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.
summit_2018
SUMMIT - Neratinib Basket Study (Multi-Institute, Nature 2018)
9 awards paid to create it · 0 awards funded work that used it
1. Awards that paid to create this data (6 of 9)
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY2025–2026
- Leadership, Planning and Evaluation · CARYN LERMAN, VERONICA WENDY SETIAWAN · FY2025–2026
- Cancer Center Support Grant · PETER W PISTERS, Fernando J Benavides · FY2025
- SPORE in Breast Cancer · JENNIFER A PIETENPOL, Ben Ho Park · FY2003–2024
- Erbb2 targeted antitumor strategies in breast cancer · Carlos L Arteaga · FY1999–2014
- Understanding Long Tail Driver Mutations in Cancer · Nikolaus Schultz, Barry Stephen Taylor · FY2017–2021
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 |
|---|---|---|
| P30CA008748 P30FY2025–2026$48.4M | Antitumor Assessment | API |
| P30CA014089 P30FY2025–2026$24.6M | Leadership, Planning and Evaluation | API |
| P30CA016672 P30FY2025$14.0M | Cancer Center Support Grant | API |
| P50CA098131 P50FY2003–2024$83.6M | SPORE in Breast Cancer | API |
| R01CA080195 R01FY1999–2014$4.1M | Erbb2 targeted antitumor strategies in breast cancer | API |
| R01CA204749 R01FY2017–2021$2.0M | Understanding Long Tail Driver Mutations in Cancer | API |
| R25CA020449 R25FY1985–2026$7.0M | Summer research experiences for medical students supervised by faculty mentors | API |
| T32CA009207 T32FY1985–2026$20.1M | Investigational Cancer Therapeutics Training Program (ICTTP) | API |
| U01CA180964 U01FY2013–2015$964k | Informatics to enable routine personalized cancer therapy | 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.