Constitutional Limits on Surveillance: Associational Freedom in the Age of Data Hoarding

"Data ownership" is actually an oxymoron, because there could not be a copyright (ownership) on facts or ideas, hence no data onwership rights and law exist. The term refers to various kinds of data protection instruments: Intellectual Property Rights (IPR) (mostly copyright) asserted to indicate some kind of data ownership, confidentiality clauses/rules, database right protection (in the European Union only), or personal data protection (GDPR) (Scassa 2018). Data protection is often realised via different mechanisms of "data hoarding", that is witholding access to data for various reasons (Sieber 1989). Data hoarding, however, does not put the data into someone's ownership. Nonetheless, the access to and the re-use of data, and biodiversuty data in particular, is hampered by technical, economic, sociological, legal and other factors, although there should be no formal legal provisions related to copyright that may prevent anyone who needs to use them (Egloff et al. 2014, Egloff et al. 2017, see also the Bouchout Declaration). One of the best ways to provide access to data is to publish these so that the data creators and holders are credited for their efforts. As one of the pioneers in biodiversity data publishing, Pensoft has adopted a multiple-approach data publishing model, resulting in the ARPHA-BioDiv toolbox and in extensive Strategies and Guidelines for Publishing of Biodiversity Data (Penev et al. 2017a, Penev et al. 2017b). ARPHA-BioDiv consists of several data publishing workflows: Deposition of underlying data in an external repository and/or its publication as supplementary file(s) to the related article which are then linked and/or cited in-tex. Supplementary files are published under their own DOIs to increase citability). Description of data in data papers after they have been deposited in trusted repositories and/or as supplementary files; the systme allows for data papers to be submitted both as plain text or converted into manuscripts from Ecological Metadata Language (EML) metadata. Import of structured data into the article text from tables or via web services and their susequent download/distribution from the published article as part of the integrated narrative and data publishing workflow realised by the Biodiversity Data Journal. Publication of data in structured, semanticaly enriched, full-text XMLs where data elements are machine-readable and easy-to-harvest. Extraction of Linked Open Data (LOD) from literature, which is then converted into interoperable RDF triples (in accordance with the OpenBiodiv-O ontology) (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph Deposition of underlying data in an external repository and/or its publication as supplementary file(s) to the related article which are then linked and/or cited in-tex. Supplementary files are published under their own DOIs to increase citability). Description of data in data papers after they have been deposited in trusted repositories and/or as supplementary files; the systme allows for data papers to be submitted both as plain text or converted into manuscripts from Ecological Metadata Language (EML) metadata. Import of structured data into the article text from tables or via web services and their susequent download/distribution from the published article as part of the integrated narrative and data publishing workflow realised by the Biodiversity Data Journal. Publication of data in structured, semanticaly enriched, full-text XMLs where data elements are machine-readable and easy-to-harvest. Extraction of Linked Open Data (LOD) from literature, which is then converted into interoperable RDF triples (in accordance with the OpenBiodiv-O ontology) (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph In combination with text and data mining (TDM) technologies for legacy literature (PDF) developed by Plazi, these approaches show different angles to the future of biodiversity data publishing and, lay the foundations of an entire data publishing ecosystem in the field, while also supplying FAIR (Findable, Accessible, Interoperable and Reusable) data to several interoperable overarching infrastructures, such as Global Biodiversity Information Facility (GBIF), Biodiversity Literature Repository (BLR), Plazi TreatmentBank, OpenBiodiv, as well as to various end users.

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Chapter 4. The Sin of Data Hoarding

The Seven Deadly Sins of Psychology ◽

10.1515/9781400884940-005 ◽

2017 ◽

pp. 75-95

Keyword(s):

Data Hoarding

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The Sin of Data Hoarding

The Seven Deadly Sins of Psychology ◽

10.2307/j.ctvc779w5.7 ◽

2017 ◽

pp. 75-95

Keyword(s):

Data Hoarding

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“Smittestopp”: If you want your freedom back, download now

Big Data & Society ◽

10.1177/2053951720939985 ◽

2020 ◽

Vol 7 (2) ◽

pp. 205395172093998 ◽

Cited By ~ 2

Author(s):

Kristin B Sandvik

Keyword(s):

Public Health ◽

Social Distancing ◽

Health Authorities ◽

Public Health Authorities ◽

Data Hoarding ◽

The Government

The intervention attempts to engage critically with the Smittestopp app as a specifically Norwegian technofix. Culturally and politically, much of the Covid-19 response and the success of social distancing rules have been organized around the widespread trust in the government and public health authorities, and a focus on the citizens’ duty to contribute to the dugnaðr. The intervention argues that Smittestopp has been co-created by the mobilization of trust and dugnaðr, resulting in the launch of an incomplete and poorly defined data-hoarding product with significant vulnerabilities.

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The Sin of Data Hoarding

The Seven Deadly Sins of Psychology ◽

10.2307/j.ctvc7742b.8 ◽

2019 ◽

pp. 75-95

Keyword(s):

Data Hoarding

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A Utility Theory of Privacy and Information Sharing

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

10.4018/978-1-7998-3479-3.ch031 ◽

2021 ◽

pp. 428-448

Author(s):

Julia Puaschunder

Keyword(s):

Decision Making ◽

Social Media ◽

Big Data ◽

Information Sharing ◽

Data Storage ◽

Computational Power ◽

Human Decision ◽

Data Hoarding ◽

Share Information ◽

Over Time

Today enormous data storage capacities and computational power in the e-big data era have created unforeseen opportunities for big data hoarding corporations to reap hidden benefits from individuals' information sharing, which occurs bit by bit in small tranches over time. Behavioral economics describes human decision-making fallibility over time but has—to this day—not covered the problem of individuals' decisions to share information about themselves in tranches on social media and big data administrators being able to reap a benefit from putting data together over time and reflecting the individual's information in relation to big data of others.

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