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.
thyroid_mskcc_2016
Poorly-Differentiated and Anaplastic Thyroid Cancers (MSK, JCI 2016)
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
- Elucidating and Targeting the Molecular Foundations of Hurthle Cell Cancer · Timothy An-thy Chan, RONALD A GHOSSEIN · FY2014–2018
- Molecular Pathophysiology of Thyroid Cell Growth · JAMES A FAGIN · FY2023
- Oncogenic Events in Thyroid Neoplasia · JAMES A FAGIN · FY1997–2020
- CONFERENCE ON HEPATIC REGENERATION AND CARCINOGENESIS · GEORGE K MICHALOPOULOS · FY1996
- MOLECULAR PATHOPHYSIOLOGY OF THYROID CELL GROWTH · JAMES A FAGIN · FY1989–1993
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 |
| P50CA172012 P50FY2014–2018$20.5M | Elucidating and Targeting the Molecular Foundations of Hurthle Cell Cancer | API |
| R01CA050706 R01FY2023$427k | Molecular Pathophysiology of Thyroid Cell Growth | API |
| R01CA072597 R01FY1997–2020$7.5M | Oncogenic Events in Thyroid Neoplasia | API |
| R13CA072012 R13FY1996$11k | CONFERENCE ON HEPATIC REGENERATION AND CARCINOGENESIS | API |
| R29CA050706 R29FY1989–1993$502k | MOLECULAR PATHOPHYSIOLOGY OF THYROID CELL GROWTH | 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.