Many managers and mentors for project teams desire more efficient and more effective ways of monitoring and predicting the quality of social relationships and the performance of teams under their purview. A previous study [13] found that one form of linguistic mimicry, linguistic style matching, and some lexical features indicated team performance and mutual attraction in short-term, laboratory tasks. In this paper, we evaluate whether these measures also work as indicators for performance, shared understanding, and team trust in longerduration project teams, using only limited, unobtrusively obtained communication traces. In our four-month evaluation using student project team emails, we found no support for LSM or most of the previously identified measures as practical indicators in our field setting. We did find some support for using future-oriented words to indicate team performance over time.

Objectives: (1) to identify common errors in data organization and metadata completeness that would preclude a "reader" from being able to interpret and re-use the data for a new purpose; and (2) to develop a set of best practices derived from these common errors that would guide researchers in creating more usable data products that could be readily shared, interpreted, and used.

Conventional wisdom suggests that there are benefits to the creation of shared repositories of scientific data. Funding agencies require that the data from sponsored projects be shared publicly, but individual researchers often see little personal benefit to offset the work of creating easily sharable data. These conflicting forces have led to the emergence of a new role to support researchers: data managers. This paper identifies key differences between the sociotechnical context of data managers and other "human infrastructure" roles articulated previously in Computer Supported Cooperative Work (CSCW) literature and summarizes the challenges that data managers face when accepting data for archival and reuse. While data managers' work is critical for advancing science and science policy, their work is often invisible and under-appreciated since it takes place behind the scenes.

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In order to encourage interdisciplinary research, the National Institutes of Health (NIH) and the National Science Foundation (NSF) are mandating that researchers make their data public in an effort to provide incentives for data sharing. While this has encouraged data sharing in some fields, other fields with little NSF or NIH funding do not have the same incentives to encourage such sharing. In this work, we find that these other funding sources either fail to encourage data sharing and in some cases actively discourage it.