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.
mixed_selpercatinib_2020
Mixed Tumors Selpercatinib RET Trial (MSK, Nat Commun. 2022)
7 awards paid to create it · 0 awards funded work that used it
1. Awards that paid to create this data (6 of 7)
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY2025–2026
- Understanding Long Tail Driver Mutations in Cancer · Nikolaus Schultz, Barry Stephen Taylor · FY2017–2021
- Targeting AKT-Mutant Human Cancers · David B. Solit, Barry Stephen Taylor · FY2016–2020
- Defining the impact of mutant oncogene zygosity · Sarat Chandarlapaty · FY2020–2024
- Predicting sensitivity and resistance in RET-driven cancers · Alexander Drilon · FY2021–2025
- Clinical Scholars Biomedical Research Training Program · Ping Chi, THOMAS J. KELLY · FY1996–2026
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 |
|---|---|---|
| P30CA008748 P30FY2025–2026$48.4M | Antitumor Assessment | API |
| R01CA204749 R01FY2017–2021$2.0M | Understanding Long Tail Driver Mutations in Cancer | API |
| R01CA207244 R01FY2016–2020$2.0M | Targeting AKT-Mutant Human Cancers | API |
| R01CA245069 R01FY2020–2024$2.0M | Defining the impact of mutant oncogene zygosity | API |
| R01CA251591 R01FY2021–2025$2.0M | Predicting sensitivity and resistance in RET-driven cancers | API |
| T32CA009512 T32FY1996–2026$7.2M | Clinical Scholars Biomedical Research Training Program | API |
| T32CA160001 T32FY2011–2020$2.0M | Translational Research in Oncology Training Program | 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.