Scholar Mesh is a scientific reviewer discovery service built on large-scale scholarly metadata, including more than 4 million authors and 8 million publications in computer science, machine learning, and related fields, helping users move from a paper description to a shortlist of relevant experts.
Why This Service Exists
Peer review is essential for publication quality, but finding qualified and independent reviewers is becoming harder as global research output grows. The number of publications grows exponentially, and as research areas expand it is harder to keep up with new researchers. Reviewing is unpaid, workloads are high, and one manuscript typically needs multiple reviewers.
Research output keeps increasing, so reviewer discovery must scale with it.
How Scholar Mesh Works
- Provide article metadata, including authors, title, area, and abstract. More complete inputs generally produce better recommendations.
- The recommender system algorithm searches for the best-fitted candidates.
- Various filters are applied to reduce conflicts of interest. For example, it never recommends direct co-authors.
Who It Helps
- Scientists looking for reviewer suggestions for manuscripts.
- Conference organizers building Programme Committees.
- Journal editors identifying reviewers and editorial board candidates.