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.

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 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

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.

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.

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

The goal of BioLINCC is to facilitate and coordinate the existing activities of the NHLBI Biorepository and the Data Repository and to expand their scope and usability to the scientific community through a single web-based user interface.

Combined Survey ResponsesThis spreadsheet shows the combined survey responses from Dryad Digital Repository, Figshare and TreeBASE survey populations.combined_survey_responses.csv

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

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".

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.

Background: With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists around the world. Methods: The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample of scientific researchers in a two-waves approach in 2017-2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and sectors of employment. Findings: Most respondents displayed what we describe as high and moderate risk data practices by storing their data on their personal computer, departmental servers or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half of them are satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to progress in science at large, yet only about a half thought that it restricted their own ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage, and educational programs for both awareness and to help engender good data practices are clearly needed.

To inform efforts to improve the discoverability of and access to biomedical datasets by providing a preliminary estimate of the number and type of datasets generated annually by National Institutes of Health (NIH)-funded researchers. Of particular interest is characterizing those datasets that are not deposited in a known data repository or registry, e.g., those for which a related journal article does not indicate that underlying data have been deposited in a known repository. Such “invisible” datasets comprise the “long tail” of biomedical data and pose significant practical challenges to ongoing efforts to improve discoverability of and access to biomedical research data. This study identified datasets used to support the NIH-funded research reported in articles published in 2011 and cited in PubMed® and deposited in PubMed Central® (PMC). After searching for all articles that acknowledged NIH support, we first identified articles that contained explicit mention of datasets being deposited in recognized repositories. Thirty members of the NIH staff then analyzed a random sample of the remaining articles to estimate how many and what types of datasets were used per article. Two reviewers independently examined each paper. Each dataset is titled Bigdata_randomsample_xxxx_xx. The xxxx refers to the set of articles the annotator looked at, while the xxidentifies the annotator that did the analysis. Within each dataset, the author has listed the number of datasets they identified within the articles that they looked at. For every dataset that was found, the annotators were asked to insert a new row into the spreadsheet, and then describe the dataset they found (e.g., type of data, subject of study, etc.). Each row in the spreadsheet was always prepended by the PubMed Identifier (PMID) where the dataset was found. Finally, the files 2013-08-07_Bigdatastudy_dataanalysis, Dataanalysis_ack_si_datasets, and Datasets additional random sample mention vs deposit 20150313 refer to the analysis that was performed based on each annotator's analysis of the publications they were assigned, and the data deposits identified from the analysis.

This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).

The supplementary information consists of three files:

SI 1 - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.

SI 2 - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.

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.

Background: Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the "citation benefit". Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results: Here, we look at citation rates while controlling for many known citation predictors, and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations th...

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.