Christine L. Borgman

PDFBOR | Descriptive Abstract | Exploratory Research | Qualitative | Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data-sharing practices in other fields vary widely. Moreover, research data take many forms are handled in many ways, using many approaches, and often are difficult to interpret once
removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders
involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice |

Contents
1. Introduction
2. Why is data Sharing Urgent?
3. What Are Data?
-- Communities and Data
-- Categories of Data
-- Purposes for Collecting Data
-- FIG. 1. Purposes for Collecting Data
-- Approaches to Handling Data
-- FIG. 2. Approaches to handling data
4. Why Share Research Data?
-- FIG. 3. Rationales for Sharing Research Data
-- To Reproduce or to Verify Research "Reproducibility or replication of research is viewed as
“the gold standard” for science (Jasny, Chin, Chong, &
Vignieri, 2011), yet it is the most problematic rationale
for sharing research data. This rationale is fundamentally
research-driven but can also be viewed as serving the public
good. Reproducing a study confirms the science, and in
doing so confirms that public monies were well spent.
However, the argument can be applied only to certain kinds
of data and types of research, and rests upon several questionable
assumptions."
-- To Make Results of Publicly Funded Research Available to
the Public
-- To Enable Others to Ask New Questions of Extant Data
-- To Advance the State of Research and Innovation
5. Discussion and Conclusions
6. Acknowledgments
7. References

SA - https://www.librarything.com/work/31585755/book/256874616 | https://www.librarything.com/work/31502625/book/255725622 | https://www.librarything.com/work/31453634/book/254994272 | https://www.librarything.com/work/31432400/book/254685366 | https://www.librarything.com/work/31575314/book/256775357 |
RT - Reproducibility
BT - Sharing
NT - Preservation
UF -This document discusses the challenges and importance of sharing research data in various fields of study.
SN - PDF copy of a journal article. (This entry does not reference a hierarchical list)… (plus d'informations)

Just finished reading Christine Borgman's book entitled "Big Data, Little Data, No Data: Scholarship in the Networked World" (MIT Press, 2015) and I found it a really interesting and thought provoking read.

A Google search for the phrase "big data" returns more than 60,000,000 results, indicating its wide usage on the big wide web. Christine Borgman, a renown author and commentator on scholarly communication, open data, digital libraries and information infrastructure, offers us a much required in-depth critical analysis and discussion of big data and insight on the importance of placing emphasis on research data management policies and strategies within an organisation. The author argues that value is not gained from the sheer size of raw data, but on how to make sense, curate (preservation for future use), share and re-use data, whether such data is big or little in its size. In essence what makes data valuable is not its size but the authenticity, provenance and the ability to re-purpose it. Big data is also about collaboration and making contextual links between data. Borgman notes "data are big or little in terms of what can be done with them, what insights they can reveal, and the scale of analysis required relative to the phenomenon of interest." She makes detail references to the notion of Derek de Solla Price's Little Science, Big Science and the invisible college.

Without denying the increase in “volume, velocity and variety” of data, Borgman argues that big data is a rather broad characterisation of an emerging paradigm in data science and scholarship which takes data seriously. The value of big data can be harnessed in the inter-connections and collaborative efforts of various bodies. Research publications are one-way of utilising data – but more can be harnessed from research data for example through data mining.

Borgman contends that data has no intrinsic value in itself as its value is derived from use and the context. She writes "data are not pure or natural objects with an essence of their own. They exist in a context, taking on meaning from that context and from the perspective of the beholder" (Borgman, 2015, p. 18).

Whilst data has always been there, the recent emphasis is on the strategic thinking of data beyond publications. Universities and research institutions should be proactive data creators, re-users and curators. Borgman accentuates the need for metadata (technical, provenance and descriptive) so as to make sense and provide context for research data. Hence, metadata supports the discovery, re-use and curation of research data.

To this end, Borgman discusses the role of data infrastructure – a data management strategy and operational data plan are considered paramount even before the data is collected. Developing a working data strategy is a serious institutional business – it requires resources and expertise. Research data management strategies should consider costs, rights, responsibilities, sensitivities, roles and risks associated with data (p.273). According to Borgman “the challenge is to make data discoverable, usable, accessible, intelligible, and interpretable, and to do so for extended periods of time” (p.287). According to Borgman, libraries are part of this data infrastructure to play an important role in the organisation, repository (data repository vs publication repositories) archival and metadata (provenance) of data.

Due to lack of incentives to re-use data, competition, data sensitivity (privacy issues), lack of trust and related factors affect data sharing. The natural sciences such as physics, Borgman argues, are relatively open to share their research data than the humanities.

Overall, I believe this book provides a good theoretical framework for the ongoing research and practice on big data. It does not however aim to answer all your practical (technical) questions but it provokes one's thinking and challenges some of the taken-for-granted assumptions about research data. The book reminded me Sir Tim Berners-Lee's call for raw data now (Ted Talk 2010) – where he asked governments/organisations to release their data in an open, re-usable format. Borgman's book is the same reverberating call for research institutions and universities to have a good research data management strategy.

Key metadata for this book : data, research data, open data, big data, big science, data collection, data processing, open access to scholarly communication, open access to data, openness, the Long Tail, data management, data preservation, data scholarship, data diversity, data sharing, data releasing, data reusing, data citation and data discovery. Hope you enjoy reading it.… (plus d'informations)