Data Collection: Overview
This section describes how the data shown on Scholar Data is collected and computed, including datasets, FAIR scores, citations, alternative mentions, and auto-generated researcher profiles.
Where the Data Comes From
Every metric on Scholar Data is derived from publicly available research infrastructure: dataset registries, citation indexes, code repositories, patent databases, and more. No data is self-reported by researchers. Scores are computed automatically from these external sources using documented, reproducible methods.
Current Data
The data currently shown on Scholar Data covers represents a point-in-time calculation rather than a live feed. Scores reflect the citations, mentions, and FAIR assessments available at the time of processing (see individual pages in this section for specific dates).
Long-Term Vision
The goal is a fully automated pipeline that runs periodically, continuously discovering new datasets, recomputing FAIR scores, identifying new citations and mentions, and updating D-index and S-index values over time (see Figure 1 below). We expect to establish that as the project progresses.
Figure 1. Overview of the targeted automated pipelines for collecting dataset-level data to compute the S-index of researchers
What's Covered in This Section
- Datasets: what datasets are in the Scholar Data database and where they come from
- FAIR Scores: how dataset FAIRness is assessed
- Citations: how formal citations to datasets are identified
- Mentions: how appearances in code repositories, patents, and other sources are found
- Research Fields: how a research field is assigned to each dataset to enable field-specific normalization
- Auto-Generated Profiles: how researcher profiles are assembled automatically and S-index scores computed