1. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN How Data Contribute to Scientific Practice Matthew J. Bietz Jeremy P. Birnholtz

2. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN What Do Data Do? Traditional view is that data are representations of the world We find that when thinking about data sharing, it is important to take a broader view of the role of data This has implications for the development of data sharing systems

3. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Data in Practice We observed scientists using data in day-to- day scientific practice – Scientific Collaborations in 3 fields We find that data – Contribute to knowledge – Contribute to community Data sharing systems need to recognize the complexity of these roles

4. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Contributing to knowledge News vs. Confirmation Confirming vs. developing theory Purpose of study affects how data are collected, stored, and analyzed Implication: Difficult to reuse data across purposes Implication: Need to capture context as well as data

5. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Contributing to knowledge Streams vs. Events Shaking events vs. Clinical trials Often difficult to define when a data set is complete Implication: When to make data public, versioning, and other time issues are critical for sharing data

6. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Contributing to community Defining boundaries Groups defined by how data are used Data can bridge or enforce boundaries – Boundary objects – Ownership and control Implication: Decisions about sharing data often involve communities, not just individuals

7. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Contributing to community Gateways Those who control access to data act as community gatekeepers Multiple levels of access to data Subtle social process Implication: Access control is poorly supported in many data sharing systems

8. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Contributing to community Status and Recognition Benefits of having one’s own data Task uncertainty and reputation Implication: Recognition and reputation are important concerns for scientists who share data

9. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Contributing to community Enabling Learning Working with data can be a trajectory into a Community of Practice Scientists learn from what others do with their data Implication: Scientists want to interact around data, not just a “hand- off”

10. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Thanks!