Christine borgman keynote

1. Data, Data, Everywhere, Nor Any Drop to Drink Chris6ne L. Borgman Professor and Presiden6al Chair in Informa6on Studies University of California, Los Angeles Keynote presenta6on Research Data Alliance, Fourth Plenary Mee6ng Amsterdam, September 2014 Gustave Dore, Rime of the Ancient Mariner, Woodcut, 1798

2. Day aSer day, day aSer day, We stuck, nor breath nor mo6on; As idle as a painted ship Upon a painted ocean. Water, water, every where, And all the boards did shrink; Water, water, every where, Nor any drop to drink. Gustave Dore, Ancient Mariner Illustra6on, 1798 Stanzas from The Rime of the Ancient Mariner Samuel Taylor Coleridge, 1798

4. Big Data, LiVle Data, No Data: Scholarship in the Networked World* • Part I: Data and Scholarship – Ch 1: Provoca6ons – Ch 2: What Are Data? – Ch 3: Data Scholarship – Ch 4: Data Diversity • Part II: Case Studies in Data Scholarship – Ch 5: Data Scholarship in the Sciences – Ch 6: Data Scholarship in the Social Sciences – Ch 7: Data Scholarship in the Humani6es • Part III: Data Policy and Prac6ce – Ch 8: Releasing, Sharing, and Reusing Data – Ch 9: Credit, AVribu6on, and Discovery – Ch 10: What to Keep and Why 4 *C. L. Borgman (2015, January) MIT Press

5. Neelie Kroes, VP European Commission: • To collect, curate, preserve and make available ever-‐increasing amounts of scien6fic data, new types of infrastructures will be needed. The poten6al benefits are enormous but the same is true for the costs. We therefore need to lay the right founda6ons and the sooner we start the beVer. 5 Wood, J., Andersson, T., Bachem, A., Best, C., Genova, F., Lopez, D. R., … Hudson, R. L. (2010). Riding the wave: How Europe can gain from the rising <de of scien<fic data. Final report of the High Level Expert Group on Scien6fic Data. Retrieved from hVp://cordis.europa.eu/fp7/ict/e-‐infrastructure/docs/hlg-‐sdi-‐report.pdf

6. Precondi6on: Researchers share data 6

7. Scholars’ perspec6ves on data sharing • Rewards • Responsibility • Data • Incen6ves 7 Persistent URL: photography.si.edu/SearchImage.aspx?id=5799 Repository: Smithsonian Ins6tu6on Archives

8. Rewards • Publica6ons • Publica6ons • Publica6ons • Publica6ons • Publica6ons • Publica6ons • Grants • Awards and honors • Teaching • Service • Data hVp://blog.starjreshtoday.com/Portals/170402/images/improve-‐credit-‐score1.jpg

9. Func6ons of Scholarly Publica6ons • Legi6miza6on – Authority, quality – Priority, trustworthiness • Dissemina6on – Awareness – Diffusion – Publicity • Access, preserva6on, cura6on – Availability – Discovery – Retrieval – Persistence Borgman, C.L. (2007). Scholarship in the Digital Age: Informa6on, Infrastructure, and the Internet. MIT Press.

11. Responsibility Publica6ons are arguments made by authors, and data are the evidence used to support the arguments. C.L. Borgman (2015). Big Data, LiBle Data, No Data: Scholarship in the Networked World. MIT Press

12. Responsibility • Publica6ons – Independent units – Authorship is nego6ated • Data – Compound objects – Ownership is rarely clear – AVribu6on • Long term responsibility: Inves6gators • Exper6se hudsonalpha.org for interpreta6on: Data collectors and analysts

13. AVribu6on of data • Legal responsibility – Licensed data – Specific aVribu6on required • Scholarly credit: contributorship – “Author” of data – Contributor of data to this publica6on – Colleague who shared data – SoSware developer – Data collector – Instrument builder – Data curator – Data manager – Data scien6st – Field site staff – Data calibra6on – Data analysis, visualiza6on – Funding source – Data repository – Lab director – Principal inves6gator – University research office – Research subjects – Research workers, e.g., ci6zen science… 13

15. What are data? http://www.census.gov/population/cen2000/map02.gif Marie Curie’s notebook aip.org ncl.ucar.edu http://onlineqda.hud.ac.uk/Intro_QDA/Examples_of_Qualitative_Data.php hudsonalpha.org NASA Astronomy Picture of the Day 15

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18. Center for Embedded Networked Sensing 18 • NSF Science & Tech Ctr, 2002-‐2012 • 5 universi6es, plus partners • 300 members • Computer science and engineering • Science applica6on areas Slide by Jason Fisher, UC-Merced, Center for Embedded Networked Sensing (CENS)

19. UCLA USC CENTER FOR EMBEDDED NETWORKED SENSING UCR CALTECH UCM Sensor Collected Proprioceptive Data Heading Roll/pitch/yaw Sensor Collected Application Data Sensor Collected Performance Data Awake time Fault detection Neighbor table Hand Collected Application Data Conductivity Flow Wind speed Wind duration Water potential Sap flow LandSat images Mosscam Bird calls GPS/location Time Water depth Ammonium Ammonia Phosphate Water temp pH Temperature Chlorophyll CO2 Humidity Rainfall Packets transmitted Packets received ORP PAR Motor speed Rudder angle Soil moisture Nitrate Calcium Chloride Wind direction Leaf wetness Routing table Organism presence Organism concentration Battery voltage Mercury Nutrient presence Methylmercury Nutrient concentration CDOM CENS data variation Borgman, et al. (2007). Drowning in data: Digital library architecture to support scientific use of embedded sensor networks. JCDL

20. Documen6ng Data for Interpreta6on Engineering researcher: “Temperature is temperature.” Biologist: “There are hundreds of ways to measure temperature. ‘The temperature is 98’ is low-‐value compared to, ‘the temperature of the surface, measured by the infrared thermopile, model number XYZ, is 98.’ That means it is measuring a proxy for a temperature, rather than being in contact with a probe, and it is measuring from a distance. The accuracy is plus or minus .05 of a degree. I [also] want to know that it was taken outside versus inside a controlled environment, how long it had been in place, and the last <me it was calibrated, which might tell me whether CENS Robo6cs team it has driYed.."

21. Center for Dark Energy Biosphere Inves6ga6ons Repository for seafloor cores. Photo: Peter Darch Interna6onal Ocean Discovery Program Iodp.tamu.org • NSF Science & Tech Ctr, 2010-‐2020 • 20 universi6es, plus partners (35 ins6tu6ons) • 90 scien6sts • Biological sciences • Physical sciences 21

22. Social science data hVp://dss.princeton.edu/images/gss.gif 22

23. Social science data hVp://dss.princeton.edu/images/gss.gif 23

24. Data are representa6ons of observa6ons, objects, or other en66es used as evidence of phenomena for the purposes of research or scholarship. 24 C.L. Borgman (2015). Big Data, LiBle Data, No Data: Scholarship in the Networked World. MIT Press hudsonalpha.org

26. Incen6ves • Publica6ons that report the research Vs. • Data that are reusable by others Image: Alyssa Goodman, Harvard Astronomy 26

27. Pepe, A., Mayernik, M. S., Borgman, C. L. & Van de Sompel, H. (2010). From Ar6facts to Aggrega6ons: Modeling Scien6fic Life Cycles on the Seman6c Web. Journal of the American Society for Informa6on Science and Technology, 61(3): 567–27 582.

28. Metadata • Metadata is structured informa6on that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an informa6on resource.* – descrip6ve – structural – administra6ve *Na6onal Informa6on Standards Organiza6on 2004 photo by @kissane

29. Provenance • Libraries: Origin or source • Museums: Chain of custody • Internet: Provenance is informa6on about en66es, ac6vi6es, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness.* *World Wide Web Consor6um (W3C) Provenance working group Bri6sh Library, provenance record: Bes6ary -‐ cap6on: 'Owl mobbed by smaller birds'

30. Reuse across place and 6me • Reuse by inves6gator • Reuse by collaborators • Reuse by colleagues • Reuse by unaffiliated others • Reuse at later 6mes – Months – Years – Decades – Centuries hVp://chandra.harvard.edu/photo/2013/kepler/kepler_525.jpg 30

31. Day aSer day, day aSer day, We stuck, nor breath nor mo6on; As idle as a painted ship Upon a painted ocean. Water, water, every where, And all the boards did shrink; Water, water, every where, Nor any drop to drink. Gustave Dore, Ancient Mariner Illustra6on, 1798 Stanzas from The Rime of the Ancient Mariner Samuel Taylor Coleridge, 1798

32. Emerging themes in data prac6ces • Scarcity or abundance of data • Centrality of data to research • Time frame of research • Heterogeneity of exper6se • Maturity of standards • Community building Borgman, C. L., et al. (2014). The Ups and Downs of Knowledge Infrastructures in Science: Implica6ons for Data Management. IEEE/ACM Digital Libraries Conference, London

33. Economics of the Knowledge Commons 33 Subtractability / Rivalry Low High Exclusion Difficult Public Goods General knowledge Public domain data Common-‐pool resources Libraries Data archives Easy Toll or Club Goods Subscrip6on journals Subscrip6on data Private Goods Printed books Raw or compe66ve data Adapted from C. Hess & E. Ostrom (Eds.), Understanding knowledge as a commons: From theory to prac<ce. MIT Press.

34. To share data, scholars need hVp://environment.na6onalgeographic.com/environment/habitats/water-‐pressure/ • Fresh water – Tools – Services – Skills – Resources – Incen6ves

35. To share data, scholars need • Life boats – Repositories – Governance models – Provenance models – Data stewardship workforce Patent Model, Life Boat, 1841; Smithsonian American History Museum

36. Knowledge Infrastructures Image: Alyssa Goodman, Harvard Astronomy 36

37. hVp://knowledgeinfrastructures.org

38. Acknowledgements UCLA Data Practices team • Peter Darch, Milena Golshan, Irene Pasquetto, Ashley Sands, Sharon Traweek • Former members: Rebekah Cummings, David Fearon, Ariel Hernandez, Elaine Levia, Jaklyn Nunga, Matthew Mayernik, Alberto Pepe, Kalpana Shankar, Katie Shilton, Jillian Wallis, Laura Wynholds, Kan Zhang • Research funding: National Science Foundation, Alfred P. Sloan Foundation, Microsoft Research • University of Oxford: Balliol College, Oliver Smithies Fellowship, Oxford Internet Institute, Oxford eResearch Center, Bodleian Library

Christine borgman keynote

Christine borgman keynote

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