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

Siegfried G Priglinger

Innsbruck Medical University

ORCID: 0000-0002-5580-612X

Also affiliated with Centre hospitalier national d'ophtalmologie des Quinze-Vingts, LMU Klinikum, Augenklinik Heidelberg, Rotterdam Eye Hospital, München Klinik, Onnuri Smile Eye Clinic, Ludwig-Maximilians-Universität München, University of Sheffield

Medicine

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 100%percentile
0Author 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.

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

Papers

Driven by 9 papers — median percentile 0. Top paper: “Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy”.

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

Reconstructing disease dynamics for mechanistic insights and clinical benefit

Nature Communications(2023)10.1038/s41467-023-42354-8
N/A
0.458DataRank · unranked
6 citations

Neta Milman, Ayelet Alpert, Hannah Spitzer, Ben Asani, Evgeny Bakin +7 more

Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases

Klinische Monatsblätter für Augenheilkunde(2023)10.1055/a-2227-3742
N/A
0.241DataRank · unranked
4 citations

Robin Mittas, Nastassya Horlava, Johannes B Schiefelbein, Ben Asani, Stylianos Michalakis +9 more

Evaluation of OCT biomarker changes in treatment-naive neovascular AMD using a deep semantic segmentation algorithm

medRxiv(2022)10.1101/2022.06.16.22276342
N/A
0.208DataRank · unranked
3 citations

Olle G. Holmberg, Johannes B Schiefelbein, Michael Hafner, Tina Herold, Hannah Spitzer +8 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
0.165DataRank · unranked
2 citations

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

Reconstructing disease dynamics for mechanistic insights and clinical benefit

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.11.17.468952
N/A
0DataRank · unranked
0 citations

Neta Milman, Ayelet Alpert, Hannah Spitzer, Ben Asani, Johannes B Schiefelbein +5 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.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.3626020
Top 100%
0DataRank
Top 100%0 citations

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