Types of data sharing (absolute values) in the examined dataset.

This dataset is an abridged version of the Information Management and Security Team log of Data Sharing Agreements. The log is used to record, track and report on the various data sharing agreements made by the FSA with other organisations to share information compliantly, and is the number as at the end of 2016. The reference numbers are not always consecutive as sometimes an initial data sharing enquiry does not result in setting up an agreement. The data sharing activity categories are: • disclosed - data is shared one way only from FSA to the other party in the data sharing agreement • received - data is shared one way only from the other party in the data sharing agreement to the FSA • both - there is a mutual exchange of data between the FSA and the other party in the agreement

According to a survey conducted among stakeholders in the healthcare industry in the United States in 2020, ** percent of respondents indicated that lack of data standardization was the biggest challenge to health data sharing between payers and providers. Furthermore, a lack of technical interoperability and quality of data that is shared was each noted by ** percent of respondents.

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 statistic illustrates banking customer responses to certain potential types of data monitoring and their associated benefits in the United Kingdom (UK) as of 2015. It can be seen that 43 percent of respondents stated that their bank analysing their account data to predict future financial needs was helpful and impressive. However, the bank analysing the future holiday plans via social media activity and offering a good rate on travel expenses was found to be creepy, by 79 percent of respondents..

Open data sharing improves the quality and reproducibility of scholarly research. Open data are defined as data that can be freely used, reused, and redistributed by anyone. Open data sharing democratizes science by making data more equitably available throughout the world regardless of funding or access to other resources necessary for generating cutting-edge data. For an interdisciplinary field like biological anthropology, data sharing is critical since one person cannot easily collect data across multiple domains. The goal of this paper is to encourage broader data sharing by exploring the state of data sharing in the field of biological anthropology. Our paper is divided into four parts: the first section describes the benefits, challenges, and emerging solutions to open data sharing; the second section presents the results of our data archiving and sharing survey that was completed by over 700 researchers; the third section presents personal experiences of data sharing by the authors; and the fourth section discusses the strengths of different types of data repositories and provides a list of recommended data repositories.

The data archiving and sharing survey and the raw data collected from the survey are deposited here.

This statistic illustrates consumers willingness to share different types of personal information and data with their insurers in the United Kingdom (UK) as of 2015, by type of data. It can be seen that 31 percent of respondents stated that they feel comfortable sharing their personal contact information with their home or motor insurer.

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.

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.

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

Methods To investigate the data-related practices of psychology researchers, we adapted a survey developed as part of our previous study of neuroimaging researchers. The survey was distributed via Qualtrics (http://www.qualtrics.com) from January 25 to March 25, 2019. Before beginning the survey, participants were required to verify that they were at least 18 years old and gave their informed consent to participate. Participants who did not meet these inclusion criteria or who did not complete at least the first section of the survey were not included in the final data analysis. After filtering, 274 psychology researchers from 31 countries participated in our survey.

All code for data collection and visualization is included in the Jupyter notebooks included here.

Data Sharing Agreements Market for Cities and OEMs Outlook

According to our latest research, the global Data Sharing Agreements market size for Cities and OEMs reached USD 2.34 billion in 2024, and is expected to grow at a robust CAGR of 18.7% from 2025 to 2033. By the end of the forecast period, the market is projected to reach USD 12.07 billion by 2033. The primary growth driver is the escalating demand for real-time, interoperable, and secure data exchange mechanisms between urban authorities and Original Equipment Manufacturers (OEMs), spurred by the rapid adoption of smart city initiatives globally.

One of the most significant factors fueling the expansion of the Data Sharing Agreements market for Cities and OEMs is the proliferation of smart city projects worldwide. Cities are increasingly leveraging advanced technologies to enhance urban mobility, public safety, environmental sustainability, and infrastructure management. The integration of connected devices and IoT solutions generates vast amounts of data that must be shared securely and efficiently among various stakeholders, including municipalities, OEMs, and technology providers. As cities aim to optimize resource allocation, improve decision-making, and deliver citizen-centric services, robust data sharing frameworks become indispensable. The need for standardized and customizable data sharing agreements is further amplified by the growing complexity of urban ecosystems and the diversity of data sources, such as traffic sensors, environmental monitors, and mobility platforms.

Another key growth factor is the evolving regulatory landscape and the increasing emphasis on data privacy and security. Governments and regulatory bodies are implementing stringent guidelines for data governance, transparency, and accountability, particularly in the context of urban data sharing. These regulations necessitate the adoption of formalized data sharing agreements that clearly define data ownership, usage rights, consent mechanisms, and security protocols. As a result, OEMs and city authorities are investing in legal and technical frameworks that facilitate compliant and secure data exchange. This regulatory impetus not only mitigates risks associated with data breaches and misuse but also fosters trust among stakeholders, thereby accelerating the adoption of data sharing agreements across cities and OEMs.

Technological advancements, particularly in artificial intelligence, machine learning, and blockchain, are also catalyzing the growth of the Data Sharing Agreements market for Cities and OEMs. These technologies enable automated data validation, real-time analytics, and tamper-proof data transactions, which are critical for managing the scale and complexity of urban data flows. The integration of advanced analytics and secure data exchange protocols empowers cities and OEMs to derive actionable insights from shared data, optimize urban operations, and drive innovation in mobility, environmental monitoring, and infrastructure development. The convergence of these technological trends is expected to further elevate the demand for sophisticated data sharing agreements tailored to the unique requirements of urban environments and OEM collaborations.

From a regional perspective, North America currently leads the global Data Sharing Agreements market for Cities and OEMs, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the early adoption of smart city technologies, robust regulatory frameworks, and the presence of leading OEMs and technology providers. Europe follows closely, driven by strong government initiatives for urban digitization and sustainability. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, propelled by rapid urbanization, increasing investments in smart infrastructure, and a growing ecosystem of technology startups. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a relatively nascent stage of adoption.

Agreement Type Analysis

The Agreement Type segment in the Data Sharing Agreements market for Cities and OEMs is categorized into Bilateral, Multilateral, Standardized, and Customized agreements. Bilateral agreements have traditionally dominated the market, particularly in cases where data sharing is limited to two parties, such as a city authority and a single OEM. These agreements are

Focusing on the type of location data French mobile users are willing to share with brands through geolocation, it appears that the majority of the respondents would not share their residence, workplace or current location. From the sample, ** percent mentioned that they were open to share their current position to receive personalized offers from brands, ** percent would share their area of residence and ** percent would share their workplace location.

Resources to facilitate data sharing.

Data from online survey among authors of the social sciences using social media data for their research and having published journal articles based on social media data between 2018 and 2021. The questionnaire consists of several closed and open-ended questions in seven main sections: a) data acquisition and use of secondary data, b) past data sharing behaviour, c) data sharing intentions, d) data documentation, e) use of other forms of data, f) personality and g) demography. The questions to measure factors that influence researchers’ data sharing decisions were designed using the Theory of Planned Behavior (Icek Ajzen).

This dataset contains responses from an online survey of 2187 participants primarily located in the UK. All participants stated that they had used the UK National Health Service (NHS) at some time in their lives. The data were collected between December 2018 and August 2019. Participants' views on data sharing - this dataset contains information about people's willingness to share mental and physical health data for research purposes. It also includes information on willingness to share other types of data, such as financial information. The dataset includes participants' responses to questions relating to mental health data sharing, including the trustworthiness of organisations which use such data, how much the presence of different governance measures (such as deidentification, opt-out, etc.) would alter their views, and whether they would be less likely to access NHS mental health services if they knew their data might be shared with researchers. Participants' satisfaction and interaction with UK mental and physical health services - the dataset includes information regarding participants' views on and interaction with NHS services. This includes ratings of satisfaction at first contact and in the previous 12 months, frequency of use, and type of treatment received. Information about participants - the dataset includes information about participants' mental and physical health, including whether or not they have experience with specific mental health conditions, and how they would rate their mental and physical health at the time of the survey. There is also basic demographic information about the participants (e.g. age, gender, location etc.). ## This item has been replaced by the one which can be found at https://hdl.handle.net/10283/4467 ##

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.

A manually curated registry of standards, split into three types - Terminology Artifacts (ontologies, e.g. Gene Ontology), Models and Formats (conceptual schema, formats, data models, e.g. FASTA), and Reporting Guidelines (e.g. the ARRIVE guidelines for in vivo animal testing). These are linked to the databases that implement them and the funder and journal publisher data policies that recommend or endorse their use.

Data Access Agreements Market Outlook

According to our latest research, the global Data Access Agreements market size reached USD 2.48 billion in 2024, with a robust year-over-year growth trajectory. The market is projected to expand at a CAGR of 11.2% from 2025 to 2033, reaching an estimated USD 6.51 billion by the end of the forecast period. This strong growth is primarily fueled by the increasing need for secure, compliant, and transparent data sharing across industries, as organizations prioritize data privacy, regulatory adherence, and efficient data management in a rapidly digitalizing world.

One of the primary growth factors in the Data Access Agreements market is the relentless surge in data generation and utilization across sectors such as healthcare, finance, government, and telecommunications. With the exponential increase in volume, variety, and velocity of data, organizations are compelled to establish clear protocols for data access, sharing, and usage. This has led to a heightened demand for robust data access agreements that ensure legal compliance, protect sensitive information, and foster collaboration between entities. The proliferation of data-driven business models, coupled with mounting regulatory pressures such as GDPR, HIPAA, and CCPA, is compelling organizations to invest in advanced solutions for managing data access agreements effectively.

Another significant growth driver for the Data Access Agreements market is the rise of cross-border data flows and collaborative research initiatives, especially in sectors like healthcare and academia. The COVID-19 pandemic underscored the importance of rapid data sharing for scientific research, public health, and global cooperation, further accelerating the adoption of standardized and customized data access agreements. As multinational enterprises and research organizations increasingly engage in data sharing across jurisdictions, the demand for agreements that address diverse legal frameworks, data sovereignty, and intellectual property rights continues to escalate. This trend is further supported by advancements in cloud computing and digital platforms, which facilitate seamless, secure, and scalable data sharing on a global scale.

Furthermore, the growing emphasis on data monetization and the emergence of data marketplaces are contributing to the expansion of the Data Access Agreements market. Organizations are recognizing the value of their data assets and are seeking mechanisms to license, sell, or share data in a controlled and compliant manner. Data access agreements play a pivotal role in defining the terms of use, restrictions, and revenue sharing models, thereby enabling organizations to unlock new revenue streams while mitigating risks associated with unauthorized access or misuse. The convergence of technologies such as blockchain, AI, and automation is also enhancing the efficiency, transparency, and enforceability of data access agreements, driving further market growth.

Regionally, North America continues to lead the Data Access Agreements market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The dominance of North America is attributed to the presence of major technology players, stringent regulatory frameworks, and a mature data governance ecosystem. Europe’s market is bolstered by robust privacy laws and cross-border research collaborations, while Asia Pacific is witnessing rapid growth due to digital transformation initiatives and increasing adoption of cloud-based solutions. Latin America and the Middle East & Africa are also experiencing steady growth, driven by regulatory modernization and rising awareness of data security and compliance.

Type Analysis

The Type segment of the Data Access Agreements market comprises Standard Data Access Agreements, Customized Data Access Agreements, and Institutional Data Access Agreements. Each type serves distinct requirements and use-cases, reflecting the diverse landscape of data sharing needs across industries. Standard Data Access Agreements are widely adopted for routine, low-risk data exchanges, offering predefined templates that streamline the negotiation process and ensure baseline compliance with regulatory standards. These agreements are particularly popular among organizations seeking to expedite data sharing without extensive legal overhead, especially in sectors with well-established data governance protocols.