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.
PDC000564
Academia Sinica LUAD ICPC-B
0 awards paid to create it · 16 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 (6 of 16)
- Cancer Center Administration · ALEX MACDONALD, SELWYN M VICKERS · FY1985–2026funded this article
- The Tisch Cancer Institute - Cancer Center Support Grant · Ramon E Parsons, Nina Bhardwaj · FY2015–2026funded this article
- Proteogenomic Predictors of Recurrence in Non-small Cell Lung Cancer · STEVEN A CARR, MICHAEL A GILLETTE · FY2022–2026funded this article
- Proteogenomic studies to understand mechanisms and drivers of resistance to imm… · Diwakar Davar, AMANDA G PAULOVICH · FY2022–2026funded this article
- Mayo Clinic Center for Clinical Proteomics · Rafael Fonseca, AKHILESH PANDEY · FY2022–2026funded this article
- Proteogenomic characterization of early and late resistance mechanisms in acute… · BRIAN J DRUKER, Paul D Piehowski · FY2022–2026funded this article
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
1 of 3 traced articles report an NCI award.
| Award | Project | Articles it is credited on | Evidence |
|---|---|---|---|
| P30CA008748 P30FY1985–2026$717.9M | Cancer Center Administration | API | |
| P30CA196521 P30FY2015–2026$63.6M | The Tisch Cancer Institute - Cancer Center Support Grant | API | |
| U01CA271402 U01FY2022–2026$5.0M | Proteogenomic Predictors of Recurrence in Non-small Cell Lung Cancer | API | |
| U01CA271407 U01FY2022–2026$5.6M | Proteogenomic studies to understand mechanisms and drivers of resistance to immunotherapies | API | |
| U01CA271410 U01FY2022–2026$5.3M | Mayo Clinic Center for Clinical Proteomics | API | |
| U01CA271412 U01FY2022–2026$5.8M | Proteogenomic characterization of early and late resistance mechanisms in acute myeloid leukemia | API | |
| U24CA210972 U24FY2016–2020$3.3M | Proteogenomic Data Analysis for Cancer Systems Biology and Clinical Translation | API | |
| U24CA210979 U24FY2016–2020$4.4M | Integrated high throughput proteogenomic data analysis center for CPTAC | API | |
| U24CA210986 U24FY2016–2020$6.4M | Center of Excellence for High Throughput Proteogenomic Characterization | API | |
| U24CA270823 U24FY2022–2026$5.3M | Center of Excellence for High Throughput Proteogenomic Characterization | API | |
| U24CA271012 U24FY2022–2026$6.6M | Center for Advanced Multi-Omic Characterization of Cancer | API | |
| U24CA271037 U24FY2022–2026$4.4M | Michigan Center for Translational Cancer Proteogenomics | API | |
| U24CA271075 U24FY2022–2026$3.9M | Center for comprehensive proteogenomic data analysis | API | |
| U24CA271076 U24FY2022–2026$5.0M | iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC | API | |
| U24CA271079 U24FY2022–2026$6.6M | Proteogenomic Characterization of Tumor Tissues and Preclinical Models with High Precision | API | |
| U24CA271114 U24FY2022–2026$5.0M | Proteogenomic translator for cancer biomarker discovery towards precision medicine | 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 | Cancer Center Administration | API |
| P30CA196521 P30FY2015–2026$63.6M | The Tisch Cancer Institute - Cancer Center Support Grant | API |
| U01CA271402 U01FY2022–2026$5.0M | Proteogenomic Predictors of Recurrence in Non-small Cell Lung Cancer | API |
| U01CA271407 U01FY2022–2026$5.6M | Proteogenomic studies to understand mechanisms and drivers of resistance to immunotherapies | API |
| U01CA271410 U01FY2022–2026$5.3M | Mayo Clinic Center for Clinical Proteomics | API |
| U01CA271412 U01FY2022–2026$5.8M | Proteogenomic characterization of early and late resistance mechanisms in acute myeloid leukemia | API |
| U24CA210972 U24FY2016–2020$3.3M | Proteogenomic Data Analysis for Cancer Systems Biology and Clinical Translation | API |
| U24CA210979 U24FY2016–2020$4.4M | Integrated high throughput proteogenomic data analysis center for CPTAC | API |
| U24CA210986 U24FY2016–2020$6.4M | Center of Excellence for High Throughput Proteogenomic Characterization | API |
| U24CA270823 U24FY2022–2026$5.3M | Center of Excellence for High Throughput Proteogenomic Characterization | API |
| U24CA271012 U24FY2022–2026$6.6M | Center for Advanced Multi-Omic Characterization of Cancer | API |
| U24CA271037 U24FY2022–2026$4.4M | Michigan Center for Translational Cancer Proteogenomics | API |
| U24CA271075 U24FY2022–2026$3.9M | Center for comprehensive proteogenomic data analysis | API |
| U24CA271076 U24FY2022–2026$5.0M | iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC | API |
| U24CA271079 U24FY2022–2026$6.6M | Proteogenomic Characterization of Tumor Tissues and Preclinical Models with High Precision | API |
| U24CA271114 U24FY2022–2026$5.0M | Proteogenomic translator for cancer biomarker discovery towards precision medicine | 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.