Metadata capital: Simulating the predictive value of Self-Generated Health Information (SGHI)

Greenberg, Jane; Ogletree, Adrian; Murillo, Angela P.; Caruso, Thomas P.; Huang, Herbie

doi:10.1109/bigdata.2014.7004209

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This strategy is closely connected to PET, as well as to the principle of data minimization, which involves a minimized amount of personal information stored and shared as well as duration of time stored. The emergence of metadata capital on the part of providers (Greenberg, Ogletree, Murillo, Caruso, & Huang, ), along with practices of participatory personal data on the part of users (Shilton, ), continue to make this aspect of platform use a matter of live debate. Notably, many of these ideas, including overarching principles of privacy by design, have been integrated into the European GDPR.…”

Section: Data Work At a Macro Levelmentioning

confidence: 99%

Data work in context: Value, risks, and governance

Foster

McLeod

Nolin

et al. 2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

While always integral to scientific activity, data work has recently emerged as a key set of processes within societal activities of all kinds. While data work presents new opportunities for discovery, value creation, and decision making, its emergence also raises significant ethical issues, including those of ownership, privacy, and trust. This article presents a review of data work, and how negotiating a trade‐off between its value and risks requires locating its processes within the contexts of its conditions and consequences. These include international, national, and sectoral conditions of law, policy, and regulation at a macro level; organizational conditions of information and data governance that aim to address the value and risks of data work at a meso level; along with attention to the everyday contexts of data and information handling by data information and other professionals at a micro level. In conclusion, a conceptual framework is presented that locates the processes of data work within the matrix of its macro meso and micro conditions, its consequences for individuals, organizations, and society, and the relations between them. Suggestions are given for how research into the study of data work—its value risks and governance— can be advanced by using this framework.

show abstract

Section: Data Work At a Macro Levelmentioning

confidence: 99%

Data work in context: Value, risks, and governance

Foster

McLeod

Nolin

et al. 2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…This research was predicated on the fact that value, as a financial indicator, can be measured. The incipient effort has chiefly applied the modified capital gains equation (Greenberg, Swauger, & Feinstein, 2013;Greenberg et al, 2014a;2014b), and calculated costs associated with metadata creation and reuse to determine value. Specifically, metadata reuse demonstrates a greater return on investment (ROI) by adding value to the initial metadata cost.…”

Section: Metadata Capitalmentioning

confidence: 99%

Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

Greenberg

2017

Journal of Data and Information Science

Self Cite

View full text Add to dashboard Cite

Purpose: The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research. This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science.Design/methodology/approach: This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings:The "utilitarian nature" and "historical and traditional views" of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part of a metadata lingua franca to help frame research in the data science research space. Research limitations:There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications:The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem.Originality/value: Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.

show abstract

Metadata capital: Simulating the predictive value of Self-Generated Health Information (SGHI)

Cited by 2 publications

References 8 publications

Data work in context: Value, risks, and governance

Data work in context: Value, risks, and governance

Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

Contact Info

Product

Resources

About