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.
bladder_columbia_msk_2018
Bladder Cancer (Columbia University/MSK, Cell 2018)
7 awards paid to create it · 0 awards funded work that used it
1. Awards that paid to create this data (6 of 7)
- Modeling bladder cancer pathogenesis and tumor evolution · Cory Abate-Shen, David B. Solit · FY2018–2022
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY2025–2026
- Biospecimen Repository Core · Hikmat Al-Ahmadie, David B. Solit · FY2024
- Preclinical analyses of advanced prostate cancer in genetically-engineered mice · Cory Abate-Shen · FY2013–2026
- Investigating the cell of origin for bladder cancer · Cory Abate-Shen · FY2015–2019
- Analysis of drug response in organoids and mouse models · Cory Abate-Shen, MICHAEL M. SHEN · FY2016–2018
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 |
|---|---|---|
| P01CA221757 P01FY2018–2022$17.1M | Modeling bladder cancer pathogenesis and tumor evolution | API |
| P30CA008748 P30FY2025–2026$48.4M | Antitumor Assessment | API |
| P50CA221745 P50FY2024$1.8M | Biospecimen Repository Core | API |
| R01CA173481 R01FY2013–2026$6.1M | Preclinical analyses of advanced prostate cancer in genetically-engineered mice | API |
| R01CA193442 R01FY2015–2019$2.4M | Investigating the cell of origin for bladder cancer | API |
| R01CA196662 R01FY2016–2018$1.8M | Analysis of drug response in organoids and mouse models | API |
| U54CA193313 U54FY2015–2020$20.4M | Topology of Cancer Evolution and Heterogeneity | 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.