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

Arunabha Majumdar

Indian Institute of Technology Hyderabad

ORCID: 0000-0002-5609-075X
Biochemistry, 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 3%percentile
1.5Author 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.

Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions

JNCI Journal of the National Cancer Institute(2023)10.1093/jnci/djad043
Top 25%
0.804DataRank
Top 25%33 citations

Lu Wang, Helian Feng, Arunabha Majumdar, Sijia Huo, James W. MacDonald +56 more

Papers

Driven by 7 papers — median percentile 74. Top paper: “Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions”.

Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions

JNCI Journal of the National Cancer Institute(2023)10.1093/jnci/djad043
Top 25%
0.804DataRank
Top 25%33 citations

Lu Wang, Helian Feng, Arunabha Majumdar, Sijia Huo, James W. MacDonald +56 more

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals

Human Genetics and Genomics Advances(2021)10.1016/j.xhgg.2021.100041
Top 27%
0.739DataRank
Top 27%21 citations

Arunabha Majumdar, Lu Wang, Siddhartha Kar, Kevin M. Brown, Helian Feng +54 more

Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improve the power of transcriptome-wide association studies

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.07.03.186247
N/A
0DataRank · unranked
2 citations

Nicholas Mancuso, Alexander Gusev, Arunabha Majumdar, Megan Major, Bogdan Paşaniuc +2 more

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Nature Communications(2020)10.1038/s41467-020-19365-w
N/A
0DataRank · unranked
68 citations

Tommer Schwarz, Arunabha Majumdar, Kangcheng Hou, Leah Briscoe, Richard K. Perez +7 more

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

PLoS Computational Biology(2021)10.1371/journal.pcbi.1008915
N/A
0DataRank · unranked
5 citations

Claudia Giambartolomei, Na Cai, Tanushree Haldar, Tommer Schwarz, Michael J. Gandal +3 more

Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies

PLoS Genetics(2021)10.1371/journal.pgen.1008973
N/A
0DataRank · unranked
93 citations

Nicholas Mancuso, Alexander Gusev, Arunabha Majumdar, Megan Major, Bogdan Paşaniuc +2 more

A two-step approach to testing overall effect of gene–environment interaction for multiple phenotypes

Bioinformatics(2020)10.1093/bioinformatics/btaa1083
N/A
0DataRank · unranked
9 citations

Kathryn S. Burch, Tanushree Haldar, Sriram Sankararaman, Bogdan Paşaniuc, W. James Gauderman +2 more