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.
EXCEPTIONAL_RESPONDERS-ER
Exceptional Responders
0 awards paid to create it · 3 awards funded work that used it
1. Awards that paid to create this data
2. The dataset
3. Articles that used it
4. Awards those articles were funded by (3)
- The BCM Tumor Genome Characterization Center · RICHARD A GIBBS, David Andrew Wheeler · FY2009–2013funded this article
- Integrative Cancer Epigenomic Data Analysis Center (ICE-DAC) · PETER W LAIRD, Benjamin P Berman · FY2016–2020funded this article
- RNA sequencing analysis of Cancer · KATHERINE A. HOADLEY, David N Hayes · FY2016–2020funded this article
Why one side of this chain is empty
- No repository or reviewer names this dataset's marker paper, so no award can be credited with creating it.
Awards that funded work using this data
1 of 4 traced articles report an NCI award.
| Award | Project | Articles it is credited on | Evidence |
|---|---|---|---|
| U24CA143843 U24FY2009–2013$9.5M | The BCM Tumor Genome Characterization Center | API | |
| U24CA210969 U24FY2016–2020$2.5M | Integrative Cancer Epigenomic Data Analysis Center (ICE-DAC) | API | |
| U24CA210988 U24FY2016–2020$2.1M | RNA sequencing analysis of Cancer | API |
Awards that maintain or redistribute this data
Resource and center grants credited on articles that used the data. Kept out of the flow above because they pay for the pipes, not for a cohort or a finding.
| Award | Project | Evidence |
|---|---|---|
| U24CA143843 U24FY2009–2013$9.5M | The BCM Tumor Genome Characterization Center | API |
| U24CA210969 U24FY2016–2020$2.5M | Integrative Cancer Epigenomic Data Analysis Center (ICE-DAC) | API |
| U24CA210988 U24FY2016–2020$2.1M | RNA sequencing analysis of 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.