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307
datasets available to search
ShareScore release 0.9.0
Dataset results
307 results for “consortium”
Great China Fatter Liver Consortium (GC_FLC) Study to Assess the Progress of NAFLD/NASH in Chinese
ClinicalTrials.gov study NCT03282305. IPD Sharing: Not stated. Countries: 0. Publications: 0.
North American Scapula Consortium
ClinicalTrials.gov study NCT02993848. IPD Sharing: NO. Countries: 0. Publications: 0.
Phase II study of therapy selected by tumor molecular profiling in patients with previously treated metastatic pancreatic cancer. A study of the Stand Up to Cancer (SU2C) study consortium
GEO Series GSE64462. Homo sapiens. 31 samples. Type: Genome variation profiling by genome tiling array.
Adipose-tissue regulatory T cells are a consortium of subtypes that evolves with age and diet
GEO Series GSE248440. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing; Other.
High Rates of Placental Inflammation in Samples Collected by the MOMI Consortium
GEO Series GSE240306. Homo sapiens. 56 samples. Type: Expression profiling by high throughput sequencing.
SmallRNA seq of leukocytes of chronic pain patients (CRPS, Nerve lesion and Diabetes polyneuropathy) and an SNI mice model ncRNA pain consortium
GEO Series GSE106837. Homo sapiens; Mus musculus. 244 samples. Type: Non-coding RNA profiling by high throughput sequencing.
LungMAP Consortium Data
GEO Series GSE128810. Mus musculus. 80 samples. Type: Expression profiling by high throughput sequencing; Other.
Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. CPTAC is a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of robust, quantitative, proteomic technologies and workflows. The overarching goal of CPTAC is to improve our ability to diagnose, treat and prevent cancer. To achieve this goal, the NCI launched CPTAC to systematically identify proteins that derive from alterations in cancer genomes and related biological processes, and provide this data with accompanying assays and protocols to the public. CPTAC has provided the GDC with genomic data from a total of 1100+ cancer patients with diverse disease types.
International Soft Tissue Sarcoma Consortium (INSTRuCT)
Researchers created this consortium with the goal of establishing a a soft tissue sarcoma data commons to share and provide access to digital objects, focusing on RMS and NRSTS clinical trials data from the United States and Europe. The INSTRuCT database integrates clinical data from the Children's Oncology Group (COG), The European Paediatric Soft Tissue Sarcoma Study Group (EpSSG), Cooperative Weichteilsarkom Studiengruppe (CWS) der Gesellschaft fur Padiatrische Onkologie und Hamatologie (GPOH), International Society for Pediatric Oncology (SIOP) Malignant Mesenchymal Tumour (MMT), and the Italian Associazione Italiana di Ematologia e Oncologia Pediatrica (AIEOP) Soft Tissue Sarcoma Committee (STSC).
Malignant Germ Cell International Consortium (MaGIC)
The Malignant Germ Cell International Consortium (MaGIC) brings together the world's leading experts in germ cell tumors (GCTs), from across the spectrum of cancer research, with the shared goal of developing more effective treatments for GCTs through scientific inquiry. Our collaboration across a wide range of disciplines - clinical trialists, basic scientists, bioinformaticists, pathologists, statisticians, surgeons, epidemiologists - is allowing us to accelerate the pace of discovery and the promise of change. Since its inception in 2009, MaGIC has grown to include clinical and basic scientists from more than 40 institutions in 11 countries. Sharing experience, expertise, and culture continues to create new opportunities for advancement in the field of GCT research.
Pediatric Preclinical Testing Consortium
Cohort of 261 xenograft models from the Pediatric Preclinical Testing Consortium.
Texas Pediatric Cancer Drug Testing Core & Pediatric Preclinical In Vivo Testing Consortium
The Pediatric Preclinical In Vivo Testing consortium (PIVOT) provides rigorous preclinical testing of novel molecularly targeted agents using in vivo models of pediatric cancer. PIVOT is an extension and expansion of two previous, highly successful NCI programs: Pediatric Preclinical Testing Program (PPTP; 2004-2014) and Pediatric Preclinical Testing Consortium (PPTC; 2015-2021). The objective of the CPRIT (Cancer Prevention and Research Institute of Texas) supported TPC-DTC is to provide reproducible high-quality in vivo data to guide pediatric clinical development of novel agents and combinations. Both initiatives have developed within the context of the Research to Accelerate Cures and Equity for Children Act (RACE for Children Act). For preclinical testing, this necessitates having sufficient models with the appropriate genetic alterations in the context of the appropriate childhood cancer types. The RACE Act requires the FDA to develop a list of molecular targets of known and new drugs/biologics. If agents are determined to be substantially relevant to the growth and progression of pediatric cancer, this may trigger the requirement for pediatric investigations. This expectation applies both to drugs/biologics being developed by Pharma and by academic centers.
Diffuse Glioma (GLASS Consortium)
Whole genome or whole exome sequencing analysis of temporally separated diffuse glioma tumor/normal pairs from 222 adult patients. The cases here are only those under the age of 40 years old.
Pediatric Preclinical Testing Consortium
Whole-exome sequencing of 261 patient derived xenografts (PDXs) samples from high-risk childhood cancers (with no matched normals).
Pediatric Preclinical Testing Consortium (PPTC)
Open the record for dataset details and reuse information.
Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin degrading consortium
<p>The study dataset contains 8 DOM data (DOM.zip), 21 GC-MS data (GC_MS.zip) and 16 LC-MS data (LC_MS.zip) obtained from “top-down” enrichment of coastal lignin degrading bacterial consortium.</p>
Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin degrading consortium
<p>The study dataset contains 8 DOM data (DOM.zip), 21 GC-MS data (GC_MS.zip) and 16 LC-MS data (LC_MS.zip) obtained from “top-down” enrichment of coastal lignin degrading bacterial consortium.</p>
Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin degrading consortium
<p>The study dataset contains DOM data (DOM.zip), GC-MS data (GC-MS.zip) and LC-MS data (LC-MS.zip), which were obtained from “top-down” enrichment of coastal lignin degrading bacterial consortium.</p>
Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin degrading consortium
<p>The study dataset contains DOM data (DOM.zip), GC-MS data (GC-MS.zip) and LC-MS data (LC-MS.zip)</p>
Santa Cruz Ellipsis Consortium Sluicing Dataset
<p>This is release 1.0 of the Santa Cruz Ellipsis Consortium Sluicing Dataset, made possible by funding from the UC Santa Cruz<br> Institute for Humanities Research, Committee on Research, as well as NSF funding for "The Implicit Content of Sluicing".</p> <p>The data comprises roughly 5000 instances of sluicing (and some related constructions) extracted from the New York Times subset of the Gigaword dataset, from years 1994 to 2000. The sluices were located by a combination of parsetree patterns and regular expressions, and we believe them to be comprehensive for those years.</p> <p>The sluices were then annotated over two years by teams of 5-6 annotators, and a final adjudication over 2018 has led to the resulting dataset.</p> <p>The sluices are annotated for antecedent, paraphrased elided content, and potential mismatches between the sluice paraphrase and antecedent.</p> <p>In this release, there are three directories:<br> Doc: More information about the extracting of the data, the annotation process, and the tagset used is available in the Data directory of the release.<br> Data: The annotated data, presented as a series of jsons<br> Explorer: a lightweight script for traversing the json data</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.