Incentives for Data Producers to Create ‘Archive-Ready’ Data*

1. Incentives for Data Producers to Create ‘Archive-Ready’ Data* Margaret Hedstrom Jinfang Niu University of Michigan NSF Award IIS-0456022

2. Why Do Producer Incentives Matter? • Basis for cooperation between producers and archives • Critical for efficient and affordable processing of data • Important for quality of archived data • The “hidden” knowledge problem

3. Where Are Incentives Operative? • Data sharing policies that require researchers to deposit data from federally-funded research (NIH, NSF, NOAA, NASA, NIJ, etc.) • Data Archive and Repository Requirements that set minimum requirements for data and documentation • Transfers of records to archival repositories

4. What Types of Producer Contributions Are Expected? • Data • Complete • Accurate • Documentation • Sampling Frame and Selection • Data Collection Instruments • Coding • Data Manipulation • Other Compliance Requirements • Confidentiality, Privacy, Other Non-disclosure Requirements

5. Why is it Difficult for Producers to Comply? • Requirements are not known • Requirements are not explicit • Extra Effort is Required • Outside normal research/business processes • Episodic • Requires additional knowledge/skill • Data Producers gain little (if anything) for their extra effort • Weak Incentives and Weak Rewards

6. Producer Concerns • Producers are rewarded for using the data directly, not for sharing it • Producers lose control and exclusive use of data when they deposit it • Benefits of depositing data are unknown or unclear • Sharing data exposes flaws and errors • Confidentiality may be compromised, especially via deductive disclosure when data sets are pooled.

7. Potential Remedies • Make data deposit requirements explicit and known • Provide sufficient support for compliance with deposit requirements (compensation for time and effort, tools, training) • Make benefits of data deposit explicit and visible • Notify producers when others use their data • Revisit citation and attribution requirements • Enforce explicit consequences for on-compliance

8. The “Hidden” Knowledge Problem • Hidden Knowledge • Difficult to articulate • Taken for granted • Not anticipated, overlooked, etc. • Hidden knowledge is difficult to transfer from the original producer to an archives, but can be critical for reuse • Standard practices rarely take hidden knowledge into account

9. Mitigation • Only the data producer can reveal hidden knowledge • Acknowledge hidden knowledge and its significance • Encourage data producers and provide means and incentives to make hidden knowledge explicit • Provide communication channels between producers and users