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.

As of 2022, more than ** percent of countries in the WHO European region reported having legislation to protect the privacy of an individual's health-related data in electronic format in an electronic health record (EHR). Moreover, ** percent of countries have legislation allowing individuals electronic access to their health data in their EHRs.

Background Data sharing is commonly seen as beneficial for science, but is not yet common practice. Research funding agencies are known to play a key role in promoting data sharing, but German funders’ data sharing policies appear to lag behind in international comparison. This study aims to answer the question of how German data sharing experts inside and outside funding agencies perceive and evaluate German funders’ data sharing policies and overall efforts to promote data sharing. Methods This study is based on sixteen guideline-structured interviews with representatives of German funding agencies and German research data experts from other organisations, who shared their perceptions of German’ funders efforts to promote data sharing. By applying the method of qualitative content analysis to our interview data, we categorise and describe noteworthy aspects of the German data sharing policy landscape and illustrate our findings with interview passages. Research data This dataset contains summaries from interviews with data sharing and funding policy experts from German funding agencies and what we call "stakeholder organisations" (e.g., universities, research data infrastructure providers, etc.). We asked the interviewees about their perspectives on German funders' data sharing policies, for example regarding the actual status quo, their expectations about the potential role that funders can play in promoting data sharing, as well as general developments in this area. Supplement_1_Interview_guideline_funders.pdf and Supplement_2_Interview_guideline_stakeholders.pdf provide supplemental information in the form of the (german) interview guidelines used in this study. Supplement_3_Transcription_and_coding_guideline.pdf lays out the rules we followed in our transcription and coding process. Supplement_4_Category_system.pdf describes the underlying category system of the qualitative content analysis we conducted.

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.

Raw data from the article entitled, "Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports"

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.

Data sharing is crucial to the advancement of science because it facilitates collaboration, transparency, reproducibility, criticism, and re-analysis. Publishers are well-positioned to promote sharing of research data by implementing data sharing policies. While there is an increasing trend toward requiring data sharing, not all journals mandate that data be shared at the time of publication. In this study, we extended previous work to analyze the data sharing policies of 447 journals across several scientific disciplines, including biology, clinical sciences, mathematics, physics, and social sciences. Our results showed that only a small percentage of journals require data sharing as a condition of publication, and that this varies across disciplines and Impact Factors. Both Impact Factors and discipline are associated with the presence of a data sharing policy. Our results suggest that journals with higher Impact Factors are more likely to have data sharing policies; use shared data in peer review; require deposit of specific data types into publicly available data banks; and refer to reproducibility as a rationale for sharing data. Biological science journals are more likely than social science and mathematics journals to require data sharing.

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!).

This dataset contains anonymized transcripts of interviews performed during a qualitative interview study with members of 16 research funding agencies. Sample: Funding agencies were located mainly, but not exclusively, in Europe. Funding agencies were selected using a purposive sampling strategy that aimed to acquire sufficient representation of (a) national and international agencies; (b) public and philanthropic agencies; and (c) agencies in continental European and the anglophone world. Suitable funding agencies were selected through various means, such as a list of health research funding organizations according to their annual expenditure on health research (https://www.healthresearchfunders.org/health-research-funding-organizations/) and Science Europe Working Groups. Background: Open science policy documents have emphasized the need to install more incentives for data sharing. These incentives are often understood as being reputational or financial. Additionally, there are other policy measures that could be taken, such as data sharing mandates. Aim: To document views of funding agencies on (1) potential alterations to recognition systems in academia; (2) incentives to enhance the sharing of cohort data; (3) data sharing policies in terms of the governance of cohort data; (4) other potential interactions between science policy and data sharing platforms for cohorts. Our study focused on the sharing of patient- and population-based cohorts through data infrastructures.

Examples of data sharing policies.

This project collects implementation guidance for the NIH Data Management and Sharing Policy (2023).

Please note: This guide is intended for educational purposes only and does not act as a substitute for official guidance from the NIH.

This project was made possible with support from the RDAP Association and with Federal funds from the National Library of Medicine (NLM), National Institutes of Health (NIH).

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

A manually curated registry of data policies from research funders, journal publishers, societies, and other organisations. These are linked to the databases and standards that they recommend for use

The rapidly growing number of digital research data and developing scientific methods has challenged science policy makers and research funders to seek tools for increasing openness and sharing of research data. In some countries, academic research funders have adopted formal data policies to guide data management and data sharing in new research projects. The International Federation of Data Organizations for Social Science (IFDO) conducted an international web-based survey of data professionals to gather information on the institutional policies of social sciences and humanities in different countries and to collect and locate sources for a more detailed analysis.

Vehicle Policy Engine for Data Sharing Market Outlook

According to our latest research, the global market size for Vehicle Policy Engine for Data Sharing reached USD 1.35 billion in 2024, reflecting a robust expansion in recent years. The market is projected to grow at a CAGR of 18.2% from 2025 to 2033, reaching an estimated USD 6.17 billion by the end of the forecast period. This remarkable growth is being driven primarily by the increasing adoption of connected vehicles, regulatory mandates for data sharing, and the rapid digitalization of the automotive industry worldwide.

One of the key growth factors for the Vehicle Policy Engine for Data Sharing market is the exponential increase in the number of connected vehicles on the road. Automakers are equipping vehicles with advanced telematics, IoT sensors, and communication modules, generating massive volumes of real-time data. This data is invaluable for a range of stakeholders, including OEMs, insurers, fleet operators, and regulators. The need for robust policy engines that can manage, authorize, and secure data sharing in compliance with privacy regulations is therefore surging. Furthermore, the rise of autonomous and semi-autonomous vehicles, which rely heavily on data exchange, is further amplifying the demand for advanced policy engines that can facilitate seamless and secure communication between vehicles, infrastructure, and third-party service providers.

Another significant driver is the evolving regulatory landscape. Governments across regions such as the European Union and North America are enacting stringent data privacy and sharing regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations mandate transparency, user consent, and strict protocols for data access and sharing. As a result, automotive stakeholders are increasingly investing in sophisticated policy engines to ensure compliance, avoid hefty penalties, and build consumer trust. The growing emphasis on cybersecurity and data integrity further underscores the necessity for advanced policy management solutions that can dynamically adapt to regulatory changes and evolving threat landscapes.

The proliferation of data-driven business models within the automotive ecosystem is also fueling market growth. Insurance companies are leveraging vehicle-generated data for usage-based insurance (UBI), risk assessment, and claims management, while fleet managers use real-time data to optimize operations and reduce costs. Smart mobility providers and government agencies are tapping into vehicle data to enhance urban planning, traffic management, and public safety initiatives. The Vehicle Policy Engine for Data Sharing market is thus benefitting from a broadening scope of applications, as stakeholders recognize the value of data in driving innovation, efficiency, and customer-centric services.

Regionally, North America currently leads the market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. North America's dominance is attributed to its early adoption of connected vehicle technologies, robust regulatory frameworks, and a strong presence of leading automotive OEMs and technology providers. Europe is also experiencing significant growth, driven by regulatory mandates and a mature automotive sector. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid urbanization, expanding automotive production, and increasing investments in smart mobility infrastructure. Latin America and the Middle East & Africa are expected to witness steady growth, supported by ongoing digital transformation initiatives and rising demand for connected mobility solutions.

Component Analysis

The Vehicle Policy Engine for Data Sharing market is segmented by component into software, hardware, and services. Software constitutes the largest segment, as it forms the core of policy engines, enabling the creation, management, and enforcement of data sharing policies across diverse platforms. The increasing complexity of data flows, coupled with evolving regulatory requirements, is driving the demand for advanced software solutions that can offer granular control, real-time monitoring, and dynamic policy updates. Leading vendors are investing heavily in the development of AI-powered and cloud-native policy engines that can scale with the growing volume and variety of vehicle data.

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Data Sharing Agreements for Cities and OEMs Market Outlook

According to our latest research, the Global Data Sharing Agreements for Cities and OEMs market size was valued at $2.3 billion in 2024 and is projected to reach $9.8 billion by 2033, expanding at a robust CAGR of 17.2% during the forecast period of 2025–2033. One of the primary drivers fueling this rapid growth is the increasing adoption of smart city initiatives worldwide, which demand seamless, secure, and standardized data sharing between municipal authorities and Original Equipment Manufacturers (OEMs). As urban environments become increasingly digitized, the ability to exchange mobility, infrastructure, and environmental data efficiently is critical to optimizing city operations, enhancing public safety, and fostering innovation in urban mobility solutions. This trend is further accelerated by growing investments in digital infrastructure, rising expectations for real-time data-driven decision-making, and the proliferation of IoT devices within urban landscapes.

Regional Outlook

North America currently holds the largest share of the Data Sharing Agreements for Cities and OEMs market, accounting for approximately 38% of the global value in 2024. This dominance is attributed to the region's mature technology ecosystem, advanced urban infrastructure, and strong regulatory frameworks that promote data interoperability and security. Cities such as New York, San Francisco, and Toronto are at the forefront of deploying smart city technologies, leveraging robust data-sharing agreements with OEMs and technology providers to enable integrated urban mobility, efficient infrastructure management, and real-time environmental monitoring. Additionally, proactive government policies and significant public-private partnerships have fostered an environment conducive to innovation and large-scale implementation of data-driven urban solutions. The presence of leading technology firms and OEMs further cements North America’s leadership position, as these organizations continuously invest in scalable and secure data exchange platforms.

Asia Pacific is projected to be the fastest-growing region in the Data Sharing Agreements for Cities and OEMs market, with a forecasted CAGR of 21.4% from 2025 to 2033. This remarkable growth is driven by rapid urbanization, substantial investments in smart city infrastructure, and increasing government focus on digital transformation across major economies such as China, India, Japan, and South Korea. Cities in Asia Pacific are witnessing a surge in demand for efficient urban mobility, sustainable infrastructure, and enhanced public safety, all of which necessitate robust data sharing frameworks between municipalities and OEMs. Strategic collaborations between governments, technology providers, and OEMs are accelerating the adoption of standardized and custom data sharing agreements, while national policies supporting open data and digital governance further boost market expansion. The region’s large population base and fast-growing urban centers create a fertile ground for innovation and the deployment of next-generation city management solutions.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing data sharing agreements for cities and OEMs, albeit at a slower pace due to unique challenges such as limited digital infrastructure, regulatory complexities, and budgetary constraints. However, there is a growing recognition of the transformative potential of data-driven urban management, especially in areas like public safety, environmental monitoring, and infrastructure optimization. Local governments are increasingly exploring partnerships with OEMs and technology providers to pilot smart city projects and establish foundational data sharing protocols. While adoption is currently hampered by concerns around data privacy, cybersecurity, and standardization, ongoing policy reforms, international collaboration, and capacity-building initiatives are expected to gradually bridge these gaps and unlock new growth opportunities for the market in these regions.

Report Scope

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

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.

It is the survey data on the data sharing policy to editors in Korea.

This study consists of data files that code the data availability policies of top-20 academic journals in the fields of Business & Finance, Economics, International Relations, Political Science, and Sociology. Journals that were ranked as top-20 titles based on 2003-vintage ISI Impact Factor scores were coded on their data policies in 2003 and on their data policies in 2015. In addition, journals that were ranked as top-20 titles based on most recent ISI Impact Factor scores were likewise coded on their data polices in 2015. The included Stata .do file imports the contents of each of the Excel files, cleans and labels the data, and produces two tables: one comparing the data policies of 2003-vintage top-20 journals in 2003 those journals' policies in 2015, and one comparing the data policies of 2003-vintage top-20 journals in 2003 to the data policies of current top-20 journals in 2015.