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

Élodie Germani

Inserm

ORCID: 0000-0002-5786-9538

Also affiliated with Laboratoire Traitement du Signal et de l'Image

NeuroscienceComputer ScienceMedicineMathematics

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 4%percentile
0.609Author 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.

The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis

arXiv (Cornell University)(2023)10.48550/arxiv.2312.14493
Top 65%
0.208DataRank
Top 65%3 citations

Élisa Fromont, Pierre Maurel, Camille Maumet, Élodie Germani

Papers

Driven by 9 papers — median percentile 35. Top paper: “The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis”.

The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis

arXiv (Cornell University)(2023)10.48550/arxiv.2312.14493
Top 65%
0.208DataRank
Top 65%3 citations

Élisa Fromont, Pierre Maurel, Camille Maumet, Élodie Germani

HCP Multi-Pipeline: a derived dataset to investigate analytical variability in fMRI

Scientific Data(2025)10.1038/s41597-025-05247-7
Top 65%
0.203DataRank
Top 65%2 citations

Élisa Fromont, Pierre Maurel, Camille Maumet, Élodie Germani

On the validity of fMRI mega-analyses using data processed with different pipelines

Imaging Neuroscience(2025)10.1162/imag_a_00522
Top 65%
0.199DataRank
Top 65%2 citations

Xavier Rolland, Pierre Maurel, Camille Maumet, Élodie Germani

Uncovering Communities Of Pipelines in the Task-FMRI Analytical Space

HAL (Le Centre pour la Communication Scientifique Directe)(2024)10.1109/icip51287.2024.10647701
N/A
0DataRank · unranked
1 citations

Élisa Fromont, Camille Maumet, Élodie Germani

On the validity of fMRI studies with subject-level data processed through different pipelines

arXiv (Cornell University)(2024)10.48550/arxiv.2402.12900
N/A
0DataRank · unranked
0 citations

Xavier Rolland, Pierre Maurel, Camille Maumet, Élodie Germani

On the benefits of self-taught learning for brain decoding

GigaScience(2022)10.1093/gigascience/giad029
N/A
0DataRank · unranked
3 citations

Élisa Fromont, Camille Maumet, Élodie Germani

Proceedings of the OHBM Hackathon 2023

Aperture Neuro(2025)10.52294/001c.145051
N/A
0DataRank · unranked
0 citations

Anibal Sólon Heinsfeld, Andrea Gondová, Bruno Hebling Vieira, Qing Wang, Sina Mansour L. +41 more

Uncovering communities of pipelines in the task-fMRI analytical space

arXiv (Cornell University)(2023)10.48550/arxiv.2312.06231
N/A
0DataRank · unranked
0 citations

Élisa Fromont, Camille Maumet, Élodie Germani

Mitigating analytical variability in fMRI results with style transfer

arXiv (Cornell University)(2024)10.48550/arxiv.2404.03703
N/A
0DataRank · unranked
3 citations

Camille Maumet, Élisa Fromont, Élodie Germani