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

Wei Qiu

University of Washington

ORCID: 0000-0001-8246-6901

Also affiliated with Changsha University of Science and Technology

Materials ScienceEngineeringComputer Science

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
0.104Author DataRank

Indexed papers

2in 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 2 indexed papers 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.

Interpretable machine learning prediction of all-cause mortality

Zenodo (CERN European Organization for Nuclear Research)(2022)10.5281/zenodo.6899541
Top 78%
0.104DataRank
Top 78%1 citations

Wei Qiu

Papers

Driven by 5 papers — median percentile 11. Top paper: “Interpretable machine learning prediction of all-cause mortality”.

Interpretable machine learning prediction of all-cause mortality

Zenodo (CERN European Organization for Nuclear Research)(2022)10.5281/zenodo.6899541
Top 78%
0.104DataRank
Top 78%1 citations

Wei Qiu

Interpretable machine learning prediction of all-cause mortality

Communications Medicine(2022)10.1038/s43856-022-00180-x
N/A
0DataRank · unranked
103 citations

Hugh Chen, Ayse B. Dincer, Scott Lundberg, Matt Kaeberlein, Su-In Lee +1 more

Interpretable Machine Learning Prediction of All-Cause Mortality

Research Square(2022)10.21203/rs.3.rs-1352145/v1
N/A
0DataRank · unranked
4 citations

Hugh Chen, Ayşe Dinçer, Scott Lundberg, Matt Kaeberlein, Su-In Lee +1 more

Interpretable machine learning prediction of all-cause mortality

Zenodo (CERN European Organization for Nuclear Research)(2022)10.5281/zenodo.6899505
Top 100%
0DataRank
Top 100%0 citations

Wei Qiu

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age

The Lancet Healthy Longevity(2023)10.1016/s2666-7568(23)00189-7
N/A
0DataRank · unranked
73 citations

Hugh Chen, Matt Kaeberlein, Su-In Lee, Wei Qiu