christine borgman keynote

Data, Data, Everywhere, Nor Any Drop to Drink

Keynote presenta6on Research Data Alliance, Fourth Plenary Mee6ng Amsterdam, September 2014

Chris6ne L. Borgman Professor and Presiden6al Chair in Informa6on Studies University of California, Los Angeles

Gustave Dore, Rime of the Ancient Mariner, Woodcut, 1798

Gustave Dore, Ancient Mariner Illustra6on, 1798

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.

Stanzas from The Rime of the Ancient Mariner Samuel Taylor Coleridge, 1798

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

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.

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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

Precondi6on:

Researchers share data 6

Scholars’ perspec6ves on data sharing

•  Rewards •  Responsibility •  Data •  Incen6ves

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Persistent URL: photography.si.edu/SearchImage.aspx?id=5799 Repository: Smithsonian Ins6tu6on Archives

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

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.



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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

Responsibility

•  Publica6ons –  Independent units – Authorship is nego6ated

•  Data –  Compound objects – Ownership is rarely clear – AVribu6on

•  Long term responsibility: Inves6gators •  Exper6se for interpreta6on: Data collectors and analysts

hudsonalpha.org

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



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http://www.census.gov/population/cen2000/map02.gif

What are data?

ncl.ucar.edu http://onlineqda.hud.ac.uk/Intro_QDA/Examples_of_Qualitative_Data.php

Marie Curie’s notebook aip.org

hudsonalpha.org

NASA Astronomy Picture of the Day

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Center for Embedded Networked Sensing

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•  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)

UCLA USC UCR CALTECH UCM CENTER FOR EMBEDDED NETWORKED SENSING

Sensor Collected Application Data

Sensor Collected Proprioceptive Data

Sensor Collected Performance Data

Hand Collected Application Data

Flow

Water depth

Ammonium

Ammonia Phosphate

Water temp

pH

Temperature

Conductivity

Chlorophyll

GPS/location Time

Sap flow

CO2

Humidity

Rainfall Packets transmitted

Packets received ORP

PAR

Motor speed

Rudder angle

Heading

Roll/pitch/yaw Soil moisture

Nitrate

Calcium

Chloride

Water potential

Wind speed

Wind direction

Wind duration

Leaf wetness

Routing table

Neighbor table

Fault detection

Awake time

Organism presence

Organism concentration

Battery voltage

Mercury

Methylmercury

Nutrient concentration

Nutrient presence

LandSat images Mosscam

CDOM

Bird calls

CENS data variation

Borgman, et al. (2007). Drowning in data: Digital library architecture to support scientific use of embedded sensor networks. JCDL

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 it has driYed.." CENS Robo6cs team

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

Social science data

hVp://dss.princeton.edu/images/gss.gif 22

Social science data

hVp://dss.princeton.edu/images/gss.gif 23

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Data are representa6ons of observa6ons, objects, or other en66es used as evidence of phenomena for the purposes of research or scholarship.

C.L. Borgman (2015). Big Data, LiBle Data, No Data: Scholarship in the Networked World. MIT Press

hudsonalpha.org



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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): 567–582.

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

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'

•  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

Reuse across place and 6me

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Gustave Dore, Ancient Mariner Illustra6on, 1798

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.

Stanzas from The Rime of the Ancient Mariner Samuel Taylor Coleridge, 1798

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

Economics of the Knowledge Commons

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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.

hVp://environment.na6onalgeographic.com/environment/habitats/water-‐pressure/

To share data, scholars need

•  Fresh water – Tools – Services – Skills – Resources –  Incen6ves

To share data, scholars need

•  Life boats – Repositories – Governance models – Provenance models – Data stewardship workforce

Patent Model, Life Boat, 1841; Smithsonian American History Museum

Image: Alyssa Goodman, Harvard Astronomy

Knowledge Infrastructures

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hVp://know

ledgeinfrastructures.org

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

Documents

datadiversity partii

datapolicyandprac6ce

dataandscholarship ch1

andreusingdata ch9

anddiscovery ch10

datascholarship ch4