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100
datasets available to search
ShareScore release 0.9.0
Dataset results
100 results for “Population Cohort”
Chinese Migrant Population Health Cohort
ClinicalTrials.gov study NCT07124949. IPD Sharing: NO. Countries: 1. Publications: 21.
Androgen-Deprivation Therapy and Cardiovascular Risk: A Nationwide Population-based Cohort Study
ClinicalTrials.gov study NCT02895230. IPD Sharing: NO. Countries: 1. Publications: 2.
Establish and Characterize an Acute HIV Infection Cohort in a High Risk Population
ClinicalTrials.gov study NCT00796146. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Hong Kong Cohort of Abnormal Sleep in Ageing Population (HK-ASAP): Focusing on Brain Health and Sleep Quality
ClinicalTrials.gov study NCT06170073. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Should Colorectal Cancer Patients be Followed After Five Years? Study of Recurrence in a Population Cohort
ClinicalTrials.gov study NCT01904955. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The FuSion Program: A Prospective and Multicenter Cohort Study of Pan-Cancer Screening in Chinese Population
ClinicalTrials.gov study NCT05159544. IPD Sharing: NO. Countries: 1. Publications: 1.
Return of Genomic Results and Aggregate Penetrance in Population-Based Cohorts
ClinicalTrials.gov study NCT04196374. IPD Sharing: NO. Countries: 1. Publications: 18.
Population Cohort Set-up for the Epidemiological Assessment of Balance Disorders in Elderly People
ClinicalTrials.gov study NCT06965660. IPD Sharing: NO. Countries: 1. Publications: 0.
Feasibility of a Creative Writing Intervention in an Advanced Cancer Population: A Single Arm, Consecutive Cohort Study
ClinicalTrials.gov study NCT02575898. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Impact of 10 Different Prior Cancer History on Survival of Patients Who Underwent Surgery for Second Primary Colorectal Cancer: a Population-based Cohort Study
ClinicalTrials.gov study NCT06189547. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Diabetes, Glucose Control, Glucose Lowering Medications, and Cancer Risk: A 10-year Population-based Historical Cohort
ClinicalTrials.gov study NCT02072902. IPD Sharing: Not stated. Countries: 1. Publications: 5.
A Community Population Screening Cohort Study Based on Polygene Methylation Detection for Colorectal Cancer in Yangzhou
ClinicalTrials.gov study NCT05336539. IPD Sharing: NO. Countries: 1. Publications: 2.
Covid-19 Vaccine Cohort in Specific Populations
ClinicalTrials.gov study NCT04824651. IPD Sharing: NO. Countries: 1. Publications: 1.
A Prospective Cohort Study on Colorectal Cancer Screening in Community Population
ClinicalTrials.gov study NCT05485077. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study
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Data from: Population and life-history consequences of within-cohort individual variation
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Data from: Repurposing population genetics data to discern genomic architecture: a case study of linkage cohort detection in mountain pine beetle (Dendroctonus ponderosae)
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Data from: Risk factors for possible REM sleep behavior disorder: a CLSA population-based cohort study
Objective: Idiopathic REM sleep behavior disorder (RBD) is a powerful marker of prodromal neurodegenerative synucleinopathy, with 80% of patients ultimately phenoconverting to defined disease. Several environmental risk factors for RBD have been suggested, but associations vary between studies. We assessed sociodemographic, socioeconomic and clinical correlates of RBD in a 30,000-subject national cohort. Methods: Subjects aged 45-85 years in Canada were collected as part of the Canadian Longitudinal Study on Aging. Possible RBD (pRBD) was screened with the RBD-1Q, a questionnaire with 94% specificity and 87% sensitivity. To improve diagnostic reliability, subjects screening positive for apnea or non-REM parasomnia (young onset pRBD), and subjects self-reporting dementia or Parkinson's disease were excluded. A series of sociodemographic, life style and mental health variables were analysed cross-sectionally. Potential correlates were assessed via multivariable logistic regression. Results: Of 30,097 subjects, 958 (3.2%) had pRBD. Male sex (OR=2.14 ,95%CI=[1.84, 2.49]) and lower education (OR=0.95 [0.92, 0.98] were associated with pRBD. pRBD subjects had smoked more (total smoking years OR=1.006 [1.010, 1.011]) and were more likely to be moderate-heavy drinkers (OR=1.25 [1.03, 1.50]). There was a strong association between pRBD and self-reported antidepressant treatment for depression (OR=2.76 [2.22, 3.44]), psychological distress (OR=1.52 [1.37, 1.69]), mental illness (OR=2.08 [1.79, 2.41]), and post-traumatic stress disorder (OR=2.84 [2.55, 3.54]). Conclusions: Our study replicated previous reported associations between pRBD and smoking, low education and male sex, and found previously-unreported links with alcohol use and psychological distress. The risk factors for pRBD differ from those previously defined for neurodegenerative synucleinopathies.
Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability.</p> <p><strong>Methods: </strong>Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics.</p> <p><strong>Results: </strong>We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). </p> <p><strong>Conclusion: </strong>A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation.</p> <p><strong>Dataset Description: </strong>We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. </p> <p>We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and <a href="https://www.ncbi.nlm.nih.gov/pubmed/31026761">https://www.ncbi.nlm.nih.gov/pubmed/31026761</a>. These coordinates can be used for registering datasets. </p> <p><strong>Publication</strong>: https://pubmed.ncbi.nlm.nih.gov/35089057/</p>
Data from: Perinatal depression and anxiety in women with MS: a population-based cohort study
<p><b>Objective: </b>To assess the occurrence of perinatal depression and anxiety in women before and after diagnosis of multiple sclerosis (MS).</p> <p><b>Methods: </b>114,629 pregnant women were included in the Norwegian Mother, Father and Child Cohort study 1999–2008. We assessed depression and anxiety by questionnaires during and after pregnancy. Women with MS were identified from national health registries and hospital records and grouped into 1) MS diagnosed before pregnancy (n = 140), MS diagnosed after pregnancy with 2) symptom onset before pregnancy (n = 98) and 3) symptom onset after pregnancy (n = 308). Thirty-five women were diagnosed with MS in the postpartum period. The reference group (n = 111,627) consisted of women without MS.</p> <p><b>Results: </b>Women with MS diagnosed before pregnancy had an adjusted odds ratio of 2.0 (95% confidence interval 1.2–3.1) for depression in the third trimester. Risk factors were adverse socioeconomic factors, history of psychiatric disease and physical/sexual abuse. The risk of anxiety was not increased. Women diagnosed with MS in the postpartum period had especially high risk of postpartum depression. Women with MS symptom onset within 5 years after pregnancy had increased risk of both depression and anxiety during pregnancy, whereas women with more than 5 years until symptom onset did not.</p> <p><b>Conclusion: </b>Women diagnosed with MS have increased risk of perinatal depression. Women with MS symptom onset within 5 years after pregnancy have increased risk of both depression and anxiety during pregnancy.</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.