A listing of NIH supported data sharing repositories that make data accessible for reuse. Most accept submissions of appropriate data from NIH-funded investigators (and others), but some restrict data submission to only those researchers involved in a specific research network. Also included are resources that aggregate information about biomedical data and information sharing systems. The table can be sorted according by name and by NIH Institute or Center and may be searched using keywords so that you can find repositories more relevant to your data. Links are provided to information about submitting data to and accessing data from the listed repositories. Additional information about the repositories and points-of-contact for further information or inquiries can be found on the websites of the individual repositories.

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

BRADS is a repository for data and biospecimens from population health research initiatives and clinical or interventional trials designed and implemented by NICHD’s Division of Intramural Population Health Research (DIPHR). Topics include human reproduction and development, pregnancy, child health and development, and women’s health. The website is maintained by DIPHR.

The table shows only the research areas with at least 100 articles published. Data is from Web of Science, which defined the research areas and categorized articles into them.

This dataset resulted from conducting focus groups with scientists from five disciplines (atmospheric and earth science, chemistry, computer science, ecology, and neuroscience) about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans. Participants identified metadata quality control and training as problem areas in data management. Participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. Our dataset includes five anonymized focus group transcripts in .pdf file format (one for each focus group with scientists from each discipline), our codebook as a spreadsheet in excel file format (.xlsx), and coded segments of our transcript text to visualize our data analysis in an excel spreadsheet in excel file format (.xlsx).

ADR provides an authoritative data store for shared administrative, demographic, enrollment, and eligibility information which is managed as a corporate asset. This administrative database system offers mission-critical database support for all VA Medical 21st Century Core applications such as Enrollment Systems, Identity Management System, Community Care Program, Veterans's Choice program, President's Affordable Care Act project, Patient Advocacy Tracking System, Veterans 360, and others.

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.

This dataset contains the data availability statements (DAS) from four Nature Portfolio journals from January 2017 to December 2021. This covers two periods; one prior to integrating the figshare repository with the submission system of each journal (January 2017 to December 2021) and one following the integration (April 2022 to July 2023).Each DAS is assigned one or more of seven categories based on its content and any links to available data. This enables analysis of changes in data sharing behaviour, for example either side of the figshare integration.Summary statistics by year and the DAS categories are provided in separate tabs of the worksheet. DAS were initially assigned by basic text-matching (for example the presence of key terms like 'request' in the DAS indicating data are available on request). A human curator then verified each article's categorisation and amended if necessary.

Instructions and guidance materials on how to prepare your research data for sharing and long-term access and how to deposit your research data in the University of Guelph Research Data Repositories (Data Repositories).How the Data Repositories work:Self-deposit with mediation data deposit service: Upon request, depositors are given dataset creator access to a collection in the Data Repositories allowing them to create new draft dataset records and submit their draft datasets for review. Repository staff review all submitted datasets for alignment with repository policies and data deposit guidelines. Repository staff will work with depositors to make any required changes to the metadata, data files, and/or supplemental documentation to improve the FAIRness (findability, accessibility, interoperability, and reusability) of the dataset. When the dataset is ready, repository staff will make the dataset publicly available in the repository on behalf of the depositor. How to start the deposit process: If you are interested in depositing data in the Data Repositories and/or have questions about preparing your data for deposit, please contact repository staff to start the process.

This is supplementary data to the article "Repository approaches to improving quality of shared data and code," and in particular, its first section on completeness of research code. Run this code on Jupyter Binder here:

Collected in this dataset are the slideset and abstract for a presentation on Toward a Reproducible Research Data Repository by the depositar team at International Symposium on Data Science 2023 (DSWS 2023), hosted by the Science Council of Japan in Tokyo on December 13-15, 2023. The conference was organized by the Joint Support-Center for Data Science Research (DS), Research Organization of Information and Systems (ROIS) and the Committee of International Collaborations on Data Science, Science Council of Japan. The conference programme is also included as a reference.

Title

Toward a Reproducible Research Data Repository

Author(s)

Cheng-Jen Lee, Chia-Hsun Ally Wang, Ming-Syuan Ho, and Tyng-Ruey Chuang

Affiliation of presenter

Institute of Information Science, Academia Sinica, Taiwan

Summary of Abstract

The depositar (https://data.depositar.io/) is a research data repository at Academia Sinica (Taiwan) open to researhers worldwide for the deposit, discovery, and reuse of datasets. The depositar software itself is open source and builds on top of CKAN. CKAN, an open source project initiated by the Open Knowledge Foundation and sustained by an active user community, is a leading data management system for building data hubs and portals. In addition to CKAN's out-of-the-box features such as JSON data API and in-browser preview of uploaded data, we have added several features to the depositar, including sourcing from Wikidata as dataset keywords, a citation snippet for datasets, in-browser Shapefile preview, and a persistent identifier system based on ARK (Archival Resource Keys). At the same time, the depositar team faces an increasing demand for interactive computing (e.g. Jupyter Notebook) which facilitates not just data analysis, but also for the replication and demonstration of scientific studies. Recently, we have provided a JupyterHub service (a multi-tenancy JupyterLab) to some of the depositar's users. However, it still requires users to first download the data files (or copy the URLs of the files) from the depositar, then upload the data files (or paste the URLs) to the Jupyter notebooks for analysis. Furthermore, a JupyterHub deployed on a single server is limited by its processing power which may lower the service level to the users. To address the above issues, we are integrating the BinderHub into the depositar. BinderHub (https://binderhub.readthedocs.io/) is a kubernetes-based service that allows users to create interactive computing environments from code repositories. Once the integration is completed, users will be able to launch Jupyter Notebooks to perform data analysis and vsualization without leaving the depositar by clicking the BinderHub buttons on the datasets. In this presentation, we will first make a brief introduction to the depositar and BinderHub along with their relationship, then we will share our experiences in incorporating interactive computation in a data repository. We shall also evaluate the possibility of integrating the depositar with other automation frameworks (e.g. the Snakemake workflow management system) in order to enable users to reproduce data analysis.

Keywords

BinderHub, CKAN, Data Repositories, Interactive Computing, Reproducible Research

The focus of the Swedish National Data Service Strategic Plan 2018-2022 is to declare our vision and describe the strategic objectives of the national research infrastructure. The strategic plan consists of two parts: The vision with a short description of the mission and the overall purpose of the infrastructure. The three overarching strategic areas with specified aims: Facilitate and Secure Research Data and Metadata Flows Assist and Benefit Users Accumulate and Maintain Expertise. Funded by the Swedish Research Council.

Dryad BioLINCC Survey Data 16-09-01This is the deidentified data from the 2015 cross-sectional survey of investigators who requested and received access to clinical research data from BioLINCC between 2007 and 2014.READ ME Dryad BioLINCC Survey 16-09-01.txtData Dictionary BioLINCC Survey 16-09-01This file lists and describes the variables from the 2015 cross-sectional BioLINCC survey.

Sharing and preserving data are central to protecting the integrity of science. DataHub, a Research Computing endeavor, provides tools and services to meet scientific data challenges at Pacific Northwest National Laboratory (PNNL). DataHub helps researchers address the full data life cycle for their institutional projects and provides a path to creating findable, accessible, interoperable, and reusable (FAIR) data products. Although open science data is a crucial focus of DataHub’s core services, we are interested in working with evidence-based data throughout the PNNL research community.

The VHA Data Sharing Agreement Repository serves as a centralized location to collect and report on agreements that share VHA data with entities outside of VA. It provides senior management an overall view of existing data sharing agreements; fosters productive sharing of health information with VHA's external partners; and streamlines data acquisition to improve data management responsibilities overall. Agreements that VHA has established with entities within the VA are not candidates for this Repository.

The purpose of this project is to investigate Alberta clinical and health researcher practices and perspectives about data sharing, governance and re-use within a broader political, legal, economic, ethical and social context. The project has potential legislative and public policy implications and will add a unique Canadian contribution to the growing literature on data governance and data sharing practice studies. The study will also inform the development of a researcher-centered data curation and repository services affiliated with the co-sponsoring agencies: the Alberta Centre for Child, Family & Community Research and University of Alberta Libraries.

Objective: To investigate clinical trialists’ opinions and experiences of sharing of clinical trial data with investigators who are not directly collaborating with the research team. Design and setting: Cross sectional, web based survey. Participants: Clinical trialists who were corresponding authors of clinical trials published in 2010 or 2011 in one of six general medical journals with the highest impact factor in 2011. Main outcome measures: Support for and prevalence of data sharing through data repositories and in response to individual requests, concerns with data sharing through repositories, and reasons for granting or denying requests. Results: Of 683 potential respondents, 317 completed the survey (response rate 46%). In principle, 236 (74%) thought that sharing de-identified data through data repositories should be required, and 229 (72%) thought that investigators should be required to share de-identified data in response to individual requests. In practice, only 56 (18%) indicated that they were required by the trial funder to deposit the trial data in a repository; of these 32 (57%) had done so. In all, 149 respondents (47%) had received an individual request to share their clinical trial data; of these, 115 (77%) had granted and 56 (38%) had denied at least one request. Respondents’ most common concerns about data sharing were related to appropriate data use, investigator or funder interests, and protection of research subjects. Conclusions: We found strong support for sharing clinical trial data among corresponding authors of recently published trials in high impact general medical journals who responded to our survey, including a willingness to share data, although several practical concerns were identified.

Repository that serves to coordinate searches across data and biospecimen collections from participants in numerous clinical trials and epidemiologic studies and to provide an electronic means for requests for additional information and the submission of requests for collections. The collections, comprising data from more than 80 trials or studies and millions of biospecimens, are available to qualified investigators under specific terms and conditions consistent with the informed consents provided by the individual study participants. Some datasets are presented with studies and supporting materials to facilitate their use in reuse and teaching. Datasets support basic research, clinical studies, observational studies, and demonstrations. Researchers wishing to apply to submit biospecimen collections to the NHLBI Biorepository for sharing with qualified investigators may also use this website to initiate that process.

A set of materials prepared by Tyng-Ruey Chuang for the session Research Data Management and Planning in the Humanities organized by Profs. Shoichiro Hara and Tatsuki Sekino at the PNC 2024 Annual Conference and Joint Meetings, held at the Korea University, Seoul, South Korea, on August 29-31, 2024.

Collected in this dataset are the abstract (in text) and slideset (in PDF) of the presentation. The abstract is also appended below.

The Active Roles of Research Data Repositories in Cultural Landscape Documentation

Tyng-Ruey Chuang (trc@iis.sinica.edu.tw)

2024-06-05

Research data repositories have been used mainly for archiving data (e.g. supporting datasets are deposited when research articles are being published). Some data repositories, however, can also be used for active data management if certain functional requirements are met (e.g. the repository will allow for flexible project organization and controlled data access). For active data management, we mean the repositories are used for data aggregation, curation, and sharing during the entire research process.

In a team effort to document cultural landscapes, the project team members will necessarily collect existing datasets from diverse sources (e.g. old photos) as well as generate new documentary materials (e.g. on-site images). These materials are heterogeneous in nature. Some data collections are about historical records and sensor datasets, while the others may consist of soundtracks and videos, aerial and panorama images, documents and pictures, plain text journals, bibliographies, and so on. For rapid research exploration, therefore, it will be crucial to streamline the management and presentation of multiple data collections related by space and time.

At Academia Sinica, we have developed the depositar (https://data.depositar.io), a data repository open to all for the deposit, discovery, and reuse of research datasets. The depositar is a generalist repository suitable for active data management, and it can serve as a data hub for collaborative data collection. In this talk, we will present a few showcases in using the depositar for collaborative cultural landscape documentation.

Project portal for publishing, citing, sharing and discovering research data. Software, protocols, and community connections for creating research data repositories that automate professional archival practices, guarantee long term preservation, and enable researchers to share, retain control of, and receive web visibility and formal academic citations for their data contributions. Researchers, data authors, publishers, data distributors, and affiliated institutions all receive appropriate credit. Hosts multiple dataverses. Each dataverse contains studies or collections of studies, and each study contains cataloging information that describes the data plus the actual data files and complementary files. Data related to social sciences, health, medicine, humanities or other sciences with an emphasis in human behavior are uploaded to the IQSS Dataverse Network (Harvard). You can create your own dataverse for free and start adding studies for your data files and complementary material (documents, software, etc). You may install your own Dataverse Network for your University or organization.