- Data analytical tools for studying scholarly discovery and collaboration, including:
- New approaches to measure and predict the impact of research and researchers in a particular fields of study;
- Searching and mining large digital libraries, repositories for scholarly publications and patents and linking to other data sources such as funded proposals and patents;
- Novel data search and mining tools for studying scholarly collaboration structure using big data, including scalable graph mining, etc.;
- Data infrastructure that supports scalable computation, e.g., document indexing with cloud computing services;
- Algorithms for accessing, extracting and recommending scholarly articles, experts and findings.
- Online scholar data platforms and systems consideration for scholarly discovery and collaboration, including:
- Heterogenous data source integration, especially with open-access, novel datasets (e.g., Wikipedia, government census data, patent data, etc.);
- Storage, indexing and query processing for research data;
- Design considerations for effectively support scholars’ engagement in using online and social platforms;
- Social and collaborative support for scholarly discovery and collaboration;
- Privacy and security issues and management in online scholarly collaboration.
- Digital data curation and management for scholarly discovery and collaboration, including:
- Issues and solutions to data curation, management, and archival;
- Existing practices for managing research data;
- Scalability and usability of managing research data
- Other aspects of scholarly discovery and collaboration, including:
- Design of next generation collaboration platforms;
- Information professionals’ role in engaging in online scholarly collaboration;
- Cultural and community acceptance and evaluation of activities in online scholarly collaboration.