A wealth of digital texts and the proliferation of automated research methodologies enable researchers to analyze large sets of data at a speed that would be impossible to achieve through manual review. When researchers use these automated techniques and methods for identifying, extracting, and analyzing patterns, trends, and relationships across large volumes of un- or thinly structured digital content, they are applying a methodology called text data mining or TDM. TDM is also referred to, with slightly different emphases, as “computational text analysis” or “content mining.”
For decades, universities, researchers, and libraries have sought a systemwide transition of scholarly publishing to open access (OA), but progress has been slow. There is now a potential for more rapid and impactful change, as new collaborative OA publishing models have taken shape. Cooperative publishing arrangements represent a viable path forward for society publishers to transition to OA as the default standard for disseminating research. The traditional article processing charge OA model has introduced sometimes unnavigable financial roadblocks, but cooperative arrangements premised on collective action principles can help to secure long-term stability and prevent the risk of free riding. Investment in cooperative arrangements does not require that cash-strapped libraries discover a new influx of money as their collection budgets continue to shrink, but rather that they purposefully redirect traditional subscription funds toward publishing support. These cooperative arrangements will require a two-way demonstration of trust: On one hand, libraries working together to provide assurances of sustained financial support, and on the other, societies’ willingness to experiment with discarding subscriptions. Organizations such as Society Publishers Coalition and Transitioning Society Publications to Open Access are committed to education about and further development of scalable and cooperative OA publishing models.
reflects the combined input of the authors listed here (in alphabetical order by last name) as well as contributions from other OSI2017 delegates. The findings and recommendations expressed herein do not necessarily reflect the opinions of the individual authors listed here, nor their agencies, trustees, officers, or staff.
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