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

Ritoban Kundu

University of Michigan

ORCID: 0000-0003-0967-6755

Also affiliated with Michigan Medicine, University of Pennsylvania

MathematicsMedicineBiochemistry, Genetics and Molecular Biology

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

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.

To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks

medRxiv(2024)10.1101/2024.02.12.24302710
Top 78%
0.104DataRank
Top 78%1 citations

Ritoban Kundu, Xu Shi, Christopher R. Friese, Seunggeun Lee, Lars G. Fritsche +5 more

Papers

Driven by 7 papers — median percentile 22. Top paper: “Extending the susceptible‐exposed‐infected‐removed (SEIR) model to handle the false negative rate and symptom‐based administration of COVID‐19 diagnostic tests: SEIR‐fansy”.

Extending the susceptible‐exposed‐infected‐removed (SEIR) model to handle the false negative rate and symptom‐based administration of COVID‐19 diagnostic tests: SEIR‐fansy

Statistics in Medicine(2022)10.1002/sim.9357
N/A
0.494DataRank · unranked
26 citations

Ritoban Kundu, Soumik Purkayastha, Michael Kleinsasser, Lauren J. Beesley, Bhramar Mukherjee +2 more

To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice

Journal of the American Medical Informatics Association(2024)10.1093/jamia/ocae098
N/A
0.416DataRank · unranked
15 citations

Ritoban Kundu, Xu Shi, Christopher R. Friese, Seunggeun Lee, Lars G. Fritsche +5 more

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

PLoS Genetics(2023)10.1371/journal.pgen.1010907
N/A
0.360DataRank · unranked
10 citations

Kisung Nam, Jiacong Du, Ritoban Kundu, Maxwell Salvatore, Xu Shi +4 more

To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks

medRxiv(2024)10.1101/2024.02.12.24302710
Top 78%
0.104DataRank
Top 78%1 citations

Ritoban Kundu, Xu Shi, Christopher R. Friese, Seunggeun Lee, Lars G. Fritsche +5 more

A framework for understanding selection bias in real-world healthcare data

Journal of the Royal Statistical Society Series A (Statistics in Society)(2024)10.1093/jrsssa/qnae039
N/A
0DataRank · unranked
25 citations

Xu Shi, Jean Morrison, Jessica Barrett, Bhramar Mukherjee, Ritoban Kundu

Comparative impact assessment of COVID-19 policy interventions in five South Asian countries using reported and estimated unreported death counts during 2020-2021

PLOS Global Public Health(2023)10.1371/journal.pgph.0002063
N/A
0DataRank · unranked
2 citations

Jyotishka Datta, Debashree Ray, Swapnil Mishra, Rupam Bhattacharyya, Lauren Zimmermann +2 more

Network Structural Equation Models for Causal Mediation and Spillover Effects

arXiv (Cornell University)(2024)10.48550/arxiv.2412.05397
N/A
0DataRank · unranked
0 citations

Peter X.‐K. Song, Ritoban Kundu