Understanding the motivators and barriers to sharing participant-level data and samples: A cross-sectional study with acute febrile illness cohort teams

Understanding the motivators and barriers to sharing participant-level data and samples: A cross-sectional study with acute febrile illness cohort teams | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding the motivators and barriers to sharing participant-level data and samples: A cross-sectional study with acute febrile illness cohort teams Priya Shreedhar, Thomas Jaenisch, Mirna Naccache, Lauren Maxwell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4541739/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in BMC Medical Ethics → Version 1 posted 10 You are reading this latest preprint version Abstract Background Sharing de-identified, participant-level clinical-epidemiological data, human biological samples, and human genetic data facilitates understanding diseases and the development of prevention strategies, diagnostics, and treatments. While there are increasing calls to share participant-level data and samples both during and outside the public health response to epidemics, several barriers remain. Methods We administered a cross-sectional, online survey to research teams that manage acute febrile illness (AFI) cohorts. We included questions on the researchers’ best and worst experiences, motivators, benefits, and barriers to sharing de-identified participant-level clin-epi data, human biological samples, and human genetic data during and outside epidemics. Using the political, ethical, administrative, regulatory, and legal (PEARL) framework, we classified the best and worst sharing experiences and employed the Wilcoxon signed-rank test to compare barriers between epidemic and non-epidemic settings. Results We received 78 responses to the survey from cohort study teams in 23 countries. Most respondents were cohort PIs, over 45, and advanced in their careers. Most cohorts were based in South America or Central America, focused on multiple pathogens, and collected and shared multiple data types and samples. Scientific collaborations with researchers outside their country were the most commonly reported best data or sample-sharing experience. Lack of benefit sharing was the most commonly reported worst sharing experience. Benefits and barriers to sharing did not vary significantly by data type or whether sharing happened during or outside of pandemics, except for regulatory barriers to sharing human biological samples which were significantly more important in epidemic than in non-epidemic settings. Conclusions The study highlights the need for stakeholders to improve data and sample-sharing practices for AFI researchers in LMICs, emphasising ethical considerations, benefit sharing, and streamlined administrative processes in both epidemic and non-epidemic settings. Acute febrile illness data sharing sample sharing genetic data sharing PEARL barriers epidemic setting non-epidemic setting Figures Figure 1 Figure 2 Background Sharing clinical-epidemiological (clin-epi) data, human biological samples, and human genetic data from health-related human subjects research is essential for informed decision-making in routine healthcare and identifying and responding to public health emergencies like epidemics. 1–3 The advantages of sharing clin-epi and genetic data and samples include maximising the value of the data, increasing opportunities to leverage the data for new insights, and translating these findings into practice, such as faster development and evaluation of diagnostics and treatments. 4,5 Data sharing also enhances research transparency, facilitates the verification of results, and fosters an environment of openness and collaboration within the research community. 4,6 Data sharing reduces the burden and costs associated with unnecessary duplication of research. 6 When data are available, researchers can reuse or build upon existing studies rather than replicating them, leading to a more efficient use of resources. 4,7,8 Biobanks play a critical role in health-related research. They provide a valuable repository of biological samples and linked clinical-epi data, which are essential for understanding complex diseases and developing new diagnostics and treatments. 9,10 Recognising these benefits, research funders and regulatory agencies increasingly advocate for and require researchers to share participant-level data and biological samples equitably. 11–14 Despite the calls for increased data and sample sharing, several barriers prevent or delay sharing during and outside epidemics. 15 Barriers to data sharing include lack of data preservation and retrieval systems and use of clin-epi or genetic data standards, resource constraints (e.g., lack of funding for curating, annotating and communication regarding the data), loss of professional development opportunities (e.g., publication, grant opportunities), loss of confidentiality and the related potential to stigmatise or otherwise target research participants, lack of benefit sharing with research study teams and source populations, as well as data misuse and misinterpretation. 15,16 Legal and regulatory issues, including regional or national policies that limit data sharing (e.g., General Data Protection Regulation [GDPR]), lack of clear guidelines for what participant-level data to share, lack of participant broad consent for future use, ethics review committee (ERC) or funders not providing permission to share, and confusion over data ownership, may also hinder data and sample sharing. 15,16 Additional barriers to sample sharing include a lack of standards for related metadata, interoperability in related contracts, funding for preparing and transporting samples or linking samples to clin-epi data, mechanisms for tracking the chain of custody, and limited sample volume. 17 Researchers in low-and-middle-income countries (LMICs) face additional barriers to data and sample sharing stemming from the long history of colonial research, helicopter research, and other exploitative research where the benefits of data and sample sharing for researchers and source populations, including novel disease prevention and treatments strategies, increased opportunities for publication and funding, disproportionately benefit researchers and populations in high-income countries. 18–20 To understand more about the barriers and facilitators to data and sample sharing within and outside epidemic settings, we conducted a cross-sectional survey with acute febrile illness (AFI) cohort researchers. AFIs are characterised by a sudden onset of fever, can represent myriad infectious etiologies, and are associated with high morbidity and mortality in LMICs. 21,22 AFIs, including Zika and Ebola viruses, have been responsible for several epidemics and are a priority for global surveillance and public health response efforts. 23,24 Accurate and rapid diagnosis of AFIs is critical for detection and treatment but is hindered by the lack of standardised case definitions, limited access to diagnostic tools, cross-reactive or inaccurate diagnostics, and limited access to data and samples. 25 We focused the survey on AFI cohorts because data and sample sharing motivations, barriers, and behaviour likely differ across disease areas. Sharing AFI-related data and samples is crucial for outbreak detection and response, developing and evaluating diagnostics, vaccines, and treatments, identifying at-risk populations, understanding infection dynamics, and guiding research funding. Methods Study population The target population for this study were research teams that manage cohorts that focus on pathogens associated with AFIs, including Zika virus (ZIKV), DENV, rickettsia/ rickettsiosis, neorickettsia, chikungunya virus (CHIKV), Rift Valley fever virus, Mansonella perstans Filariasis, Leishmaniasis, yellow fever virus, leptospirosis/Leptospira, Japanese encephalitis virus, tick-borne encephalitis virus, hepatitis virus (any form), and Mayaro virus, as well as SARS‑CoV‑2 as cohorts expanded to address concerns surrounding the COVID-19 pandemic. Participants were recruited through lists of febrile illness cohort studies. 26,27 and from existing consortia, including the ZIKV Individual Participant Data (IPD) Consortium, 28 ZIKAlliance, 29 ZikaPLAN, 30 AEDES Network, 31 and the International Research Consortium on Dengue Risk Assessment, Management and Surveillance (IDAMS). 32 We requested survey participation from cohort principal investigators (PIs) and laboratory and field data managers, as the benefits and burdens of data and sample sharing may vary between these roles. We excluded cohorts focused on human immunodeficiency virus (HIV), tuberculosis (TB), malaria, or Ebola, as data and sample sharing within these groups may differ significantly from sharing in AFI cohorts that focus on other pathogens. Infectious diseases (IDs) that are not considered neglected tropical diseases (NTDs), like HIV, and IDs that are well studied, like tuberculosis and malaria, are associated with long-standing data-sharing efforts, such as the WorldWide Antimalarial Resistance Network (WWARN), from which the Infectious Diseases Data Observatory (IDDO) was founded. 33 At the time of data collection, IDDO had developed a data-sharing platform for Ebola, which we felt might have changed data-sharing norms in that community. 34 Survey development & administration We developed the survey through information from a systematic review of political, ethical, administrative, regulatory, and legal (PEARL) barriers to data reuse, 15 our review of the literature on PEARL barriers to data and sample reuse, and conversations with colleagues working on data and sample reuse in the context of AFIs and EIDs. We used the PEARL rather than the ethical, legal, and social issues (ELSI) approach to classifying barriers because the PEARL approach has been used in a systematic review of barriers to data reuse 15 and provided a more nuanced understanding of political, administrative, and regulatory barriers than the ELSI framework. The survey was piloted with four colleagues working with AFI cohorts, and following the pilot, the format was adjusted to differentiate between data and sample sharing within and outside of epidemics to capture potential differences. The survey took around 20 minutes to complete. We administered the one-time, online, cross-sectional survey using REDCap, a GDPR and Health Insurance Portability and Accountability Act (HIPAA)--compliant survey research platform. 35 Please see Appendix File 2 for the REDCap survey. In August 2020, 285 AFI cohort PIs were contacted by email with the link and details of the survey. We asked PIs to forward the survey to their teams since we only had the PIs' email contact for most cohorts. We sent monthly reminders to complete the survey until December 2020. The survey covered the following areas: respondent demographic information; basic information about the AFI cohort (e.g., pathogen of interest, source population, types of data and samples collected); information on sharing de-identified participant-level clin-epi data, human genetic data, and samples; open questions on the respondent’s best and worse experience sharing clin-epi data, genetic data or samples; motivators for and barriers to data and sample sharing both during and outside of epidemic settings; details of COVID-19 data and sample sharing; information on the cohort’s broad consent for future use of data and samples; data and sample sharing-related processes and repository or platform; participation in cross-cohort data or sample sharing initiatives; and benefit sharing. Analysis Descriptive statistics are presented as frequencies and percentages. In the survey, respondents used sliders to score the importance of “technical,” “motivational,” “economic,” “regulatory,” “legal,” “ethical,” and “political” barriers to sharing participant-level clin-epi data, human biological samples, and human genetic data in both epidemic and non-epidemic settings. These categories of barriers are based on a previously developed taxonomy of barriers to sharing data in public health. 15 We considered scores between 0–33 for the barriers to be classified as “not important,” 34–67 as “somewhat important,” and 68–100 as “very important.” We used the Wilcoxon signed-rank test to compare the differences in scores between epidemic and non-epidemic settings for each barrier type (α = 0.05). We asked participants to describe their best and worst experiences sharing participant-level clin-epi data, human biological samples, or human genetic data in free text fields. We then categorised responses according to the PEARL framework. For motivators, benefits, and barriers to data and sample sharing, we compared the responses from principal investigators (PIs) to those from non-PIs. Respondents identifying as co-investigators and investigators were grouped and classified under the PI category. We conducted the statistical analyses and created the figures with R Studio version 2023.06.1 + 524. Ethical clearance The University of Heidelberg Faculty of Medicine's ERC approved this study’s research protocol and survey. Upon accessing the survey link, participants received detailed information about the voluntary nature of their participation, their right to withdraw, the survey's objectives, funding sources, and ethical approval. Informed consent was obtained when participants elected to begin the survey after reviewing this information. Results Characteristics of the survey respondents and cohorts We obtained 78 survey responses, either complete or partial, from PIs and study teams representing cohorts in 23 countries. We could not determine an exact non-response rate as we asked study PIs to distribute the survey to their staff, and the total number of staff members who received the survey is unknown. Sixty-nine respondents (89%) indicated their cohort PI’s name, which we used, along with the cohort location and pathogen focus, to estimate that responses represented 62 unique cohorts. As shown in Table 1 , 40 (51%) of the survey respondents were cohort PIs, 9 (12%) laboratory scientists, 6 (8%) local investigators, and 4 (5%) laboratory data managers, field data managers, and field staff or data collectors respectively, and 2 (3%) statisticians. Most respondents were 46–55 (n = 27; 35%) or over 55 years old (n = 24; 31%) and advanced in their career (n = 48; 62%) or mid-career (n = 19; 24%). More than half (n = 56; 72%) were from the same country where the cohort was located. About half (n = 45; 58%) of the cohorts were located in South America, 22 (28%) were in North or Central America, 6 (8%) each in Asia and Africa, and 2 (3%) in Europe. Over half of the respondents said their cohort (n = 46; 59%) focused on multiple pathogens. Twelve respondents (15%) represented cohorts focused on ZIKV only, and 7 (9%) on DENV only. Respondents indicated that the main source population for most cohorts was hospitals (n = 55; 71%), followed by the community (n = 47; 60%). Two respondents (3%) were from cohorts recruited from schools, and two were from other sources. Sixty respondents (77%) reported that their cohort collected multiple types of data and samples, with 32 (41%) collecting SARS-CoV-2-related data or samples. Another 10 (13%) respondents indicated that their cohort collected clin-epi data only, and 3 (4%) indicated that they only collected human-derived samples. Close to half (n = 42; 54%) of cohorts shared multiple data types or samples, 22 (28%) shared clin-epi data only, and one respondent indicated that their cohort shared human samples only. Eight respondents (10%) said their cohort was sharing or planning to share SARS-CoV-2 data to existing platforms, including ISARIC, COVID-19 Data Portal, GitHub, and COVI-PREG. Seven respondents (9%) reported that their cohort was sharing human genetic data to a platform; 20 (26%) respondents reported using a biobanking platform to make sample data visible. In terms of timelines for sharing, for clin-epi data, respondents indicated that 12 (15%) of cohorts shared data in real-time, 18 (23%) cohorts within a one year of data collection, 25 (32%) cohorts over one year after collection, and 24 (32%) after publication. For sharing human OMICs data, respondents reported that 3 (4%) cohorts shared data in real-time, 2 (3%) within one year after collection, 4 (5%) over one year after collection, and 3 (4%) after publication. For the sharing of human biological samples, respondents indicated that 12 (15%) cohorts shared samples in real-time, 16 (21%) within one year of collection, 19 (24%) over one year after collection, and 13 (17%) after publication. Twenty-nine (37%) of respondents said that they obtained broad consent for future use of clin-epi data, 25 (32%) for human biological samples, and 7 (9%) for human genetic data. Twenty (26%) respondents indicated that their cohort had a documented process for external groups to access clin-epi data, 15 (19%) for human biological samples, and 3 (4%) for human genetic data. Regarding data standards, less than 25% (n = 18) of survey respondents reported that the variables collected in their cohort corresponded to an internationally accepted standard. Data standards used included International Classification of Diseases (ICD)-9, 10, or 11, NCI Thesaurus, Medical Dictionary for Regulatory Activities (MedDRA), and Health Level Seven International (HL7). Best and worst sharing experiences Table 2 presents respondents’ best and worst experiences sharing participant-level clin-epi or human genetic data or human biological samples from their cohort, classified according to the PEARL framework. Fewer than one-third of survey respondents provided their best (n = 24; 31%) and worst sharing experience (n = 18; 23%). The most commonly reported best data or sample-sharing experiences were classified as political, including eight respondents who indicated that collaborating with, exchanging ideas, and forming partnerships with experts and researchers worldwide were their best data or sample-sharing experiences. Ethical experiences, chiefly the lack of benefit sharing, were the most commonly reported worst sharing experiences. Three respondents indicated that they did not have any bad experiences to share. Motivators for sharing Using pre-specified, multiple-choice options, we asked survey respondents to identify their top three motivators for sharing de-identified participant-level clin-epi data, human biological samples, and human genetic data (Table 3 ). Sharing participant-level clin-epi data for a public health rationale (other than the development of novel vaccines, treatments, or therapies) (n = 32; 50%), increased funding opportunities or opportunities for collaboration (n = 21; 33%), and conducting a cross-cohort or international study with funding support (n = 20; 31%) were the three most frequently chosen motivators for cohorts to share participant-level clin-epi data. Increased funding opportunities or opportunities for collaboration (n = 22; 41%), public health rationale (other than development of novel vaccines, treatments, or therapies) (n = 20; 37%), and the development of novel vaccines, treatments, or therapies (n = 19; 35%) were the top three motivators for cohorts to share human biological samples. Lastly, public health rationale (other than the development of novel vaccines, treatments, or therapies) (n = 13; 65%), the conduct of a cross-cohort or international study with funding support (n = 10; 50%), and the development of novel vaccines, treatments, or therapies (n = 5; 25%) tied with informing future research investments (n = 5; 25%) and increased funding opportunities or opportunities for collaboration (n = 5; 25%) were the top three motivators for cohorts to share human genetic data. Two survey respondents each indicated that there were no motivators for sharing participant-level clin-epi data and human biological samples. One survey respondent indicated that there were no motivators for sharing human genetic data from their cohort. Figure 1 presents the motivators for sharing participant-level clin-epi data, human genetic data, and human biological samples for PIs and non-PIs. A greater percentage of PIs than non-PIs indicated increased authorship opportunities (76% vs 24%), and the ability to conduct a cross-cohort or international study (67% vs 33%) were motivators for sharing. Appendix Figs. 1A–C present the motivators for sharing participant-level clin-epi data, human biological samples, and human genetic data for PIs and non-PIs. Reflecting the overall results, compared to other study staff members, a higher percentage of PIs identified increased authorship opportunities as a motivator for sharing participant-level clin-epi data (80% vs 20%), human biological samples (70% vs 30%) and human genetic data where 100% of PIs from cohorts sharing human genetic data voted for this motivator. Funder requirements were also more cited as a motivator by PIs than other cohort staff for sharing participant-level clin-ep data (67% vs 33%) and human genetic data (75% vs 25%). The opposite was observed for sharing human biological samples, where more non-PIs than PIs saw funder requirements as a motivator (60% vs 40%). Reflecting the overall results, more PIs than non-PIs identified being able to conduct a cross-cohort study with funding support as a motivator for sharing participant-level clin-epi data (65% vs 35%), human biological samples (67% vs 33%), and human genetic data (70% vs 30%). Figure 1. Motivators for sharing participant-level clin-epi data, human genetic data, and human biological samples for PIs and non-PIs *Other than the development of novel vaccines, treatments, or therapies Benefits of sharing We present respondents’ top three benefits of sharing de-identified participant-level clin-epi data, human biological samples, and human genetic data in Table 3 . The three most frequently chosen benefits of sharing participant-level clin-epi data were enhanced insights through collaboration (n = 37; 67%), increased opportunities for authorship (n = 34; 62%) and increased funding opportunities (n = 29; 53%). The three most frequently chosen benefits of sharing human biological samples from cohorts were expanded grant opportunities (n = 24; 52%), long-term capacity-building investment (n = 21; 46%), and free or reduced-cost access to novel diagnostics, treatments, or prevention (n = 20; 44%). For sharing human genetic data from cohorts, the three most commonly chosen benefits were enhanced insights through collaboration (n = 11; 79%), reduced duplication of efforts (n = 9; 64%), and increased funding opportunities (n = 7; 50%) tied with increased opportunities for authorship (n = 7; 50%) and free or reduced-cost access to novel diagnostics, treatments, or prevention (n = 7; 50%). One survey respondent indicated that there were no benefits to sharing participant-level clin-epi data, and two respondents indicated that there were no benefits to sharing human biological samples. Appendix Figures 2A–C present the most important benefits of sharing participant-level clin-epi data, human biological samples, and human genetic data for PIs versus non-PIs. Short-term capacity building was seen as a benefit of sharing by a greater percentage of PIs than non-PIs for clin-epi data (56% vs 44%), human biological samples (56% vs 44%), and human genetic data (60% vs 40%). Reduced duplication of efforts was also seen as a benefit of sharing by a greater percentage of PIs than non-PIs for clin-epi data (57% vs 43%) and human genetic data (56% vs 44%). We were unable to combine the benefits of sharing participant-level clin-epi data, human genetic data, and human biological samples for PIs and non-PIs into one figure (as done for motivators in Figure 1) as the benefits for sharing human biological samples that respondents could choose in the survey were different from those provided for clin-epi and human genetic data. Barriers to sharing Figures 2A and B present respondent ratings for barriers to sharing participant-level clin-epi data, human biological samples, and human genetic data within epidemic (2A) and non-epidemic settings (2B). Barriers most frequently scored as “very important” in epidemic settings were technical barriers to sharing participant-level clin-epi data and human genetic data and regulatory barriers to sharing human biological samples. Technical barriers were also frequently scored as “very important” for sharing clin-epi data and human biological samples outside of epidemic settings. In contrast, ethical barriers were more frequently highlighted as “very important” for sharing human genetic data outside epidemic settings. The only barriers that were found to be significantly different between epidemic and non-epidemic settings were regulatory barriers to sharing human biological samples (Table 4), which were scored higher in epidemic settings versus non-epidemic settings (94 vs 83; p<0.05). Political barriers were most often cited as “not very important” across data and sample types in both epidemic and non-epidemic settings. Figure 2. Barriers to sharing participant-level clin-epi data, human biological samples, and human genetic data within epidemic (2A) and non-epidemic settings (2B) Appendix Figure 3 compares barriers to sharing for PIs and non-PIs. Appendix Figure 4 compares barriers for sharing data and samples separately for PIs and non-PIs. Appendix Figures 5A and 5B compare barriers for sharing data and samples for PIs and non-PIs in both epidemic (5A) and non-epidemic settings (5B). A greater percentage of PIs than non-PIs scored economic barriers for sharing participant-level clin-epi data, human genetic data, and human biological samples as “very important” in both epidemic (clin-epi: 41% vs 23%; genetic: 40% vs 20%; samples: 42% vs 31%) and non-epidemic (clin-epi: 39% vs 20%; genetic: 42% vs 25%; samples: 36% vs 21%) settings. A higher percentage of PIs vs non-PIs scored motivational barriers in epidemic settings as “very important” for participant-level clin-epi data (34% vs 19%), human biological samples (36% vs 25%), and human genetic data (40% vs 10%). The rating of which barriers to sharing were the least important was generally consistent for PIs and other members of the cohort staff, with political barriers cited as the least important (Appendix Figure 3). That said, when considered at the data type level, PIs and non-PIs rated barriers differently. Other cohort members were less concerned with legal barriers to reusing participant-level data than PIs. In contrast, PIs were less concerned with legal barriers to sharing human genetic data than other cohort members (Appendix Figure 4). Both PIs and other cohort members agreed that technical barriers to sharing participant-level data and samples were important or very important. Discussion We conducted a cross-sectional survey to understand the experiences, motivators, benefits and barriers in epidemic and non-epidemic settings to share clin-epi data, human biological samples, and human genetic data from AFI-related cohorts. The 78 survey responses from AFI researchers representing cohorts in 23 countries highlight diverse experiences with data and sample sharing. Most respondents were older and at an advanced career stage. Most cohorts collected multiple data types and samples; about half of the respondents reported that their cohorts had shared data or samples. A small proportion of respondents indicated that their cohort shared clin-epi data or human samples in real-time. Rapid or real-time data and sample sharing can fast-track research and development for diagnostic, treatment, and prevention-related tools during and outside of epidemics and is especially important for addressing EIDs. 2,36–38 Ongoing and developing outbreaks, such as Ebola, Zika, and COVID-19, underscore the importance and challenges of quick, open, and effective sharing of high-quality samples and related clin-epi data and human genetic data from various populations for an informed response. 39–41 Best and worst data and sample-sharing experiences In this study, the opportunity to form partnerships with experts and researchers worldwide was the most frequently cited best aspect of data and sample sharing, which is similar to findings from other studies. In a qualitative study with public health and biomedical researchers in Thailand, senior researchers highlighted the importance of sharing health research data to foster cross-national collaborations and improve their research portfolio. 4 In a survey disseminated to life scientists in 13 countries in sub-Saharan Africa, close to half of the respondents indicated that the most significant benefit of sharing their data was networking and collaboration opportunities. 42 Lack of communication regarding the results of analyses made possible through data or sample reuse and lack of benefit sharing, including not being credited in resulting publications, were the most commonly reported worst sharing experiences. The study mentioned above involving Thai researchers reflects this finding, wherein researchers acknowledged that the benefits of sharing can only be realised with appropriate acknowledgement and attribution of the data providers. 4 Several researchers recounted personal experiences where data users did not adequately acknowledge them. They highlighted the harms of this, including reducing future funding opportunities, impeding career advancement, and damaging their reputation. 4 A systematic review of factors associated with data sharing also highlighted investigator concerns around benefit sharing, which included being appropriately credited and involved in future funding opportunities made possible through data and sample reuse. 43 Another scoping review highlighted that the lack of benefit sharing that researchers from LMICs face, including being stuck as data producers only, lack of recognition, and lack of career progression, are all threats to equitable global health research. 44 Motivators for data and sample sharing Leading motivators for sharing participant-level data and samples included public health reasons other than developing novel vaccines, treatments, or therapies, increased funding or collaboration opportunities, funding support for cross-study analyses, and developing novel vaccines, treatments, or therapies. Our finding that improving public health is one of the most important motivators for data and sample sharing is reflected in empirical research with investigators from the Ebola and Yellow Fever epidemics. 45 Reflecting researchers’ best sharing experiences, increased funding and collaboration opportunities, and funding support for cross-cohort or international studies were top motivators for sharing data and human biological samples. We also compared the motivators for sharing participant-level clin-epi data, human biological samples, and human genetic data between study PIs and non-PIs. A substantially higher percentage of PIs found increased authorship opportunities to be a strong motivator for sharing participant-level clin-epi data, human biological samples and human genetic data compared to other study staff members. This difference may relate to different authorship opportunities within cohorts for PIs vs non-PIs. 46 PIs occupying leadership positions are likelier to see their contributions recognised and acknowledged in publications. 47 Senior researchers in Vietnam indicated that academic recognition in the form of authorship on papers that use the data they produce is crucial for them. Researchers in the same paper also saw authorship as a way of being responsible for the ethical and scientific integrity of the research. 48 More PIs also considered engagement in cross-cohort or international studies with funding support a motivator than non-PIs for all data types. This difference could relate to PIs’ responsibility to fund cohorts or the belief that the cross-national collaboration will increase research quality or impact. Benefits of data and sample sharing The top benefits for sharing participant-level clin-epi data and human genetic data indicated by survey respondents were enhanced insights through collaboration, increased opportunities for authorship, reduced duplication of efforts, and increased funding opportunities. The top benefits of sharing human biological samples included expanded grant opportunities, long-term capacity-building investments, and free or reduced-cost access to novel diagnostics, treatments, or prevention. Increased collaboration, authorship, and funding opportunities reflect the best data and sample-sharing experiences and motivators for sharing in this study. These have also been acknowledged to be essential benefits that need to be provided to LMIC researchers to ensure equitable data sharing in global health research. 44 Reduced duplication of research efforts has also been seen as a benefit of data sharing by other health research stakeholders, research participants, and community representatives in India and Kenya. 7,49 We also compared the benefits of sharing participant-level clin-epi data, human biological samples, and human genetic data between study PIs and non-PIs. For clin-epi data, human biological samples, and human genetic data, a greater percentage of PIs than non-PIs saw short-term capacity building as a benefit of sharing. A qualitative study of Vietnamese researchers indicated that their view of sustainable and acceptable data sharing included reciprocity, wherein capacity building and strengthening of data producers were prioritised. 48 Barriers to data and sample sharing We did not find any significant differences in the importance of barriers to sharing data or samples in epidemic vs. non-epidemic settings, except regulatory barriers for sharing biological samples which were scored higher in epidemic settings versus non-epidemic settings. Similar studies on data and sample sharing in infectious disease-related observational research have identified regulatory obstacles as a primary challenge. One study examining barriers and enablers in responding to the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak highlighted legal frameworks and regulatory procedures as the primary obstacles. 38 These barriers ultimately resulted in delays in MERS-CoV infection notifications, prolonged processes for data-sharing authorization, and difficulties in shipping/importing samples. 38 Similarly, another study that examined barriers to information and sample sharing in the aftermath of response efforts to combat the Zika virus outbreak highlighted regulatory delays, specifically in regulatory approvals, as one of the three significant bottlenecks faced by researchers. 50 Our finding that barriers and facilitators are different for cohort PIs compared to other members of the cohort staff suggests that barriers and enablers to biomedical research data reuse should be disaggregated by the respondents' roles in the study. Future qualitative research could explore why barriers and facilitators may be different for PIs and other cohort staff. Strengths and limitations This research has several strengths. We developed the survey through consultations with AFI researchers and an extensive literature review on data and sample sharing in infectious diseases. To our knowledge, this is the first study to assess barriers and facilitators to data and sample sharing within AFI cohorts both during and outside of epidemics and using the PEARL framework. Findings from this survey can help develop meaningful interventions for funders, cross-national surveillance systems, and other stakeholders to increase the rapidity, incidence, and quality of data and sample sharing. The survey also has several limitations. Most AFI cohorts were based in South, North, or Central America. However, we only circulated the survey in English, which could have prevented some respondents from answering the survey. We had more extensive lists of AFI studies and closer connections to AFI researchers from the Americas because of the recent ZIKV epidemic. AFI cohorts based in Asia and Africa were less likely to have been contacted and responded to the survey. Results should be interpreted with caution as they could be subject to self-reported survey-related recall and response bias. Furthermore, as the names of the respondents and the cohorts were not collected, we had to estimate the number of cohorts that the survey respondents represented. Conclusions Compared to populations in HICs, LMICs face a disproportionate burden of IDs. Data and sample sharing are essential during and outside epidemics as they facilitate rapid identification and understanding of the disease, enable the development of diagnostics, treatments, and vaccines, and support a coordinated global response. Understanding cohort teams' motivations for sharing data and samples during and outside epidemics helps regulators and funders understand what barriers to address and incentives to support. While we did not find differences in barriers and motivations for sharing different data types during and outside of epidemics, we did find differences between cohort PIs and other members of cohort teams. Addressing barriers to data sharing, including loss of publication opportunities and misinterpretation of data, and sample sharing, including regulatory issues, could help improve the sharing of data and samples. Supporting equitable data and sample reuse in keeping with cohort teams’ main motivations, including building cross-national collaborations and advancing public health, can facilitate the long-standing partnerships needed to facilitate data and sample reuse during and outside of epidemics. Abbreviations AFI Acute febrile illness CHIKV Chikungunya virus Clin-epi Clinical-epidemiological DENV Dengue virus ERC Ethics Review Committee GDPR General Data Protection Regulation HIPPA Health Insurance Portability and Accountability Act HIV Human immunodeficiency virus IDAMS International Research Consortium on Dengue Risk Assessment, Management and Surveillance ID Infectious disease IDDO Infectious Diseases Data Observatory LMICs Low-and-middle-income countries MERS-CoV Middle East Respiratory Syndrome Coronavirus NTDs Neglected tropical diseases PEARL Political, Ethical, Administrative, Regulatory, and Legal PIs Principal Investigators WHO World Health Organization WWARN World-Wide Malaria Research Network ZIKV Zika virus Declarations Ethics approval and consent to participate The research protocol and survey were reviewed and approved by the Ethics Review Committee of the University of Heidelberg Faculty of Medicine. After reading the study description and consent form, participants provided informed consent by clicking the survey link. Consent for publication Not applicable. Availability of data and materials The survey and de-identified dataset of survey responses are available on OSF (doi: 10.17605/OSF.IO/EDPSH). Competing interests The authors declare that they have no competing interests. Funding This study is part of the ReCoDID project, which is funded by the EU Horizon 2020 research and innovation programme (grant agreement 825746) and the Canadian Institutes of Health Research (CIHR) Institute of Genetics (grant agreement 01886-000). Authors' contributions PS and LM designed the survey protocol, survey, and analysis plan. PS conducted the analysis. PS, LM and MN wrote the first draft of the manuscript. PS, TJ, MN, and LM provided critical feedback and revisions to the manuscript. All authors read and approved the final manuscript. Acknowledgements We want to acknowledge Claudia Emerson, Martine van Roode, and María Consuelo Miranda Montoya’s review and comments on the draft survey. References Schwalbe N, Wahl B, Song J, Lehtimaki S. Data Sharing and Global Public Health: Defining What We Mean by Data. Front Digit Health . 2020;2:612339. doi:10.3389/FDGTH.2020.612339 Giri J, Pezzi L, Cachay R, et al. Specimen sharing for epidemic preparedness: Building a virtual biorepository system from local governance to global partnerships. PLOS Global Public Health . 2023;3(10):e0001568. doi:10.1371/JOURNAL.PGPH.0001568 Pratt B, Bull S. Equitable data sharing in epidemics and pandemics. BMC Med Ethics . 2021;22(1). doi:10.1186/S12910-021-00701-8 Cheah PY, Tangseefa D, Somsaman A, et al. Perceived Benefits, Harms, and Views About How to Share Data Responsibly: A Qualitative Study of Experiences With and Attitudes Toward Data Sharing Among Research Staff and Community Representatives in Thailand. 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Survey respondent and cohort characteristics (N=78) N (%) Age range of respondent s Under 25 1 (1) 25-35 9 (12) 36-45 15 (19) 46-55 27 (35) Over 55 24 (31) Not indicated 2 (3) Respondent’s role within the cohort Principal Investigator/co-investigator 40 (51) Local investigator 6 (8) Laboratory data manager 4 (5) Laboratory scientist 9 (12) Field data manager 4 (5) Field staff/data collector 4 (5) Statistician 2 (3) Others 7 (9) Not indicated 2 (3) Respondent career stage Early career 8 (10) Mid-career 19 (24) Advanced career 48 (62) Not indicated 3 (4) Respondent is from the country where the cohort is located 56 (72) Cohort location South America 45 (58) North or Central America 22 (28) Asia 6 (8) Africa 6 (8) Europe 2 (3) Cohort source population Community 47 (60) Hospital 55 (71) School 2 (3) Other 2 (3) Cohort pathogen of interest ZIKV only 12 (15) DENV only 7 (9) Multiple 46 (59) Types of data or samples collected Clin-epi data only 10 (13) Human OMICs data only 0 Human samples only 3 (4) Multiple data or sample types 60 (77) Collecting SARS-CoV-2 data or samples 32 (41) Types of data or samples shared Clin-epi data only 22 (28) Human OMICs data only 0 Human samples only 1 (1) Multiple data or sample types 42 (54) Sharing or planning to share SARS-CoV-2 data with existing platform 8 (10) Sharing human OMICs data to a platform 7 (9) Using a biobanking platform 20 (26) When clin-epi data is shared Real-time 12 (15) Less than or equal to 1 year after collection 18 (23) Over one year after the collection 25 (32) After publication 25 (32) Other 3 (4) When human OMICs shared Real-time 3 (4) Less than or equal to 1 year after collection 2 (3) Over one year after the collection 4 (5) After publication 3 (4) Other 3 (4) When human samples are shared Real-time 12 (15) Less than or equal to 1 year after collection 16 (21) Over one year after the collection 19 (24) After publication 13 (17) Other 3 (4) Do variables correspond to an internationally accepted standard 18 (23) Consent for use of data/samples beyond the study Clin-epi data 29 (37) Human samples 25 (32) Human OMICs 7 (9) Documented process for external groups to access data/samples Clin-epi data 20 (26) Human samples 15 (19) Human OMICs 3 (4) Table 2. Best and worst experiences with data, sample, or genetic data sharing Best sharing experiences (n=24) Worst sharing experiences (n=18) Political Collaborating with, exchanging ideas, and forming partnerships with experts and researchers across the world (n=8) Contributing to a multi-national research initiative (n=5) Developing high-impact scientific publications (n=5) Providing data to the MoH to encourage them to formulate disease control measures (n=1) Comparing results with other countries (n=1) Lack of clarity on the reason for wanting data by the research group requesting the data, especially in the case of researchers with external funding from HICs requesting data from LMICs (n=1) Ethical Contributing to improved quality and rigour of data analysis, i.e., through larger sample size and support of a larger team of researchers (n=5) Improving knowledge, awareness, and control of diseases (n=3) Improving the diagnostic capabilities of different laboratories via the data sharing project (n=1) No communication regarding the results of the analysis of shared data/samples (n=4) Not being recognised and credited in publications or receiving any other type of benefit for sharing data/samples (n=4) Unclear or lack of benefit sharing/collaboration plan from the research group that obtained that data (n=3) Administrative Centrally organised logistics for sample sharing (n=2) Clear study plan in place (n=1) Ability to contribute to analysis plans for the shared data (n=1) Lengthy duration of processes and overwhelming paperwork needed for sharing or receiving data (n=2) Lack of metadata needed to prepare data for sharing (n=2) Difficult to manage laboratory structures in small urban hospitals in areas with low human resources for sample sharing (n=1) Not being consulted regarding data analysis plans (n=1) Lack of funding to prepare the data and metadata for sharing (n=1) Long duration of time needed to format data and metadata for sharing (n=1) Regulatory Generating data to aid in the approval of diagnostics and reagents during critical periods of epidemics (n=1) Accidentally uploading images with personal identifiers to the central database (n=1) Legal Helping to initiate conversations with sponsors to increase budgets to cover a more extensive scope of analysis (n=2) Experiencing a well-defined contract in place between parties (n=1) Data recipients trying to patent a product based on the shared data/samples (n=2) Suffering from lack of a well-defined contract in place between parties (n=1) Long duration of time needed to obtain relevant permissions for sharing data (n=1) Long duration of time required to obtain an export license for sharing samples (n=1) No good/bad experience * n=0 n=3 No data-sharing experience was mentioned † n=54 n=57 *Explicitly stated that they did not have any good/bad experiences with sharing data or samples; † Irrespective of whether or not they indicated that they shared data or samples. Table 3. Most important motivators and benefits of data or sample sharing with groups outside of pre-established partnerships with cohorts Motivators Benefits De-identified, participant-level clin-epi data (N=64) De-identified, participant-level clin-epi data (N=55) Public health rationale, other than development of novel vaccines, treatments, or therapies (n=32; 50%) Increased funding opportunities or opportunities for collaboration (n=21; 33%) Cross-cohort or international study with funding support (n=20; 31%) Development of novel vaccines, treatments, or therapies (n=16; 25%) Cross-cohort or international study without funding support (e.g. individual participant data meta-analysis) (n=14; 22%) Prevent duplication of efforts (n=13; 20%) Funder requirement (n=12; 19%) Inform future research investments (n=10; 16%) Increased authorship opportunities (n=10; 16%) National MoH requirement (n=7; 11%) Local health department (state/department or regional level) requirement (n=6; 9%) Moral obligation to understand the underlying pathology of diseases (n=1; 2%) Obtaining insight into rare disease outcomes (n=1; 2%) No motivation for sharing de-identified participant-level clin-epi data (n=2; 3% ) Enhanced insights through collaboration (n=37; 67%) Increased opportunities for authorship (n=34; 62%) Increased funding opportunities (n=29; 53%) Reduced duplication of efforts (n=23; 42%) Long-term capacity building investment (e.g. funded postdoc) (n=24; 44%) Short-term capacity building investment (e.g. short course) (n=19; 35%) Free or reduced-cost access to novel diagnostics, treatments, or prevention (n=14; 25%) No benefits of sharing de-identified participant-level clin-epi data (n=1; 2%) Human biological samples (N=54) Human biological samples (N=46) Increased funding opportunities or opportunities for collaboration (n=22; 41%) Public health rationale, other than development of novel vaccines, treatments, or therapies (n=20; 37%) Development of novel vaccines, treatments, or therapies (n=19; 35%) Cross-cohort or international study with funding support (n=18; 33%) Prevent duplication of efforts (n=11; 20%) Funder requirement (n=10; 19%) Increased authorship opportunities (n=10; 19%) Inform future research investments (n=9; 17%) Cross-cohort or international study without funding support (e.g. individual participant data meta-analysis) (n=8; 15%) National MoH requirement (n=6; 11%) Local health department (state/department or regional level) requirement (n=3; 6%) To access better technology (n=1; 2%) No motivation for sharing human biological samples (n=2; 4% ) Expanded grant opportunities (n=24; 52%) Long-term capacity building investment (e.g. funded postdoc) (n=21; 46%) Free or reduced-cost access to novel diagnostics, treatments, or prevention (n=20; 44%) Short-term capacity building investment (e.g. short course) (n=18; 39%) Joint ownership of rights to IP produced using samples (n=17; 37%) Participation in a multi-site biobanking network (n=17; 37%) Infrastructure funding for biorepository (n=17; 37%) Access fee (n=3; 7%) No benefits of sharing human biological samples (n=2; 4%) Human genetic/OMICs data (N=20) Human genetic/OMICs data (N=14) Public health rationale, other than development of novel vaccines, treatments, or therapies (n=13; 65%) Cross-cohort or international study with funding support (n=10; 50%) Development of novel vaccines, treatments, or therapies (n=5; 25%) Inform future research investments (n=5; 25%) Increased funding opportunities or opportunities for collaboration (n=5; 25%) Funder requirement (n=4; 20%) Cross-cohort or international study without funding support (e.g. individual participant data meta-analysis) (n=4; 20%) Prevent duplication of efforts (n=2; 10%) Local health department (state/department or regional level) requirement (n=2; 10%) National MoH requirement (n=1; 5%) Increased authorship opportunities (n=1; 5%) No motivation for sharing human genetic data (n=1; 5% ) Enhanced insights through collaboration (n=11; 79%) Reduced duplication of efforts (n=9; 64%) Increased funding opportunities (n=7; 50%) Increased opportunities for authorship (n=7; 50%) Free or reduced-cost access to novel diagnostics, treatments, or prevention (n=7; 50%) Long-term capacity building investment (e.g. funded postdoc) (n=6; 43%) Short-term capacity building investment (e.g. short course) (n=5; 36%) Table 4. Comparison of barriers to sharing participant-level data and samples within and outside of epidemic settings Median p-value Participant-level clin-epi data Technical barriers (n=52) Epidemic setting 75.0 0.09 Non-epidemic setting 78.0 Motivational barriers (n=45) Epidemic setting 75.0 0.68 Non-epidemic setting 70.0 Economic barriers (n=48) Epidemic setting 73.5 0.19 Non-epidemic setting 70.0 Regulatory barriers (n=48) Epidemic setting 87.0 0.34 Non-epidemic setting 86.0 Legal barriers (n=47) Epidemic setting 57.0 0.66 Non-epidemic setting 65.0 Ethical barriers (n=47) Epidemic setting 73.5 0.46 Non-epidemic setting 81.0 Political barriers (n=39) Epidemic setting 50.0 0.40 Non-epidemic setting 50.0 Human biological samples Technical barriers (n=46) Epidemic setting 81.0 0.68 Non-epidemic setting 83.0 Motivational barriers (n=42) Epidemic setting 70.0 0.94 Non-epidemic setting 70.0 Economic barriers (n=45) Epidemic setting 81.0 0.14 Non-epidemic setting 71.0 Regulatory barriers (n=46) Epidemic setting 94.0 0.0014* Non-epidemic setting 82.5 Legal barriers (n=40) Epidemic setting 65.5 0.98 Non-epidemic setting 64.0 Ethical barriers (n=45) Epidemic setting 93.0 0.10 Non-epidemic setting 89.0 Political barriers (n=40) Epidemic setting 61.0 0.11 Non-epidemic setting 50.0 Human genetic data Technical barriers (n=10) Epidemic setting 79.0 0.94 Non-epidemic setting 69.5 Motivational barriers (n=10) Epidemic setting 62.5 0.29 Non-epidemic setting 67 Economic barriers (n=10) Epidemic setting 74.0 0.68 Non-epidemic setting 78 Regulatory barriers (n=10) Epidemic setting 78.0 0.83 Non-epidemic setting 94 Legal barriers (n=10) Epidemic setting 60.0 0.67 Non-epidemic setting 81 Ethical barriers (n=10) Epidemic setting 66.5 0.27 Non-epidemic setting 95 Political barriers (n=10) Epidemic setting 81.5 0.28 Non-epidemic setting 50 Additional Declarations No competing interests reported. Supplementary Files AFIsurvey.pdf Appendix22052024.docx Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2026 Read the published version in BMC Medical Ethics → Version 1 posted Editorial decision: Revision requested 06 Mar, 2025 Reviews received at journal 02 Feb, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviews received at journal 17 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers invited by journal 06 Aug, 2024 Editor invited by journal 20 Jun, 2024 Editor assigned by journal 20 Jun, 2024 Submission checks completed at journal 20 Jun, 2024 First submitted to journal 06 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Maxwell","email":"data:image/png;base64,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","orcid":"","institution":"Universitätsklinikum Heidelberg","correspondingAuthor":true,"prefix":"","firstName":"Lauren","middleName":"","lastName":"Maxwell","suffix":""}],"badges":[],"createdAt":"2024-06-06 16:38:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4541739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4541739/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12910-026-01399-2","type":"published","date":"2026-02-11T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60597938,"identity":"5f42a3fe-6e44-4a6c-a4c3-05e71833d36d","added_by":"auto","created_at":"2024-07-18 15:52:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72876,"visible":true,"origin":"","legend":"\u003cp\u003eMotivators for sharing participant-level clin-epi data, human genetic data, and human biological samples for PIs and non-PIs\u003c/p\u003e\n\u003cp\u003e*Other than the development of novel vaccines, treatments, or therapies\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4541739/v1/eb859b8b18590d7f5f642303.png"},{"id":60597936,"identity":"d85405aa-f9e5-4f00-9d45-f732a2e5f3c2","added_by":"auto","created_at":"2024-07-18 15:52:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82389,"visible":true,"origin":"","legend":"\u003cp\u003eBarriers to sharing participant-level clin-epi data, human biological samples, and human genetic data within epidemic (2A) and non-epidemic settings (2B)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4541739/v1/11fc496b6dba8a833bfed4a9.png"},{"id":102785364,"identity":"8969ce75-304d-41af-a658-5fb814663b01","added_by":"auto","created_at":"2026-02-16 16:05:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1885430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4541739/v1/9a80b69f-3021-4d2f-9371-c6819638dec5.pdf"},{"id":60597935,"identity":"c689d47c-f763-4ab5-9a59-d61da0d5995b","added_by":"auto","created_at":"2024-07-18 15:52:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":108723,"visible":true,"origin":"","legend":"","description":"","filename":"AFIsurvey.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4541739/v1/e02b2db55b2561c896fa0504.pdf"},{"id":60597934,"identity":"bd503393-c3fc-431a-b829-edee108d35e1","added_by":"auto","created_at":"2024-07-18 15:52:07","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3204212,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix22052024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4541739/v1/bb2b9fd260a670b764951e20.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the motivators and barriers to sharing participant-level data and samples: A cross-sectional study with acute febrile illness cohort teams","fulltext":[{"header":"Background","content":"\u003cp\u003eSharing clinical-epidemiological (clin-epi) data, human biological samples, and human genetic data from health-related human subjects research is essential for informed decision-making in routine healthcare and identifying and responding to public health emergencies like epidemics.\u003csup\u003e1\u0026ndash;3\u003c/sup\u003eThe advantages of sharing clin-epi and genetic data and samples include maximising the value of the data, increasing opportunities to leverage the data for new insights, and translating these findings into practice, such as faster development and evaluation of diagnostics and treatments.\u003csup\u003e4,5\u003c/sup\u003e Data sharing also enhances research transparency, facilitates the verification of results, and fosters an environment of openness and collaboration within the research community.\u003csup\u003e4,6\u003c/sup\u003e Data sharing reduces the burden and costs associated with unnecessary duplication of research.\u003csup\u003e6\u003c/sup\u003e When data are available, researchers can reuse or build upon existing studies rather than replicating them, leading to a more efficient use of resources.\u003csup\u003e4,7,8\u003c/sup\u003e Biobanks play a critical role in health-related research. They provide a valuable repository of biological samples and linked clinical-epi data, which are essential for understanding complex diseases and developing new diagnostics and treatments.\u003csup\u003e9,10\u003c/sup\u003e Recognising these benefits, research funders and regulatory agencies increasingly advocate for and require researchers to share participant-level data and biological samples equitably.\u003csup\u003e11\u0026ndash;14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite the calls for increased data and sample sharing, several barriers prevent or delay sharing during and outside epidemics.\u003csup\u003e15\u003c/sup\u003e Barriers to data sharing include lack of data preservation and retrieval systems and use of clin-epi or genetic data standards, resource constraints (e.g., lack of funding for curating, annotating and communication regarding the data), loss of professional development opportunities (e.g., publication, grant opportunities), loss of confidentiality and the related potential to stigmatise or otherwise target research participants, lack of benefit sharing with research study teams and source populations, as well as data misuse and misinterpretation.\u003csup\u003e15,16\u003c/sup\u003e Legal and regulatory issues, including regional or national policies that limit data sharing (e.g., General Data Protection Regulation [GDPR]), lack of clear guidelines for what participant-level data to share, lack of participant broad consent for future use, ethics review committee (ERC) or funders not providing permission to share, and confusion over data ownership, may also hinder data and sample sharing.\u003csup\u003e15,16\u003c/sup\u003e Additional barriers to sample sharing include a lack of standards for related metadata, interoperability in related contracts, funding for preparing and transporting samples or linking samples to clin-epi data, mechanisms for tracking the chain of custody, and limited sample volume.\u003csup\u003e17\u003c/sup\u003e Researchers in low-and-middle-income countries (LMICs) face additional barriers to data and sample sharing stemming from the long history of colonial research, helicopter research, and other exploitative research where the benefits of data and sample sharing for researchers and source populations, including novel disease prevention and treatments strategies, increased opportunities for publication and funding, disproportionately benefit researchers and populations in high-income countries.\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo understand more about the barriers and facilitators to data and sample sharing within and outside epidemic settings, we conducted a cross-sectional survey with acute febrile illness (AFI) cohort researchers. AFIs are characterised by a sudden onset of fever, can represent myriad infectious etiologies, and are associated with high morbidity and mortality in LMICs.\u003csup\u003e21,22\u003c/sup\u003e AFIs, including Zika and Ebola viruses, have been responsible for several epidemics and are a priority for global surveillance and public health response efforts.\u003csup\u003e23,24\u003c/sup\u003e Accurate and rapid diagnosis of AFIs is critical for detection and treatment but is hindered by the lack of standardised case definitions, limited access to diagnostic tools, cross-reactive or inaccurate diagnostics, and limited access to data and samples.\u003csup\u003e25\u003c/sup\u003e We focused the survey on AFI cohorts because data and sample sharing motivations, barriers, and behaviour likely differ across disease areas. Sharing AFI-related data and samples is crucial for outbreak detection and response, developing and evaluating diagnostics, vaccines, and treatments, identifying at-risk populations, understanding infection dynamics, and guiding research funding.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThe target population for this study were research teams that manage cohorts that focus on pathogens associated with AFIs, including Zika virus (ZIKV), DENV, rickettsia/ rickettsiosis, neorickettsia, chikungunya virus (CHIKV), Rift Valley fever virus, Mansonella perstans Filariasis, Leishmaniasis, yellow fever virus, leptospirosis/Leptospira, Japanese encephalitis virus, tick-borne encephalitis virus, hepatitis virus (any form), and Mayaro virus, as well as SARS‑CoV‑2 as cohorts expanded to address concerns surrounding the COVID-19 pandemic. Participants were recruited through lists of febrile illness cohort studies.\u003csup\u003e26,27\u003c/sup\u003e and from existing consortia, including the ZIKV Individual Participant Data (IPD) Consortium,\u003csup\u003e28\u003c/sup\u003e ZIKAlliance,\u003csup\u003e29\u003c/sup\u003e ZikaPLAN,\u003csup\u003e30\u003c/sup\u003e AEDES Network,\u003csup\u003e31\u003c/sup\u003e and the International Research Consortium on Dengue Risk Assessment, Management and Surveillance (IDAMS).\u003csup\u003e32\u003c/sup\u003e We requested survey participation from cohort principal investigators (PIs) and laboratory and field data managers, as the benefits and burdens of data and sample sharing may vary between these roles. We excluded cohorts focused on human immunodeficiency virus (HIV), tuberculosis (TB), malaria, or Ebola, as data and sample sharing within these groups may differ significantly from sharing in AFI cohorts that focus on other pathogens. Infectious diseases (IDs) that are not considered neglected tropical diseases (NTDs), like HIV, and IDs that are well studied, like tuberculosis and malaria, are associated with long-standing data-sharing efforts, such as the WorldWide Antimalarial Resistance Network (WWARN), from which the Infectious Diseases Data Observatory (IDDO) was founded.\u003csup\u003e33\u003c/sup\u003e At the time of data collection, IDDO had developed a data-sharing platform for Ebola, which we felt might have changed data-sharing norms in that community.\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSurvey development \u0026amp; administration\u003c/h2\u003e \u003cp\u003eWe developed the survey through information from a systematic review of political, ethical, administrative, regulatory, and legal (PEARL) barriers to data reuse,\u003csup\u003e15\u003c/sup\u003e our review of the literature on PEARL barriers to data and sample reuse, and conversations with colleagues working on data and sample reuse in the context of AFIs and EIDs. We used the PEARL rather than the ethical, legal, and social issues (ELSI) approach to classifying barriers because the PEARL approach has been used in a systematic review of barriers to data reuse\u003csup\u003e15\u003c/sup\u003e and provided a more nuanced understanding of political, administrative, and regulatory barriers than the ELSI framework. The survey was piloted with four colleagues working with AFI cohorts, and following the pilot, the format was adjusted to differentiate between data and sample sharing within and outside of epidemics to capture potential differences. The survey took around 20 minutes to complete. We administered the one-time, online, cross-sectional survey using REDCap, a GDPR and Health Insurance Portability and Accountability Act (HIPAA)--compliant survey research platform.\u003csup\u003e35\u003c/sup\u003e Please see Appendix File 2 for the REDCap survey.\u003c/p\u003e \u003cp\u003eIn August 2020, 285 AFI cohort PIs were contacted by email with the link and details of the survey. We asked PIs to forward the survey to their teams since we only had the PIs' email contact for most cohorts. We sent monthly reminders to complete the survey until December 2020. The survey covered the following areas: respondent demographic information; basic information about the AFI cohort (e.g., pathogen of interest, source population, types of data and samples collected); information on sharing de-identified participant-level clin-epi data, human genetic data, and samples; open questions on the respondent\u0026rsquo;s best and worse experience sharing clin-epi data, genetic data or samples; motivators for and barriers to data and sample sharing both during and outside of epidemic settings; details of COVID-19 data and sample sharing; information on the cohort\u0026rsquo;s broad consent for future use of data and samples; data and sample sharing-related processes and repository or platform; participation in cross-cohort data or sample sharing initiatives; and benefit sharing.\u003c/p\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003cp\u003eDescriptive statistics are presented as frequencies and percentages. In the survey, respondents used sliders to score the importance of \u0026ldquo;technical,\u0026rdquo; \u0026ldquo;motivational,\u0026rdquo; \u0026ldquo;economic,\u0026rdquo; \u0026ldquo;regulatory,\u0026rdquo; \u0026ldquo;legal,\u0026rdquo; \u0026ldquo;ethical,\u0026rdquo; and \u0026ldquo;political\u0026rdquo; barriers to sharing participant-level clin-epi data, human biological samples, and human genetic data in both epidemic and non-epidemic settings. These categories of barriers are based on a previously developed taxonomy of barriers to sharing data in public health.\u003csup\u003e15\u003c/sup\u003e We considered scores between 0\u0026ndash;33 for the barriers to be classified as \u0026ldquo;not important,\u0026rdquo; 34\u0026ndash;67 as \u0026ldquo;somewhat important,\u0026rdquo; and 68\u0026ndash;100 as \u0026ldquo;very important.\u0026rdquo; We used the Wilcoxon signed-rank test to compare the differences in scores between epidemic and non-epidemic settings for each barrier type (α\u0026thinsp;=\u0026thinsp;0.05). We asked participants to describe their best and worst experiences sharing participant-level clin-epi data, human biological samples, or human genetic data in free text fields. We then categorised responses according to the PEARL framework. For motivators, benefits, and barriers to data and sample sharing, we compared the responses from principal investigators (PIs) to those from non-PIs. Respondents identifying as co-investigators and investigators were grouped and classified under the PI category. We conducted the statistical analyses and created the figures with R Studio version 2023.06.1\u0026thinsp;+\u0026thinsp;524.\u003c/p\u003e \u003cp\u003eEthical clearance\u003c/p\u003e \u003cp\u003e The University of Heidelberg Faculty of Medicine's ERC approved this study\u0026rsquo;s research protocol and survey. Upon accessing the survey link, participants received detailed information about the voluntary nature of their participation, their right to withdraw, the survey's objectives, funding sources, and ethical approval. Informed consent was obtained when participants elected to begin the survey after reviewing this information.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the survey respondents and cohorts\u003c/h2\u003e \u003cp\u003eWe obtained 78 survey responses, either complete or partial, from PIs and study teams representing cohorts in 23 countries. We could not determine an exact non-response rate as we asked study PIs to distribute the survey to their staff, and the total number of staff members who received the survey is unknown. Sixty-nine respondents (89%) indicated their cohort PI\u0026rsquo;s name, which we used, along with the cohort location and pathogen focus, to estimate that responses represented 62 unique cohorts. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 40 (51%) of the survey respondents were cohort PIs, 9 (12%) laboratory scientists, 6 (8%) local investigators, and 4 (5%) laboratory data managers, field data managers, and field staff or data collectors respectively, and 2 (3%) statisticians. Most respondents were 46\u0026ndash;55 (n\u0026thinsp;=\u0026thinsp;27; 35%) or over 55 years old (n\u0026thinsp;=\u0026thinsp;24; 31%) and advanced in their career (n\u0026thinsp;=\u0026thinsp;48; 62%) or mid-career (n\u0026thinsp;=\u0026thinsp;19; 24%). More than half (n\u0026thinsp;=\u0026thinsp;56; 72%) were from the same country where the cohort was located. About half (n\u0026thinsp;=\u0026thinsp;45; 58%) of the cohorts were located in South America, 22 (28%) were in North or Central America, 6 (8%) each in Asia and Africa, and 2 (3%) in Europe. Over half of the respondents said their cohort (n\u0026thinsp;=\u0026thinsp;46; 59%) focused on multiple pathogens. Twelve respondents (15%) represented cohorts focused on ZIKV only, and 7 (9%) on DENV only. Respondents indicated that the main source population for most cohorts was hospitals (n\u0026thinsp;=\u0026thinsp;55; 71%), followed by the community (n\u0026thinsp;=\u0026thinsp;47; 60%). Two respondents (3%) were from cohorts recruited from schools, and two were from other sources.\u003c/p\u003e \u003cp\u003eSixty respondents (77%) reported that their cohort collected multiple types of data and samples, with 32 (41%) collecting SARS-CoV-2-related data or samples. Another 10 (13%) respondents indicated that their cohort collected clin-epi data only, and 3 (4%) indicated that they only collected human-derived samples. Close to half (n\u0026thinsp;=\u0026thinsp;42; 54%) of cohorts shared multiple data types or samples, 22 (28%) shared clin-epi data only, and one respondent indicated that their cohort shared human samples only. Eight respondents (10%) said their cohort was sharing or planning to share SARS-CoV-2 data to existing platforms, including ISARIC, COVID-19 Data Portal, GitHub, and COVI-PREG. Seven respondents (9%) reported that their cohort was sharing human genetic data to a platform; 20 (26%) respondents reported using a biobanking platform to make sample data visible. In terms of timelines for sharing, for clin-epi data, respondents indicated that 12 (15%) of cohorts shared data in real-time, 18 (23%) cohorts within a one year of data collection, 25 (32%) cohorts over one year after collection, and 24 (32%) after publication. For sharing human OMICs data, respondents reported that 3 (4%) cohorts shared data in real-time, 2 (3%) within one year after collection, 4 (5%) over one year after collection, and 3 (4%) after publication. For the sharing of human biological samples, respondents indicated that 12 (15%) cohorts shared samples in real-time, 16 (21%) within one year of collection, 19 (24%) over one year after collection, and 13 (17%) after publication. Twenty-nine (37%) of respondents said that they obtained broad consent for future use of clin-epi data, 25 (32%) for human biological samples, and 7 (9%) for human genetic data. Twenty (26%) respondents indicated that their cohort had a documented process for external groups to access clin-epi data, 15 (19%) for human biological samples, and 3 (4%) for human genetic data. Regarding data standards, less than 25% (n\u0026thinsp;=\u0026thinsp;18) of survey respondents reported that the variables collected in their cohort corresponded to an internationally accepted standard. Data standards used included International Classification of Diseases (ICD)-9, 10, or 11, NCI Thesaurus, Medical Dictionary for Regulatory Activities (MedDRA), and Health Level Seven International (HL7).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eBest and worst sharing experiences\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents respondents\u0026rsquo; best and worst experiences sharing participant-level clin-epi or human genetic data or human biological samples from their cohort, classified according to the PEARL framework. Fewer than one-third of survey respondents provided their best (n\u0026thinsp;=\u0026thinsp;24; 31%) and worst sharing experience (n\u0026thinsp;=\u0026thinsp;18; 23%). The most commonly reported best data or sample-sharing experiences were classified as political, including eight respondents who indicated that collaborating with, exchanging ideas, and forming partnerships with experts and researchers worldwide were their best data or sample-sharing experiences. Ethical experiences, chiefly the lack of benefit sharing, were the most commonly reported worst sharing experiences. Three respondents indicated that they did not have any bad experiences to share.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eMotivators for sharing\u003c/h2\u003e \u003cp\u003eUsing pre-specified, multiple-choice options, we asked survey respondents to identify their top three motivators for sharing de-identified participant-level clin-epi data, human biological samples, and human genetic data (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sharing participant-level clin-epi data for a public health rationale (other than the development of novel vaccines, treatments, or therapies) (n\u0026thinsp;=\u0026thinsp;32; 50%), increased funding opportunities or opportunities for collaboration (n\u0026thinsp;=\u0026thinsp;21; 33%), and conducting a cross-cohort or international study with funding support (n\u0026thinsp;=\u0026thinsp;20; 31%) were the three most frequently chosen motivators for cohorts to share participant-level clin-epi data. Increased funding opportunities or opportunities for collaboration (n\u0026thinsp;=\u0026thinsp;22; 41%), public health rationale (other than development of novel vaccines, treatments, or therapies) (n\u0026thinsp;=\u0026thinsp;20; 37%), and the development of novel vaccines, treatments, or therapies (n\u0026thinsp;=\u0026thinsp;19; 35%) were the top three motivators for cohorts to share human biological samples. Lastly, public health rationale (other than the development of novel vaccines, treatments, or therapies) (n\u0026thinsp;=\u0026thinsp;13; 65%), the conduct of a cross-cohort or international study with funding support (n\u0026thinsp;=\u0026thinsp;10; 50%), and the development of novel vaccines, treatments, or therapies (n\u0026thinsp;=\u0026thinsp;5; 25%) tied with informing future research investments (n\u0026thinsp;=\u0026thinsp;5; 25%) and increased funding opportunities or opportunities for collaboration (n\u0026thinsp;=\u0026thinsp;5; 25%) were the top three motivators for cohorts to share human genetic data. Two survey respondents each indicated that there were no motivators for sharing participant-level clin-epi data and human biological samples. One survey respondent indicated that there were no motivators for sharing human genetic data from their cohort.\u003c/p\u003e \u003cp\u003eFigure 1 presents the motivators for sharing participant-level clin-epi data, human genetic data, and human biological samples for PIs and non-PIs. A greater percentage of PIs than non-PIs indicated increased authorship opportunities (76% vs 24%), and the ability to conduct a cross-cohort or international study (67% vs 33%) were motivators for sharing. Appendix Figs.\u0026nbsp;1A\u0026ndash;C present the motivators for sharing participant-level clin-epi data, human biological samples, and human genetic data for PIs and non-PIs. Reflecting the overall results, compared to other study staff members, a higher percentage of PIs identified increased authorship opportunities as a motivator for sharing participant-level clin-epi data (80% vs 20%), human biological samples (70% vs 30%) and human genetic data where 100% of PIs from cohorts sharing human genetic data voted for this motivator. Funder requirements were also more cited as a motivator by PIs than other cohort staff for sharing participant-level clin-ep data (67% vs 33%) and human genetic data (75% vs 25%). The opposite was observed for sharing human biological samples, where more non-PIs than PIs saw funder requirements as a motivator (60% vs 40%). Reflecting the overall results, more PIs than non-PIs identified being able to conduct a cross-cohort study with funding support as a motivator for sharing participant-level clin-epi data (65% vs 35%), human biological samples (67% vs 33%), and human genetic data (70% vs 30%).\u003c/p\u003e \u003cp\u003eFigure 1. Motivators for sharing participant-level clin-epi data, human genetic data, and human biological samples for PIs and non-PIs\u003c/p\u003e \u003cp\u003e*Other than the development of novel vaccines, treatments, or therapies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBenefits of sharing\u003c/h2\u003e \u003cp\u003eWe present respondents\u0026rsquo; top three benefits of sharing de-identified participant-level clin-epi data, human biological samples, and human genetic data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The three most frequently chosen benefits of sharing participant-level clin-epi data were enhanced insights through collaboration (n\u0026thinsp;=\u0026thinsp;37; 67%), increased opportunities for authorship (n\u0026thinsp;=\u0026thinsp;34; 62%) and increased funding opportunities (n\u0026thinsp;=\u0026thinsp;29; 53%). The three most frequently chosen benefits of sharing human biological samples from cohorts were expanded grant opportunities (n\u0026thinsp;=\u0026thinsp;24; 52%), long-term capacity-building investment (n\u0026thinsp;=\u0026thinsp;21; 46%), and free or reduced-cost access to novel diagnostics, treatments, or prevention (n\u0026thinsp;=\u0026thinsp;20; 44%). For sharing human genetic data from cohorts, the three most commonly chosen benefits were enhanced insights through collaboration (n\u0026thinsp;=\u0026thinsp;11; 79%), reduced duplication of efforts (n\u0026thinsp;=\u0026thinsp;9; 64%), and increased funding opportunities (n\u0026thinsp;=\u0026thinsp;7; 50%) tied with increased opportunities for authorship (n\u0026thinsp;=\u0026thinsp;7; 50%) and free or reduced-cost access to novel diagnostics, treatments, or prevention (n\u0026thinsp;=\u0026thinsp;7; 50%). One survey respondent indicated that there were no benefits to sharing participant-level clin-epi data, and two respondents indicated that there were no benefits to sharing human biological samples.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003eAppendix Figures 2A–C present the most important benefits of sharing participant-level clin-epi data, human biological samples, and human genetic data for PIs versus non-PIs. Short-term capacity building was seen as a benefit of sharing by a greater percentage of PIs than non-PIs for clin-epi data (56% vs 44%), human biological samples (56% vs 44%), and human genetic data (60% vs 40%). Reduced duplication of efforts was also seen as a benefit of sharing by a greater percentage of PIs than non-PIs for clin-epi data (57% vs 43%) and human genetic data (56% vs 44%). We were unable to combine the benefits of sharing participant-level clin-epi data, human genetic data, and human biological samples for PIs and non-PIs into one figure (as done for motivators in Figure 1) as the benefits for sharing human biological samples that respondents could choose in the survey were different from those provided for clin-epi and human genetic data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBarriers to sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures 2A and B present respondent ratings for barriers to sharing participant-level clin-epi data, human biological samples, and human genetic data within epidemic (2A) and non-epidemic settings (2B). Barriers most frequently scored as “very important” in epidemic settings were technical barriers to sharing participant-level clin-epi data and human genetic data and regulatory barriers to sharing human biological samples. Technical barriers were also frequently scored as “very important” for sharing clin-epi data and human biological samples outside of epidemic settings. In contrast, ethical barriers were more frequently highlighted as “very important” for sharing human genetic data outside epidemic settings. The only barriers that were found to be significantly different between epidemic and non-epidemic settings were regulatory barriers to sharing human biological samples (Table 4), which were scored higher in epidemic settings versus non-epidemic settings (94 vs 83; p\u0026lt;0.05). \u0026nbsp;Political barriers were most often cited as “not very important” across data and sample types in both epidemic and non-epidemic settings.\u003c/p\u003e\n\u003cp\u003eFigure 2. Barriers to sharing participant-level clin-epi data, human biological samples, and human genetic data within epidemic (2A) and non-epidemic settings (2B)\u003c/p\u003e\n\u003cp\u003eAppendix Figure 3 compares barriers to sharing for PIs and non-PIs. Appendix Figure 4 compares barriers for sharing data and samples separately for PIs and non-PIs. Appendix Figures 5A and 5B compare barriers for sharing data and samples for PIs and non-PIs in both epidemic (5A) and non-epidemic settings (5B). A greater percentage of PIs than non-PIs scored economic barriers for sharing participant-level clin-epi data, human genetic data, and human biological samples as “very important” in both epidemic (clin-epi: 41% vs 23%; genetic: 40% vs 20%; samples: 42% vs 31%) and non-epidemic (clin-epi: 39% vs 20%; genetic: 42% vs 25%; samples: 36% vs 21%) settings. A higher percentage of PIs vs non-PIs scored motivational barriers in epidemic settings as “very important” for participant-level clin-epi data (34% vs 19%), human biological samples (36% vs 25%), and human genetic data (40% vs 10%). \u0026nbsp;The rating of which barriers to sharing were the least important was generally consistent for PIs and other members of the cohort staff, with political barriers cited as the least important (Appendix Figure 3). That said, when considered at the data type level, PIs and non-PIs rated barriers differently. \u0026nbsp;Other cohort members were less concerned with legal barriers to reusing participant-level data than PIs. In contrast, PIs were less concerned with legal barriers to sharing human genetic data than other cohort members (Appendix Figure 4). Both PIs and other cohort members agreed that technical barriers to sharing participant-level data and samples were important or very important.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a cross-sectional survey to understand the experiences, motivators, benefits and barriers in epidemic and non-epidemic settings to share clin-epi data, human biological samples, and human genetic data from AFI-related cohorts. The 78 survey responses from AFI researchers representing cohorts in 23 countries highlight diverse experiences with data and sample sharing. Most respondents were older and at an advanced career stage. Most cohorts collected multiple data types and samples; about half of the respondents reported that their cohorts had shared data or samples. A small proportion of respondents indicated that their cohort shared clin-epi data or human samples in real-time. Rapid or real-time data and sample sharing can fast-track research and development for diagnostic, treatment, and prevention-related tools during and outside of epidemics and is especially important for addressing EIDs.\u003csup\u003e2,36\u0026ndash;38\u003c/sup\u003e Ongoing and developing outbreaks, such as Ebola, Zika, and COVID-19, underscore the importance and challenges of quick, open, and effective sharing of high-quality samples and related clin-epi data and human genetic data from various populations for an informed response.\u003csup\u003e39\u0026ndash;41\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBest and worst data and sample-sharing experiences\u003c/h2\u003e \u003cp\u003eIn this study, the opportunity to form partnerships with experts and researchers worldwide was the most frequently cited best aspect of data and sample sharing, which is similar to findings from other studies. In a qualitative study with public health and biomedical researchers in Thailand, senior researchers highlighted the importance of sharing health research data to foster cross-national collaborations and improve their research portfolio.\u003csup\u003e4\u003c/sup\u003e In a survey disseminated to life scientists in 13 countries in sub-Saharan Africa, close to half of the respondents indicated that the most significant benefit of sharing their data was networking and collaboration opportunities.\u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLack of communication regarding the results of analyses made possible through data or sample reuse and lack of benefit sharing, including not being credited in resulting publications, were the most commonly reported worst sharing experiences. The study mentioned above involving Thai researchers reflects this finding, wherein researchers acknowledged that the benefits of sharing can only be realised with appropriate acknowledgement and attribution of the data providers.\u003csup\u003e4\u003c/sup\u003e Several researchers recounted personal experiences where data users did not adequately acknowledge them. They highlighted the harms of this, including reducing future funding opportunities, impeding career advancement, and damaging their reputation.\u003csup\u003e4\u003c/sup\u003e A systematic review of factors associated with data sharing also highlighted investigator concerns around benefit sharing, which included being appropriately credited and involved in future funding opportunities made possible through data and sample reuse.\u003csup\u003e43\u003c/sup\u003e Another scoping review highlighted that the lack of benefit sharing that researchers from LMICs face, including being stuck as data producers only, lack of recognition, and lack of career progression, are all threats to equitable global health research.\u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMotivators for data and sample sharing\u003c/h2\u003e \u003cp\u003eLeading motivators for sharing participant-level data and samples included public health reasons other than developing novel vaccines, treatments, or therapies, increased funding or collaboration opportunities, funding support for cross-study analyses, and developing novel vaccines, treatments, or therapies. Our finding that improving public health is one of the most important motivators for data and sample sharing is reflected in empirical research with investigators from the Ebola and Yellow Fever epidemics.\u003csup\u003e45\u003c/sup\u003e Reflecting researchers\u0026rsquo; best sharing experiences, increased funding and collaboration opportunities, and funding support for cross-cohort or international studies were top motivators for sharing data and human biological samples.\u003c/p\u003e \u003cp\u003eWe also compared the motivators for sharing participant-level clin-epi data, human biological samples, and human genetic data between study PIs and non-PIs. A substantially higher percentage of PIs found increased authorship opportunities to be a strong motivator for sharing participant-level clin-epi data, human biological samples and human genetic data compared to other study staff members. This difference may relate to different authorship opportunities within cohorts for PIs vs non-PIs.\u003csup\u003e46\u003c/sup\u003e PIs occupying leadership positions are likelier to see their contributions recognised and acknowledged in publications.\u003csup\u003e47\u003c/sup\u003e Senior researchers in Vietnam indicated that academic recognition in the form of authorship on papers that use the data they produce is crucial for them. Researchers in the same paper also saw authorship as a way of being responsible for the ethical and scientific integrity of the research.\u003csup\u003e48\u003c/sup\u003e More PIs also considered engagement in cross-cohort or international studies with funding support a motivator than non-PIs for all data types. This difference could relate to PIs\u0026rsquo; responsibility to fund cohorts or the belief that the cross-national collaboration will increase research quality or impact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBenefits of data and sample sharing\u003c/h2\u003e \u003cp\u003eThe top benefits for sharing participant-level clin-epi data and human genetic data indicated by survey respondents were enhanced insights through collaboration, increased opportunities for authorship, reduced duplication of efforts, and increased funding opportunities. The top benefits of sharing human biological samples included expanded grant opportunities, long-term capacity-building investments, and free or reduced-cost access to novel diagnostics, treatments, or prevention. Increased collaboration, authorship, and funding opportunities reflect the best data and sample-sharing experiences and motivators for sharing in this study. These have also been acknowledged to be essential benefits that need to be provided to LMIC researchers to ensure equitable data sharing in global health research.\u003csup\u003e44\u003c/sup\u003e Reduced duplication of research efforts has also been seen as a benefit of data sharing by other health research stakeholders, research participants, and community representatives in India and Kenya.\u003csup\u003e7,49\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe also compared the benefits of sharing participant-level clin-epi data, human biological samples, and human genetic data between study PIs and non-PIs. For clin-epi data, human biological samples, and human genetic data, a greater percentage of PIs than non-PIs saw short-term capacity building as a benefit of sharing. A qualitative study of Vietnamese researchers indicated that their view of sustainable and acceptable data sharing included reciprocity, wherein capacity building and strengthening of data producers were prioritised.\u003csup\u003e48\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBarriers to data and sample sharing\u003c/h2\u003e \u003cp\u003eWe did not find any significant differences in the importance of barriers to sharing data or samples in epidemic vs. non-epidemic settings, except regulatory barriers for sharing biological samples which were scored higher in epidemic settings versus non-epidemic settings. Similar studies on data and sample sharing in infectious disease-related observational research have identified regulatory obstacles as a primary challenge. One study examining barriers and enablers in responding to the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak highlighted legal frameworks and regulatory procedures as the primary obstacles.\u003csup\u003e38\u003c/sup\u003e These barriers ultimately resulted in delays in MERS-CoV infection notifications, prolonged processes for data-sharing authorization, and difficulties in shipping/importing samples.\u003csup\u003e38\u003c/sup\u003e Similarly, another study that examined barriers to information and sample sharing in the aftermath of response efforts to combat the Zika virus outbreak highlighted regulatory delays, specifically in regulatory approvals, as one of the three significant bottlenecks faced by researchers.\u003csup\u003e50\u003c/sup\u003e Our finding that barriers and facilitators are different for cohort PIs compared to other members of the cohort staff suggests that barriers and enablers to biomedical research data reuse should be disaggregated by the respondents' roles in the study. Future qualitative research could explore why barriers and facilitators may be different for PIs and other cohort staff.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis research has several strengths. We developed the survey through consultations with AFI researchers and an extensive literature review on data and sample sharing in infectious diseases. To our knowledge, this is the first study to assess barriers and facilitators to data and sample sharing within AFI cohorts both during and outside of epidemics and using the PEARL framework. Findings from this survey can help develop meaningful interventions for funders, cross-national surveillance systems, and other stakeholders to increase the rapidity, incidence, and quality of data and sample sharing. The survey also has several limitations. Most AFI cohorts were based in South, North, or Central America. However, we only circulated the survey in English, which could have prevented some respondents from answering the survey. We had more extensive lists of AFI studies and closer connections to AFI researchers from the Americas because of the recent ZIKV epidemic. AFI cohorts based in Asia and Africa were less likely to have been contacted and responded to the survey. Results should be interpreted with caution as they could be subject to self-reported survey-related recall and response bias. Furthermore, as the names of the respondents and the cohorts were not collected, we had to estimate the number of cohorts that the survey respondents represented.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCompared to populations in HICs, LMICs face a disproportionate burden of IDs. Data and sample sharing are essential during and outside epidemics as they facilitate rapid identification and understanding of the disease, enable the development of diagnostics, treatments, and vaccines, and support a coordinated global response. Understanding cohort teams' motivations for sharing data and samples during and outside epidemics helps regulators and funders understand what barriers to address and incentives to support. While we did not find differences in barriers and motivations for sharing different data types during and outside of epidemics, we did find differences between cohort PIs and other members of cohort teams. Addressing barriers to data sharing, including loss of publication opportunities and misinterpretation of data, and sample sharing, including regulatory issues, could help improve the sharing of data and samples. Supporting equitable data and sample reuse in keeping with cohort teams\u0026rsquo; main motivations, including building cross-national collaborations and advancing public health, can facilitate the long-standing partnerships needed to facilitate data and sample reuse during and outside of epidemics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAFI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Acute febrile illness\u003c/p\u003e\n\u003cp\u003eCHIKV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chikungunya virus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClin-epi \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Clinical-epidemiological\u003c/p\u003e\n\u003cp\u003eDENV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Dengue virus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eERC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Ethics Review Committee\u003c/p\u003e\n\u003cp\u003eGDPR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;General Data Protection Regulation\u003c/p\u003e\n\u003cp\u003eHIPPA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Health Insurance Portability and Accountability Act\u003c/p\u003e\n\u003cp\u003eHIV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Human immunodeficiency virus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIDAMS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;International Research Consortium on Dengue Risk Assessment, Management \u0026nbsp;and Surveillance\u003c/p\u003e\n\u003cp\u003eID \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Infectious disease\u003c/p\u003e\n\u003cp\u003eIDDO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Infectious Diseases Data Observatory\u003c/p\u003e\n\u003cp\u003eLMICs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low-and-middle-income countries\u003c/p\u003e\n\u003cp\u003eMERS-CoV \u0026nbsp; \u0026nbsp; \u0026nbsp;Middle East Respiratory Syndrome Coronavirus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNTDs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Neglected tropical diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePEARL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Political, Ethical, Administrative, Regulatory, and Legal\u003c/p\u003e\n\u003cp\u003ePIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Principal Investigators\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; World Health Organization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWWARN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; World-Wide Malaria Research Network\u003c/p\u003e\n\u003cp\u003eZIKV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Zika virus\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe research protocol and survey were reviewed and approved by the Ethics Review Committee of the University of Heidelberg Faculty of Medicine. After reading the study description and consent form, participants provided informed consent by clicking the survey link.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey and de-identified dataset of survey responses are available on OSF (doi: 10.17605/OSF.IO/EDPSH).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study is part of the ReCoDID project, which is funded by the EU Horizon 2020 research and innovation programme (grant agreement 825746) and the Canadian Institutes of Health Research (CIHR) Institute of Genetics (grant agreement 01886-000).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003ePS and LM designed the survey protocol, survey, and analysis plan. PS conducted the analysis. PS, LM and MN wrote the first draft of the manuscript. PS, TJ, MN, and LM provided critical feedback and revisions to the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge Claudia Emerson, Martine van Roode, and Mar\u0026iacute;a Consuelo Miranda Montoya\u0026rsquo;s review and comments on the draft survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchwalbe N, Wahl B, Song J, Lehtimaki S. Data Sharing and Global Public Health: Defining What We Mean by Data. \u003cem\u003eFront Digit Health\u003c/em\u003e. 2020;2:612339. doi:10.3389/FDGTH.2020.612339\u003c/li\u003e\n\u003cli\u003eGiri J, Pezzi L, Cachay R, et al. 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Zika virus epidemiology: from Uganda to world pandemic, an update. \u003cem\u003eEpidemiol Infect\u003c/em\u003e. 2018;146(6):673-679. doi:10.1017/S0950268818000419\u003c/li\u003e\n\u003cli\u003eHolmes EC, Dudas G, Rambaut A, Andersen KG. The evolution of Ebola virus: Insights from the 2013-2016 epidemic. \u003cem\u003eNature\u003c/em\u003e. 2016;538(7624):193-200. doi:10.1038/NATURE19790\u003c/li\u003e\n\u003cli\u003eTam PYI, Obaro SK, Storch G. Challenges in the Etiology and Diagnosis of Acute Febrile Illness in Children in Low- and Middle-Income Countries. \u003cem\u003eJ Pediatric Infect Dis Soc\u003c/em\u003e. 2016;5(2):190. doi:10.1093/JPIDS/PIW016\u003c/li\u003e\n\u003cli\u003eHome - PREPARE Europe. Accessed June 5, 2024. https://www.prepare-europe.eu/index.html\u003c/li\u003e\n\u003cli\u003eNon-malaria febrile illness (NMFI) surveyor - fever series. Accessed June 5, 2024. http://www.wwarn.org/surveyor/NMFIv3/#0\u003c/li\u003e\n\u003cli\u003eAlger J, De Alencar Ximenes RA, Avelino-Silva VI, et al. The Zika Virus Individual Participant Data Consortium: A Global Initiative to Estimate the Effects of Exposure to Zika Virus during Pregnancy on Adverse Fetal, Infant, and Child Health Outcomes. \u003cem\u003eTrop Med Infect Dis\u003c/em\u003e. 2020;5(4). doi:10.3390/TROPICALMED5040152\u003c/li\u003e\n\u003cli\u003eObadia T, Gutierrez-Bugallo G, Duong V, et al. Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project. \u003cem\u003eNat Commun\u003c/em\u003e. 2022;13(1). doi:10.1038/S41467-022-32234-Y\u003c/li\u003e\n\u003cli\u003eWilder-Smith A, Brickley EB, Ximenes RA de A, et al. The legacy of ZikaPLAN: a transnational research consortium addressing Zika. \u003cem\u003eGlob Health Action\u003c/em\u003e. 2021;14(sup1). doi:10.1080/16549716.2021.2008139\u003c/li\u003e\n\u003cli\u003eAEDES - Red de Conocimiento y Cooperaci\u0026oacute;n. Accessed June 6, 2024. https://www.redaedes.org/\u003c/li\u003e\n\u003cli\u003eHOME. Accessed June 3, 2024. https://www.idams.eu/\u003c/li\u003e\n\u003cli\u003eWWARN | Infectious Diseases Data Observatory. Accessed June 3, 2024. https://www.iddo.org/wwarn\u003c/li\u003e\n\u003cli\u003eAccessing data | Infectious Diseases Data Observatory. Accessed June 3, 2024. https://www.iddo.org/ebola/data-sharing/accessing-data\u003c/li\u003e\n\u003cli\u003eHarris PA, Taylor R, Minor BL, et al. 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Why you should share your data during a pandemic. \u003cem\u003eBMJ Glob Health\u003c/em\u003e. 2021;6(3):e004940. doi:10.1136/BMJGH-2021-004940\u003c/li\u003e\n\u003cli\u003eDickmann P, Kitua A, Kaczmarek P, et al. Using Lessons Learned from Previous Ebola Outbreaks to Inform Current Risk Management. \u003cem\u003eEmerg Infect Dis\u003c/em\u003e. 2015;21(5). doi:10.3201/EID2105.142016\u003c/li\u003e\n\u003cli\u003eSims JM, Lawrence E, Glazer K, et al. Lessons learned from the COVID-19 pandemic about sample access for research in the UK. \u003cem\u003eBMJ Open\u003c/em\u003e. 2022;12(4):e047309. doi:10.1136/BMJOPEN-2020-047309\u003c/li\u003e\n\u003cli\u003eBezuidenhout L, Chakauya E. Hidden concerns of sharing research data by low/middle-income country scientists. \u003cem\u003eGlobal Bioethics\u003c/em\u003e. 2018;29(1):39. doi:10.1080/11287462.2018.1441780\u003c/li\u003e\n\u003cli\u003eZuiderwijk A, Shinde R, Jeng W. What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption. \u003cem\u003ePLoS One\u003c/em\u003e. 2020;15(9):e0239283. doi:10.1371/JOURNAL.PONE.0239283\u003c/li\u003e\n\u003cli\u003eEvertsz N, Bull S, Pratt B. What constitutes equitable data sharing in global health research? A scoping review of the literature on low-income and middle-income country stakeholders\u0026rsquo; perspectives. \u003cem\u003eBMJ Glob Health\u003c/em\u003e. 2023;8(3):10157. doi:10.1136/BMJGH-2022-010157\u003c/li\u003e\n\u003cli\u003eTappan J, Varanda-Ferreira J, Mason K, Beyer M. \u003cem\u003ePublic Health Emergencies: Anthropological and Historical Perspectives on Data Sharing during The\u003c/em\u003e.; 2014.\u003c/li\u003e\n\u003cli\u003eMartins RS, Mustafa MA, Fatimi AS, Nasir N, Pervez A, Nadeem S. The CalculAuthor: determining authorship using a simple-to-use, fair, objective, and transparent process. \u003cem\u003eBMC Res Notes\u003c/em\u003e. 2023;16(1):329. doi:10.1186/S13104-023-06597-4\u003c/li\u003e\n\u003cli\u003eSmith E, Williams-Jones B, Master Z, et al. Researchers\u0026rsquo; Perceptions of Ethical Authorship Distribution in Collaborative Research Teams. \u003cem\u003eSci Eng Ethics\u003c/em\u003e. 2020;26(4):1995-2022. doi:10.1007/S11948-019-00113-3\u003c/li\u003e\n\u003cli\u003eMerson L, Phong TV, Nhan LNT, et al. Trust, Respect, and Reciprocity: Informing Culturally Appropriate Data-Sharing Practice in Vietnam. \u003cem\u003eJournal of Empirical Research on Human Research Ethics\u003c/em\u003e. 2015;10(3):251-263. doi:10.1177/1556264615592387\u003c/li\u003e\n\u003cli\u003eJao I, Kombe F, Mwalukore S, et al. 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Familiar barriers still unresolved-a perspective on the Zika virus outbreak research response. \u003cem\u003eLancet Infect Dis\u003c/em\u003e. 2019;19(2):e59-e62. doi:10.1016/S1473-3099(18)30497-3\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Survey respondent and cohort characteristics (N=78)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge range of respondent\u003c/strong\u003es\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Under 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; 25-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e9 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; 36-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e15 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; 46-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e27 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Over 55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e24 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Not indicated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespondent\u0026rsquo;s role within the cohort\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Principal Investigator/co-investigator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e40 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Local investigator\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Laboratory data manager\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e4 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Laboratory scientist\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e9 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Field data manager\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e4 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Field staff/data collector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e4 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Statistician\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Others\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e7 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Not indicated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespondent career stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Early career\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e8 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Mid-career\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e19 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Advanced career\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e48 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Not indicated\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespondent is from the country where the cohort is located\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e56 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; South America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e45 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; North or Central America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e22 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort source population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e47 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e55 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCohort pathogen of interest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; ZIKV only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e12 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DENV only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e7 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e46 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of data or samples collected\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Clin-epi data only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e10 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Human OMICs data only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Human samples only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Multiple data or sample types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e60 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollecting SARS-CoV-2 data or samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e32 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of data or samples shared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Clin-epi data only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e22 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003eHuman OMICs data only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003eHuman samples only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple data or sample types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e42 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSharing or planning to share SARS-CoV-2 data with existing platform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e8 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSharing human OMICs data to a platform\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e7 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsing a biobanking platform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e20 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhen clin-epi data is shared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Real-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e12 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Less than or equal to 1 year after collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e18 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Over one year after the collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e25 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; After publication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e25 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhen human OMICs shared\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Real-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Less than or equal to 1 year after collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Over one year after the collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e4 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; After publication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhen human samples are shared\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Real-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e12 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Less than or equal to 1 year after collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e16 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Over one year after the collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e19 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; After publication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e13 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo\u003c/strong\u003e \u003cstrong\u003evariables correspond to an internationally accepted standard\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e18 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsent for use of data/samples beyond the study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Clin-epi data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e29 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Human samples\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e25 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Human OMICs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e7 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocumented process for external groups to access data/samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Clin-epi data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e20 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Human samples\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e15 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.70707070707071%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Human OMICs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Best and worst experiences with data, sample, or genetic data sharing\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"945\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBest sharing experiences\u0026nbsp;\u003c/strong\u003e(n=24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorst sharing experiences\u003c/strong\u003e (n=18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolitical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eCollaborating with, exchanging ideas, and forming partnerships with experts and researchers across the world (n=8)\u003c/p\u003e\n \u003cp\u003eContributing to a multi-national research initiative (n=5)\u003c/p\u003e\n \u003cp\u003eDeveloping high-impact scientific publications (n=5)\u003c/p\u003e\n \u003cp\u003eProviding data to the MoH to encourage them to formulate disease control measures (n=1)\u003c/p\u003e\n \u003cp\u003eComparing results with other countries (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eLack of clarity on the reason for wanting data by the research group requesting the data, especially in the case of researchers with external funding from HICs requesting data from LMICs (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eContributing to improved quality and rigour of data analysis, i.e., through larger sample size and support of a larger team of researchers (n=5)\u003c/p\u003e\n \u003cp\u003eImproving knowledge, awareness, and control of diseases (n=3)\u003c/p\u003e\n \u003cp\u003eImproving the diagnostic capabilities of different laboratories via the data sharing project (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eNo communication regarding the results of the analysis of shared data/samples (n=4)\u003c/p\u003e\n \u003cp\u003eNot being recognised and credited in publications or receiving any other type of benefit for sharing data/samples (n=4)\u003c/p\u003e\n \u003cp\u003eUnclear or lack of benefit sharing/collaboration plan from the research group that obtained that data (n=3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdministrative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eCentrally organised logistics for sample sharing (n=2)\u003c/p\u003e\n \u003cp\u003eClear study plan in place (n=1)\u003c/p\u003e\n \u003cp\u003eAbility to contribute to analysis plans for the shared data (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eLengthy duration of processes and overwhelming paperwork needed for sharing or receiving data (n=2)\u003c/p\u003e\n \u003cp\u003eLack of metadata needed to prepare data for sharing (n=2)\u003c/p\u003e\n \u003cp\u003eDifficult to manage laboratory structures in small urban hospitals in areas with low human resources for sample sharing (n=1)\u003c/p\u003e\n \u003cp\u003eNot being consulted regarding data analysis plans (n=1)\u003c/p\u003e\n \u003cp\u003eLack of funding to prepare the data and metadata for sharing (n=1)\u003c/p\u003e\n \u003cp\u003eLong duration of time needed to format data and metadata for sharing (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eGenerating data to aid in the approval of diagnostics and reagents during critical periods of epidemics (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eAccidentally uploading images with personal identifiers to the central database (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLegal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eHelping to initiate conversations with sponsors to increase budgets to cover a more extensive scope of analysis (n=2)\u003c/p\u003e\n \u003cp\u003eExperiencing a well-defined contract in place between parties (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003eData recipients trying to patent a product based on the shared data/samples (n=2)\u003c/p\u003e\n \u003cp\u003eSuffering from lack of a well-defined contract in place between parties (n=1)\u003c/p\u003e\n \u003cp\u003eLong duration of time needed to obtain relevant permissions for sharing data (n=1)\u003c/p\u003e\n \u003cp\u003eLong duration of time required to obtain an export license for sharing samples (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo good/bad experience\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003en=0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003en=3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.497354497354497%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo data-sharing experience was mentioned\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003en=54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.75132275132275%\" valign=\"top\"\u003e\n \u003cp\u003en=57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Explicitly stated that they did not have any good/bad experiences with sharing data or samples; \u003csup\u003e\u0026dagger;\u003c/sup\u003e Irrespective of whether or not they indicated that they shared data or samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Most important motivators and benefits of data or sample sharing with groups outside of pre-established partnerships with cohorts\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMotivators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDe-identified, participant-level clin-epi data (N=64)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDe-identified, participant-level clin-epi data (N=55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003ePublic health rationale, other than development of novel vaccines, treatments, or therapies (n=32; 50%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIncreased funding opportunities or opportunities for collaboration (n=21; 33%)\u003c/p\u003e\n \u003cp\u003eCross-cohort or international study with funding support (n=20; 31%)\u003c/p\u003e\n \u003cp\u003eDevelopment of novel vaccines, treatments, or therapies (n=16; 25%)\u003c/p\u003e\n \u003cp\u003eCross-cohort or international study without funding support (e.g. individual participant data meta-analysis) (n=14; 22%)\u003c/p\u003e\n \u003cp\u003ePrevent duplication of efforts (n=13; 20%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFunder requirement (n=12; 19%)\u003c/p\u003e\n \u003cp\u003eInform future research investments (n=10; 16%)\u003c/p\u003e\n \u003cp\u003eIncreased authorship opportunities (n=10; 16%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNational MoH requirement (n=7; 11%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLocal health department (state/department or regional level) requirement (n=6; 9%)\u003c/p\u003e\n \u003cp\u003eMoral obligation to understand the underlying pathology of diseases (n=1; 2%)\u003c/p\u003e\n \u003cp\u003eObtaining insight into rare disease outcomes (n=1; 2%)\u003c/p\u003e\n \u003cp\u003eNo motivation for sharing de-identified participant-level clin-epi data (n=2; 3%\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eEnhanced insights through collaboration (n=37; 67%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIncreased opportunities for authorship (n=34; 62%)\u003c/p\u003e\n \u003cp\u003eIncreased funding opportunities (n=29; 53%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eReduced duplication of efforts (n=23; 42%)\u003c/p\u003e\n \u003cp\u003eLong-term capacity building investment (e.g. funded postdoc) (n=24; 44%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShort-term capacity building investment (e.g. short course) (n=19; 35%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFree or reduced-cost access to novel diagnostics, treatments, or prevention (n=14; 25%)\u003c/p\u003e\n \u003cp\u003eNo benefits of sharing de-identified participant-level clin-epi data (n=1; 2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman biological samples (N=54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman biological samples (N=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eIncreased funding opportunities or opportunities for collaboration (n=22; 41%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePublic health rationale, other than development of novel vaccines, treatments, or therapies (n=20; 37%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDevelopment of novel vaccines, treatments, or therapies (n=19; 35%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCross-cohort or international study with funding support (n=18; 33%)\u003c/p\u003e\n \u003cp\u003ePrevent duplication of efforts (n=11; 20%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFunder requirement (n=10; 19%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIncreased authorship opportunities (n=10; 19%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eInform future research investments (n=9; 17%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCross-cohort or international study without funding support (e.g. individual participant data meta-analysis) (n=8; 15%)\u003c/p\u003e\n \u003cp\u003eNational MoH requirement (n=6; 11%)\u003c/p\u003e\n \u003cp\u003eLocal health department (state/department or regional level) requirement (n=3; 6%)\u003c/p\u003e\n \u003cp\u003eTo access better technology (n=1; 2%)\u003c/p\u003e\n \u003cp\u003eNo motivation for sharing human biological samples (n=2; 4%\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eExpanded grant opportunities (n=24; 52%)\u003c/p\u003e\n \u003cp\u003eLong-term capacity building investment (e.g. funded postdoc) (n=21; 46%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFree or reduced-cost access to novel diagnostics, treatments, or prevention (n=20; 44%)\u003c/p\u003e\n \u003cp\u003eShort-term capacity building investment (e.g. short course) (n=18; 39%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eJoint ownership of rights to IP produced using samples (n=17; 37%)\u003c/p\u003e\n \u003cp\u003eParticipation in a multi-site biobanking network (n=17; 37%)\u003c/p\u003e\n \u003cp\u003eInfrastructure funding for biorepository (n=17; 37%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAccess fee (n=3; 7%)\u003c/p\u003e\n \u003cp\u003eNo benefits of sharing human biological samples (n=2; 4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman genetic/OMICs data (N=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman genetic/OMICs data (N=14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003ePublic health rationale, other than development of novel vaccines, treatments, or therapies (n=13; 65%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCross-cohort or international study with funding support (n=10; 50%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDevelopment of novel vaccines, treatments, or therapies (n=5; 25%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eInform future research investments (n=5; 25%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIncreased funding opportunities or opportunities for collaboration (n=5; 25%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFunder requirement (n=4; 20%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCross-cohort or international study without funding support (e.g. individual participant data meta-analysis) (n=4; 20%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePrevent duplication of efforts (n=2; 10%)\u003c/p\u003e\n \u003cp\u003eLocal health department (state/department or regional level) requirement (n=2; 10%) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNational MoH requirement (n=1; 5%)\u003c/p\u003e\n \u003cp\u003eIncreased authorship opportunities (n=1; 5%)\u003c/p\u003e\n \u003cp\u003eNo motivation for sharing human genetic data (n=1; 5%\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eEnhanced insights through collaboration (n=11; 79%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eReduced duplication of efforts (n=9; 64%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIncreased funding opportunities (n=7; 50%)\u003c/p\u003e\n \u003cp\u003eIncreased opportunities for authorship (n=7; 50%)\u003c/p\u003e\n \u003cp\u003eFree or reduced-cost access to novel diagnostics, treatments, or prevention (n=7; 50%)\u003c/p\u003e\n \u003cp\u003eLong-term capacity building investment (e.g. funded postdoc) (n=6; 43%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShort-term capacity building investment (e.g. short course) (n=5; 36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Comparison of barriers to sharing participant-level data and samples within and outside of epidemic settings\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipant-level clin-epi data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnical barriers (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMotivational barriers (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic barriers (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory barriers (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e86.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLegal barriers (n=47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e65.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthical barriers (n=47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolitical barriers (n=39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman biological samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnical barriers (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMotivational barriers (n=42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic barriers (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory barriers (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e94.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.0014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLegal barriers (n=40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e64.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthical barriers (n=45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e93.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolitical barriers (n=40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e61.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman genetic data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnical barriers (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e69.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMotivational barriers (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic barriers (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e74.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory barriers (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLegal barriers (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthical barriers (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e66.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolitical barriers (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp; Epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e81.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-epidemic setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-ethics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meth","sideBox":"Learn more about [BMC Medical Ethics](http://bmcmedethics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meth/default.aspx","title":"BMC Medical Ethics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute febrile illness, data sharing, sample sharing, genetic data sharing, PEARL barriers, epidemic setting, non-epidemic setting","lastPublishedDoi":"10.21203/rs.3.rs-4541739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4541739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSharing de-identified, participant-level clinical-epidemiological data, human biological samples, and human genetic data facilitates understanding diseases and the development of prevention strategies, diagnostics, and treatments. While there are increasing calls to share participant-level data and samples both during and outside the public health response to epidemics, several barriers remain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe administered a cross-sectional, online survey to research teams that manage acute febrile illness (AFI) cohorts. We included questions on the researchers\u0026rsquo; best and worst experiences, motivators, benefits, and barriers to sharing de-identified participant-level clin-epi data, human biological samples, and human genetic data during and outside epidemics. Using the political, ethical, administrative, regulatory, and legal (PEARL) framework, we classified the best and worst sharing experiences and employed the Wilcoxon signed-rank test to compare barriers between epidemic and non-epidemic settings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe received 78 responses to the survey from cohort study teams in 23 countries. Most respondents were cohort PIs, over 45, and advanced in their careers. Most cohorts were based in South America or Central America, focused on multiple pathogens, and collected and shared multiple data types and samples. Scientific collaborations with researchers outside their country were the most commonly reported best data or sample-sharing experience. Lack of benefit sharing was the most commonly reported worst sharing experience. Benefits and barriers to sharing did not vary significantly by data type or whether sharing happened during or outside of pandemics, except for regulatory barriers to sharing human biological samples which were significantly more important in epidemic than in non-epidemic settings.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e The study highlights the need for stakeholders to improve data and sample-sharing practices for AFI researchers in LMICs, emphasising ethical considerations, benefit sharing, and streamlined administrative processes in both epidemic and non-epidemic settings.\u003c/p\u003e","manuscriptTitle":"Understanding the motivators and barriers to sharing participant-level data and samples: A cross-sectional study with acute febrile illness cohort teams","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 15:51:31","doi":"10.21203/rs.3.rs-4541739/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-06T15:52:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-02T07:47:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-01-29T12:22:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-17T19:51:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41355319039564543603327922652897619610","date":"2024-08-07T16:54:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-06T18:04:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-20T06:26:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T06:14:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T06:12:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Ethics","date":"2024-06-06T16:35:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-ethics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meth","sideBox":"Learn more about [BMC Medical Ethics](http://bmcmedethics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meth/default.aspx","title":"BMC Medical Ethics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b16ee15f-ae44-4792-96f3-fa54b55ed367","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:02:28+00:00","versionOfRecord":{"articleIdentity":"rs-4541739","link":"https://doi.org/10.1186/s12910-026-01399-2","journal":{"identity":"bmc-medical-ethics","isVorOnly":false,"title":"BMC Medical Ethics"},"publishedOn":"2026-02-11 15:57:50","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2024-07-18 15:51:31","video":"","vorDoi":"10.1186/s12910-026-01399-2","vorDoiUrl":"https://doi.org/10.1186/s12910-026-01399-2","workflowStages":[]},"version":"v1","identity":"rs-4541739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4541739","identity":"rs-4541739","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}