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

Chris Sidey‐Gibbons

ORCID: 0000-0002-4732-7305
MedicineBiochemistry, Genetics and Molecular BiologyEconomics, Econometrics and Finance

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
0.907Author 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.

Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data

Scientific Reports(2022)10.1038/s41598-022-22614-1
Top 25%
0.803DataRank
Top 25%18 citations

Charlotte C. Sun, Amy Schneider, Sheng-Chieh Lu, Karen H. Lu, Alexi A. Wright +2 more

Papers

Driven by 6 papers — median percentile 49. Top paper: “Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy”.

Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy

European Journal of Cancer(2020)10.1016/j.ejca.2020.11.006
N/A
1.9DataRank · unranked
70 citations

Chris Sidey‐Gibbons, Han‐Byoel Lee, Marios Konstantinos Tasoulis, Vivian Koelbel, Michael Golatta +10 more

Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data

Scientific Reports(2022)10.1038/s41598-022-22614-1
Top 25%
0.803DataRank
Top 25%18 citations

Charlotte C. Sun, Amy Schneider, Sheng-Chieh Lu, Karen H. Lu, Alexi A. Wright +2 more

Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data.

Journal of Clinical Oncology(2021)10.1200/jco.2021.39.15_suppl.e13555
Top 78%
0.104DataRank
Top 78%1 citations

Charlotte C. Sun, Cai Xu, Amy Schneider, Sheng-Chieh Lu, Ishwaria M. Subbiah +3 more

Towards data-driven decision-making for breast cancer patients undergoing mastectomy and reconstruction: Prediction of individual patient-reported outcomes at two-year follow-up using machine learning.

Journal of Clinical Oncology(2020)10.1200/jco.2020.38.15_suppl.520
N/A
0DataRank · unranked
5 citations

Babak J. Mehrara, Jonas A. Nelson, Edwin G. Wilkins, Andrea L. Pusic, Chris Sidey‐Gibbons +1 more

Short-form adaptive measure of financial toxicity from the Economic Strain and Resilience in Cancer (ENRICh) study: Derivation using modern psychometric techniques

PLoS ONE(2022)10.1371/journal.pone.0272804
N/A
0DataRank · unranked
20 citations

Grace L. Smith, Ying‐Shiuan Chen, Cristina M. Checka, Sharon H. Giordano, Kelsey Kaiser +10 more

Development of a PROMIS multidimensional cancer-related fatigue (mCRF) form using modern psychometric techniques

Quality of Life Research(2024)10.1007/s11136-024-03705-0
N/A
0DataRank · unranked
1 citations

Chris Sidey‐Gibbons, Tamara E. Lacourt, Cai Xu