🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Ge Shi

Beijing Institute of Technology

ORCID: 0000-0002-6944-6874
NeuroscienceComputer ScienceMedicine

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 7%percentile
0.117Author DataRank

Indexed papers

1in pilot corpus
datarank_citation_only_1hop_v6· scope data_onlyMethodology
Why this DataRank?

An author's DataRank is the sum of the DataRanks of all 1 indexed paper attributed to them. A prolific author with many moderate-impact papers can outrank one with a single high-impact paper.

Author scores recompute whenever paper DataRanks are refreshed, so this number lags the underlying paper scores by at most one batch run.

Read the full methodology →

Top data-sharing exemplar

The highest-impact dataset this researcher has shared, ranked by DataRank — the single contribution doing the most to lift their data-sharing standing.

Deep Learning for Prognosis Using Task-fMRI

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(2022)10.1145/3534678.3539362
Top 72%
0.117DataRank
Top 72%1 citations

Jason Smucny, Ian Davidson, Ge Shi

Papers

Driven by 2 papers — median percentile 28. Top paper: “Deep Learning for Prognosis Using Task-fMRI”.

Deep Learning for Prognosis Using Task-fMRI

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(2022)10.1145/3534678.3539362
Top 72%
0.117DataRank
Top 72%1 citations

Jason Smucny, Ian Davidson, Ge Shi

Deep Learning in Neuroimaging: Overcoming Challenges With Emerging Approaches

Frontiers in Psychiatry(2022)10.3389/fpsyt.2022.912600
N/A
0DataRank · unranked
41 citations

Ge Shi, Ian Davidson, Jason Smucny