Difference between revisions of "Journal:Open data: Accountability and transparency"

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|caption      =  
|caption      =  
|title_full  = Open data: Accountability and transparency
|title_full  = Open data: Accountability and transparency
|journal      = ''Big Data and Society''
|journal      = ''Big Data & Society''
|authors      = Mayernik, Matthew S.
|authors      = Mayernik, Matthew S.
|affiliations = University Corporation for Atmospheric Research
|affiliations = University Corporation for Atmospheric Research
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==Introduction==
==Introduction==
The movements by national governments, funding agencies, universities, and research communities toward “open data” face many difficult challenges. As a slate of recent studies have shown, the phrase “open data” itself faces at least two central questions, namely (1) what are “data”?<ref name="BorgmanBig15">{{cite book |title=Big Data, Little Data, No Data: Scholarship in the Networked World |author=Borgman, C.L. |publisher=MIT Press |pages=416 |year=2015 |isbn=9780262028561}}</ref><ref name="LeonelliWhatCounts15">{{cite journal |title=What Counts as Scientific Data? A Relational Framework |journal=Philosophy of Science |author=Leonelli, S. |volume=82 |issue=5 |pages=810–821 |year=2015 |doi=10.1086/684083 |pmid=26869734 |pmc=PMC4747116}}</ref> and (2) what is “open”?<ref name="LevinHowDo16">{{cite journal |title=How Do Scientists Define Openness? Exploring the Relationship Between Open Science Policies and Research Practice |journal=Bulletin of Science, Technology, and Society |author=Levin, N.; Leonelli, S.; Weckowska, D. et al. |volume=36 |issue=2 |pages=128–141 |year=2016 |doi=10.1177/0270467616668760 |pmid=27807390 |pmc=PMC5066505}}</ref><ref name="PasquettoOpenData16">{{cite journal |title=Open Data in Scientific Settings: From Policy to Practice |journal=Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems |author=Pasquetto, I.V.; Sands, A.E.; Darch, P.T. et al. |volume=2016 |pages=1585-1596  |year=2016 |doi=10.1145/2858036.2858543}}</ref><ref name="PomerantzFifty16">{{cite journal |title=Fifty shades of open |journal=First Monday |author=Pomerantz, J.; Peek, R. |volume=21 |issue=5 |year=2016 |doi=10.5210/fm.v21i5.6360}}</ref> In the face of the vagueness of these terms, individuals, research projects, communities, and organizations define “data” and “openness” in a variety of ways, often via informal norms in lieu of codified policies.
The movements by national governments, funding agencies, universities, and research communities toward “open data” face many difficult challenges. As a slate of recent studies have shown, the phrase “open data” itself faces at least two central questions, namely (1) what are “data”?<ref name="BorgmanBig15">{{cite book |title=Big Data, Little Data, No Data: Scholarship in the Networked World |author=Borgman, C.L. |publisher=MIT Press |pages=416 |year=2015 |isbn=9780262028561}}</ref><ref name="LeonelliWhatCounts15">{{cite journal |title=What Counts as Scientific Data? A Relational Framework |journal=Philosophy of Science |author=Leonelli, S. |volume=82 |issue=5 |pages=810–821 |year=2015 |doi=10.1086/684083 |pmid=26869734 |pmc=PMC4747116}}</ref> and (2) what is “open”?<ref name="LevinHowDo16">{{cite journal |title=How Do Scientists Define Openness? Exploring the Relationship Between Open Science Policies and Research Practice |journal=Bulletin of Science, Technology, and Society |author=Levin, N.; Leonelli, S.; Weckowska, D. et al. |volume=36 |issue=2 |pages=128–141 |year=2016 |doi=10.1177/0270467616668760 |pmid=27807390 |pmc=PMC5066505}}</ref><ref name="PasquettoOpenData16">{{cite journal |title=Open Data in Scientific Settings: From Policy to Practice |journal=Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems |author=Pasquetto, I.V.; Sands, A.E.; Darch, P.T. et al. |volume=2016 |pages=1585-1596  |year=2016 |doi=10.1145/2858036.2858543}}</ref><ref name="PomerantzFifty16">{{cite journal |title=Fifty shades of open |journal=First Monday |author=Pomerantz, J.; Peek, R. |volume=21 |issue=5 |year=2016 |doi=10.5210/fm.v21i5.6360}}</ref> In the face of the vagueness of these terms, individuals, research projects, communities, and organizations define “data” and “openness” in a variety of ways, often via informal norms in lieu of codified policies.
The concepts of “accountability” and “transparency” provide insight in understanding how open data requirements and expectations are achieved in different circumstances. An individual or organization is accountable for “open data” when they are answerable for the act(s) of making data open, whatever those acts might be. Being accountable means having to justify actions and decisions to some individual or organization. Transparency, on the other hand, refers to the notion that information about an individual or organization’s actions can be seen from the outside. Both concepts feature prominently in research and policy discussions concerning the relations that governments, organizations, and other social bodies have with their constituents or communities.<ref name="LeshnerAccount09">{{cite journal |title=Accountability and Transparency |journal=Science |author=Leshner, A.I. |volume=324 |issue=5925 |pages=313 |year=2009 |doi=10.1126/science.1174215}}</ref><ref name="LessigAgainst09">{{cite web |url=https://newrepublic.com/article/70097/against-transparency |title=Against Transparency |work=New Republic |author=Lessig, L. |publisher=Hamilton Fish |date=08 October 2009}}</ref><ref name="McNuttLiberating16">{{cite journal |title=Liberating field science samples and data |journal=Science |author=McNutt, M.; Lehnert, K.; Hanson, B. et al. |volume=351 |issue=6277 |pages=1024-1026 |year=2016 |doi=10.1126/science.aad7048}}</ref>
==Accountability and transparency==
In high-level visions of open data, researchers’ data, and metadata practices are expected to be robust and structured. The integration of the internet into scientific institutions amplifies these expectations, as it provides a seemingly ubiquitous data distribution mechanism.<ref name="AgreRealTime11">{{cite journal |title=Real-Time Politics: The Internet and the Political Process |journal=The Information Society |author=Agre, P.E. |volume=18 |issue=5 |pages=311–331 |year=2011 |doi=10.1080/01972240290075174}}</ref> When examined critically, however, the data and metadata practices of scholarly researchers often appear incomplete or deficient.<ref name="VanTuylWater16">{{cite journal |title=Water, water, everywhere: Defining and assessing data sharing in academia |journal=PLOS ONE |author=Van Tuyl, S.; Whitmire, A.L. |volume=11 |issue=2 |pages=e0147942 |year=2016 |doi=10.1371/journal.pone.0147942 |pmid=26886581 |pmc=PMC4757565}}</ref><ref name="VinesTheAvail14">{{cite journal |title=The availability of research data declines rapidly with article age |journal=Current Biology |author=Vines, T.H.; Albert, A.Y.; Andrew, R.L. et al. |volume=24 |issue=1 |pages=94–7 |year=2014 |doi=10.1016/j.cub.2013.11.014 |pmid=24361065}}</ref> The concept of accountability helps to guide explanations for data practices that seem, on the surface, to be insufficient. “Accountability” is a concept drawn from multiple social science traditions, including studies of governance in organizations and nations<ref name="BovensTheQuest98">{{cite book |title=The Quest for Responsibility: Accountability and Citizenship in Complex Organisations |series=Theories of Institutional Design |author=Bovens, M. |publisher=Cambridge University Press |pages=266 |year=1998 |isbn=9780521481632}}</ref>, and studies of mundane activities in everyday life.<ref name="GarfinkelStudies67">{{cite book |title=Studies in Ethnomethodology |author=Garfinkle, H. |publisher=Prentice-Hall, Inc |pages=288 |year=1967 |isbn=10987654321}}</ref><ref name="WoolgarMundane14">{{cite book |title=Mundane Governance: Ontology and Accountability |author=Woolgar, S.; Neyland, D. |publisher=Oxford University Press |pages=328 |year=2014 |isbn=9780199584741}}</ref> It is important to remember that for most researchers, working with data is a very mundane activity. As Pink et al.<ref name="PinkMundane17">{{cite journal |title=Mundane data: The routines, contingencies and accomplishments of digital living |journal=Big Data & Society |author=Pink, S.; Sumartojo, S.; Lipton, D. et al. |volume=4 |issue=1 |year=2017 |doi=10.1177/2053951717700924}}</ref> note, data are intertwined with everyday routines, and often entail significant improvisation, both in data generation and use. For field-based scientists, such as ecologists and archaeologists, data may literally emerge from the dirt. For laboratory and computational scientists, data generation and management are less obviously subject to worldly interference, but are nevertheless imperfect human activities.<ref name="GitelmanRawData13">{{cite book |title=Raw Data Is an Oxymoron |author=Gitelman |publisher=MIT Press |pages=192 |year=2013 |isbn=9780262518284}}</ref> To be accountable for data, researchers must be able to describe in a way sufficient for the social situation at hand how any perceived data problems are anomalous, correctable, or in fact not problematic at all — they must be “answerable” for their data. Simply being answerable for data can be called soft accountability. When soft accountability is coupled with the possibility of sanctions for non-compliance, such as loss of research funding or journal article rejections for a lack of data archiving, researchers face hard accountability.<ref name="FoxTheUncertain07">{{cite journal |title=The Uncertain Relationship between Transparency and Accountability |journal=Development in Practice |author=Fox, J. |volume=17 |issue=4/5 |pages=663-71 |year=2007}}</ref>


==References==
==References==

Revision as of 16:58, 24 October 2017

Full article title Open data: Accountability and transparency
Journal Big Data & Society
Author(s) Mayernik, Matthew S.
Author affiliation(s) University Corporation for Atmospheric Research
Primary contact Email: mayernik at ucar dot edu
Year published 2017
Volume and issue 4(2)
DOI 10.1177/2053951717718853
ISSN 2053-9517
Distribution license Creative Commons Attribution-NonCommercial 4.0 International
Website http://journals.sagepub.com/doi/10.1177/2053951717718853
Download http://journals.sagepub.com/doi/pdf/10.1177/2053951717718853 (PDF)
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Abstract

The movements by national governments, funding agencies, universities, and research communities toward “open data” face many difficult challenges. In high-level visions of open data, researchers’ data and metadata practices are expected to be robust and structured. The integration of the internet into scientific institutions amplifies these expectations. When examined critically, however, the data and metadata practices of scholarly researchers often appear incomplete or deficient. The concepts of “accountability” and “transparency” provide insight in understanding these perceived gaps. Researchers’ primary accountabilities are related to meeting the expectations of research competency, not to external standards of data deposition or metadata creation. Likewise, making data open in a transparent way can involve a significant investment of time and resources with no obvious benefits. This paper uses differing notions of accountability and transparency to conceptualize “open data” as the result of ongoing achievements, not one-time acts.

Keywords: Open data, accountability, transparency, data policy, data, metadata

Introduction

The movements by national governments, funding agencies, universities, and research communities toward “open data” face many difficult challenges. As a slate of recent studies have shown, the phrase “open data” itself faces at least two central questions, namely (1) what are “data”?[1][2] and (2) what is “open”?[3][4][5] In the face of the vagueness of these terms, individuals, research projects, communities, and organizations define “data” and “openness” in a variety of ways, often via informal norms in lieu of codified policies.

The concepts of “accountability” and “transparency” provide insight in understanding how open data requirements and expectations are achieved in different circumstances. An individual or organization is accountable for “open data” when they are answerable for the act(s) of making data open, whatever those acts might be. Being accountable means having to justify actions and decisions to some individual or organization. Transparency, on the other hand, refers to the notion that information about an individual or organization’s actions can be seen from the outside. Both concepts feature prominently in research and policy discussions concerning the relations that governments, organizations, and other social bodies have with their constituents or communities.[6][7][8]

Accountability and transparency

In high-level visions of open data, researchers’ data, and metadata practices are expected to be robust and structured. The integration of the internet into scientific institutions amplifies these expectations, as it provides a seemingly ubiquitous data distribution mechanism.[9] When examined critically, however, the data and metadata practices of scholarly researchers often appear incomplete or deficient.[10][11] The concept of accountability helps to guide explanations for data practices that seem, on the surface, to be insufficient. “Accountability” is a concept drawn from multiple social science traditions, including studies of governance in organizations and nations[12], and studies of mundane activities in everyday life.[13][14] It is important to remember that for most researchers, working with data is a very mundane activity. As Pink et al.[15] note, data are intertwined with everyday routines, and often entail significant improvisation, both in data generation and use. For field-based scientists, such as ecologists and archaeologists, data may literally emerge from the dirt. For laboratory and computational scientists, data generation and management are less obviously subject to worldly interference, but are nevertheless imperfect human activities.[16] To be accountable for data, researchers must be able to describe in a way sufficient for the social situation at hand how any perceived data problems are anomalous, correctable, or in fact not problematic at all — they must be “answerable” for their data. Simply being answerable for data can be called soft accountability. When soft accountability is coupled with the possibility of sanctions for non-compliance, such as loss of research funding or journal article rejections for a lack of data archiving, researchers face hard accountability.[17]

References

  1. ↑ Borgman, C.L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press. pp. 416. ISBN 9780262028561. 
  2. ↑ Leonelli, S. (2015). "What Counts as Scientific Data? A Relational Framework". Philosophy of Science 82 (5): 810–821. doi:10.1086/684083. PMC PMC4747116. PMID 26869734. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747116. 
  3. ↑ Levin, N.; Leonelli, S.; Weckowska, D. et al. (2016). "How Do Scientists Define Openness? Exploring the Relationship Between Open Science Policies and Research Practice". Bulletin of Science, Technology, and Society 36 (2): 128–141. doi:10.1177/0270467616668760. PMC PMC5066505. PMID 27807390. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066505. 
  4. ↑ Pasquetto, I.V.; Sands, A.E.; Darch, P.T. et al. (2016). "Open Data in Scientific Settings: From Policy to Practice". Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems 2016: 1585-1596. doi:10.1145/2858036.2858543. 
  5. ↑ Pomerantz, J.; Peek, R. (2016). "Fifty shades of open". First Monday 21 (5). doi:10.5210/fm.v21i5.6360. 
  6. ↑ Leshner, A.I. (2009). "Accountability and Transparency". Science 324 (5925): 313. doi:10.1126/science.1174215. 
  7. ↑ Lessig, L. (8 October 2009). "Against Transparency". New Republic. Hamilton Fish. https://newrepublic.com/article/70097/against-transparency. 
  8. ↑ McNutt, M.; Lehnert, K.; Hanson, B. et al. (2016). "Liberating field science samples and data". Science 351 (6277): 1024-1026. doi:10.1126/science.aad7048. 
  9. ↑ Agre, P.E. (2011). "Real-Time Politics: The Internet and the Political Process". The Information Society 18 (5): 311–331. doi:10.1080/01972240290075174. 
  10. ↑ Van Tuyl, S.; Whitmire, A.L. (2016). "Water, water, everywhere: Defining and assessing data sharing in academia". PLOS ONE 11 (2): e0147942. doi:10.1371/journal.pone.0147942. PMC PMC4757565. PMID 26886581. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757565. 
  11. ↑ Vines, T.H.; Albert, A.Y.; Andrew, R.L. et al. (2014). "The availability of research data declines rapidly with article age". Current Biology 24 (1): 94–7. doi:10.1016/j.cub.2013.11.014. PMID 24361065. 
  12. ↑ Bovens, M. (1998). The Quest for Responsibility: Accountability and Citizenship in Complex Organisations. Theories of Institutional Design. Cambridge University Press. pp. 266. ISBN 9780521481632. 
  13. ↑ Garfinkle, H. (1967). Studies in Ethnomethodology. Prentice-Hall, Inc. pp. 288. ISBN 10987654321. 
  14. ↑ Woolgar, S.; Neyland, D. (2014). Mundane Governance: Ontology and Accountability. Oxford University Press. pp. 328. ISBN 9780199584741. 
  15. ↑ Pink, S.; Sumartojo, S.; Lipton, D. et al. (2017). "Mundane data: The routines, contingencies and accomplishments of digital living". Big Data & Society 4 (1). doi:10.1177/2053951717700924. 
  16. ↑ Gitelman (2013). Raw Data Is an Oxymoron. MIT Press. pp. 192. ISBN 9780262518284. 
  17. ↑ Fox, J. (2007). "The Uncertain Relationship between Transparency and Accountability". Development in Practice 17 (4/5): 663-71. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version — by design — lists them in order of appearance.

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