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

Lukasz Kurgan

Virginia Commonwealth University

ORCID: 0000-0002-7749-0314
Biochemistry, Genetics and Molecular BiologyMaterials ScienceComputer 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 2%percentile
2.6Author DataRank

Indexed papers

3in 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 3 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.

DescribePROT: database of amino acid-level protein structure and function predictions

Nucleic Acids Research(2020)10.1093/nar/gkaa931
Top 10%
2.1DataRank
Top 10%86 citations

Akila Katuwawala, Christopher J. Oldfield, A. Keith Dunker, Eshel Faraggi, Jörg Gsponer +9 more

Papers

Driven by 8 papers — median percentile 75. Top paper: “DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions”.

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

PROTEOMICS(2019)10.1002/pmic.201900019
N/A
3.6DataRank · unranked
119 citations

Hong Song, Min Zeng, Yaohang Li, Lukasz Kurgan, Min Li +1 more

DescribePROT: database of amino acid-level protein structure and function predictions

Nucleic Acids Research(2020)10.1093/nar/gkaa931
Top 10%
2.1DataRank
Top 10%86 citations

Akila Katuwawala, Christopher J. Oldfield, A. Keith Dunker, Eshel Faraggi, Jörg Gsponer +9 more

DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options

Nucleic Acids Research(2023)10.1093/nar/gkad985
Top 41%
0.469DataRank
Top 41%14 citations

Bi Zhao, Bálint Biró, Eshel Faraggi, Jörg Gsponer, Gang Hu +11 more

Systematic investigation of sequence and structural motifs that recognize ATP

Computational Biology and Chemistry(2015)10.1016/j.compbiolchem.2015.04.008
N/A
0.269DataRank · unranked
5 citations

Dacheng Wang, Lukasz Kurgan, Ke Chen

Computational prediction of disordered binding regions

Computational and Structural Biotechnology Journal(2023)10.1016/j.csbj.2023.02.018
N/A
0DataRank · unranked
44 citations

Daisuke Kihara, Lukasz Kurgan, Sushmita Basu

iFeatureOmega:an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets

Nucleic Acids Research(2022)10.1093/nar/gkac351
N/A
0DataRank · unranked
97 citations

Xuhan Liu, Pei Zhao, Chen Li, Yanan Wang, Fuyi Li +9 more

Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences

Briefings in Bioinformatics(2024)10.1093/bib/bbaf016
N/A
0DataRank · unranked
11 citations

Jing Yu, Daisuke Kihara, Lukasz Kurgan, Sushmita Basu

iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

Nucleic Acids Research(2021)10.1093/nar/gkab122
N/A
0DataRank · unranked
284 citations

Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yongzi Chen +7 more