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

Jana Kemnitz

Paracelsus Medical University

ORCID: 0000-0003-0342-4952

Also affiliated with Siemens (Austria), Ludwig Boltzmann Institute for Digital Health and Prevention

MedicineEngineeringComputer 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 3%percentile
1.5Author DataRank

Indexed papers

1in 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 1 indexed paper 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.

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort

Magnetic Resonance Materials in Physics Biology and Medicine(2020)10.1007/s10334-020-00889-7
Top 14%
1.5DataRank
Top 14%46 citations

F. Eckstein, Jana Kemnitz, Christian F. Baumgartner, Ender Konukoğlu, David Fuerst +2 more

Papers

Driven by 3 papers — median percentile 86. Top paper: “Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort”.

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort

Magnetic Resonance Materials in Physics Biology and Medicine(2020)10.1007/s10334-020-00889-7
Top 14%
1.5DataRank
Top 14%46 citations

F. Eckstein, Jana Kemnitz, Christian F. Baumgartner, Ender Konukoğlu, David Fuerst +2 more

Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep‐Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative

Arthritis Care & Research(2020)10.1002/acr.24539
N/A
0.903DataRank · unranked
39 citations

Akshay Chaudhari, David Fuerst, Martin Gaisberger, Jana Kemnitz, Christian F. Baumgartner +4 more

Local MRI-based measures of thigh adipose tissue derived from fully automated deep convolutional neural network-based segmentation show a comparable responsiveness to bidirectional change in body weight as from quality controlled manual segmentation

Annals of Anatomy - Anatomischer Anzeiger(2021)10.1016/j.aanat.2021.151866
N/A
0DataRank · unranked
7 citations

Eva Steidle‐Kloc, W. Wirth, David Fuerst, A. Wisser, Sebastian K. Eder +2 more