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Funding in, findings out

For any dataset: the NCI award that paid to create it, and the awards that got a published finding out of it afterwards.

332 of 602 datasets have at least one award resolved. 31 have awards on both sides.

Datasets with awards on both sides - awards that paid to create it awards that funded work using it

luad_mskcc_2020

Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)

6 awards paid to create it · 0 awards funded work that used it

Direct evidence: the award is on the paper, or the accession is in the articleWeaker: infrastructure support, or use declared but not shownHover any card to follow its chain

Why one side of this chain is empty

  • This dataset has no accession that articles quote, so no reuse can be traced and nothing it enabled is visible.

Awards that paid to create this data

Credited on the dataset's own marker paper.

AwardProjectEvidence
P30CA008748
P30FY2025–2026$48.4M
Antitumor Assessment
Elisa de Stanchina, FRANCESCA M GANY · SLOAN-KETTERING INST CAN RESEARCH
API
R01CA192399
R01FY2015–2019$1.9M
Identifying tumor suppressor targets of SIRT6 in lung cancer
MARTY W MAYO · UNIVERSITY OF VIRGINIA
API
R01CA217169
R01FY2018–2022$2.5M
BRMS1-mediated suppression of metastases in p53 mutant lung adenocarcinoma
David R Jones · SLOAN-KETTERING INST CAN RESEARCH
API
R01CA236615
R01FY2018–2022$2.0M
Image-guided irreversible electroporation directed CAR T-cell delivery to solid tumors
Prasad S. Adusumilli, Stephen Barnett Solomon · SLOAN-KETTERING INST CAN RESEARCH
API
R01CA240472
R01FY2019–2023$2.4M
Targeting SNP BRMS1v2 A273V/A273V to reduce metastases in lung adenocarcinoma
David R Jones · SLOAN-KETTERING INST CAN RESEARCH
API
T32CA009501
T32FY1995–2026$8.3M
Surgical Oncology Research Training Grant
Babak J Mehrara, Ronald P. Dematteo · SLOAN-KETTERING INST CAN RESEARCH
API

Where the links come from

NIH RePORTER maps a PMID to the awards that reported it. On a dataset’s marker paper that gives the awards which created it; on an article that used the data, the awards which funded that use.

The two sides are not measured alike. Generation is complete: a dataset has one marker paper and every award on it is resolved. Reuse is a sample - the articles using a dataset can number in the thousands, and only the few dozen strongest are retrieved and looked up. Every award shown here funded reuse; there are more that are not shown.

How far it reaches

283 datasets have an award credited with creating them, 80 with using them, and 31 both.

What is missing

No citable accession means no traceable articles; no authoritative marker paper means no attributable generation award. Absence is a gap in the record. Methods.

Start from an award instead, and see every dataset it touches.