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.

This file contains the data sharing statistics for the completion of the GISR database. The readMe file in the same fileset describes the content. The accompanying paper is about to be submitted to Computers & Geosciences.

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.

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 document presents the standard starting point language to use when drafting a formal data sharing agreement between a City entity and either another City entity or an outside party when two parties seek to share non-public data with one another. The document outlines the following major concerns:Parties to the agreementPurpose of the data sharing effort Period of the agreementDescription of the data to be sharedTiming and frequency of updates to the shared dataPoint(s) of contactCustodial responsibilitiesMethod of data transferPublication ReviewOther City terms and conditions This version 1.1 makes minor corrections of language originally formalized by the City's Data Governance Committee in June of 2020. Note that a data sharing agreement is not final or authorized without appropriate signatures from all parties represented by the agreement.

This data sharing and storage plan outlines how collected data will be managed, stored securely during and after the study, and shared with others while adhering to ethical, legal, and privacy considerations. It details data organization, storage locations (physical and digital), access controls, long-term preservation, anonymization for privacy, metadata documentation, data sharing procedures, and compliance with relevant regulations and policies. This plan ensures research transparency, reproducibility, and accessibility, promoting collaboration and the responsible use of research data.

No description was included in this Dataset collected from the OSF

These are the Data Sharing Agreements (DSAs) that the London Borough of Barnet has made with other organisations. Most of the time, a DSA is used when there is no contract between the council and another public sector organisation. People who sign a DSA do not get legal rights to share data; sharing data must already be legal. In DSAs, the rules for sharing personal information for the given reasons are spelt out.

Personal data, such as the names and signatures of officers have been removed from DSAs for GDPR reasons

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 February 2023 survey in the United Kingdom (UK) found that about 15 percent of the respondents between 16 and 24 years were willing to share personal data in return for free online services. Older respondents, meanwhile, were less likely to do so. Over 20 percent of the respondents aged 55 years and older said they would strongly disagree with sharing personal information for free online services, like news articles, games, and social media.

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.

Transcripts of in-depth interviews and group discussions with managers, researchers, ethics committee members, field data collectors and community members on the issues around ethical data sharing in the context of research involving women and children in urban India. We interviewed researchers, managers, and research participants associated with a Mumbai non-governmental organization, as well as researchers from other organizations and members of ethics committees. We conducted 22 individual semi-structured interviews and involved 44 research participants in focus group discussions. We used framework analysis to examine ideas about data and data sharing in general; its potential benefits or harms, barriers, obligations, and governance; and the requirements for consent. Both researchers and participants were generally in favor of data sharing, although limited experience amplified their reservations.

It is increasingly recognized that effective and appropriate data sharing requires the development of models of good data sharing practice capable of taking seriously both the potential benefits to be gained and the importance of ensuring that the rights and interests of participants are respected and that risk of harms is minimized. Calls for the greater sharing of individual level data from biomedical and public health research are receiving support among researchers and research funders. Despite its potential importance, data sharing presents important ethical, social, and institutional challenges in low income settings. This dataset comprises qualitative research conducted in India, exploring the experiences of key research stakeholders and their views about what constitutes good data sharing practice.

This research examines the factors affecting biological scientists' data sharing. The online survey was conducted with the scientists who are registered as biological scientists in the CoS scholar database. Among 90 thousand scientists registered under the main discipline of biological sciences in the U.S., we randomly selected a total of 8,000 potential survey participants. Throughout the survey distribution, 2,014 messages were returned or were not delivered correctly due to invalid email addresses or spam filters. Therefore, a total of 5,986 messages were delivered to any potential survey participants. Among those responses, we excluded any responses missing more than 20% of answers in the online survey; therefore, we only have 680 valid responses.

This dataset collects the slides that were presented at the Data Collaborations Across Boundaries session in SciDataCon 2022, part of the International Data Week.

The following session proposal was prepared by Tyng-Ruey Chuang and submitted to SciDataCon 2022 organizers for consideration on 2022-02-28. The proposal was accepted on 2022-03-28. Six abstracts were submitted and accepted to this session. Five presentations were delivered online in a virtual session on 2022-06-21.

Data Collaborations Across Boundaries

There are many good stories about data collaborations across boundaries. We need more. We also need to share the lessons each of us has learned from collaborating with parties and communities not in our familiar circles.

By boundaries, we mean not just the regulatory borders in between the nation states about data sharing but the various barriers, readily conceivable or not, that hinder collaboration in aggregating, sharing, and reusing data for social good. These barriers to collaboration exist between the academic disciplines, between the economic players, and between the many user communities, just to name a few. There are also cross-domain barriers, for example those that lay among data practitioners, public administrators, and policy makers when they are articulating the why, what, and how of "open data" and debating its economic significance and fair distribution. This session aims to bring together experiences and thoughts on good data practices in facilitating collaborations across boundaries and domains.

The success of Wikipedia proves that collaborative content production and service, by ways of copyleft licenses, can be sustainable when coordinated by a non-profit and funded by the general public. Collaborative code repositories like GitHub and GitLab demonstrate the enormous value and mass scale of systems-facilitated integration of user contributions that run across multiple programming languages and developer communities. Research data aggregators and repositories such as GBIF, GISAID, and Zenodo have served numerous researchers across academic disciplines. Citizen science projects and platforms, for instance eBird, Galaxy Zoo, and Taiwan Roadkill Observation Network (TaiRON), not only collect data from diverse communities but also manage and release datasets for research use and public benefit (e.g. TaiRON datasets being used to improve road design and reduce animal mortality). At the same time large scale data collaborations depend on standards, protocols, and tools for building registries (e.g. Archival Resource Key), ontologies (e.g. Wikidata and schema.org), repositories (e.g. CKAN and Omeka), and computing services (e.g. Jupyter Notebook). There are many types of data collaborations. The above lists only a few.

This session proposal calls for contributions to bring forward lessons learned from collaborative data projects and platforms, especially about those that involve multiple communities and/or across organizational boundaries. Presentations focusing on the following (non-exclusive) topics are sought after:

1. Support mechanisms and governance structures for data collaborations across organizations/communities.

2. Data policies --- such as data sharing agreements, memorandum of understanding, terms of use, privacy policies, etc. --- for facilitating collaborations across organizations/communities.

3. Traditional and non-traditional funding sources for data collaborations across multiple parties; sustainability of data collaboration projects, platforms, and communities.

4. Data workflows --- collection, processing, aggregation, archiving, and publishing, etc. --- designed with considerations of (external) collaboration.

5. Collaborative web platforms for data acquisition, curation, analysis, visualization, and education.

6. Examples and insights from data trusts, data coops, as well as other formal and informal forms of data stewardship.

7. Debates on the pros and cons of centralized, distributed, and/or federated data services.

8. Practical lessons learned from data collaboration stories: failure, success, incidence, unexpected turn of event, aftermath, etc. (no story is too small!).

The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3–4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.

Use this guide to find information on Tempe data policy and standards.Open Data PolicyEthical Artificial Intelligence (AI) PolicyEvaluation PolicyExpedited Data Sharing PolicyData Sharing Agreement (General)Data Sharing Agreement (GIS)Data Quality Standard and ChecklistDisaggregated Data StandardsData and Analytics Service Standard

During a 2020 survey carried out among senior industry experts from companies involved in the use of data and data collaboration from the United States, 64.3 percent of respondents stated they were currently collaborating with a third party to share first-party data for insights, activation, measurements, or attribution; 7.1 percent said they were not collaborating with anybody to such an end but that they used to in the past.

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.