Table 2. Changed parameters and associated measures.

Overview of considered parameters determining reuse and sharing habits of researchers, and possible measures to improve these in a realistic setting.

Parameters investigated in the model	Possible associated measures to improve this
Time ‘tr’ spent to assess and include an external dataset	• Improve data quality, for instance by the use of data journals (Costello et al., 2013; Atici et al., 2013; Gorgolewski, Margulies & Milham, 2013), or peer review of datasets (i.e., a ‘comment’ field in data repositories). • Offer techniques or tools for easy assessment of dataset quality i.e., (Eijssen et al., 2013), faster pre-processing or data cleaning (i.e., ‘OpenRefine’ or ‘R statistical language’).
Chance ‘f’ to find an external dataset	• Harvest databases through data portals to reduce ‘scattering’ of datasets. • Standardization of metadata and documentation. • Advanced community and project-specific databases. • Library assistance in finding and using appropriate datasets.
Time ‘tc’ associated with sharing of research data	• Offer a good storing & sharing IT infrastructure. • Assistance with good data management planning at the early stages of a research project.
Benefit in citation per paper ’b’ associated with sharing of research data	• Provide a permanent link between paper and dataset. • Increase attribution to datasets by citation rules . • Establish impact metrics for datasets.
Percentage of scientists sharing their research data	• Promote sharing by a top down policy from an institute, funder, or journal. • Promote sharing bottom up by offering education on the benefits of sharing, to change researchers’ mind set.