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.

Pick a dataset

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

TCGA-THYM 17 → 22 ORGANOID-PANCREATIC 13 → 49 CPTAC-3 10 → 36 WCDT-MCRPC 9 → 43 TCGA-STAD 18 → 10 BEATAML1.0-COHORT 7 → 33

egc_msk_tp53_ccr_2022

Esophagogastric Cancer (MSK, Clin Cancer Res 2022)

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

Awards that paid to create this data (3)

Credited on the dataset's own marker paper

The dataset

Articles that used it

The accession appears in their methods section

Awards those articles were funded by

NCI money spent on data it did not pay to generate

egc_msk_tp53_ccr_2022Esophagogastric Cancer (MSK, Clin Cancer Res 2022)cBioPortal

No article can be traced to this dataset, so nothing it enabled is visible here.

None of the traced articles reports an NCI award.

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.

Award	Project	Evidence
K08CA230213 K08FY2018–2022$1.3M	Investigating L1CAM-dependent stem cell regeneration in metastasis Karuna Ganesh · SLOAN-KETTERING INST CAN RESEARCH	API
P30CA008748 P30FY2025–2026$48.4M	Antitumor Assessment Elisa de Stanchina, FRANCESCA M GANY · SLOAN-KETTERING INST CAN RESEARCH	API
R37CA266185 R37FY2022–2026$2.6M	Mechanisms of Dynamic Transcriptional Reprogramming in Metastasis Stem Cells Karuna Ganesh · 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.