biodumpy : A Comprehensive Biological Data Downloader

biodumpy: A Comprehensive Biological Data Downloader | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results biodumpy : A Comprehensive Biological Data Downloader View ORCID Profile Tommaso Cancellario , View ORCID Profile Tomas Golomb Durán , View ORCID Profile Antoni Josep Far , View ORCID Profile Alejandro Roldán , View ORCID Profile Maria Capa doi: https://doi.org/10.1101/2025.07.26.666724 Tommaso Cancellario 1 Centre Balear de Biodiversitat, Universitat de les Illes Balears , Palma, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tommaso Cancellario For correspondence: t.cancellario{at}uib.eu tommaso.cancellario{at}gmail.com Tomas Golomb Durán 1 Centre Balear de Biodiversitat, Universitat de les Illes Balears , Palma, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tomas Golomb Durán Antoni Josep Far 1 Centre Balear de Biodiversitat, Universitat de les Illes Balears , Palma, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Antoni Josep Far Alejandro Roldán 1 Centre Balear de Biodiversitat, Universitat de les Illes Balears , Palma, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alejandro Roldán Maria Capa 1 Centre Balear de Biodiversitat, Universitat de les Illes Balears , Palma, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Capa Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT In recent years, the expansion of public biodiversity platforms and associated datasets has greatly improved access to ecological and biological information. These resources now cover vast geographic areas, extended temporal scales, and diverse taxonomic groups, becoming essential for ecological studies by enabling more comprehensive analyses and novel hypotheses testing. Concurrently, the development of programming packages has facilitated data access and interaction, streamlining their retrieval processes. However, most existing tools are limited to specific databases, posing challenges for studies requiring seamless integration of data from multiple sources. The growing availability of biodiversity data highlights the urgent need for robust tools to efficiently process, analyse, and interpret ecological and biological information. To address this limitation, we introduce biodumpy , a new Python package developed for the retrieval, management, and integration of biological data from various public databases. biodumpy provides access to up-to-date and comprehensive datasets spanning genetic, distributional, taxonomic, and bibliographic sources. It includes specialized modules for efficient data retrieval across taxonomic lists, with the capability to process multiple modules simultaneously. By integrating diverse data sources, biodumpy enhances data acquisition, providing researchers with a powerful framework for comprehensive analyses and supporting ecological research to tackle complex environmental challenges. 1. INTRODUCTION Ecology serves as a bridge that integrates diverse scientific disciplines and its interdisciplinary nature is essential for understanding the complex interactions between organisms and their environments, as well as the relationships among biological entities ( Goring et al., 2014 ; Odum, 1971 ). A deep comprehension of the ecological dynamics is crucial not only for advancing ecological theories but also for addressing global pressures such as climate change, habitat degradation, and biodiversity loss ( Banks-Leite et al., 2020 ). Addressing these challenges require the development of robust strategies to catalogue, assess, and predict biodiversity variations along with their associated ecosystem services. A key step toward achieving this goal is the collection and sharing of diverse ecological data that span fine to broad spatial and temporal scales, encompass multiple biological levels (e.g., from populations to communities), and include various data types (e.g., species distributions, genetic information). In recent years, the scientific community has witnessed a remarkable expansion of public biodiversity platforms and related datasets ( Heberling et al., 2021 ). These digital infrastructures serve as extensive repositories, often containing highly structured biodiversity data encompassing diverse issues, including taxonomy (e.g., Catalogue of Life; Bánki et al., 2024 ), species distributions (e.g., Global Biodiversity Information Facility; GBIF Secretariat), and genetic information (e.g., Barcode of Life Data System; Ratnasingham et al., 2024 ). Nowadays, such digital resources are indispensable for many ecological studies, as they enable users to perform comprehensive analyses or test and develop new hypothesis that were once unfeasible due to limited data availability, poor quality, and low variability. Alongside the growth of these datasets, there has also been a notable rise in programming packages, particularly for the R programming language, designed to connect directly with these databases, including a range of functions that simplify access and interaction, simplifying the retrieval process for users. However, these tools are often limited to specific databases, creating challenges for researchers seeking to seamlessly integrate data from multiple sources. The development of a toolbox capable of combining data from various biodiversity databases would significantly enhance data acquisition processes, providing researchers with a robust starting point for comprehensive and interdisciplinary analyses, representing a valuable advancement for ecological research and its capacity to address complex environmental challenges. Here, we introduce biodumpy , a new Python package designed to simplify the process of retrieving diverse biological information from several public databases. With biodumpy , users can easily download, manage, and integrate data from different sources, ensuring access to the most up-to-date and comprehensive biological information available. The package includes specialized modules designed to efficiently download biological data for a list of given taxa, with the option to process multiple modules simultaneously. These modules provide ideal access to various types of biological information, including genetic, taxonomic, distributional, and bibliographic databases. In this framework, biodumpy will significantly enhance data acquisition by integrating diverse datasets, providing researchers with a robust foundation for comprehensive analyses. 2. SOFTWARE TOOL DESCRIPTION The biodumpy package is developed in Python programming language, and its stable version (v.0.1.8) can be installed from the official repository of Python PyPI using the command: pip install biodumpy . For those interested in the development version (v.0.1.18) the package is available from TestPyPI with the following command: pip install biodumpy -i https://test.pypi.org/simple/--extra-index-url https://pypi.org/simple/ . Interestingly, biodumpy can also be used in R through the reticulate package ( Ushey et al., 2024 ), which allows seamless integration of Python code within the R environment. 2.1 Starting with biodumpy The use of biodumpy is intuitive, with a general structure that is consistent across all modules. Below we describe an overview of the main steps to get started with the package: Load the package into your Python environment. Import one or more specific modules needed to retrieve the data. Define a list of taxa (except for the Crossref module, which accepts a list of DOIs). Configure the biodumpy module/s with the required parameters and start the download. Although each module of biodumpy is characterized by specific parameters, three parameters are common across all modules: bulk , output_format , and sleep . bulk is a Boolean parameter allowing users to customize the data structure according to their needs during the saving process ( Fig. 1 ). When bulk is True , the information downloaded for each taxon is stored into a single file. This configuration may be useful if the amount of the total data is limited, for consolidating data, and simplifying file management. Whereas, if bulk is False , the information for each taxon is saved in a separate file each one named as the taxon. This option is useful for detailed analysis, particularly when individual taxon-files are required or when the data for each taxon is extensive. Download figure Open in new tab Figure 1: Example illustrating the folder hierarchy structure, with the distinction based on the bulk parameter being set to either True (A) or False (B). By default, biodumpy saves the resulting file in a folder named “downloads” within the user’s working directory. Inside this folder, a subfolder is automatically created, named after the current date and, within it, additional subfolders corresponding to each of the modules used are generated. A general folder hierarchy structure is described in Fig. 1 . Depending on the module, biodumpy provides the option to store files in two output formats: JSON or FASTA. Although the JSON format is not commonly used in biological or ecological fields, we have deliberately chosen it as default format due to its flexibility to support nested and complex data structures, such as arrays and key-value pairs. This makes JSON particularly well-suited for representing hierarchical or relational data, such as taxonomy and observations or nested genetic information, within a single file. Additionally, JSON integrates easily with the raw data returned by the Application Programming Interfaces (APIs) used to develop the biodumpy modules and can be easily converted into a tabular format (e.g., .csv). On the other hand, the FASTA format is specifically designed for storing nucleotide or protein sequences. The BOLD and NCBI modules offer the option to download data in FASTA format, making them ideal to retrieve data to perform sequence-based analysis. Finally, the sleep parameter is useful for controlling the delay between successive data retrieval requests. It enables users to regulate data download speed, thereby preventing server overloads and avoiding rate limit violations. By default, sleep is set to 0.1 seconds. However, we encourage users to adjust this parameter according to the API’s rate policies to ensure responsible data access and avoid overwhelming external servers. 2.2 biodumpy modules To date, biodumpy includes 10 modules that cater to different aspects of biodiversity data management, providing functionalities for working with a wide range of data types. Below, we describe the main functionalities and parameters for each module. Furthermore, for more detailed information, users can consult the biodumpy manual available at https://biodumpy.readthedocs.io/en/latest/ . BOLD The BOLD module allows users to retrieve information from the Barcode of Life Data System ( Ratnasingham et al., 2024 ). To select the output format users can simply set the parameter output_format to ‘json’ or ‘fasta’ . Additionally, this module provides the option to download a summarized version of the data by setting the summary parameter to True , offering a more concise and manageable dataset ( Supplementary Material 1 - S1 ). COL The COL module allows users to easily retrieve taxonomic nomenclature from the Catalogue of Life ( Bánki et al., 2024 ). This module provides users with the option to verify taxon synonyms by setting the parameter check_syn to True . When this parameter is enabled, only the accepted name is stored in the classification section of the JSON file. Conversely, both the synonym and the accepted name are included in the classification section ( Supplementary Material 1 - S2 ). Crossref The Crossref module allows users to retrieve scientific bibliographic metadata from Crossref database. Unlike other modules that use a list of taxa as an input parameter, this module requires a list of DOIs to download the available bibliographic metadata. Users can also obtain a summarized version of the downloaded data by setting the summary parameter to True ( Supplementary Material 1 - S3 ). GBIF The GBIF module enables users to access taxonomic and distributional data from the GBIF platform (GBIF Secretariat). Taxonomic information can be downloaded using the default parameters, while occurrence information can be obtained by setting the parameter occ to True . Additionally, the module supports filtering occurrences based on a specified geographic region using the geometry parameter. When a geometry is provided, only occurrences that fall within the defined polygon are included in the outcome. Finally, this module allows the user to check for possible nomenclature synonyms using the parameter accepted_only . If this parameter is False , nomenclature includes possible synonyms; conversely, the information retrieved corresponds only to accepted names ( Supplementary Material 1 - S4 ). INaturalist The INaturalist module permit users to retrieve taxon photo metadata from iNaturalist (iNaturalist). Notably, the module does not directly download photos from iNaturalist; instead, it retrieves the metadata required to access them (e.g., 34826202/medium.jpg – photo id and its size). To retrieve a photo, the photo-ID (e.g., 34826202) and the image size (e.g., medium) must be concatenated with the following base URL: https://inaturalist-open-data.s3.amazonaws.com/photos/ (i.e., https://inaturalist-open-data.s3.amazonaws.com/photos/34826202/medium.jpg ). To avoid copyright issues, the module retrieves photos classified under public licenses, including: CC0, CC BY, CC BY-NC, CC BY-NC-ND, CC BY-SA, CC BY-ND, and CC BY-NC-SA ( Supplementary Material 1 - S5 ). IUCN The IUCN module enables users to efficiently retrieve information on species assessments, habitats, and threats from the IUCN Red List (IUCN) by specifying a species name and providing a personal API key. Moreover, the module allows users to retrieve data for a specific list of species from one or more areas by specifying a list of regions in the scope parameter (e.g., scope = [‘Global’, ‘Europe’] ; Supplementary Material 1 - S6 ). NCBI The NCBI module enables users to efficiently retrieve data from the National Center for Biotechnology Information database (NCBI). To obtain data in FASTA format, users must set the rettype and output_format parameters to ‘fasta’ . By providing a list of taxa, users can refine their searches using the query_type parameter, which allows for the combination of multiple search criteria to better target the desired data. For example, to download sequences related to the cytochrome c oxidase subunit I, users can set the query_type to ‘[Organism] AND (“CO1”[GENE] OR “COI”[GENE] OR “COX1”[GENE] OR “COXI”[GENE]) ’ ( Porter & Hajibabaei, 2018 ). Module performance can be further optimized using the step_id and step_seq parameters, which control the chunk size for downloading IDs and sequences, respectively. While relatively large chunk sizes are manageable for ID downloads (e.g., step_id = 500 ), setting a high value for step_seq can lead to memory issues due to the potentially large size of the sequence data. To avoid such problems, we recommend using moderate values for the step_seq parameter (e.g., step_seq = 100 ). Lastly, users can retrieve a summary of metadata associated with the retrieved data by setting the summary parameter to True ( Supplementary Material 1 - S7 ). OBIS The OBIS module facilitates the retrieval of data from the Ocean Biodiversity Information System (OBIS). Users can download taxonomic information from OBIS and, like the GBIF module, this module enables users to download species occurrences by setting the occ parameter to True . Moreover, the module supports filtering occurrences based on specific geographic regions using the geometry or areaid parameters. These spatial parameters can be applied independently or in combination. When a geometry or areaid is specified, the results include only occurrences that fall within the defined polygons ( Supplementary Material 1 - S8 ). WORMS The WORMS module enables users to easily retrieve nomenclature information from the World Register of Marine Species database ( Ahyong et al., 2024 ). Users can set the parameter marine_only to True to restrict the search only for species belonging to the marine environment. Moreover, it is possible to retrieve the distribution data for the taxa setting the parameter distribution to True ( Supplementary Material 1 - S9 ). ZOOBANK The ZooBank module allows users to easily retrieve scientific bibliographic information about taxa from the Official Registry of Zoological Nomenclature database (International Commission on Zoological Nomenclature) by providing a taxa name. To optimize the performance of the module, this function includes the parameter dataset_size . This parameter can set to ‘small’ or ‘large’ depending on the amount of data to download. Users can also download additional information setting the parameter info to True . When this parameter is activated, the outcome includes the main information of a bibliographic resource along with additional details such as the DOI. Examples of retrieved data can be found in Supplementary Material 1 - S10 . 2.3 Customizable models Although several modules have been implemented in biodumpy , some digital sources may remain unexplored, limiting the scope of data retrieval. To address this issue, we offer users the possibility to create their own modules, leveraging the inherent hierarchical structure of biodumpy . The commented code to create your own module is available in Supplementary Material 1 - S11 . 2.4 biodumpy tests To further improve the quality and reliability of biodumpy , we incorporated a comprehensive suite of tests designed to stress all individual modules and assess their performance under various conditions. We implemented the testing framework using pytest (v. 8.3.3) ( Krekel et al., 2004 ). In Table 1 , we reported the coverage result for each module. View this table: View inline View popup Download powerpoint Table 1. Summary the test coverage results for each module. Stmts: Total number of statements (lines of executable code) in the file or module analysed. Miss: Number of statements that were not executed (i.e., not covered by any test). Cover: Percentage of statements that were executed during testing. Missing: Specific line numbers or ranges of lines where statements were not executed. 2.6 Dependencies of biodumpy The biodumpy package relies on several Python libraries, listed below: Biopython (v. 1.84): ( Cock et al., 2009 ); numpy (v. 1.26.4): ( Harris et al., 2020 ); tqdm (v. 4.66.4): (Casper da Costa-Luis et al., 2024 ); requests (v. 2.32.3): ( Reitz, 2024 ); lxml (v. 5.2.2): ( Behnel et al., 2024 ); pandas (v. 2.2.2) ( The pandas development team, 2020 ); beautifulsoup4 (v. 4.12.3): ( Richardson, 2024 ); pytest (v. 8.3.3): ( Krekel et al., 2004 ). 3 EXAMPLE APPLICATION To demonstrate the basic usage of biodumpy , we provide the commented code of three comprehensive examples in Supplementary Material 2 , which can be useful for introducing the potential of this new tool. In the “Example 1”, we aim to work with several biodumpy modules and create a general pipeline to retrieve basic ecological information as occurrences and bibliography from a list of taxa. More specifically, we created a simple pipeline to store in a single file the taxonomic classification about a target species, its occurrences and an environmental variable (i.e., bathymetry) across a specified geographic area. Moreover, we retrieve the bibliographic information available in Zoobank. We divided this example into five steps: i) loading the necessary packages; ii) creating a taxa list and downloading information from GBIF, OBIS, COL, and ZooBank; iii) filtering the species-specific information and creating separate datasets containing taxonomy, distribution, and ZooBank bibliographic information; iv) saving each dataset into separate Excel sheets; v) creating two simple plots one to show the distribution of occurrences and another to depict the environmental variable. In the “Example 2”, we focus on genetic modules, and we illustrate how to download FASTA files and perform an alignment with MAFFT ( Katoh et al., 2019 ). To do that, we divided the process in five steps: i) loading the necessary packages; ii) creating a function to remove duplicated in shared records between BOLD and NCBI module outcomes; iii) downloading the FASTA files using BOLD and NCBI modules; iv) extracting only the sequences labeled as COI and merging the data into a single dataset; v) performing an alignment using MAFFT and save the result. In the “Example 3”, we demonstrate how to use biodumpy within the R environment. This can be particularly useful for users not strictly familiarized with Python. To facilitate the integration of the package in R, we utilize the reticulate package ( Ushey et al., 2024 ). We divide the example into three steps: i) install and load the reticulate package; ii) create a virtual environment in R; iii) use two biodumpy modules to download the data. While the examples provided are useful for showing the potential of biodumpy , they do not fully capture the complexity or scope required for a comprehensive biological or ecological data management and analysis. Therefore, they should be considered as a simple guide to help the users in performing their own customized pipeline. A Python script and the downloaded data for each example are available at: Example link. 4. DISCUSSION Recent advancements in computational power, coupled with the growing application of big data in various ecological domains, have significantly improved our capacity to analyse and understand the natural environment. Here, we introduced biodumpy , a novel and customizable Python package developed for the retrieval of biological data from multiple public databases. Unlike existing tools, which are often tailored to interact with a single data source, biodumpy includes several modules that enable users to download, manage, and integrate diverse types of biological information, primarily starting from a user-defined list of taxa. With the ability to retrieve different kinds of information simultaneously, biodumpy streamlines data collection and ensures compatibility across datasets, providing a robust foundation for ecological and biological analyses. Overall, biodumpy represents a significant step toward enhancing the efficiency of data acquisition in ecological research, promoting interdisciplinary studies and enabling scientists to focus more on analysis and interpretation rather than on data download. Future versions of the package will enhance flexibility for more biological data sources, integrate advanced data cleaning functions, and optimize performance for large-scale data processing. AUTHOR CONTRIBUTIONS CT and GDT conceived the ideas, designed methodology, create the documentation and test the software. CT wrote the original draft and led the writing of the manuscript. FA and RA designed methodology, tested the software, reviewed and edited the manuscript. CM reviewed and edited the manuscript. All authors contributed critically to the final version of manuscript and gave final approval for publication. CONFLICT OF INTEREST STATEMENT The authors have declared that no competing interests exist. DATA ACCESSIBILITY OSF (Python scripts): https://osf.io/tn3au/?view_only=4ba55ec76e364afaa71f70778e0e69e5 GitHub: The code is available at: https://github.com/centrebalearbiodiversitat/biodumpy . To report a bug or issue, please contact the corresponding author or open an issue or pull request on the GitHub project. ACKNOWLEDGMENTS This work has been partially funded and promoted by the Comunitat Autonoma de les Illes Balears through the Conselleria d’Educació i Universitats and by the European Union-Next Generation EU/PRTR-C17. I1 (SINCO2022/6717). Nevertheless, the views and opinions expressed are solely those of the author or authors, and do not necessarily reflect those of the Conselleria d’Educació i Universitats, the European Union or the European Commission. Therefore, none of these organizations shall not be held liable. This study has been partially funded by GOIB/Conselleria d’Educació i Universitats through the project “SINCO2022/18146” and co-funded by the European Union. Funder Information Declared This work has been partially funded and promoted by the Comunitat Autonoma de les Illes Balears through the Conselleria d’Educació i Universitats and by the European Union- Next Generation EU/PRTR-C17. I1 (SINCO2022/6717). Nevertheless, the views and opinions expressed are solely those of the author or authors, and do not necessarily reflect those of the Conselleria d’Educació i Universitats, the European Union or the European Commission. Therefore, none of these organizations shall not be held liable. This study has been partially funded by GOIB/Conselleria d’Educació i Universitats through the project “SINCO2022/18146” and co-funded by the European Union. Footnotes Minor format changes. Updated package versions. https://osf.io/tn3au/?view_only=4ba55ec76e364afaa71f70778e0e69e5 https://github.com/centrebalearbiodiversitat/biodumpy REFERENCES 1. ↵ Ahyong , S. , Boyko , C.B. , Bernot , J. , Brandão , S.N. , Daly , M. , De Grave , S. , de Voogd , N.J. , Gofas , S. , Hernandez , F. , Hughes , L. , Neubauer , T.A. , Paulay , G. , van der Meij , S. , Boydens , B. , Decock , W. , Dekeyzer , S. , Goharimanesh , M. , Vandepitte , L. , Vanhoorne , B. , … Zullini , A. ( 2024 ). World Register of Marine Species (WoRMS) . WoRMS Editorial Board . https://www.marinespecies.org 2. ↵ Bánki , O. , Roskov , Y. , Döring , M. , Ower , G. , Hernández Robles , D.R. , Plata Corredor , C.A. , Stjernegaard Jeppesen , T. , Örn , A. , Vandepitte , L. , Pape , T. , Hobern , D. , Garnett , S. , Little , H. , DeWalt , R.E. , Ma , K. , Miller , J. , Orrell , T. , Aalbu , R. , Abbott , J. , & al , et . ( 2024 ). Catalogue of Life (Version 2024-09-25) . doi: 10.48580/dgh3g OpenUrl CrossRef 3. ↵ Banks-Leite , C. , Ewers , R.M. , Folkard-Tapp , H. , & Fraser , A. ( 2020 ). Countering the effects of habitat loss, fragmentation, and degradation through habitat restoration . One Earth , 3 ( 6 ), 672 – 676 . doi: 10.1016/j.oneear.2020.11.016 OpenUrl CrossRef 4. ↵ Behnel , S. , Faassen , M. , Bicking , I. , Joukl , H. , Sapin , S. , Parent , M.-A. , Grisel , O. , Buchcik , K. , Wagner , F. , Kroymann , E. , Everitt , P. , Ng , V. , Kern , R. , Pakulat , A. , Sankel , D. , Kasperski , M. , Silva , da S. , & Oberndörfer , P. ( 2024 ). lxml—XML and HTML with Python (Version v5.2.2) [Computer software] . https://lxml.de/index.html 5. ↵ Casper da Costa-Luis , Larroque , S.K. , Altendorf , K. , Mary , H. , richardsheridan Korobov , M. , Yorav-Raphael , N. , Ivanov , I. , Bargull , M. , Rodrigues , N. , Shawn Dektyarev , M. , Górny , M. , mjstevens777, Pagel , M.D. , Zugnoni , M. , JC , CrazyPython , Newey , C. , … McCracken , J. ( 2024 ). tqdm: A fast, Extensible Progress Bar for Python and CLI (Version v4.67.0) [Computer software] . Zenodo . doi: 10.5281/ZENODO.14047011 OpenUrl CrossRef 6. ↵ Cock , P.J.A. , Antao , T. , Chang , J.T. , Chapman , B.A. , Cox , C.J. , Dalke , A. , Friedberg , I. , Hamelryck , T. , Kauff , F. , Wilczynski , B. , & de Hoon , M.J.L. ( 2009 ). Biopython: Freely available Python tools for computational molecular biology and bioinformatics . Bioinformatics , 25 ( 11 ), 1422 – 1423 . doi: 10.1093/bioinformatics/btp163 OpenUrl CrossRef PubMed Web of Science 7. GBIF Secretariat . ( 2025 ). GBIF . Global Biodiversity Information Facility . https://www.gbif.org/ 8. ↵ Goring , S.J. , Weathers , K.C. , Dodds , W.K. , Soranno , P.A. , Sweet , L.C. , Cheruvelil , K.S. , Kominoski , J.S. , Rüegg , J. , Thorn , A.M. , & Utz , R.M. ( 2014 ). Improving the culture of interdisciplinary collaboration in ecology by expanding measures of success . Frontiers in Ecology and the Environment , 12 ( 1 ), 39 – 47 . doi: 10.1890/120370 OpenUrl CrossRef 9. ↵ Harris , C.R. , Millman , K.J. , Van Der Walt , S.J. , Gommers , R. , Virtanen , P. , Cournapeau , D. , Wieser , E. , Taylor , J. , Berg , S. , Smith , N.J. , Kern , R. , Picus , M. , Hoyer , S. , Van Kerkwijk , M.H. , Brett , M. , Haldane , A. , Del Río , J.F. , Wiebe , M. , Peterson , P. , … Oliphant , T.E. ( 2020 ). Array programming with NumPy . Nature , 585 ( 7825 ), 357 – 362 . doi: 10.1038/s41586-020-2649-2 OpenUrl CrossRef PubMed 10. ↵ Heberling , J.M. , Miller , J.T. , Noesgaard , D. , Weingart , S.B. , & Schigel , D. ( 2021 ). Data integration enables global biodiversity synthesis . Proceedings of the National Academy of Sciences , 118 ( 6 ), e2018093118 . doi: 10.1073/pnas.2018093118 OpenUrl Abstract / FREE Full Text 11. iNaturalist . ( 2025 ). iNaturalist . https://www.inaturalist.org/ 12. International Commission on Zoological Nomenclature . ( 2025 ). ZooBank: The Official Registry of Zoological Nomenclature . https://www.zoobank.org 13. IUCN . ( 2025 ). IUCN Red List of Threatened Species . https://www.iucnredlist.org 14. ↵ Katoh , K. , Rozewicki , J. , & Yamada , K.D. ( 2019 ). MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization . Briefings in Bioinformatics , 20 ( 4 ), 1160 – 1166 . doi: 10.1093/bib/bbx108 OpenUrl CrossRef PubMed 15. ↵ Krekel , H. , Oliveira , B. , Pfannschmidt , R. , Bruynooghe , F. , Laugher , B. , & Bruhin , F. ( 2004 ). Pytest 8.3.3 [Computer software] . https://github.com/pytest-dev/pytest 16. NCBI . ( 2025 ). National Center for Biotechnology Information (NCBI) . https://www.ncbi.nlm.nih.gov/ 17. OBIS Task Teams and Project Teams . ( 2025 ). OBIS . Ocean Biodiversity Information System . Retrieved 2024, from https://obis.org/ 18. ↵ Odum , E.P. ( 1971 ). Fundamentals of ecology . Printing Company Ltd . 19. ↵ Porter , T. M. , & Hajibabaei , M. ( 2018 ). Over 2.5 million COI sequences in GenBank and growing . PloS one , 13 ( 9 ), e0200177 . OpenUrl CrossRef PubMed 20. ↵ R. DeSalle Ratnasingham , S. , Wei , C. , Chan , D. , Agda , J. , Agda , J. , Ballesteros-Mejia , L. , Boutou , H.A. , El Bastami , Z.M. , Ma , E. , Manjunath , R. , Rea , D. , Ho , C. , Telfer , A. , McKeowan , J. , Rahulan , M. , Steinke , C. , Dorsheimer , J. , Milton , M. , & Hebert , P.D.N. ( 2024 ). BOLD v4: A Centralized Bioinformatics Platform for DNA-Based Biodiversity Data . In R. DeSalle (Ed.), DNA Barcoding: Methods and Protocols (pp. 403 – 441 ). Springer US . doi: 10.1007/978-1-0716-3581-0_26 OpenUrl CrossRef 21. ↵ Reitz , K. ( 2024 ). Requests: HTTP for HumansTM (Version v2.32.3) [Computer software] . https://requests.readthedocs.io/en/latest/ 22. ↵ Richardson , L. ( 2024 ). Beautifulsoup4 (Version latest) [Computer software] . https://www.crummy.com/software/BeautifulSoup/ 23. ↵ The pandas development team . ( 2020 ). pandas-dev/pandas: Pandas (Version latest) [Computer software] . Zenodo . doi: 10.5281/zenodo.3509134 OpenUrl CrossRef 24. ↵ Ushey , K. , Allaire , J.J. , & Tang , Y. ( 2024 ). reticulate: Interface to “Python.” https://rstudio.github.io/reticulate/ View the discussion thread. Back to top Previous Next Posted October 27, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following biodumpy: A Comprehensive Biological Data Downloader Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share biodumpy : A Comprehensive Biological Data Downloader Tommaso Cancellario , Tomas Golomb Durán , Antoni Josep Far , Alejandro Roldán , Maria Capa bioRxiv 2025.07.26.666724; doi: https://doi.org/10.1101/2025.07.26.666724 Share This Article: Copy Citation Tools biodumpy : A Comprehensive Biological Data Downloader Tommaso Cancellario , Tomas Golomb Durán , Antoni Josep Far , Alejandro Roldán , Maria Capa bioRxiv 2025.07.26.666724; doi: https://doi.org/10.1101/2025.07.26.666724 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Ecology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17691) Bioengineering (13892) Bioinformatics (41937) Biophysics (21452) Cancer Biology (18588) Cell Biology (25504) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88605) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15156) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)