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

Sara Merkaj

Humboldt-Universität zu Berlin

ORCID: 0000-0003-1147-4968
MedicineNeurosciencePhysics and Astronomy

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
1.2Author DataRank

Indexed papers

2in 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 2 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.

A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information

Scientific Data(2024)10.1038/s41597-024-03021-9
Top 21%
0.980DataRank
Top 21%25 citations

Leon Jekel, Saahil Chadha, Anastasia Janas, Harrison Moy, Nazanin Maleki +17 more

Papers

Driven by 9 papers — median percentile 57. Top paper: “A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information”.

A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information

Scientific Data(2024)10.1038/s41597-024-03021-9
Top 21%
0.980DataRank
Top 21%25 citations

Leon Jekel, Saahil Chadha, Anastasia Janas, Harrison Moy, Nazanin Maleki +17 more

Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction

Frontiers in Neuroscience(2022)10.3389/fnins.2022.860208
N/A
0.529DataRank · unranked
33 citations

Khaled Bousabarah, Eve Kazarian, Tal Zeevi, Wolfgang Holler, Sara Merkaj +14 more

Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery

Neuro-Oncology Advances(2022)10.1093/noajnl/vdac116
N/A
0.464DataRank · unranked
21 citations

Khaled Bousabarah, T. Verma, Marc von Reppert, Leon Jekel, Ayyüce Gordem +10 more

A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information

arXiv (Cornell University)(2023)10.48550/arxiv.2309.05053
Top 65%
0.195DataRank
Top 65%2 citations

Leon Jekel, Saahil Chadha, Anastasia Janas, Harrison Moy, Nazanin Maleki +17 more

Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries

Neuro-Oncology Advances(2022)10.1093/noajnl/vdac093
N/A
0DataRank · unranked
7 citations

Avery E Lum, Gabriel Cassinelli, Sara Merkaj, T. Verma, Tal Zeevi +18 more

Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging

Neuro-Oncology Advances(2024)10.1093/noajnl/vdae157
N/A
0DataRank · unranked
5 citations

Nader A. Fawzy, Leon Jekel, Marc von Reppert, Niklas Tillmanns, Klara Willms +13 more

Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment

American Journal of Neuroradiology(2022)10.3174/ajnr.a7473
N/A
0DataRank · unranked
22 citations

Julia Shatalov, T. Verma, Waverly Rose Brim, Harry Subramanian, Alexandria Brackett +10 more

Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction

American Journal of Neuroradiology(2023)10.3174/ajnr.a8000
N/A
0DataRank · unranked
7 citations

Leon Jekel, Marc von Reppert, Niklas Tillmanns, Sara Merkaj, Gabriel Cassinelli Petersen +19 more

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis

Frontiers in Oncology(2022)10.3389/fonc.2022.856231
N/A
0DataRank · unranked
18 citations

Sara Merkaj, Gabriel Cassinelli Petersen, Niklas Tillmanns, Harry Subramanian, Waverly Rose Brim +13 more