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_spectrum_tme_2022
Ovarian Cancer - MSK SPECTRUM (MSK, Nature 2022)
6 awards paid to create it · 0 awards funded work that used it
1. Awards that paid to create this data (6)
- Multimodal computational models to stratify ovarian cancer patients · Kevin Michael Boehm · FY2021–2022
- Towards a spatial view of Ras signaling and immune cell function in NF-1 mutant… · Maryam Pourmaleki · FY2022–2023
- MSK Paul Calabresi Career Development Award for Clinical Oncology · Omar Abdel-Wahab, Simon N. Powell · FY2015–2026
- Single cell quantification of genomic instability in cancer as a determinant of… · Marc Williams · FY2021–2022
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY2025–2026
- MSK SPORE in Genomic Instability in Breast Cancer · Simon N. Powell, Sohrab P Shah · FY2023–2025
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 |
|---|---|---|
| F30CA257414 F30FY2021–2022$83k | Multimodal computational models to stratify ovarian cancer patients | API |
| F31CA271778 F31FY2022–2023$75k | Towards a spatial view of Ras signaling and immune cell function in NF-1 mutant glioblastoma | API |
| K12CA184746 K12FY2015–2026$8.3M | MSK Paul Calabresi Career Development Award for Clinical Oncology | API |
| K99CA256508 K99FY2021–2022$200k | Single cell quantification of genomic instability in cancer as a determinant of therapeutic response | API |
| P30CA008748 P30FY2025–2026$48.4M | Antitumor Assessment | API |
| P50CA247749 P50FY2023–2025$9.5M | MSK SPORE in Genomic Instability in Breast Cancer | 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.