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.
dlbcl_dfci_2018
Diffuse Large B cell Lymphoma (DFCI, Nat Med 2018)
5 awards paid to create it · 0 awards funded work that used it
1. Awards that paid to create this data (5)
- The Epigenetic Enzyme CARM1 as a Negative Regulator of Dendritic cells and CD8… · Kai W Wucherpfennig, DAVID E FISHER · FY2025–2026
- Lymphoma Specialized Program of Research Excellence (SPORE) in Human Cancer · George J. Weiner, Thomas E. Witzig · FY2002–2021
- Personal tumor neoantigens for immunity against chronic lymphocytic leukemia · Catherine Ju-Ying Wu · FY2011–2026
- The Cancer Genome Atlas Data Analysis Center · LYNDA CHIN, GAD A GETZ · FY2009–2016
- Global Infrastructure for Collaborative High-throughput Cancer Genomics Analys… · GAD A GETZ · FY2016–2020
2. The dataset
3. Articles that used it
4. Awards those articles were funded by
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 |
|---|---|---|
| P01CA163222 P01FY2025–2026$10.0M | The Epigenetic Enzyme CARM1 as a Negative Regulator of Dendritic cells and CD8 T cells in Melanoma | API |
| P50CA097274 P50FY2002–2021$82.5M | Lymphoma Specialized Program of Research Excellence (SPORE) in Human Cancer | API |
| R01CA155010 R01FY2011–2026$9.6M | Personal tumor neoantigens for immunity against chronic lymphocytic leukemia | API |
| U24CA143845 U24FY2009–2016$16.0M | The Cancer Genome Atlas Data Analysis Center | API |
| U24CA210999 U24FY2016–2020$4.7M | Global Infrastructure for Collaborative High-throughput Cancer Genomics Analysis | 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.