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

Janick Weberpals

Brigham and Women's Hospital

ORCID: 0000-0003-0404-7394
MathematicsMedicineComputer 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 100%percentile
0Author 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.

Using real-world data to predict findings of an ongoing phase IV trial: glycemic control of semaglutide versus standard of care

BMJ Open Diabetes Research & Care(2025)10.1136/bmjdrc-2025-005180
Top 100%
0DataRank
Top 100%0 citations

Sebastian Schneeweiß, Elvira D’Andrea, Janick Weberpals, ELYSE C. DICESARE, Elisabetta Patorno +5 more

Papers

Driven by 7 papers — median percentile 0. Top paper: “Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research”.

Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research

Epidemiology(2021)10.1097/ede.0000000000001338
N/A
1.1DataRank · unranked
35 citations

Tim Becker, Jessica Davies, Fabian Schmich, Dominik Rüttinger, Fabian Joachim Theis +3 more

The Impact of Longitudinal Data‐Completeness of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores

Clinical Pharmacology & Therapeutics(2023)10.1002/cpt.2901
N/A
0DataRank · unranked
4 citations

Janick Weberpals, Shirley Wang, Rishi Desai, David Merola, Kueiyu Joshua Lin +1 more

High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates

arXiv (Cornell University)(2024)10.48550/arxiv.2405.10925
N/A
0DataRank · unranked
0 citations

Pamela A. Shaw, Kueiyu Joshua Lin, Richard Wyss, Joseph M. Plasek, Li Zhou +15 more

Natural language processing for scalable feature engineering and ultra-high-dimensional confounding adjustment in healthcare database studies

Journal of Biomedical Informatics(2025)10.1016/j.jbi.2025.104882
N/A
0DataRank · unranked
4 citations

Jie Yang, Sebastian Schneeweiß, Joseph M. Plasek, Li Zhou, Thomas Deramus +5 more

High-dimensional multiple imputation for partially observed confounders including natural language processing–derived auxiliary covariates

American Journal of Epidemiology(2025)10.1093/aje/kwaf017
N/A
0DataRank · unranked
4 citations

Pamela A. Shaw, Kueiyu Joshua Lin, Richard Wyss, Joseph M. Plasek, Li Zhou +15 more

Natural language processing for scalable feature engineering and ultra-high-dimensional confounding adjustment in healthcare database studies

medRxiv(2025)10.1101/2025.01.30.25321403
N/A
0DataRank · unranked
1 citations

Jie Yang, Sebastian Schneeweiß, Joseph M. Plasek, Li Zhou, Thomas Deramus +5 more

Using real-world data to predict findings of an ongoing phase IV trial: glycemic control of semaglutide versus standard of care

BMJ Open Diabetes Research & Care(2025)10.1136/bmjdrc-2025-005180
Top 100%
0DataRank
Top 100%0 citations

Sebastian Schneeweiß, Elvira D’Andrea, Janick Weberpals, ELYSE C. DICESARE, Elisabetta Patorno +5 more