These data were generated for an investigation of research data repository (RDR) mentions in biuomedical research articles.

Supplementary Table 1 is a discrete subset of SciCrunch RDRs used to study RDR mentions in biomedical literature. We generated this list by starting with the top 1000 entries in the SciCrunch database, measured by citations, removed entries for organizations (such as universities without a corresponding RDR) or non-relevant tools (such as reference managers), updated links, and consolidated duplicates resulting from RDR mergers and name variations. The resulting list of 737 RDRs is shown in with as a base based on a source list of RDRs in the SciCrunch database. The file includes the Research Resource Identifier (RRID), the RDR name, and a link to the RDR record in the SciCrunch database.

Supplementary Table 2 shows the RDRs, associated journals, and article-mention pairs (records) with text snippets extracted from mined Methods text in 2020 PubMed articles. The dataset has 4 components. The first shows the list of repositories with RDR mentions, and includes the Research Resource Identifier (RRID), the RDR name, the number of articles that mention the RDR, and a link to the record in the SciCrunch database. The second shows the list of journals in the study set with at least 1 RDR mention, andincludes the Journal ID, nam, ESSN/ISSN, the total count of publications in 2020, the number of articles that had text available to mine, the number of article-mention pairs (records), number of articles with RDR mentions, the number of unique RDRs mentioned, % of articles with minable text. The third shows the top 200 journals by RDR mention, normalized by the proportion of articles with available text to mine, with the same metadata as the second table. The fourth shows text snippets for each RDR mention, and includes the RRID, RDR name, PubMedID (PMID), DOI, article publication date, journal name, journal ID, ESSN/ISSN, article title, and snippet.

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.

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.

Survey period: 08 April - 08 May, 2014 Top 10 Impact Factor journals in each of 22 categories

Figures https://doi.org/10.6084/m9.figshare.6857273.v1

Article https://doi.org/10.20651/jslis.62.1_20 https://doi.org/10.15068/00158168

These data were captured as part of the CSIRO Data Practices and Attitudes Survey. The data files contain the R script and the set of measures used for the multi-level modelling of organisational, disciplinary and domain factors which influence research data sharing practices. For this reason there are four data files, one representing the data aggregated by disciplinary group (discipline_aggregation), one representing the data aggregated by organisational unit (orgunit_aggregation), one representing the data aggregated by domain group (domain_aggregation), and the corresponding R script.

In 2020, 54 percent of healthcare providers and 50 percent of healthcare payers surveyed in the United States indicated that lack of technical interoperability was the biggest challenge around health data sharing. Among 52 percent of providers, noted that timeliness of data that is shared was a challenge, in comparison only 21 percent of payers shared the same concern.

This is a collaborative research project to share datasets with some Japanese researchers. The 1st wave of the investigation was conducted on January 2017, and the 2nd wave was on January 2019.

This survey was part of a joint project between Humboldt-Universität zu Berlin and Elsevier to understand research data sharing and re-use practices, and the drivers (and/or barriers) acting on researchers in this regard. For comparability, the individual items were constructed in accordance with the Open Data Survey (Centre for Science and Technology Studies, Elsevier and Leiden University, 2017) conducted in the year 2016.

The online survey was from 30 September 2020 till 5 November 2020 to researchers worldwide, in all scientific fields. 99,667 individuals randomly selected from Scopus author database, with added 801 individuals picked from Peru (as one of the target group for Katarzyna Biernacka’s thesis) were contacted via e-mail. A personalised link guided the researchers to the Confirmit survey website.

Upload content: - Readme.txt - OpenDataSharingReuse2020_Questionnaire.pdf - the survey questions including question codes - OpenDataSharingReuse2020_Responses_anonymised.csv - the anonymised responses to the survey

This is a dataset of a number of Data Sharing Agreements (DSAs) Camden has with other bodies. A DSA is only used when the bodies do not have a contract, and are usually used between the council and another public sector organisation. A DSA does not give the parties any legal rights to share the data: the sharing must already be legal. DSAs are used to set out the terms and conditions to cover sharing of personal information for the reasons given in the DSA. Some may be redacted due to security or legal reasons. This is not a comprehensive set but we are working to add to it. The Metropolitan Police/Council DSAs are being developed and more will be added in 2022 as they are launched. For more information please contact DPA@camden.gov.uk

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.

This dataset contains Data Availability Statements from 47,593 papers published in PLOS ONE between March 2014 (when the policy went into effect) and May 2016, analyzed for type of statement.

A survey in 2020 during the COVID-19 pandemic found that the majority of consumers were happy to share their data with companies if it improved their experience. Meanwhile, the second largest portion at 30 percent of users said they did not want to share more data than they already were. Only eight percent did not have doubts, admitting that they would always share their data with companies.

follow up study to Wolins, L. (1962), Craig, J. R. & Reese, R. C. (1973) and Wicherts, J.M., Borsboom, D., Kats, J., & Molenaar, D. (2006)

The need for data sharing and rapid data access has become central with the rise of collaborative research in many disciplines. For the general public, several file sharing products are available that post and share files using web browsers. But for science data and research use, these products are not well suited. While consumer products get by with manual user interfaces to add and remove a few shared files, this is not practical for sharing large numbers of science data files, like those generated during and after large-scale computation. Instead, automated and scriptable mechanisms are required that can integrate into computation workflows to post files during and after computation jobs. Scientific data sharing also requires support for collaborative discussion of research results, quick rough-draft visualizations to analyze the data, and support for metadata and descriptive information that can record job and compute platform characteristics, input data, job parameters, job completion status, and other provenance information.Here we describe work in progress under the umbrella of the SeedMe (Stream, Encode, Explore and Disseminate My Experiments) project that is developing scientific data-sharing and data management tools that cater to the unique needs of computational scientists. These tools support automated and scriptable access to shared data, browser-based data access, secure data storage, sharing with a project workgroup, data descriptions and metadata, threaded collaborative discussion, and light-weight visualization.

Seven data sharing stories, in seven minutes or less. The video recordings, presentation slides and live scribe illustrations are included.Featuring:The citation advantage of linking publications to research data - Barbara McGillivray, The Alan Turing Institute and University of CambridgeBibliographic Data Science: Open Ecosystems for Scalable Collaboration - Leo Lahti, University of Turku Reproducible Research - Why and How? -Yasemin Turkyilmaz-van der Velden, TU DelftResearch Data Management Using Open Source Off The Shelf Tools -Graham Addis, Nuffield Department of Experimental MedicineX chromosome genetic data in a Spanish children cohort, dataset description and analysis pipeline - Augusto Anguita-Ruiz, University of GranadaParticipatory Science to Empower: Building a Citizen Science Platform - Georgia Aitkenhead, The Alan Turing Institute‘Open Science’ opens doors: How #Scidata18 helped me unlock career opportunities - Connie Clare, University of Nottingham

This bucket contains multiple neuroimaging datasets that are part of the International Neuroimaging Data-Sharing Initiative. Raw human and non-human primate neuroimaging data include 1) Structural MRI; 2) Functional MRI; 3) Diffusion Tensor Imaging; 4) Electroencephalogram (EEG) In addition to the raw data, preprocessed data is also included for some datasets. A complete list of the available datasets can be seen in the documentation lonk provided below.

This policy applies to SPREP’s own data as well as data held by SPREP on behalf of government agencies and partners within the Pacific.

A survey questionnaire was conducted as an independent study looking at the research data sharing practices and preferences of engineering faculty in the Faculty of Applied Science at the University of British Columbia. It also includes questions related to open access publishing practices and types of research data being generated within this Faculty. The survey ran from July to September 2022.

As phylogenetic data becomes increasingly available, along with associated data on species' genomes, traits, and geographic distributions, the need to ensure data availability and reuse become more and more acute. In this paper, we provide ten "simple rules" that we view as best practices for data sharing in phylogenetic research. These rules will help lead towards a future phylogenetics where data can easily be archived, shared, reused, and repurposed across a wide variety of projects.