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

Peter Eastman

Stanford University

ORCID: 0000-0002-9566-9684
Biochemistry, Genetics and Molecular BiologyMaterials ScienceComputer ScienceChemistry

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
4.4Author 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.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Scientific Data(2023)10.1038/s41597-022-01882-6
Top 7%
3.3DataRank
Top 7%171 citations

Pavan Kumar Behara, David Dotson, Raimondas Galvelis, John E. Herr, Joshua T. Horton +7 more

Papers

Driven by 7 papers — median percentile 79. Top paper: “SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials”.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Scientific Data(2023)10.1038/s41597-022-01882-6
Top 7%
3.3DataRank
Top 7%171 citations

Pavan Kumar Behara, David Dotson, Raimondas Galvelis, John E. Herr, Joshua T. Horton +7 more

Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning

Journal of Chemical Theory and Computation(2024)10.1021/acs.jctc.4c00794
Top 21%
0.955DataRank
Top 21%37 citations

Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland, Peter Eastman

Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning

arXiv (Cornell University)(2024)10.48550/arxiv.2406.13112
Top 72%
0.115DataRank
Top 72%1 citations

Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland, Peter Eastman

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

Journal of Chemical Theory and Computation(2024)10.1021/acs.jctc.4c00253
N/A
0DataRank · unranked
52 citations

Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr +4 more

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv (Cornell University)(2023)10.48550/arxiv.2310.03121
N/A
0DataRank · unranked
20 citations

Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio +20 more

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics

Journal of Chemical Information and Modeling(2023)10.1021/acs.jcim.3c00773
N/A
0DataRank · unranked
92 citations

Alejandro Varela‐Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland +3 more

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

The Journal of Physical Chemistry B(2023)10.1021/acs.jpcb.3c06662
N/A
0DataRank · unranked
495 citations

Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio +20 more