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.
TARGET-RT
Rhabdoid Tumor
0 awards paid to create it · 15 awards funded work that used it
1. Awards that paid to create this data
2. The dataset
3. Articles that used it (4)
4. Awards those articles were funded by (6 of 15)
- Antitumor Assessment · Elisa de Stanchina, FRANCESCA M GANY · FY1985–2026funded 1 of these 4 articles
- Cancer Center Support Grant · PETER W PISTERS, FUNDA MERIC-BERNSTAM · FY1985–2025funded 1 of these 4 articles
- Patient-Derived Xenograft Shared Resource · Michael T. Lewis, Carlos Almeida Ramos · FY2007–2026funded 1 of these 4 articles
- Methyltransferase Contributions to Genomic Stability and Cancer · WENDY KIMRYN RATHMELL, Cheryl L. Walker · FY2016–2020funded 1 of these 4 articles
- Genetic Loss-of-Function Studies of SETD2 in Kidney Tumorigenesis · EMILY H CHENG · FY2018–2022funded 1 of these 4 articles
- A New Target for Chromatin Remodeler Defects in Cancer · Cheryl L. Walker · FY2018–2024funded 1 of these 4 articles
The best connected are shown.
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
4 of 10 traced articles report an NCI award.
| Award | Project | Articles it is credited on | Evidence |
|---|---|---|---|
| P30CA008748 P30FY1985–2026$716.0M | Antitumor Assessment | API | |
| P30CA016672 P30FY1985–2025$532.6M | Cancer Center Support Grant | API | |
| P30CA125123 P30FY2007–2026$131.4M | Patient-Derived Xenograft Shared Resource | API | |
| R01CA203012 R01FY2016–2020$2.2M | Methyltransferase Contributions to Genomic Stability and Cancer | API | |
| R01CA223231 R01FY2018–2022$2.7M | Genetic Loss-of-Function Studies of SETD2 in Kidney Tumorigenesis | API | |
| R35CA231993 R35FY2018–2024$6.2M | A New Target for Chromatin Remodeler Defects in Cancer | API | |
| T32CA009666 T32FY1994–2026$10.4M | T32 Research Training in Academic Medical Oncology | API | |
| K08CA273527 K08FY2022–2026$1.3M | Pathogenic role of human papillomavirus (HPV) DNA Integration and clonal expansion in cervical tumorigenesis | API | |
| R50CA211461 R50FY2016–2020$1.7M | Open Software and Resource Development Leveraging Next-gen Sequencing Data to Facilitate Computational Analysis of Cancer Biology | API | |
| U24CA180922 U24FY2013–2022$7.5M | Trinity: Transcriptome assembly for genetic and functional analysis of cancer | API | |
| U24CA258406 U24FY2021–2025$4.4M | The Integrative Genomics Viewer (IGV) for Cancer Research | API | |
| R01CA236791 R01FY2019–2022$2.0M | Perceptual and Adaptive Learning in Cancer Image Interpretation | API | |
| F31CA236300 F31FY2019–2020$81k | Development of Mithramycin Analogues to Target Transcriptional Vulnerabilities in Rhabdoid Tumor | API | |
| F99CA253749 F99FY2020$46k | Characterizing Chromatin Remodeling Mechanisms of Chemo-Sensitivity and Resistance in Pediatric Solid Tumors | API | |
| K00CA253749 K00FY2021–2024$409k | Characterizing Chromatin Remodeling Mechanisms of Chemo-Sensitivity and Resistance in Pediatric Solid Tumors | 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$716.0M | Antitumor Assessment | API |
| P30CA016672 P30FY1985–2025$532.6M | Cancer Center Support Grant | API |
| P30CA125123 P30FY2007–2026$131.4M | Patient-Derived Xenograft Shared Resource | API |
| U24CA180922 U24FY2013–2022$7.5M | Trinity: Transcriptome assembly for genetic and functional analysis of cancer | API |
| U24CA258406 U24FY2021–2025$4.4M | The Integrative Genomics Viewer (IGV) for Cancer Research | 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.