We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white-and blacklisting, and use them through three heuristics to identify 17 264 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.

The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. The objective of this study is to examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. Users of data sets appreciate high data quality and are discouraged by lack of replicability of data set construction. Data related to machine learning or derived from the manufacturing sector are two suggestions of the respondents for future data papers. Overall, data papers have provided the foundation for a significant number of studies, but there is room for improvement in their utilization. This can be done by setting a higher bar for their publication, by encouraging their use, by promoting open science initiatives, and by providing incentives for the enrichment of existing data collections.