Making Energy Data Generative

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Keynote slides at the Generative Data Workshop, National Renewable Energy Lab.

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  • This one’s mine. As you can see, I have a high risk of prostate cancer, as well as Alzheimer’s disease. \n
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  • Making Energy Data Generative

    1. 1. 1. big data as we experience it.
    2. 2. 2
    3. 3. 3
    4. 4. unexpected costs of engaging with data...
    5. 5. 7
    6. 6. assume 1,000,000 downloadsassume 10% false positive rate 100,000 doctor visits $1000 per biopsy
    7. 7. 19 of 20 times, you didn’t need to go.
    8. 8. 2. how might we assess the economic value of data?
    9. 9. commitment.
    10. 10. commitment.
    11. 11. citation.
    12. 12. citation.
    13. 13. price.
    14. 14. price.
    15. 15. “capacity to produce unanticipated changethrough unfiltered contributions from broad and varied audiences”
    16. 16. 21
    17. 17. http://synapse.sagebase.org
    18. 18. http://opensnp.org/users/615
    19. 19. http://files.snpedia.com/reports/promethease_data/genome_jtw_ui2.html
    20. 20. Also there is no suggestion ofconsanguinity in your pedigree.http://www.ianlogan.co.uk/
    21. 21. 3. how might we “score” generativity?
    22. 22. what things regulate?
    23. 23. copyrightdatabase rights patents trade secrets contracts
    24. 24. ontology API format OAuthversion control provenance
    25. 25. open by default safe harbors estuary modelsfund once, use many times
    26. 26. tension between anonymity and utility ease of re-identification changing norms of privacy
    27. 27. what tools and approachesincrease the generativity of data?
    28. 28. 3. “scoring” generativity in open energy data
    29. 29. accessibility 25 0 25adaptability ease of mastery 25 25 leverage
    30. 30. Why study OED?• Big push around open data generally, open energy data (OED) specifically• No non-fuzzy understanding of why this is useful (“open data is good” is not enough) or of what interventions make OED more useful• By more useful, we mean: more likely to result in unexpected repurposing of data
    31. 31. The AOED• Qualitative analysis of OED to explore what underlying attributes of data sets make them more or less able to be repurposed in unexpected ways• Cases, analysis, recommendations, and associated materials on how to increase “reuse potential” of data sets
    32. 32. Methods• Adopted “generativity” as our scale for reuse potential – Adaptability – Accessibility – Leverage – Ease of mastery• Created a measuring scale (25 per factor) and qualitatively assigned to selected data sets
    33. 33. 50
    34. 34. 52
    35. 35. 54
    36. 36. accessibility EASY TO USE NO OPEN LICENSE 25 NO DOWNLOADadaptability 0 25 ease of mastery 25 25 leverage
    37. 37. accessibility IMPACT OF 25 POLICY INTERVENTION: CC0 license Raw download Annotationadaptability 0 25 ease of mastery 25 25 leverage
    38. 38. observations• Adaptability correlates to accessibility – Legal and technical (both formats and raw downloads) accessibility makes data sets more obvious targets for adaptation – Not necessarily causal – some data sets are poorly accessible, but so important, that they are good targets anyway – Data.gov sets a good example – Accessibility is necessary, but not sufficient, for adaptability• Accessibility is perhaps the easiest switch to flip if data is not of immediate economic
    39. 39. observations• Adaptability correlates to leverage – Easier to do something unexpected if data is adaptable – Good annotations / metadata increase adaptability and leverage at the same time – Not necessarily causal – Domain expertise often needed to make leverage out of data, but is often not the domain expertise used to create the data or annotate it – Adaptability and accessibility necessary, but not sufficient, for leverage• Adaptability is costly compared to accessibility due to need for human engagement in creation of metadata and annotation – and thus is more rare than accessibility.
    40. 40. observations• Pursuit of ease of mastery can ignore other three – Focus on user experience assumes naïve user, often doesn’t expose raw data or address licensing or annotations – “Accessibility” when converted to “accessible to the layman” doesn’t count in generativity terms for acceess – Ease of mastery often focuses on obscuring the complexity of the data, which can correlate negatively to adaptability or leverage• However, a focus on ease of mastery + accessibility may be the sweet spot – click here for laypeople and get UX, click here if a data
    41. 41. the point. 60
    42. 42. design for asymmetric use. 61

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