The TRUST Principles for digital repositories

Lin, Dawei; Crabtree, Jonathan; Dillo, Ingrid; Downs, Robert R.; Edmunds, Rorie; Giaretta, David; Giusti, Marisa Raquel De; L’Hours, Hervé; Hugo, Wim; Jenkyns, Reyna; Khodiyar, Varsha; Martone, Maryann E.; Mokrane, Mahdi; Navale, Vivek; Petters, Jonathan; Sierman, Barbara; Sokolova, Dina V.; Stockhause, Martina; Westbrook, John D.

doi:10.1038/s41597-020-0486-7

Cited by 255 publications

(198 citation statements)

References 14 publications

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“…For example, in 2020 the Open Data Institute put forward a working definition which draws upon the concept of a legal trust with trustees and beneficiaries: "a data trust provides independent, fiduciary stewardship of data" [14]. In addition, there are many other labels applied to endeavors to responsibly share and provide access to data including: digital trusts, data cooperatives, data commons, data clubs, data institutions, data banks, data stewardships, data collaboratives, data safe havens, trustworthy digital repositories and trusted research environments [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…In our experience, one of the negative effects of the multiple labels and conflicting definitions is that it can obscure commonalities behind approaches to data sharing and data access. For example, the authors of this report have, at times, used several of the labels above to describe our work, while having common goals related to data that are FAIR (findable, accessible, interoperable, reusable), and well governed and managed as per the Five Safes, the recently published TRUST principles and other frameworks [27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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Essential Requirements for Establishing and Operating Data Trusts

Paprica

Sutherland

Smith

et al. 2020

IJPDS

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Introduction: Increasingly, the label “data trust” is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe and equitable way. However, there is a gap in terms of practical guidance about how to establish and operate a data trust. Aim and Approach: In December 2019, the Canadian Institute for Health Information and the Vector Institute for Artificial Intelligence convened a working meeting of 19 people representing 15 Canadian organizations/initiatives involved in data sharing, most of which focus on public sector health data. The objective was to identify essential requirements for the establishment and operation of data trusts. Preliminary findings were presented during the meeting then refined as participants and co-authors identified relevant literature and contributed to this manuscript. Results: Twelve (12) minimum specification requirements (“min specs”) for data trusts were identified. The foundational min spec is that data trusts must meet all legal requirements, including legal authority to collect, hold or share data. In addition, there was agreement that data trusts must have (i) an accountable governing body which ensures the data trust advances its stated purpose and is transparent, (ii) comprehensive data management including responsible parties and clear processes for the collection, storage, access, disclosure and use of data, (iii) training and accountability requirements for all data users and (iv) ongoing public and stakeholder engagement. Conclusion / Implications: Based on a review of the literature and advice from participants from 15 Canadian organizations/initiatives, practical guidance in the form of twelve min specs for data trusts were agreed on. Public engagement and continued exchange of insights and experience is recommended on this evolving topic.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Essential Requirements for Establishing and Operating Data Trusts

Paprica

Sutherland

Smith

et al. 2020

IJPDS

View full text Add to dashboard Cite

show abstract

“…That said, GitHub repositories can be integrated with a trusted digital repository like Zenodo (2020) to create a citable software archive. Trusted digital repositories provide guarantees for digital permanence and have backup and contingency plans for migrating content should the repository cease operations ( Lin et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

On code sharing and model documentation of published individual and agent-based models

Janssen

Pritchard

Lee

2020

Environmental Modelling & Software

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Being able to replicate research results is the hallmark of science. Replication of research findings using computational models should, in principle, be possible. In this manuscript, we assess code sharing and model documentation practices of 7500 publications about individual-based and agent-based models. The code availability increased over the years, up to 18% in 2018. Model documentation does not include all the elements that could improve the transparency of the models, such as mathematical equations, flow charts, and pseudocode. We find that articles with equations and flow charts being cited more among other model papers, probably because the model documentation is more transparent. The practices of code sharing improve slowly over time, partly due to the emergence of more public repositories and archives, and code availability requirements by journals and sponsors. However, a significant change in norms and habits need to happen before computational modeling becomes a reproducible science.

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“…The prospect for widely available data for all has garnered the attention of scholars from various fields of specialization (Borgman et al, 2019;Chawinga & Zinn, 2019); Wiggins et al, (2018), and featured earlier publications in Nature (Campbell, 2008;2009), Science (Kum et al, 2011;Einav & Levin, 2014), as well as the Journal of the American society for information science & technology (Borgman, 2012). Testament to its ongoing significance among scholars of diverse background is the recently published comment in nature's journal Scientific Data (Lin et al, 2020).…”

Section: ) Literature Reviewmentioning

confidence: 99%

Research Data Recycling through Open Sharing and Reuse: A Case Study of Sustainable Digital Good Consumption in the Sharing Economy

Niankara¹

2020

Preprint

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In order to meet the needs of an increasingly complex research landscape, researchers engage in “collaborative prosumption” through open data sharing and reuse. Although significant gains have been achieved in this regards because of growing requirements from funding agencies, governments and journals, the question of how reuse of openly available data for new research contribute to sustainability is yet to be appropriately addressed in the literature. Therefore, relying on a three stage stratified clustered random sampling of the Journal of Applied Econometrics data archive (JAEDA), the present research provides a case study of the value of research data recycling for sustainable research and economic development. More specifically our analysis show that reformatting from wide to long format, openly shared equity price index data on eleven European countries’ extracted from JAEDA, and augmented with country level geospatial Meta data, provides a new basis for interesting descriptive analytics and spatio-temporal econometric modeling and inference. Given the ever-increasing volume of openly available research data, our study provides a first-hand insight on open data reuse, which should benefit all stakeholders in the research community, as they seek sustainable solutions for scientific productivity and progress.

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The TRUST Principles for digital repositories

Cited by 255 publications

References 14 publications

Essential Requirements for Establishing and Operating Data Trusts

Essential Requirements for Establishing and Operating Data Trusts

On code sharing and model documentation of published individual and agent-based models

Research Data Recycling through Open Sharing and Reuse: A Case Study of Sustainable Digital Good Consumption in the Sharing Economy

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