Data and code for paper: "Understanding Research Data Repositories as Infrastructures" (2021). This study discusses the properties of research data repositories and analyzes metadata about 2,646 entries in the Registry of Research Data Repositories (r3data.org) to identify which of the characteristics attributed to infrastructures they exhibit. The results reveal how research data repositories function as information infrastructure for members of the scientific community and contribute to the small body of literature that examines data repositories through a socio-technical lens.

On January 13–15, 2021, the Big Data Interagency Working Group (BD IWG) of the Networking and Information Technology Research and Development Program held a workshop on Pioneering the Future of Federally Supported Data Repositories to explore opportunities and challenges for the future of federally supported data repositories (FSDRs). FSDRs facilitate access to federally funded research data and play a pivotal role in enabling machine learning, artificial intelligence, and other data-driven science and discovery. FSDRs also play a critical role as building blocks for a future data ecosystem that emerged during the workshop...

This dataset aggregates information about 191 research data repositories that were shut down. The data collection was based on the registry of research data repositories re3data and a comprehensive content analysis of repository websites and related materials. Documented in the dataset are the period in which a repository was active, the risks resulting in its shutdown, and the repositories taking over custody of the data after.

Spreadsheet listing data repositories that are recommended by Scientific Data (Springer Nature) as being suitable for hosting data associated with peer-reviewed articles. Please see the repository list on Scientific Data's website for the most up to date list.

Data collected from major Canadian and international research data repositories cover data storage, preservation, metadata, interchange, data file types, and other standard features used in the retention and sharing of research data. The outputs of this project primarily aim to assist in the establishment of recommended minimum requirements for a Canadian research data infrastructure. The committee also aims to further develop guidelines and criteria for the assessment and selection o f repositories for deposit of Canadian research data by researchers, data managers, librarians, archivists etc.

Collecting data isn’t that hard, but what’s hard is creating and maintaining a data repository. Even harder is making sense out of a data repository.

The concept of a data repository has grown in popularity in order to efficiently manage and utilize this data. A data repository is a centralized storage site for data that allows for easy access, data management, and analysis.

This dataset provides guidance materials and templates to help you prepare your research datasets for deposit in the U of G Research Data Repositories.Please refer to the U of G Research Data Repositories LibGuide for detailed information about the U of G Research Data Repositories including additional resources for preparing datasets for deposit. The library offers a self-deposit with curation service. The deposit workflow is as follows:Create your repository account.If you are a first-time depositor, complete the U of G Research Data Repositories Dataset Deposit Intake Form.Activate your Data Repositories account by logging in with your U of G central login account.Once your account is created, contact us to set up your dataset creator access to your home department’s collection in the Data Repositories.Note: If you already have a Data Repositories account and dataset creator access, you can log in and begin a new deposit to your home department’s collection right away.Prepare your dataset.Assemble your dataset following the Dataset Deposit Guidelines. Use the README file template to capture data documentation.Create a draft dataset record.Log in to the Data Repositories and create a draft dataset record following the instructions in the Dataset Submission Guide.Submit your draft dataset for review.Dataset review.Data Repositories staff will review (also referred to as curate) your dataset for alignment with the Dataset Deposit Guidelines using a standard curation workflow.The curator will collaborate with you to enhance the dataset.Public release.Once ready, the dataset curator will make the dataset publicly available in the Data Repositories, with appropriate file access controls. Support: If you have any questions about preparing and depositing your dataset, please make a Publishing and Author Support Request.

Overview This directory was developed to provide discovery information for anyone looking for publicly accessible repositories that house geological materials in the U.S. and Canada. In addition, this resource is intended to be a tool to facilitate a community of practice. The need for the directory was identified during planning for and follow-up from a drill core repository webinar series in Spring 2020 for public repository curators and staff in the U.S. and Canada hosted by the Minnesota Geological Survey and the Minnesota Department of Natural Resources. Additional supporting sponsors included the U.S. Geological Survey National Geological and Geophysical Data Preservation Program and the Association of American State Geologists Data Preservation Committee. The 10-part webinar series provided overviews of state, provincial, territorial, and national repositories that house drill core, other geoscience materials, and data. When the series concluded a small working group of the participants continued to meet to facilitate the development and production of a directory of repositories that maintain publicly-accessible geological materials throughout the U.S. and Canada. The group used previous directory efforts described in the next section, Summary of Historical Repository Directory Compilation Efforts, as guides for content during development. The working group prepared and compiled responses from a call for repository information and characterization. This directory is planned to be a living resource for the geoscience community with updates every other year to accommodate changes. The updates will facilitated through versioned updates of this data release. Summary of Historical Repository Directory Compilation Efforts 1957 – Sample and Core Repositories of the United States, Alaska, and Canada. Published by AAPG. Committee on Preservation of Samples and Cores. 13 members from industry, academia, and government. 1977 – Well-Sample and Core Repositories of the Unites States and Canada, C.K. Fisher; M.P. Krupa, USGS Open file report 77-567.USGS wanted to update the original index. Includes a map showing core repositories by “State” “University” “Commercial” and “Federal”. Also includes a “Brief Statement of Requirements for the Preservation of Subsurface Material and Data” and referral to state regulations for details on preserved materials. 1984 - Nonprofit Sample and Core Repositories Open to the Public in the United States – USGS Circular 942. James Schmoker, Thomas Michalski, Patricia Worl. The survey was conducted by a questionnaire mailed to repository curators. Information on additions, corrections, and deletions to earlier (1957,1977) directories from state geologists, each state office of the Water Resources Division of the U.S. Geological Survey, additional government agencies and colleagues were also used. 1997 - The National Directory of Geoscience Data Repositories, edited by Nicholas H. Claudy – American Geologic Institute. To prepare the directory, questionnaires were mailed to state geologists, more than 60 geological societies, private-sector data centers selected from oil and gas directories, and to the membership committee of the American Association of Petroleum Geologists, one of AGI's member societies. The directory contains 124 repository listings, organized alphabetically by state. 2002 – National Research Council 2002. Geoscience Data and Collections: National resources in Peril. Washington, D.C.: The National Academies Press 2005 – The National Geological and Geophysical Data Preservation Program (NGGDPP) of the United States Geological Survey (USGS) was established by The Energy Policy Act of 2005, and reauthorized in the Consolidated Appropriations Act, 2021, “to preserve and expose the Nation’s geoscience collections (samples, logs, maps, data) to promote their discovery and use for research and resource development”. The Program provides “technical and financial assistance to state geological surveys and U.S. Department of the Interior (DOI) bureaus” to archive “geological, geophysical, and engineering data, maps, photographs, samples, and other physical specimens”. Metadata records describing the preserved assets are cataloged in the National Digital Catalog (NDC). References American Association of Petroleum Geologists, 1957, Sample and core repositories of the United States, Alaska, and Canada: American Association of Petroleum Geologists, Committee on Preservation of Samples and Cores, 29 p. American Association of Petroleum Geologists, 2018, US Geological Sample and Data Repositories: American Association of Petroleum Geologists, Preservation of Geoscience Data Committee, Unpublished, (Contact: AAPG Preservation of Geoscience Data Committee) American Geological Institute, 1997, National Geoscience Data Repository System, Phase II. Final report, January 30, 1995--January 28, 1997. United States. https://doi.org/10.2172/598388 American Geological Institute, 1997, National Directory of Geoscience Data Repositories, Claudy, N. H., (ed.), 91pp. Claudy N., Stevens D., 1997, AGI Publishes first edition of national directory of geoscience data repositories. American Geological Institute Spotlight, https://www.agiweb.org/news/datarep2.html Consolidated Appropriations Act, 2021 (Public Law 116-260, Sec.7002) Davidson, E. D., Jr., 1981, A look at core and sample libraries: Bureau of Economic Geology, The University of Texas at Austin, 4 p. and Appendix. Deep Carbon Observatory (DCO) Data Portal, Scientific Collections, https://info.deepcarbon.net/vivo/scientific-collections; Keyword Search: sample repository, https://info.deepcarbon.net/vivo/scientific-collections?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22sample%20repository%20%22%2C%22default_operator%22%3A%22OR%22%7D%7D%2C%22sort%22%3A%5B%7B%22_score%22%3A%7B%22order%22%3A%22asc%22%7D%7D%5D%2C%22from%22%3A0%2C%22size%22%3A200%7D: Accessed September 29, 2021 Fisher, C. K., and Krupa, M. P., 1977, Well-sample and core repositories of the United States and Canada: U.S. Geological Survey Open-File Report 77-567, 73 p. https://doi.org/10.3133/ofr77567 Fogwill, W.D., 1985, Drill Core Collection and Storage Systems in Canada, Manitoba Energy & Mines. https://www.ngsc-cptgs.com/files/PGJSpecialReport_1985_V03b.pdf Goff, S., and Heiken, G., eds., 1982, Workshop on core and sample curation for the National Continental Scientific Drilling Program: Los Alamos National Laboratory, May 5-6, 1981, LA-9308-C, 31 p. https://www.osti.gov/servlets/purl/5235532 Lonsdale, J. T., 1953, On the preservation of well samples and cores: Oklahoma City Geological Society Shale Shaker, v. 3, no. 7, p. 4. National Geological and Geophysical Data Preservation Program. https://www.usgs.gov/core-science-systems/national-geological-and-geophysical-data-preservation-program National Research Council. 2002. Geoscience Data and Collections: National Resources in Peril. Washington, DC: The National Academies Press, 107 p. https://doi.org/10.17226/10348 Pow, J. R., 1969, Core and sample storage in western Canada: Bulletin of Canadian Petroleum Geology, v. 17, no. 4, p. 362-369. DOI: 10.35767/gscpgbull.17.4.362 Ramdeen, S., 2015. Preservation challenges for geological data at state geological surveys, GeoResJ 6 (2015) 213-220, https://doi.org/10.1016/j.grj.2015.04.002 Schmoker, J. W., Michalski, T. C., and Worl, P. B., 1984, Nonprofit sample and core repositories of the United States: U.S. Geological Survey Circular 942. https://doi.org/10.3133/cir942 Schmoker, J. W., Michalski, T. C., and Worl, P. B., 1984, Addresses, telephone numbers, and brief descriptions of publicly available, nonprofit sample and core repositories of the United States: U.S. Geological Survey Open-File Report 84-333, 13 p. (Superseded by USGS Circular 942) https://doi.org/10.3133/ofr84333 The Energy Policy Act of 2005 (Public Law 109-58, Sec. 351) The National Digital Catalog (NDC). https://www.usgs.gov/core-science-systems/national-geological-and-geophysical-data-preservation-program/national-digital U.S. Bureau of Mines, 1978, CORES Operations Manual: Bureau of Mines Core Repository System: U.S. Bureau of Mines Information Circular IC 8784, 118 p. https://digital.library.unt.edu/ark:/67531/metadc170848/

This dataset contains 189 survey responses from a respository/ data managers' survey where we explored the current status, needs and challenges of research data repositories.

This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.

Please cite this data collection as: von der Heyde, M. (2019). Data and tools of the landscape and cost analysis of data repositories currently used by the Swiss research community. Retrieved from https://doi.org/10.5281/zenodo.2643495

Connected data papers are: von der Heyde, M. (2019). Open Data Landscape: Repository Usage of the Swiss Research Community: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643430 von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450

Connected data sets are: von der Heyde, M. (2019). Data from the Swiss Open Data Repository Landscape survey. Retrieved from https://doi.org/10.5281/zenodo.2643487 von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, _domain-specific databases, and the top journals compare how much data is in institutional vs. _domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find _domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known _domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were _domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of _domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared _domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the _domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

and how these can be affected by the presence of human body nearby.

Our dataset "repository_survey" summarizes a comprehensive survey of over 150 data repositories, characterizing their metadata documentation and standardization, data curation and validation, and tracking of dataset use in the literature. In addition, "survey_model_evaluation" includes our findings on model evaluation for five methodological repositories. Column descriptions and further details can be found in "README.pdf." The data are associated with our paper "Towards an Ideal Methodological Data Repository: Lessons and Recommendations."

Machine-readable metadata available from landing pages for datasets facilitate data citation by enabling easy integration with reference managers and other tools used in a data citation workflow. Embedding these metadata using the schema.org standard with the JSON-LD is emerging as the community standard. This dataset is a listing of data repositories that have implemented this approach or are in the progress of doing so.

This is the first version of this dataset and was generated via community consultation. We expect to update this dataset, as an increasing number of data repositories adopt this approach, and we hope to see this information added to registries of data repositories such as re3data and FAIRsharing.

In addition to the listing of data repositories we provide information of the schema.org properties supported by these data repositories, focussing on the required and recommended properties from the "Data Citation Roadmap for Scholarly Data Repositories".

The Bear Lake Data Repository (BLDR) is an active archive, containing a growing compilation of biological, chemical, and physical datasets collected from Bear Lake and its surrounding watershed. The datasets herein have been digitized from historical records and reports, extracted from papers and theses, and obtained from public and private entities, including the United States Geological Survey, PacifiCorp, and, inter alia, Ecosystems Research Institute.

Contributions are welcome. The BLDR accepts biological, chemical, or physical datasets obtained at Bear Lake, irrespective of funding source. There is no submission size limit at present—workarounds will be found if submissions exceed Hydroshare limits (20 GB). Contributions are published with an open access license and will serve many use cases. The current repository steward, Bear Lake Watch, will advise on submissions and make accepted contributions available promptly.

Metadata files are provided for each dataset, however, contact with original contributor(s) is encouraged for questions and additional details prior to data usage. The BLDR and its contributors shall not be liable for any damages resulting from misinterpretation or misuse of the data or metadata.

This dataset documents findings form a survey on the status quo of data quality assurance practices at research data repositories.

The personalized online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information, and yielded 332 complete responses.

The dataset comprises a documentation file, the data file, a codebook, and the survey instrument.

The documentation file (documentation.pdf) outlines details of the survey design and administration, survey response, and data processing. The data file (01_survey_data.csv) contains all 332 complete responses to 19 survey questions, fully anonymized. The codebook (02_codebook.csv) describes the variables, and the survey instrument (03_survey_instrument.pdf) comprises the questionnaire that was distributed to survey participants.

In the age of digital transformation, scientific and social interest for data and data products is constantly on the rise. The quantity as well as the variety of digital research data is increasing significantly. This raises the question about the governance of this data. For example, how to store the data so that it is presented transparently, freely accessible and subsequently available for re-use in the context of good scientific practice. Research data repositories provide solutions to these issues.

Considering the variety of repository software, it is sometimes difficult to identify a fitting solution for a specific use case. For this purpose a detailed analysis of existing software is needed. Presented table of requirements can serve as a starting point and decision-making guide for choosing the most suitable for your purposes repository software. This table is dealing as a supplementary material for the paper "How to choose a research data repository software? Experience report." (persistent identifier to the paper will be added as soon as paper is published).

Clinical Trial Data Repository Market Outlook

The global clinical trial data repository market size was estimated to be approximately $1.8 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 9.5% to reach around $4.1 billion by 2032. The primary growth factors include the increasing volume and complexity of clinical trials, rising need for efficient data management systems, and stringent regulatory requirements for data accuracy and integrity. The advent of advanced technologies such as artificial intelligence and big data analytics further drives market expansion by enhancing data processing capabilities and providing actionable insights.

The growth of the clinical trial data repository market is significantly influenced by the increasing number of clinical trials being conducted globally. With the rise in chronic diseases, the need for innovative treatments and therapies has surged, leading to an upsurge in clinical trials. This increase in clinical trials necessitates robust data management systems to handle vast amounts of data generated, thereby propelling the demand for clinical trial data repositories. Moreover, the complexity of modern clinical trials, which often involve multiple sites and diverse patient populations, further amplifies the need for sophisticated data management solutions.

Another critical driver for the market is the stringent regulatory landscape governing clinical trial data. Regulatory bodies such as the FDA, EMA, and other local authorities mandate rigorous data management standards to ensure data integrity, accuracy, and accessibility. These regulations necessitate the adoption of advanced data repository systems that can comply with regulatory requirements, thereby fueling market growth. Additionally, regulatory frameworks are becoming increasingly stringent, prompting pharmaceutical and biotechnology companies to invest in state-of-the-art data management systems to avoid compliance issues and potential financial penalties.

Technological advancements play a pivotal role in the market's growth. The integration of artificial intelligence, machine learning, and big data analytics into data repository systems enhances data processing and analysis capabilities. These technologies enable real-time data monitoring, predictive analytics, and improved decision-making, thereby improving the efficiency of clinical trials. Furthermore, the shift towards cloud-based solutions offers scalability, flexibility, and cost-effectiveness, making advanced data management systems accessible to even small and medium-sized enterprises.

Regionally, North America dominates the clinical trial data repository market owing to its robust healthcare infrastructure, high R&D investments, and presence of major pharmaceutical and biotechnology companies. Europe follows closely due to stringent regulatory standards and a strong focus on clinical research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to increasing clinical trial activities, growing healthcare expenditure, and the rising adoption of advanced technologies. Latin America and the Middle East & Africa are also likely to experience growth, albeit at a slower pace, driven by improving healthcare systems and increasing focus on clinical research.

Component Analysis

The clinical trial data repository market is segmented by components into software and services. The software segment is anticipated to hold a significant share of the market due to the essential role software plays in data management. Advanced software solutions offer capabilities such as data storage, management, retrieval, and analysis, which are critical for effective clinical trial management. The integration of AI and machine learning algorithms into these software systems further enhances their efficiency by enabling predictive analytics and real-time monitoring, thus driving the software segment's growth.

Software solutions in clinical trial data repositories also offer interoperability, enabling seamless integration with other clinical trial management systems (CTMS) and electronic data capture (EDC) systems. This interoperability is crucial for ensuring data consistency and accuracy across different platforms, thereby enhancing overall data management. Additionally, the increasing adoption of cloud-based software solutions provides scalability, cost-effectiveness, and remote acce

The NSF Public Access Repository contains an initial collection of journal publications and the final accepted version of the peer-reviewed manuscript or the version of record. To do this, NSF draws upon services provided by the publisher community including the Clearinghouse of Open Research for the United States, CrossRef, and International Standard Serial Number. When clicking on a Digital Object Identifier number, you will be taken to an external site maintained by the publisher. Some full text articles may not be available without a charge during the embargo, or administrative interval. Some links on this page may take you to non-federal websites. Their policies may differ from this website.

In order to better understand the factors that most influence where researchers deposit their data when they have a choice, we collected survey data from researchers who deposited phylogenetic data in either the TreeBASE or Dryad data repositories. Respondents were asked to rank the relative importance of eight possible factors. We found that factors differed in importance for both TreeBASE and Dryad, and that the rankings differed subtly but significantly between TreeBASE and Dryad users. On average, TreeBASE users ranked the domain specialization of the repository highest, while Dryad users ranked as equal highest their trust in the persistence of the repository and the ease of its data submission process. Interestingly, respondents (particularly Dryad users) were strongly divided as to whether being directed to choose a particular repository by a journal policy or funding agency was among the most or least important factors. Some users reported depositing their data in multiple repositories and archiving their data voluntarily.