Why data science matters and what we can do with it


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Presentation at the Deep Carbon Observatory Summer School 2014, Big Sky, MT, USA.

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  • We saw this figure at the beginning of this presentation. So, now what we can do with the provenance tracing?
  • (a) Instances of calibration, model and software underpinning “paper/103” and (b) Instances of sensor, instrument and platform underpinning that paper.
  • (a) Instances of calibration, model and software underpinning “paper/103” and (b) Instances of sensor, instrument and platform underpinning that paper.
  • Why data science matters and what we can do with it

    1. 1. deepcarbon.net Xiaogang (Marshall) Ma and DCO-Data Science Team Tetherless World Constellation Rensselaer Polytechnic Institute Why Data Science Matters? and what can we do with it
    2. 2. Outline • Data Management and Publication • Interoperability of Data • Provenance of Research • Era of Science 2.0 2
    3. 3. Data Management and Publication 3
    4. 4. • Meet grant requirements – Many funding agencies now require researchers formally state how they will manage and preserve datasets generated from a research project. 4 … … Why Manage and Publish Data
    5. 5. • Increase your research efficiency – Have you ever had a hard time understanding the data that you or your colleagues have collected? 5 Data work
    6. 6. 6 Image courtesy of British Geological Survey Nice, now I have my DATA well managed, and next…
    7. 7. • Increase the visibility of your research – Making your data available to other researchers through widely-searched repositories can increase your prominence and demonstrate continued use of the data and relevance of your research. • Facilitate new discoveries – Enabling other researchers to use your data reinforces open scientific inquiry and can lead to new and unanticipated discoveries. And doing so prevents duplication of effort by enabling others to use your data rather than trying to gather the data themselves. 7
    8. 8. Data Management Plan: What and How • What is a Data Management Plan? – A data management plan is a formal document that outlines what you will do with your data during and after you complete your research. • What is involved in developing one? – Developing a data management plan can be time-consuming, tedious, and daunting, but it's a very important step in ensuring that your research data is safe and sound for the present and future. – With the right process and framework it does not take too long time and can pay-off enormously in the long-run. 8
    9. 9. • Topics in a data management plan include – Introduction and context – Data types, formats, standards and capture methods – Short-term storage and data management – Deposit and long-term preservation – Data sharing, access and re-use – Resourcing – Adherence and review
    10. 10. • Resources/Tools help create DMPs: – DCC Data Management Plans: http://www.dcc.ac.uk/resources/data-management-plans – MIT Data Management and Publishing: http://libraries.mit.edu/data-management/ – NSF Data Management Plan Requirements: http://www.nsf.gov/eng/general/dmp.jsp – DMPTool: https://dmptool.org – IEDA Data Management Plan Tool: http://www.iedadata.org/compliance/plan – DCC DMPOnline: https://dmponline.dcc.ac.uk 10
    11. 11. 12 Image from WWW
    12. 12. Data Publication & Citation • Data as first class products of research – NSF bio-sketches can include data publications 13 Image from j4h.net
    13. 13. • Ways of data publication – Data as supplemental material of a paper – Standalone data – Data paper: data + descriptive ‘data paper’ 14 (Strasser, 2014) Examples: • Standalone data journals: Nature Scientific Data, Geoscience Data Journal, Ecological Archives • Journals that publish data papers: GigaScience, F1000 Research, Internet Archaeology
    14. 14. 15 What does a DCO data publication look like?
    15. 15. • Data Citation Index – Indexes the world's leading data repositories – Records for the datasets are connected to related peer- reviewed literature indexed in the Web of Science™ – Allow researchers to efficiently access to data across subjects and regions 16
    16. 16. Interoperability of Data 17
    17. 17. A good example • OneGeology 18 • Web-accessible geologic map data worldwide (scale ~1:1 million) • Stimulate a rapid increase in interoperability (i.e. disseminate GeoSciML and vocabularies further and faster) • 120 participating countries (July 2014)
    18. 18. http://portal.onegeology.org 19
    19. 19. 20 Wyoming Colorado More challenges are still to be addressed http://mrdata.usgs.gov/
    20. 20. 21 Earth Resource Form Environmental Impact Value Exploration Activity Type Exploration Result UNFC Value Earth Resource Expression Earth Resource Shape Enduse Potential Mineral Occurrence Type Mining Activity Type Processing Activity Type Mining Waste Type Value Commodity Code Mineral Deposit Group Mineral Deposit Type Product Value A list of recently finished vocabularies CGI Geoscience Terminology Workgroup • Construct a collection of vocabularies for populating information interchange documents and enabling interoperability • Provide labels for concepts, scope to various communities defined by language, science domain, or application domain
    21. 21. 22 Another major effort... And there is a vocabulary created by the CGI Geoscience Terminology Workgroup!
    22. 22. Golden Spike information portal http://geotime.tw.rpi.edu/ 23 Golden spike - Global Boundary Stratotype Section and Point (GSSP)
    23. 23. (Haq, 2007) 24 Still, challenges …
    24. 24. 25 (Ma et al., 2011) Interoperable: “Data should be discoverable, accessible, decodable, understandable and usable, and data sharing should be legal and ethical for all participants.”
    25. 25. • Interoperability does not mean that all data should be mediated or standardized. • However, it is important that data archives are accompanied by detailed documentation, clarifying data provenance, data model, vocabularies used, etc. 26 (Ma et al., 2011)
    26. 26. Provenance of Research 27
    27. 27. Provenance capture • Documenting provenance – Linking a range of observations and model outputs, research activities, people and organizations involved in the production of scientific findings with the supporting data sets and methods used to generate them. 28 Well-curated provenance information makes scientific workflows transparent and improves the credibility and trustworthiness of their outputs. It also facilitates informed and rational policy and decision-making. Image from nature.com (Ma et al., 2014)
    28. 28. “Figure 1.2: Sea Level Rise: Past, Present, and Future” in the Third National Climate Assessment report draft of USA (NCA3) 29 What is the provenance of this figure?
    29. 29. • Detailed caption of that figure: – Estimated, observed and possible amounts of global sea level rise from 1800 to 2100. Proxy estimates (Kemp et al. 2012) (for example, based on sediment records) are shown in red (pink band shows uncertainty), tide gauge data in blue (Church and White 2011a), and satellite observations are shown in green (Nerem et al. 2010). The future scenarios range from 0.66 feet to 6.6 feet in 2100 (Parris et al. 2012). Higher or lower amounts of sea level rise are considered implausible, as represented by the gray shading. The orange line at right shows the currently projected range of sea level rise of 1 to 4 feet by 2100, which falls within the larger risk- based scenario range. The large projected range reflects uncertainty about how glaciers and ice sheets will react to the warming ocean, the warming atmosphere, and changing winds and currents. As seen in the observations, there are year-to- year variations in the trend. (Figure source: Josh Willis, NASA Jet Propulsion Laboratory) 30 As a case study, let’s trace the provenance of this paper.
    30. 30. Provenance tracing of NASA contributions to Figure 1.2 in draft NCA3 Here only the details of Topex-Poseidon mission are shown Here only the details of one paper (i.e., “paper/103”) cited by that figure are shown (a) Instances of calibration, model and software underpinning “paper/103” (b) Instances of sensor, instrument and platform underpinning that paper 31
    31. 31. 34
    32. 32. 35 http://data.globalchange.gov
    33. 33. Era of Science 2.0 36 Practice
    34. 34. • Science 2.0 – New practices of scientists who post raw experimental results, nascent theories, claims of discovery and draft papers on the Web for others to see and comment on. – Proponents say these “open access” practices make scientific progress more collaborative and therefore more productive. – Critics say scientists who put preliminary findings online risk having others copy or exploit the work to gain credit or even patents. 37 (Waldrop, 2008)
    35. 35. 38
    36. 36. • Social scholarship: Reconsidering scholarly practices in the age of social media – Polled 1,600 US and Canadian faculty members – Found that 15% use Twitter, 28% use YouTube and 39% use Facebook for scholarly activity 39 (Greenhow and Gleason, 2014) Using social media more often would help scientists to disseminate their results, debate findings and engage a wider audience Researchers must learn to create a robust online presence Social-media metrics to be added to the tenure process
    37. 37. • Altmetrics – A very broad group of metrics, capturing various parts of impact a paper or work can have. 40 (Lin and Fenner, 2013) The ImpactStory Altermetrics Classifications
    38. 38. • altmetric.com – already a product used by NPG, Springer, etc. 41 This Altmetric score means that the article is: • in the 99 percentile (ranked 181st) of the 81,582 tracked articles of a similar age in all journals • in the 93 percentile (ranked 69th) of the 992 tracked articles of a similar age in Nature http://www.nature.com/nature/journal/v497/n7449/nature12127/metrics
    39. 39. Summary make data count 42
    40. 40. 43 max7@rpi.edu Thank you!