Building Community around Open Source vs. Open Data

host: Michael Burnstein

notes: Andrew Davis similarities and differences b/w open data communities and open source communities*open data communities have more internal debate ovver licensing

• the field is new and there has not been enough litigation for businesses to guage risk
• Copyright restrictions are weightier for data vs software

• • in the US a database (of copyrightable facts) cannot itself be copyrighted -- aggregation isn't "creative"
  • in Europe aggregation is considered to be a creative task (this has hindered open data startups from starting in Europe)
  • in the US you can extract the facts from a dataset and not be liable for copyright infringement -- The copyright situation in europe is more tenuous
  • open data communities are difficult to start because rules are not the same all over the world
  • restrictions require that those joining/using data startups to be known personally to insure that they are not litigous
  • open street maps had to abandon a CC viral license model

 How do we incentiveize contribution to Open Data?*people will put their data into the public domain to contribute to a specific cause

• You don't have to appeal to "what's right" or "social good"

• • bringing business to open data requires showing business benefits
  • Opening research data

• • • peer review in the public space is one way of convincing academia
    • getting rid of database access fees is something academics are highly interested in
    • scandals in falsafied data has brought public knowledge of open data to the netherlands (stoppel, stannel (sp?))

"don't try to evangelize non-geeks about the benefit of open data because they don't care"http://xkcd.com/743/ Read vs. Write Access in Open Data (access to data vs. contributing to data)*how do you verify public contribution

• • community verification (10 people agree, so it's probably right)
  • trusted users
  • community users can crowsource coverage of data verification
  • don't allow public access
  • multiple repositories can be used to verify each other
  • Closed Source code can restrict duplication and insures quality demands
  • humans must be involved, you can't autiomate all verification
• What happens when bots target a dataset for corruption?

• • time thresholds are often used to prevent bot corruption

Public vs. Private Data*you must track the source of all data

• medical data has certain fields that can never be shared
• lawyer wiki is completely closed to allow for open discussion
• is it enough to close or denormalize some data to the public to maintain privacy?
• allow users to express their level of consent with clear wording

Examples*Tri-Met routing built upon open street map

• • tri met is responsible for route and timetable accuracy
  • tri met is not responsible for map accuracy
  • tri-met verifies the open street map data every night and submits corrections back to the community
• First Monday
• AOL anonymized search query data was quickly de-anonymized