Research data serves important roles in scientific discovery and academic innovation. To
appropriately assign credit for data work and to measure the value of research data, it is …

In recent years, there has been a dramatic increase in scientific publications across the
globe. To help navigate this overabundance of information, methods have been devised to …

Discovering authoritative links between publications and the datasets that they use can be a
labor‐intensive process. We introduce a natural language processing pipeline that retrieves …

Integrating concept and citation networks on a specific research subject can help
researchers focus their own work or use methods described in prior works. In this paper, we …

We propose a method for improving access to scientific literature by analyzing the content of
research papers beyond citation links and topic tracking. Our model relies on a typology of …

The Inter-university Consortium for Political and Social Research (ICPSR) is developing a
machine learning approach using natural language processing (NLP) to assist in the …

Data citations provide a foundation for studying research data impact. Collecting and
managing data citations is a new frontier in archival science and scholarly communication …

In the wake of information overload in academia, methodologies and systems for search,
recommendation, and prediction to aid researchers in identifying relevant research are …

Realizing the vision of networked data collections and services requires large bodies of
scientific data that can be used in new ways. Adapting the concept of epistemological …

Research data sharing has been proved to be key for accelerating scientific progress and
fostering interdisciplinary research; hence, the ability to search, discover and reuse data …