Below are some relevant studies on data discovery, reuse, and collaboration.
Sidlauskas, B., Ganeshkumar, G., Hazkani-Covo, E., Jenkins, K., Lapp, H., McCall, L.W., . . . Kidd, D.M. (2010). Linking big: The continuing promise of evolutionary synthesis. Evolution, 64(4), 871–880.
Summary: 'Lessons learned' from running the National Evolutionary Synthesis Center (NESCent) - provides four modes of 'synthesis in action': Data aggregation; Reuse of Results; Methodological Integration; Conceptual Synthesis and Methodological Integration. The "Overcoming Technological Impediments to Synthesis" is not particularly helpful your study, but interesting background info.
Young, Alyson L., and Wayne G. Lutters. "Infrastructuring for Cross-Disciplinary Synthetic Science: Meta-Study Research in Land System Science." Computer Supported Cooperative Work (CSCW) 26.1-2 (2017): 165-203.
Summary: The first half of this paper is ethnographic description of requirements gathering - the second half (which I think will be of interest) is about translating those requirements into a system (called GLOBE). See in particular section 6.
Jirotka, Marina, Charlotte P. Lee, and Gary M. Olson. "Supporting scientific collaboration: Methods, tools and concepts." Computer Supported Cooperative Work (CSCW) 22.4-6 (2013): 667-715.
Summary: A generic, but helpful overview of terminology used in CSCW, HCI, and Science Studies to describe collaboration around shared resources.
Rolland, Betsy, et al. "Toward rigorous data harmonization in cancer epidemiology research: one approach." American journal of epidemiology 182.12 (2015): 1033-1038.
Summary: Describes process of data harmonization - a form of synthesis that combines pooled data sources into a single integrated dataset. Describe a six-step harmonization process which may be helpful for understanding stages of synthesis work.
Liu, Can, et al. "Shared interaction on a wall-sized display in a data manipulation task." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016.
Summary: Not directly related to collaboration around shared data, but an interesting look at how people collaborate on shared data manipulation tasks. The discussion of "collaborative styles" will be of interest.
Edwards, P. N., Mayernik, M. S., Batcheller, A. L., Bowker, G. C., & Borgman, C. L. (2011). Science friction: Data, metadata, and collaboration. Social Studies of Science, 41(5), 667-690.
Summary: Much more critical theory than you're probably looking for, but an interesting look at use of data and collaboration across domains.
Blake, Catherine, and Wanda Pratt. "Collaborative information synthesis I: A model of information behaviors of scientists in medicine and public health." Journal of the Association for Information Science and Technology 57.13 (2006): 1740-1749. + Blake, Catherine, and Wanda Pratt. "Collaborative information synthesis II: Recommendations for information systems to support synthesis activities." Journal of the Association for Information Science and Technology 57.14 (2006): 1888-1895.
Summary: These two studies are a bit dated, but they were pretty influential in thinking about how Medline (as a comprehensive archive of open access lit) changed information behaviors within the field of public health research. The overall model (CIS) may be of interest - for a generic overview see Table 2 (in the first publication).
Shah, C. (2014). Collaborative information seeking. Journal of the Association for Information Science and Technology, 65(2), 215-236. + Twidale, M., & Nichols, D. (1998). Designing interfaces to support collaboration in information retrieval. Interacting with computers, 10(2), 177-193.
Summary: Again, a bit dated but these are two concepts that information systems designers often use in describing collaborative, small-group work.
Kim, Y., & Yoon, A. (2017). Scientists' data reuse behaviors: A multilevel analysis. Journal of the Association for Information Science and Technology.
Summary: Not a great study (imho) but the literature review is pretty through in terms of what other people have presented as research findings about the "reuse" of data.