LAUSR

This special issue features the work of authors originally coming from different communities: bibliometrics/scientometrics (SCIM), information retrieval (IR) and, as an emerging player gaining more relevance for both aforementioned fields, natural language processing (NLP). The work presented in their papers combine ideas from all these fields, having in common that they all are using the scholarly data well known in scientometrics and solving problems typical to scientometric research. They model and mine citations, as well as metadata of bibliographic records (authorships, titles, abstracts sometimes), which is common practice in SCIM. They also mine and process fulltexts (including in-text references and equations) which is common practice in IR and requires established NLP text mining techniques. IR collections are utilised to ensure reproducible evaluations; creating and sharing test collections in evaluation initiatives such as CLEF eHealth1 is common IR tradition that is also prominent in NLP, eg., by the CL-SciSumm shared task.2 From an IR perspective, surprisingly, scholarly information retrieval and recommendation, though gaining momentum, have not always been the focus of research in the past. Besides operating on a rich set of data for researchers in all three disciplines to play with, scholarly search poses challenges in particular for IR due to the complex information needs that require different approaches than known from, e.g., Web search, where information needs are simpler in many cases. As an example, the current COVID-19 crisis shows that hybrid SCIM/IR/NLP approaches are increasingly required to ensure researchers get access