Data Stewardship: Data Sharing and (EU) Privacy Legislation (Presentation Slides)

PurposeThe progress of life science and social science research is contingent on effective modes of data storage, data sharing and data reproducibility. In the present digital era, data storage and data sharing play a vital role. For productive data-centric tasks, findable, accessible, interoperable and reusable (FAIR) principles have been developed as a standard convention. However, FAIR principles have specific challenges from computational implementation perspectives. The purpose of this paper is to identify the challenges related to computational implementations of FAIR principles. After identification of challenges, this paper aims to solve the identified challenges.Design/methodology/approachThis paper deploys Petri net-based formal model and Petri net algebra to implement and analyze FAIR principles. The proposed Petri net-based model, theorems and corollaries may assist computer system architects in implementing and analyzing FAIR principles.FindingsTo demonstrate the use of derived petri net-based theorems and corollaries, existing data stewardship platforms – FAIRDOM and Dataverse – have been analyzed in this paper. Moreover, a data stewardship model – “Datalection” has been developed and conversed about in the present paper. Datalection has been designed based on the petri net-based theorems and corollaries.Originality/valueThis paper aims to bridge information science and life science using the formalism of data stewardship principles. This paper not only provides new dimensions to data stewardship but also systematically analyzes two existing data stewardship platforms FAIRDOM and Dataverse.

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A beginner’s guide to data stewardship and data sharing

Spinal Cord ◽

10.1038/s41393-018-0232-6 ◽

2019 ◽

Vol 57 (3) ◽

pp. 169-182 ◽

Cited By ~ 8

Author(s):

Marcel P. Dijkers

Keyword(s):

Data Sharing ◽

Data Stewardship

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Data Governance in the Dairy Industry

Animals ◽

10.3390/ani11102981 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2981

Author(s):

Roger Cue ◽

Mark Doornink ◽

Regi George ◽

Benjamin Griffiths ◽

Matthew W. Jorgensen ◽

...

Keyword(s):

Data Sharing ◽

Data Privacy ◽

Dairy Farm ◽

Legal Regulation ◽

Data Governance ◽

Codes Of Practice ◽

The Status ◽

Data Stewardship ◽

Data Ecosystem ◽

Diverse Data

Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts. However, all these codes are voluntary, which limits their adoption. We propose a Farmers Bill of Rights for the dairy data ecosystem to address some key components around data ownership and transparency in data sharing. Our hope is to start the discussion to create a balanced environment to promote equity within the data economy, encourage proper data stewardship, and to foster trust and harmony between the industry companies and the farmers when it comes to sharing data.

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Technical Note: Open-paleo-data implementation pilot – The PAGES 2k special issue

10.5194/cp-2017-157 ◽

2017 ◽

Cited By ~ 1

Author(s):

Darrell Kaufman ◽

Keyword(s):

Data Sharing ◽

Open Data ◽

Essential Element ◽

Technical Note ◽

Published Data ◽

Special Issue ◽

Publication Process ◽

The Past ◽

Data Stewardship ◽

Abstract Data

Abstract. Data stewardship is an essential element of the publication process. Knowing how to enact generally described data policies can be difficult, however. Examples are needed to model the implementation of open-data polices in actual studies. Here we explain the procedure used to attain a high and consistent level of data stewardship across a special issue of the journal, Climate of the Past. We discuss the challenges related to (1) determining which data are essential for public archival, (2) using previously published data, and (3) understanding how to cite data. We anticipate that open-data sharing in paleo sciences will accelerate as the advantages become more evident and the practice becomes a standard part of publication.

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Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape

International Journal of Digital Curation ◽

10.2218/ijdc.v10i2.359 ◽

2016 ◽

Vol 10 (2) ◽

pp. 157-175 ◽

Cited By ~ 7

Author(s):

Yi Shen

Keyword(s):

Data Sharing ◽

Current Knowledge ◽

Virginia Tech ◽

Research Data ◽

Future Research ◽

User Engagement ◽

Strategic Development ◽

Academic Faculty ◽

Data Stewardship ◽

Existing Data

This paper presents the results of a research data assessment and landscape study in the institutional context of Virginia Tech to determine the data sharing and reuse practices of academic faculty researchers. Through mapping the level of user engagement in “openness of data,” “openness of methodologies and workflows,” and “reuse of existing data,” this study contributes to the current knowledge in data sharing and open access, and supports the strategic development of institutional data stewardship. Asking faculty researchers to self-reflect sharing and reuse from both data producers’ and data users’ perspectives, the study reveals a significant gap between the rather limited sharing activities and the highly perceived reuse or repurpose values regarding data, indicating that potential values of data for future research are lost right after the original work is done. The localized and sporadic data management and documentation practices of researchers also contribute to the obstacles they themselves often encounter when reusing existing data.

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Blockchain-based platforms for genomic data sharing: a de-centralized approach in response to the governance problems?

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy149 ◽

2018 ◽

Vol 26 (1) ◽

pp. 76-80 ◽

Cited By ~ 18

Author(s):

Mahsa Shabani

Keyword(s):

Access Control ◽

Data Sharing ◽

Genomic Data ◽

Data Access ◽

Distributed Data ◽

Data Ownership ◽

Governance Challenges ◽

Data Stewardship

Abstract Blockchain-based platforms are emerging to provide solutions for technical and governance challenges associated with genomic data sharing. Providing capabilities for distributed data stewardship and participatory access control along with effective ways for enforcement of the data access agreements and data ownership are among the major promises of these platforms.

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Fostering trustworthy data sharing: Establishing data foundations in practice

Data & Policy ◽

10.1017/dap.2020.24 ◽

2021 ◽

Vol 3 ◽

Author(s):

Sophie Stalla-Bourdillon ◽

Laura Carmichael ◽

Alexsis Wintour

Keyword(s):

Data Sharing ◽

Personal Data ◽

Channel Islands ◽

Independent Data ◽

Data Governance ◽

Good Data ◽

Governance Model ◽

Data Stewardship ◽

The Guardian

Abstract Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation.

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