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.
msk_ch_2020
Cancer Therapy and Clonal Hematopoiesis (MSK, Nat Genet 2020)
9 awards paid to create it · 0 awards funded work that used it
1. Awards that paid to create this data (6 of 9)
- Impact of Oncologic Therapy on Clonal Hematopoiesis and Subsequent Risk of Ther… · Kelly Leigh Bolton · FY2019–2023
- UNC Oncology K12 · WILLIAM Y. KIM, NORMAN E SHARPLESS · FY2007–2026
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY2025–2026
- Admin-Core · David Darr, Chad V Pecot · FY2025–2026
- Cancer Center Support Grant · PETER W PISTERS, Fernando J Benavides · FY2025
- Administrative Core · BRIAN SPRINGER, Frederick Locke · FY2025–2026
The best connected are shown.
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 |
|---|---|---|
| K08CA241318 K08FY2019–2023$1.2M | Impact of Oncologic Therapy on Clonal Hematopoiesis and Subsequent Risk of Therapy-Related Leukemia | API |
| K12CA120780 K12FY2007–2026$9.6M | UNC Oncology K12 | API |
| P30CA008748 P30FY2025–2026$48.4M | Antitumor Assessment | API |
| P30CA016086 P30FY2025–2026$16.9M | Admin-Core | API |
| P30CA016672 P30FY2025$14.0M | Cancer Center Support Grant | API |
| P30CA076292 P30FY2025–2026$8.6M | Administrative Core | API |
| P50CA172012 P50FY2014–2018$20.5M | Elucidating and Targeting the Molecular Foundations of Hurthle Cell Cancer | API |
| P50CA217694 P50FY2023–2025$10.1M | Project 3: Identifying Targetable Epigenetic and Immuno-Oncologic Vulnerabilities in Synovial Sarcoma | API |
| R35CA253125 R35FY2020–2024$4.9M | The role of clonal hematopoiesis in the development and therapy of myeloid malignancies | 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.