A list of NIH-supported repositories that accept submissions of appropriate scientific research data from biomedical researchers. It includes resources that aggregate information about biomedical data and information sharing systems. 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.

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.

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.

The NIH Common Data Elements (CDE) Repository has been designed to provide access to structured human and machine-readable definitions of data elements that have been recommended or required by NIH Institutes and Centers and other organizations for use in research and for other purposes. Visit the NIH CDE Resource Portal for contextual information about the repository.

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).

This dataset tracks the updates made on the dataset "NIH Data Sharing Repositories" as a repository for previous versions of the data and metadata.

The attached data sets provides an overview of the landscape of research data repositories in 2015. They are based on an analysis of the re3data - registry of research data repositories from December 2015.

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.

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 from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493

Further information is given in the corresponding data paper: 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

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

This blog post was posted by Elizabeth Kittrie on July 21, 2016 It was written by Elizabeth Kittrie and Shubham Chattopadhyay.

Sharing data can increase scientific transparency and reproducibility, allow for novel uses, and spur discovery and innovation beyond the research enterprise. To maximize these benefits, the US National Institutes of Health (NIH) has implemented several data sharing efforts, including the 2014 Genomic Data Sharing (GDS) Policy, which promotes the broad and responsible sharing of genomic data. The Policy expects large-scale human genomic data resulting from NIH funding to be shared through controlled-access data repositories, including the NIH the database of Genotypes and Phenotypes (dbGaP). To understand how sharing genomic data through controlled access repositories contributes to secondary research and facilitates the translation of research to broader societal benefits, we assessed the impact of research that reused data accessed through dbGaP’s Authorized Access System. This dataset contains the results of our analysis.For our analysis, a list of “Manuscripts Citing dbGaP Authorized Access System Data” was downloaded from https://www.ncbi.nlm.nih.gov/gap/summary/pub/ on April 26, 2023 and curated to ensure accuracy and compatibility with our analysis tools. Publications were categorized as a “deposit” if authored by a data submitter, “secondary use” if acknowledging a phs accession number as a source of data, or “reference” if referencing a phs accession number as a resource but not a specific source of data. Publications meeting the criteria of both a deposit and secondary use publication were manually categorized as secondary use. Publications not yet categorized were labeled as “undetermined.” To ensure compatibility with our analysis tools, the PubMed Central IDs (PMCIDs) of all remaining publications on the list were converted to their corresponding PubMed ID (PMID) using the NIH conversion tool (https://www.ncbi.nlm.nih.gov/pmc/tools/idconv/). Publications lacking a corresponding PMID were removed from further analysis.To determine the scientific influence of secondary use publications, we used the NIH Office of Portfolio Analysis’ (OPA) iCite tool (https://icite.od.nih.gov/) to calculate their Relative Citation Ratios (RCRs). For additional bibliometric analysis, we used iSearch, a portfolio analysis platform internal to NIH that contains linked datasets of publications, clinical trials, global grants, and patents.For additional details, please refer to README.docx. For the methodology used to generate each file, please refer to the README tab of the corresponding CSV file.

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.

Over the last decade, there have been significant changes in data sharing policies and in the data sharing environment faced by life science researchers. Using data from a 2013 survey of over 1600 life science researchers, we analyze the effects of sharing policies of funding agencies and journals. We also examine the effects of new sharing infrastructure and tools (i.e., third party repositories and online supplements). We find that recently enacted data sharing policies and new sharing infrastructure and tools have had a sizable effect on encouraging data sharing. In particular, third party repositories and online supplements as well as data sharing requirements of funding agencies, particularly the NIH and the National Human Genome Research Institute, were perceived by scientists to have had a large effect on facilitating data sharing. In addition, we found a high degree of compliance with these new policies, although noncompliance resulted in few formal or informal sanctions. Despite the overall effectiveness of data sharing policies, some significant gaps remain: about one third of grant reviewers placed no weight on data sharing plans in their reviews, and a similar percentage ignored the requirements of material transfer agreements. These patterns suggest that although most of these new policies have been effective, there is still room for policy improvement.

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

This dataset is derived from a master's research focused on the study of guidelines from research data repositories in Ibero America that adopt self-archiving, specifically the atribuition of keywords.

This dataset outlines a proposed set of core, minimal metadata elements that can be used to describe biomedical datasets, such as those resulting from research funded by the National Institutes of Health. It can inform efforts to better catalog or index such data to improve discoverability. The proposed metadata elements are based on an analysis of the metadata schemas used in a set of NIH-supported data sharing repositories. Common elements from these data repositories were identified, mapped to existing data-specific metadata standards from to existing multidisciplinary data repositories, DataCite and Dryad, and compared with metadata used in MEDLINE records to establish a sustainable and integrated metadata schema. From the mappings, we developed a preliminary set of minimal metadata elements that can be used to describe NIH-funded datasets. Please see the readme file for more details about the individual sheets within the spreadsheet.

The NIMH Repository and Genomics Resource (RGR) stores biosamples, genetic, pedigree and clinical data collected in designated NIMH-funded human subject studies. The RGR database likewise links to other repositories holding data from the same subjects, including dbGAP, GEO and NDAR. The NIMH RGR allows the broader research community to access these data and biospecimens (e.g., lymphoblastoid cell lines, induced pluripotent cell lines, fibroblasts) and further expand the genetic and molecular characterization of patient populations with severe mental illness.

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 NIDDK Central Repository stores biosamples, genetic and other data collected in designated NIDDK-funded clinical studies. The purpose of the NIDDK Central Repository is to expand the usefulness of these studies by allowing a wider research community to access data and materials beyond the end of the study.

Use of a persistent identifier for access to journal articles (the DOI) is now almost universal amongst researchers. It directs to the journal landing page where the human has to then take over navigation (or payment). Recently, the deposition of data into open access repositories and the resulting assignment of a data-DOI to the data or fileset has started to be increasingly adopted, and in the near future probably mandated by funders. Unfortunately, mechanisms for the retrieval and application of the data from such sources are still inherited from those developed for journal articles. We argue these mechanisms are not fit for (data) purpose. In these three demonstrations, we show how existing standards can be used to automate the data retrieval process, starting purely from the DOI assigned to the objects. The first of these utilises the 10320/loc method (see doi:10.1021/ci500302p) which is flexible and efficient, but is not supported by the DataCite registry. The next two schemes were developed to achieve such interoperability, the first using the DataCite Media API and the second exploiting added metadata such as relatedMetadataScheme = ORE to use the repository ORE resource map. We have embedded these methods into a Javascript-based data viewing demonstrator (JSmol), which is designed to display molecular information. Handlers for other types of data could be readily incorporated, and the system could also be exploited for data-mining. Examples of recently published journal articles which use such data-DOI handling will be cited.