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384
datasets available to search
ShareScore release 0.7.1
Dataset results
384 results for “risk model”
Esmadem Education Model on High-Risk Pregnant Women
ClinicalTrials.gov study NCT06786143. IPD Sharing: Not stated. Countries: 0. Publications: 0.
AI Prediction Model and Risk Stratification for Lung Metastasis in Colorectal Cancer
ClinicalTrials.gov study NCT05816902. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Model for Risk Prediction of Fracture in Diabetic Patients With Osteoporosis
ClinicalTrials.gov study NCT04534166. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
A Risk Prediction Model of Postoperative Nausea and Vomiting in Patients With Liver Cancer
ClinicalTrials.gov study NCT06356623. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Gene network analysis reveals a role for striatal glutamatergic receptors in dysregulated risk-assessment behavior of autism mouse models
GEO Series GSE138539. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Cytomegalovirus as a risk factor for exacerbation of Mycobacterium tuberculosis: development of a Mtb-CMV mouse model infection
GEO Series GSE315842. Mus musculus. 54 samples. Type: Expression profiling by high throughput sequencing.
Clinical and molecular risk factors in extracranial malignanat rhabdoid tumors - towards an integrated model of high-risk tumors
GEO Series GSE246036. Homo sapiens. 85 samples. Type: Methylation profiling by genome tiling array.
Application of Transcriptomics Dose-Response Modeling to Risk-Based Prioritization of Contaminants Detected in Tributaries of the North American Great Lakes
GEO Series GSE268998. Raphidocelis subcapitata. 270 samples. Type: Expression profiling by high throughput sequencing.
A novel prognostic risk model for cervical cancer based on immune checkpoint HLA-G-driven differentially expressed genes [HeLa]
GEO Series GSE208117. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Application of Transcriptomics Dose-Response Modeling to Risk-Based Prioritization of Contaminants Detected in Tributaries of the North American Great Lakes [D. magna]
GEO Series GSE269991. Daphnia magna. 335 samples. Type: Expression profiling by high throughput sequencing.
A novel prognostic risk model for cervical cancer based on immune checkpoint HLA-G-driven differentially expressed genes [SiHa]
GEO Series GSE208118. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome response of mouse mammary gland from age 12 to 30 months of age in Esr1 and CYP19A1 genetically engineered mouse models of breast cancer risk in the presence and absence of transgene ind
GEO Series GSE201767. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome response to anti-hormonals in Esr1 and CYP19A1 genetically engineered mouse models of breast cancer risk during reproductive senescence
GEO Series GSE201326. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
APP and its intracellular domain modulate Alzheimer’s disease risk gene networks in transgenic APPsw and PSEN1M146I porcine models [fibroblasts]
GEO Series GSE254570. Sus scrofa. 29 samples. Type: Expression profiling by high throughput sequencing.
APP and its intracellular domain modulate Alzheimer’s disease risk gene networks in transgenic APPsw and PSEN1M146I porcine models
GEO Series GSE254571. Sus scrofa. 41 samples. Type: Expression profiling by high throughput sequencing.
A Gene Expression-Based Risk Stratification Model for Newly Diagnosed Multiple Myeloma
GEO Series GSE8991. Homo sapiens. 288 samples. Type: Expression profiling by array.
A novel prognostic risk model for cervical cancer based on immune checkpoint HLA-G-driven differentially expressed genes
GEO Series GSE208119. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Database for Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy
<p>Database (2014-2018) used in the paper entitled "Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy".</p> <p>This database includes :</p> <ul> <li>the inputs and outputs of the probabilistic forecaster which predicts the Belgian system imbalance.</li> <li>the market data related to the construction of the balancing market.</li> </ul> <p>These data are obtained from the Belgian transmission system operator (Elia) and the European Network of Transmission system Operators (ENTSO-E).</p> <p>If you use these data, please refer to the following paper:</p> <p>J. Bottieau, K. Bruninx, A. Sanjab, Z. De Grève, F. Vallée and J-F. Toubeau, “Automatic Risk Tuning in Short-Term Electricity Market Models Using a Machine Learning Proxy,”.</p>
Dataset of e-Poster titled "A Simplistic Unsaturated Zone Leaching Model-based Probabilistic Human Health Risk Assessment of Groundwater around the Ariyamangalam Dumping Site"
<p>This database contains an "excel sheet" and ".docx" files having input parameters and health risk metrics generated from a Simplistic Unsaturated Zone Leaching Model-based Probabilistic Human Health Risk Assessment Framework for Ariyamangalam dumping site.</p>
EHR readmission risk model in OPAT patients
<p><strong>Objective:</strong> The primary aim of this study is to determine the ability of the electronic health record-embedded EPIC Unplanned Readmission Model 1 to predict all-cause 30-day hospital unplanned readmissions in discharged patients receiving OPAT through the Duke University Heath System (DUHS) OPAT program. We then explored the impact of OPAT-specific variables on model performance.</p> <p><strong>Methods:</strong> This retrospective cohort study included patients > 18 years of age discharged to home or skilled nursing facility between July 1, 2019 - February 1, 2020 with OPAT care initiated inpatient and coordinated by the DUHS OPAT program and with at least one Epic readmission score during the index hospitalization. Those with a planned duration of OPAT < 7 days, receiving OPAT administered in a long-term acute care facility (LTAC), or ongoing renal replacement therapy were excluded. The relationship between the primary outcome (unplanned readmission during 30-day post-index discharge) and Epic readmission scores during the index admission (discharge and maximum) was examined using multivariable logistic regression models adjusted for additional predictors. The performance of the models was assessed with the scaled Brier score for overall model performance, the area under the receiver operating characteristics curve (C-index) for discrimination ability, calibration plot for calibration, and Hosmer-Lemeshow goodness-of-fit test for model fit.</p> <p>Files include the de-identified dataset and a data dictionary.</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.