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.
FM-AD
Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)
0 awards paid to create it · 8 awards funded work that used it
1. Awards that paid to create this data
2. The dataset
3. Articles that used it (2)
4. Awards those articles were funded by (6 of 8)
- Dissecting Therapeutic Resistance and Progression in Metastatic Melanoma Throug… · David Liu · FY2018–2022funded 1 of these 2 articles
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY1985–2026funded 1 of these 2 articles
- Automated in vivo analysis of tumor growth rate as a guide for therapeutic deci… · Mizuki Nishino · FY2017–2021funded 1 of these 2 articles
- Integrative Somatic and Germline Computational Biology to Redefine Clinical Act… · Eliezer M Van Allen · FY2018–2022funded 1 of these 2 articles
- Physician Scientist Training in Cancer Research · Jennifer R Brown, James A. DeCaprio · FY1985–2026funded 1 of these 2 articles
- The Boston Lung Cancer Survival Cohort · David C Christiani · FY2017–2026funded 1 of these 2 articles
The best connected are shown.
Why one side of this chain is empty
- This dataset's marker paper is this pipeline's own nomination, not a repository's or a reviewer's. Attributing generation funding from a guess about which paper describes the cohort is exactly the mistake this project argues against, so no award is claimed.
Awards that funded work using this data
2 of 9 traced articles report an NCI award.
| Award | Project | Articles it is credited on | Evidence |
|---|---|---|---|
| K08CA234458 K08FY2018–2022$1.2M | Dissecting Therapeutic Resistance and Progression in Metastatic Melanoma Through Clinical Computational Oncology | API | |
| P30CA008748 P30FY1985–2026$717.9M | Antitumor Assessment | API | |
| R01CA203636 R01FY2017–2021$2.9M | Automated in vivo analysis of tumor growth rate as a guide for therapeutic decisions to advance personalized cancer treatment | API | |
| R01CA227388 R01FY2018–2022$2.0M | Integrative Somatic and Germline Computational Biology to Redefine Clinical Actionability in Solid Tumors | API | |
| T32CA009172 T32FY1985–2026$23.7M | Physician Scientist Training in Cancer Research | API | |
| U01CA209414 U01FY2017–2026$12.2M | The Boston Lung Cancer Survival Cohort | API | |
| T32CA009666 T32FY1994–2026$10.4M | T32 Research Training in Academic Medical Oncology | API | |
| U01CA247760 U01FY2020–2022$1.2M | Informatics for Functional Integration of Heterogeneous Cancer Genome and Transcriptome Sequencing Data | 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 |
|---|---|---|
| P30CA008748 P30FY1985–2026$717.9M | Antitumor Assessment | API |
| U01CA209414 U01FY2017–2026$12.2M | The Boston Lung Cancer Survival Cohort | API |
| U01CA247760 U01FY2020–2022$1.2M | Informatics for Functional Integration of Heterogeneous Cancer Genome and Transcriptome Sequencing Data | 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.