Peter Granda Archival Assistant Director / Data Archives and Data Producers: A Cooperative Partnership

What I plan to discuss Reasons to share social science data Obstacles to share social science data Role of data archives Best practices for preparing data for archiving New developments to facilitate archiving process and improve cooperation between data archives and data producers

Why should data producers share research data? Data sharing achieves many important goals for the scientific community, such as:  reinforcing open scientific inquiry  encouraging diversity of analysis and opinion  promoting new research, testing of new or alternative hypotheses and methods of analysis  supporting studies on data collection methods and measurement  facilitating education of new researchers  enabling the exploration of topics not envisioned by the initial investigators permitting the creation of new datasets by combining data from multiple sources

Obstacles/Challenges to data sharing and archiving: Reasons not to share  Costs to producer in creating public-use files  Maintaining respondent confidentiality  Use by potential competitors  Less credit given for archiving data than for continually collecting new data especially within the academic community  Unexpected duplication of effort possible in using public-use files for research

Result of potential conflict between sharing data and the difficulties of doing so Even in places where there is a tradition of archiving social science survey and aggregate data, it is not always done or not done correctly Funds not always available or, more commonly, all of the funds are spent on data collection process Insufficient thought given to preparing materials throughout the data “life-cycle” process that could be easily used by other researchers

ROLE OF DATA ARCHIVE Assist data producers by providing advice regarding procedures to use when archiving their data and documentation Consult with data producers regarding respondent confidentiality Discuss best strategy and location to preserve the data in perpetuity

Methods of Data Sharing - Versioning  Importance of this issue for replication: e.g. users need to know which version of data file was used in publications  Increasing trend: Data files stored on data producer Web sites: –Greater number of interim or ‘early release’ versions now appearing –Need to have “versioning” system in place if data files are updated  Archives/data depositories usually preserve “final” versions of data files and also have systems in place to record history of each data collection they receive

Public Files Restricted Files Available to all users Available to general research community Available to members of a specific research team Accessible only through a formal application process Accessible only at a specific location under very restricted conditions CONFIDENTIALITYCONCERNSCONFIDENTIALITYCONCERNS

Defining Best Practices – General Goals Maintain respondent confidentiality while releasing the maximum amount of data publicly Archive materials in a format that will insure long-term preservation Provide sufficient information so that users who are not expert in the subject matter of the data collection could still use it effectively

Best Practices – Confidentiality Dangers of direct identifiers and potential dangers of indirect identifiers Solutions: removal, bracketing, top- coding, collapsing and/or combining variables, sampling, swapping, disturbing Restricted-use files or licenses Data enclaves

Best Practices – Data Formats Options: ASCII data files and record layouts ASCII data plus setup files Software-specific system files Portable software-specific files Online analysis-ready files *** IMPORTANCE OF ASCII AS A PRESERVATION FORMAT ***

Best Practices – Documentation Project description Sample and sampling procedures Weighting Date, geographic location of data collection, and time period covered Data source(s) Unit(s) of analysis/observation Variables Technical information on files Data collection instruments Interviewer guide, recode logic, coding instrument

Best Practices: Yes, in theory, but What is the Real Situation? Even on well-funded projects archiving is often given little attention It is not unusual that the vast majority of project funds are spent on data collection Documentation is often prepared hastily with insufficient thought given to how other researchers might use it

Best Practices: Yes, in theory, but What is the Real Situation? Experience from the Archival Perspective: Full compliance with submission requirements is often the exception rather than the rule

New Project between ICPSR and School of Information at the University of Michigan Purpose: to identify barriers and develop incentives for data producers to deposit “archive-ready” datasets Archive-ready: data and documentation files that are supplied to the archive in a format based on a specific agreement with the data producer

What are some of these barriers? Archiving process requires time, resources, and attention to detail In academic settings, researchers are awarded for publishing not for archiving Few “formal” professional rewards for depositing “archive-ready” datasets

What rewards and incentives are now offered to data producers? Appeal to self-interest Appeal to altruism Reputation effects Archive services Professional norms

What rewards and incentives might be offered to data producers in the future? Make it easier to collect and report information about uses of the data collection by other researchers (“reputation through citation”) Scoring rule: how “archive-ready” was the collection submitted? Enhanced service from the archive Publications: “reviews” of datasets

Implementation in different social science research environments Archive resources may vary affecting how much guidance and assistance they can provide to data producers Technical standards could also vary: in some places, the importance of certain data formats (e.g., SPSS files) may be paramount The key: what is most important for local researchers?

Спасибо !