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!).

Background: Multiple factors, including experiences with unethical research practices, have made some Indigenous groups in the United States and Canada reticent to participate in potentially beneficial health-related research. Yet, Indigenous peoples have also expressed a willingness to participate in research when certain conditions related to the components of data management—including data collection, analysis, security and storage, sharing, dissemination, and withdrawal—are met. A scoping review was conducted to better understand the terms of data management employed in health-related research involving Indigenous communities in the United States and Canada.Methods: PubMed, Embase, PsychINFO, and Web of Science were searched using terms related to the populations and topics of interest. Results were screened and articles deemed eligible for inclusion were extracted for content on data management, community engagement, and community-level research governance.Results: The search strategy returned 734 articles. 31 total articles were extracted, of which nine contained in-depth information on data management and underwent detailed extraction. All nine articles reported the development and implementation of data management tools, including research ethics codes, data-sharing agreements, and biobank access policies.These articles reported that communities were involved in activities and decisions related to data collection (n=7), data analysis (n=5), data-sharing (n=9), dissemination (n=7), withdrawal (n=4), and development of data management tools (n=9). The articles also reported that communities had full or shared ownership of (n=5), control over (n=9), access to (n=1), and possession of data (n=5).All nine articles discussed the role of community engagement in research and community-level research governance as means for aligning the terms of data management with the values, needs, and interests of communities.Conclusions: There is need for more research and improved reporting on data management in health-related research involving Indigenous peoples in the United States and Canada. Findings from this review can provide guidance for the identification of data management terms and practices that may be acceptable to Indigenous communities considering participation in health-related research.

According to a survey conducted in the fiscal year 2019, 80 community health information networks in Japan were using the system ID-Link. ID-Link is a cloud network system for medical institutions developed by Japanese technology and electronics company NEC. The community health information network is a system in which medical information of patients can be shared within the regions. The Japanese government has been promoting the system to improve comprehensive medical care offered in local areas.

Collaboratory is a software product developed and maintained by HandsOn Connect Cloud Solutions. It is intended to help higher education institutions accurately and comprehensively track their relationships with the community through engagement and service activities. Institutions that use Collaboratory are given the option to opt-in to a data sharing initiative at the time of onboarding, which grants us permission to de-identify their data and make it publicly available for research purposes. HandsOn Connect is committed to making Collaboratory data accessible to scholars for research, toward the goal of advancing the field of community engagement and social impact.Collaboratory is not a survey, but is instead a dynamic software tool designed to facilitate comprehensive, longitudinal data collection on community engagement and public service activities conducted by faculty, staff, and students in higher education. We provide a standard questionnaire that was developed by Collaboratory’s co-founders (Janke, Medlin, and Holland) in the Institute for Community and Economic Engagement at UNC Greensboro, which continues to be closely monitored and adapted by staff at HandsOn Connect and academic colleagues. It includes descriptive characteristics (what, where, when, with whom, to what end) of activities and invites participants to periodically update their information in accordance with activity progress over time. Examples of individual questions include the focus areas addressed, populations served, on- and off-campus collaborators, connections to teaching and research, and location information, among others.The Collaboratory dataset contains data from 37 institutions beginning in March 2016and continues to grow as more institutions adopt Collaboratory and continue to expand its use. The data represent over 3,600 published activities (and additional associated content) across our user base.Please cite this data as:Medlin, Kristin and Seto, Matthew. Dataset on Higher Education Community Engagement and Public Service Activities, 2016-2021. Collaboratory [producer], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-11-01. doi: _v1.When you cite this data, please also include: ORIGINS PAPER CITATION

Provides resources related to Data Sharing for the AmeriGEOSS community.

HydroShare is an open-source general purpose data repository for the water community, created with the goal of increasing data sharing and lowering the barrier to entry for FAIR data practices. We approach this goal by removing both traditional and perceived barriers to data sharing through an intuitive and general purpose, file agnostic repository. We have found that abundant and easy to access instruction in various formats, one on one interactions, and a flexible interface eases the burden of learning to use a data sharing platform.

Resource Use
License
Creative Commons Attribution (CC-BY 4.0)
Recommended Citation
Cogswell C. 2023. HydroShare and the Water Community, removing perceived barriers to data sharing. University of Idaho. https://doi.org/10.7923/2kae-fr20

Funding
National Science Foundation, EngageINFEWS Research Coordination Network: 1856059, 1856084
US National Science Foundation (NSF), Award numbers: 1148453, 1148090, 1664018, 1664061, 1338606, 1664119, 1849458

This database represents a list of community solar projects, as well as the low-income (LI) and low- and moderate-income (LMI) provisions for both complete and pending projects identified through various sources. The dataset is updated multiple times per year. The current version is the first file located below. Previous versions of the dataset published before June of 2024 can be found in the dataset below labeled “ARCHIVE_Sharing the Sun Community Solar Project Data_Before 06.24.“ The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects.Updated information can be submitted to Sudha Kannan (sudha.kannan@nrel.gov).

This dataset contains supplementary data and R scripts to generate figures for the paper 'The time efficiency gain in sharing and reuse of research data'. This version contains new R scripts to generate Figures of the revised manuscript. Abstract: Among the frequently stated benefits of sharing research data are time efficiency or increased productivity. The assumption is that reuse or secondary use of research data saves researchers time in not having to produce data for a publication themselves. This can make science more efficient and productive. However, if there is no reuse, time costs in making data available for reuse will have been made with no return on this investment. In this paper a mathematical model is used to calculate the break-even point for time spent sharing in a scientific community, versus time gain by reuse. This is done for several scenarios; from simple to complex datasets to share and reuse, and at different sharing rates. The results indicate that sharing research data can indeed cause an efficiency revenue for the scientific community. However, this is not a given in all modeled scenarios. The most efficient scientific community is one that has few sharing researchers, a high reuse rate, and low time investments for sharing and reuse. This suggests it would be beneficial to have a critical selection of datasets that are worth the effort to prepare for reuse in other scientific studies. In addition, stimulating reuse of datasets in itself would be beneficial to increase efficiency in scientific communities.

The objective of this research is to investigate the factors influencing scientists’ data sharing behaviors in different scientific communities by examining both discipline and individual level predictors together. The target population of this research included faculty members and post-doctoral researchers in U.S. academic institutions who belong to STEM disciplines. The sampling frame of this research was identified from the scholar list in the Community of Science’s (CoS) Scholar Database (http://pivot.cos.com), which provides a researcher profile directory in the world mainly from universities and colleges. The final field survey instrument was distributed to the 16,165 potential survey participants in 56 STEM disciplines. From November 19, 2012 to February 15, 2013, a total of 2,470 valid responses were received for the initial data analysis (15.28% of response rate).

The spreadsheets in the present dataset (CSV format) include the anonymised responses to our online survey of signatories of the Joint Statement on open research and data sharing. Responses have been split into quantitative responses (i.e., closed survey questions) and qualitative responses (i.e., free text survey questions).

This data has been used to inform our final report, which is available in our Zenodo Project Community.

Community portal for researchers and content management system for data and databases. Intended to provide common source of data to research community and data about Research Resource Identifiers (RRIDs), which can be used in scientific publications. Central service where RRIDs can be searched and created. Designed to help communities of researchers create their own portals to provide access to resources, databases and tools of relevance to their research areas. Adds value to existing scientific resources by increasing their discoverability, accessibility, visibility, utility and interoperability, regardless of their current design or capabilities and without need for extensive redesign of their components or information models. Resources can be searched and discovered at multiple levels of integration, from superficial discovery based on limited description of resource at SciCrunch Registry, to deep content query at SciCrunch Data Federation.

Collaboration is central to CIROH (https://ciroh.ua.edu/). Advancing the knowledge needed to support research to operations in hydrology depends on collaboration around model and data sharing. It requires open data supporting the integration of information from multiple sources; easy to use, generally accessible, shareable computing; and working together as a team and community. The CUAHSI HydroShare platform was developed to advance water research by enabling communities of researchers to more easily and freely share digital products resulting from their research, not just the scientific publications summarizing a study, but the data, models and workflows used to produce the results, consistent with Findable, Accessible, Interoperable, and Reusable (FAIR) principles of present-day research. HydroShare supports and enables private (e.g., social science) and open data sharing, transparent workflows, and computational reproducibility, thereby improving reliability and trust in research findings. These are crucial as research is transferred into operations.

The goal of this project is to enhance the performance, reliability, usability, and scalability of HydroShare’s linkages with cloud storage and computational systems to fulfill CIROH’s community collaboration and linked computing needs and enable CIROH researchers to easily integrate and analyze national scale datasets required for their research using high-performance and cloud computing systems.

The objectives are to (1) enhance community data access; (2) establish interoperability with scalable computing; (3) demonstrate computational reproducibility; and (4) establish and grow a CIROH Community on HydroShare. Work under objective (1) will use community input to identify, prioritize, and establish easy to use access to multiple high-value community datasets. Work under objective (2) will establish or extend interfaces to high performance computing, leveraging tools for model input preparation such as the CUAHSI Domain Subsetter and I-GUIDE (the Institute for Geospatial Understanding through an enhanced Discovery Environment, https://iguide.illinois.edu). Work under objective (3) will establish and document CIROH community best practices for enhancing the reproducibility of high-performance computing and analysis workflows so that CIROH modeling workflows can be accessed, re-executed, and analyzed by multiple researchers. Work under objective (4) will establish a CIROH “Community” within the HydroShare repository to support collaboration around and sharing of CIROH research products.

Forecasting operations will benefit from the transparency of research products hosted in HydroShare and linked to computing platforms for reproducibility and evaluation. Linking publications, data, and code (often in GitHub), with methods and findings that are well documented and tested will support their evaluation by the National Water Center for operational adoption.

This project runs 6/1/2023 to 5/31/2025.

The public sharing of primary research datasets potentially benefits the research community but is not yet common practice. In this pilot study, we analyzed whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact. Across 397 recent biomedical microarray studies, we found investigators were more likely to publicly share their raw dataset when their study was published in a high-impact journal and when the first or last authors had high levels of career experience and impact. We estimate the USA's National Institutes of Health (NIH) data sharing policy applied to 19% of the studies in our cohort; being subject to the NIH data sharing plan requirement was not found to correlate with increased data sharing behavior in multivariate logistic regression analysis. Studies published in journals that required a database submission accession number as a condition of publication were more likely to share their data, but this trend was not statistically significant. These early results will inform our ongoing larger analysis, and hopefully contribute to the development of more effective data sharing initiatives. Earlier version presented at ASIS&T and ISSI Pre-Conference: Symposium on Informetrics and Scientometrics 2009

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of June 2022. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

Open data policies have been introduced by governments, funders, and publishers over the past decade. Previous research showed a growing recognition by scientists of the benefits of data-sharing and reuse, but actual practices lag and are not always compliant with new regulations. The goal of this study is to investigate motives, attitudes, and data practices of the community of earth and planetary geophysicists, a discipline believed to have accepting attitudes towards data sharing and reuse. A better understanding of the attitudes and current data-sharing practices of this scientific community could enable funders, publishers, data managers, and librarians to design systems and services that help scientists understand and adhere to mandates and to create practices, tools, and services that are scientist-focused. An online survey was distributed to the members of the American Geophysical Union (AGU), producing 1372 responses from 116 countries. The attitudes of researchers to data shar...

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.

Data management, sharing, and publication are integral parts of a robust data management plan, a core requirement of all NSF funded research grants and many other funding agencies. This seminar will discuss some common challenges and present solutions for managing and sharing data using CUAHSI tools, specifically utilizing HydroShare. HydroShare is an online repository system for water data and models that aims to advance hydrologic science through enabling users to manage, share, and publish products resulting from their research and data collection. We will introduce attendees to approaches for managing current and archived data, collaboration within a research group, documentation of metadata, and publication. The webinar will center around tools and techniques within HydroShare to facilitate these activities, employing both discussions and demos.

1200+ ''resting state'' functional MRI (R-fMRI) datasets independently collected at 33 sites and donated by the principal investigators for the purpose of providing the broader imaging community complete access to a large-scale functional imaging dataset. Age, sex and imaging center information are provided for each of the datasets. In accordance with HIPAA guidelines, all datasets are anonymous, with no protected health information included. We anticipate this data-sharing effort will equip researchers with a means of exploring and refining R-fMRI approaches, and facilitate the growing ethos of sharing and collaboration. Disclaimer: The ''1000 Functional Connectomes Project'' datasets are provided freely without assurance of quality or appropriateness for usage.

In this paper, we address the issues associated with the development and sustainability of a network of data sites and databases hosted by academic-based groups. These are the groups who, over the years, have conducted the majority of research on geothermal systems while educating the next generation of geothermal professionals and researchers. A network can be envisioned as an Internet-connected series of nodes (data sites), that allow for a common approach to finding data among the linked sites. Each of the co-authors' groups have for years collected and provided data to researchers, industry, state and federal agencies, and the public. Over the last ten years, the better management of and access to data have become an increasingly important part of not only the research process, but also for industry as they push forward with delineating and producing geothermal resources, for state and federal agencies to help them meet their missions and mandates, and to inform the public on the importance of geothermal energy. Indeed both Congress and the White House continue to strengthen the bipartisan goal of free and open access to all data created by the federal dollar. For academic-based data sites, the challenges are many, but not insurmountable. For example, we need to provide seamless linkages of data to analysis and visualization tools, in particular high-level modeling programs and required computational resources. When dealing with research results and industry partnerships, moratorium and proprietary data must be handled carefully and securely. At the same time, we have to make it as easy as possible for users to discover, aggregate and synthesize data in ways that allow them to focus on the analysis of these data rather than on finding and compiling them. We also must better utilize these research-level data within the education enterprise to train and attract our next generation of geoscientists and geoengineers. Data discovery and sharing among multiple data sites is a persistent issue. Each of the sites in the network hosts some unique data but a substantial number of data types may overlap among some of the sites, complicating the process. The notion of the "semantic web" as the solution for data integration has sparked continued debate because of its underlying requirement for ontologies and single-definition vocabularies. A hybrid approach is now evolving that utilizes some tools of the semantic web but recognizes the investment in current data sites and the different needs of the various user communities. This approach also recognizes that much "knowledge" (the ultimate value of a data network) is wrapped up in differences among vocabularies, languages and concepts and that forcing singularity decreases the knowledge value of data and their resulting data products. This approach makes data sharing more difficult, but not impossible, and importantly opens up the system for broader participation and collaboration. Sustainability will always remain an issue for an academic-based data network. Self-funding from the home institution is not feasible. However as agencies continue to increase their efforts to manage and provide access to data generated by projects and activities they fund, a long-term agency-academic partnership will evolve that includes both funding academic-based data networks and relying on these networks to provide some of that public access to federally-funded data. Finally, it is important to note that these academic-based data networks have to be self-governing for them to work at all, much less be sustainabl

The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative—the Data Ark—to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006–2011 (n = 48) and 2014–2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship.