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Elizabeth Churchill, "Data by Design"
- 1. Data by Design Elizabeth F. Churchill
- 2. Design/Science of participation (1) Science through (platforms for mediated communication) TMSP (2) Science on (social science contributions about fundamentals of psychology/communication/collaboration/cooperation) “Hubble telescope” of social science WE NEED TO ADDRESS THE DESIGN OF DATA (FOR) SCIENCE ISSUE DIRECTLY
- 3. On (1) – TMSP via SMPs Awareness Conversation and content exchange good; content storage, indexing and search poor Content sharing Malleable as well as stable content Coordination Long and short term Collaborative production Lightweight to complex Longevity Currently questionable….
- 4. Cooperative activities, centralised Collective action, centralised Collective action, decentralised
- 5. On (2)- Sciences of the social Data quality descriptive/predictive; observed/understood; local/universal; reactive/proactive; stand- alone/replicated Science quality Data stability/longevity, TOS, content and social responsibility WE NEED TO ADDRESS THE DESIGN OF DATA (FOR) SCIENCE ISSUE DIRECTLY Designers : Statisticians : Computer scientists : Data Scientists : Social scientists
- 6. Focus on (2) Mike Loukides http://radar.oreilly.com/2010/06/what-is-data-science.html
- 7. On Data Science “What differentiates data science from statistics is that data science is a holistic approach. We’re increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.” The first step of any data analysis project is “data conditioning,” or getting data into a state where it’s usable.
- 8. On Data Science The most meaningful definition I’ve heard: “big data” is when the size of the data itself becomes part of the problem. The need to define a schema in advance conflicts with reality of multiple, unstructured data sources, in which you may not know what’s important until after you’ve analyzed the data.
- 9. On Data Science Data scientists … come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?” The future belongs to the companies who figure out how to collect and use data successfully. …and the scientists?
- 10. Business logic is not science logic
- 12. Data – the ‘this is the dataset’ problem
- 14. Interface elements ….lead to data, inviting action and inviting information
- 15. Facebook
- 18. Dating
- 20. profile creation explicit versus passive “personalisation”
- 23. Anxiety, self reflection, identity…. Eva Illouz
- 24. Flickr
- 26. Recording and Sharing Documenting Personal and Collective Memory Competition Status Affiliation Group Membership Learning Emulating Awareness Near and Far Curiosity/Voyeuris m
- 27. Flickr – Photo sharing by user location
- 28. The Library of Congress, the Powerhouse Museum, the Smithsonian, New York Public Library, and Cornell University Library
- 34. Data longevity “Like all Commons members, the other qualitative measure we value highly is the sheer inventiveness of Flickr members who engage with the photographs. Currently, Cornell saves links to examples of reuse on delicious (http://www.delicious.com) and displays them as a feed on its website.
- 44. Business logic is not science logic
- 45. Design/Science of participation (1) Science through (platforms for mediated communication) TMSP (2) Science on (social science contributions about fundamentals of collaboration/cooperation) “Hubble telescope” of social science
- 46. Reflections on requirements Stability – the existence of content in an accessible (and hopefully the same) format over time Science requires Consistency: consistently re-code the same data in the same way over a period of time Reproducibility: the tendency for a group of coders to classify categories membership in the same way Accuracy: or the extent to which the classification of a text corresponds to a standard or norm statistically. Validity correspondence of the categories to the conclusions, avoiding ambiguity and addressing multiple possible classifications Proof: trust in the inferential procedures and clarity of what level of implication is allowed. i.e. do the conclusions follow from the data or are they explainable due to some other phenomenon Generalizability of results to a theory Cross-setting comparative interventions
- 47. On (2)- Sciences of the social Data quality descriptive/predictive; observed/understood; local/universal; reactive/proactive; stand- alone/replicated Science quality Data stability/longevity, TOS, content and social responsibility WE NEED TO ADDRESS THE DESIGN OF DATA (FOR) SCIENCE ISSUE DIRECTLY Designers : Statisticians : Computer scientists : Data Scientists : Social scientists
- 49. Acknowledgements On dating: Elizabeth Goodman; on Flickr: Shyong (Tony) Lam, on instrumentation and analysis: David Ayman Shamma & M. Cameron Jones; on Flickr Commons: George Oates Flickr photographers: Marina Noordegraaf (Verbeeldingskr8), Tim Jagenberg, Nicolas Nova
Hinweis der Redaktion
- Define serious and casual daters Serious: Looking for a long term partner Casual: Looking for short-term or one-time encounters Industry assumptions: Division between “ serious ” and “ casual ” daters in terms of what they ’ d pay for and the effort they put in Interviews were semi-structured, asking people to talk about the experience of planning and going on dates after only communicating online. Focusing on the work of dating -- the management of schedules, choosing locations, dealing with unexpected delays in traffic, handling anxiety.