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

Niklas D. Köhler

ORCID: 0000-0003-2726-0518

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 1%percentile
5.0Author DataRank

Indexed papers

5in 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 5 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.

The proteome landscape of the kingdoms of life

Nature(2020)10.1038/s41586-020-2402-x
Top 5%
4.7DataRank
Top 5%224 citations

Philipp E. Geyer, Ana R. Colaço, Peter V. Treit, Maximilian T. Strauss, Mario Oroshi +9 more

Papers

Driven by 10 papers — median percentile 0. Top paper: “The proteome landscape of the kingdoms of life”.

The proteome landscape of the kingdoms of life

Nature(2020)10.1038/s41586-020-2402-x
Top 5%
4.7DataRank
Top 5%224 citations

Philipp E. Geyer, Ana R. Colaço, Peter V. Treit, Maximilian T. Strauss, Mario Oroshi +9 more

Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy

Nature Machine Intelligence(2020)10.1038/s42256-020-00247-1
N/A
3.5DataRank · unranked
87 citations

Niklas D. Köhler, Jakob Siedlecki, Tina Herold, Leonie Keidel, Ben Asani +6 more

Deep learning does not outperform classical machine learning for cell-type annotation

bioRxiv (Cold Spring Harbor Laboratory)(2019)10.1101/653907
N/A
1.7DataRank · unranked
36 citations

Maren Büttner, Niry Andriamanga, Fabian Joachim Theis, Niklas D. Köhler

Reconstructing cell cycle and disease progression using deep learning

bioRxiv (Cold Spring Harbor Laboratory)(2016)10.1101/081364
N/A
0.869DataRank · unranked
21 citations

Niklas D. Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees +4 more

Reconstructing cell cycle and disease progression using deep learning

Nature Communications(2017)10.1038/s41467-017-00623-3
N/A
0.858DataRank · unranked
304 citations

Niklas D. Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees +3 more

Deep learning the collisional cross sections of the peptide universe from a million training samples

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.05.19.102285
Top 53%
0.325DataRank
Top 53%5 citations

Niklas D. Köhler, Andreas-David Brunner, Jean-Marc H. Wanka, Eugenia Voytik, Maximilian T. Strauss +3 more

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

bioRxiv (Cold Spring Harbor Laboratory)(2019)10.1101/861757
N/A
0.208DataRank · unranked
3 citations

Niklas D. Köhler, Thiago Gonçalves dos Santos Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel +6 more

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

Zenodo (CERN European Organization for Nuclear Research)(2020)10.5281/zenodo.3625995
Top 100%
0DataRank
Top 100%0 citations

Niklas D. Köhler, Thiago Gonçalves dos Santos Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel +6 more

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

Zenodo (CERN European Organization for Nuclear Research)(2020)10.5281/zenodo.3626854
Top 100%
0DataRank
Top 100%0 citations

Niklas D. Köhler, Thiago Gonçalves dos Santos Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel +6 more

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

Zenodo (CERN European Organization for Nuclear Research)(2020)10.5281/zenodo.3626020
Top 100%
0DataRank
Top 100%0 citations

Niklas D. Köhler, Thiago Gonçalves dos Santos Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel +6 more