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

T. Lancaster

Durham University

ORCID: 0000-0003-1322-2449

Also affiliated with University of Bath

NeuroscienceBiochemistry, Genetics and Molecular BiologyMaterials ScienceMedicine

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
0.606Author 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.

Population neuroimaging: generation of a comprehensive data resource within the ALSPAC pregnancy and birth cohort

Wellcome Open Research(2020)10.12688/wellcomeopenres.16060.1
Top 38%
0.502DataRank
Top 38%20 citations

Nancy McBride, Amy Howell, John Evans, Derek K. Jones, Gavin Perry +12 more

Papers

Driven by 14 papers — median percentile 42. Top paper: “The genetic architecture of the human cerebral cortex”.

The genetic architecture of the human cerebral cortex

Science(2020)10.1126/science.aay6690
N/A
1.0DataRank · unranked
899 citations

Neda Jahanshad, Jodie N. Painter, Lucía Colodro‐Conde, Janita Bralten, Derrek P. Hibar +95 more

In vivo hippocampal subfield volumes in bipolar disorder—A mega‐analysis from The Enhancing Neuro Imaging Genetics through Meta‐Analysis Bipolar Disorder Working Group

Human Brain Mapping(2020)10.1002/hbm.25249
N/A
0.661DataRank · unranked
81 citations

Tiril P. Gurholt, Stener Nerland, Torbjørn Elvsåshagen, Theophilus N. Akudjedu, Martin Alda +93 more

Population neuroimaging: generation of a comprehensive data resource within the ALSPAC pregnancy and birth cohort

Wellcome Open Research(2020)10.12688/wellcomeopenres.16060.1
Top 38%
0.502DataRank
Top 38%20 citations

Nancy McBride, Amy Howell, John Evans, Derek K. Jones, Gavin Perry +12 more

Polygenic risk for Alzheimer's disease shapes hippocampal scene-selectivity

Neuropsychopharmacology(2020)10.1038/s41386-019-0595-1
N/A
0.396DataRank · unranked
13 citations

Carl J. Hodgetts, Xavier Caseras, Kevin Murphy, T. Lancaster, Hannah Chandler

Subiculum–BNST structural connectivity in humans and macaques

NeuroImage(2022)10.1016/j.neuroimage.2022.119096
N/A
0.335DataRank · unranked
6 citations

Andrew D. Lawrence, T. Lancaster, Chiara Casella, John P. Aggleton, Mark Postans +1 more

Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model

Molecular Psychiatry(2025)10.1038/s41380-025-03273-w
N/A
0.208DataRank · unranked
3 citations

Tracy Erwin-Grabner, Ling‐Li Zeng, Christopher R. K. Ching, André Alemán, Alyssa R. Amod +82 more

Alzheimer’s genetic risk effects on cerebral blood flow are spatially consistent and proximal to gene expression across the lifespan

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2020.12.31.424949
N/A
0.165DataRank · unranked
2 citations

Richard G. Wise, David E.J. Linden, Julie Williams, Kevin Murphy, T. Lancaster +2 more

Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model

arXiv (Cornell University)(2023)10.48550/arxiv.2311.11046
N/A
0.165DataRank · unranked
2 citations

Tracy Erwin-Grabner, Ling‐Li Zeng, Christopher R. K. Ching, André Alemán, Alyssa R. Amod +82 more

Regional brain morphology and current antidepressant use: findings from 32 international cohorts from the ENIGMA major depressive disorder working group

Molecular Psychiatry(2025)10.1038/s41380-025-03310-8
Top 78%
0.104DataRank
Top 78%1 citations

Yara J. Toenders, Elena Pozzi, André Alemán, Nina Alexander, Zeynep Başgöze +95 more

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study

Cortex(2023)10.1016/j.cortex.2023.11.015
N/A
0DataRank · unranked
8 citations

Vilte Baltramonaityte, Doretta Caramaschi, Laura K. M. Han, T. Lancaster, Stanley Zammit +3 more

Reductions in brainstem volume as a key macrostructural indicator in at-risk populations for Alzheimer’s disease

Alzheimer s Research & Therapy(2025)10.1186/s13195-025-01829-0
N/A
0DataRank · unranked
0 citations

Kevin Murphy, Hannah Chandler, T. Lancaster

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Scientific Reports(2024)10.1038/s41598-023-47934-8
N/A
0DataRank · unranked
35 citations

Tracy Erwin-Grabner, Moji Aghajani, André Alemán, Alyssa R. Amod, Zeynep Başgöze +61 more

Subiculum – BNST Structural Connectivity in Humans and Macaques

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.11.11.468209
N/A
0DataRank · unranked
0 citations

Andrew D. Lawrence, T. Lancaster, Chiara Casella, John P. Aggleton, Mark Postans +1 more

Multimodal hippocampal and amygdala subfield volumetry in polygenic risk for Alzheimer's disease

Neurobiology of Aging(2020)10.1016/j.neurobiolaging.2020.08.022
N/A
0DataRank · unranked
29 citations

Hannah Chandler, T. Lancaster, Amy N. Murray