Text and Data Mining: Seeking Traction
Thomas G. Padilla · 2018
Working in libraries affords many opportunities to engage challenges associated with text and data mining (TDM) limited access datasets. Challenges afford themselves across interactions with diverse disciplinary communities at institutions with varying resources. Increasingly, data requested by research communities fall outside the traditional purview of library collection development and research support. Solutions to TDM challenges are few given gaps in understanding and misaligned values. Continued activity in light of these factors fosters an environment where weaknesses and threats are many and seemingly tractable opportunities are few. In the space of this brief statement I will introduce four challenges: (1) underdeveloped and inconsistent content provider effort to meet text and data mining need, (2) misaligned values reinforced by ambiguous and/or overly restrictive content provider terms, (3) debt incurred by technical abstraction, and (4) purposeful technical opacity.