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3,919 results for “cohort”
Data analysis of an LC-MS dataset from a human urine biofluid cohort study
<p>Supplementary dataset and tutorials for the "<strong>Statistical analysis in metabolic phenotyping"</strong></p> <p> </p> <p>This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed<sup>1</sup> and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and <sup>1</sup>H NMR, using the methods described by Lewis <em>et al</em><sup>2</sup> and Dona <em>et al</em>. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE<sup>3</sup> repository, which can be accessed via the Sage BioNetworks portal (<a href="https://doi.org/10.7303/syn22252881">https://doi.org/10.7303/syn22252881</a>). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: <a href="https://www.ebi.ac.uk/metabolights/MTBLS719">https://www.ebi.ac.uk/metabolights/MTBLS719</a>.</p> <p> </p> <p>1. Lovestone, S. <em>et al.</em> AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in <em>Annals of the New York Academy of Sciences</em> (2009). doi:10.1111/j.1749-6632.2009.05064.x</p> <p>2. Lewis, M. R. <em>et al.</em> Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. <em>Anal. Chem.</em> <strong>88</strong>, acs.analchem.6b01481 (2016).</p> <p>3. Birkenbihl, C. <em>et al.</em> ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. <em>medRxiv</em> (2020). doi:10.1101/2020.08.04.20168229</p>
Data from: Variations in regional white matter volumetry and microstructure during the post-adolescence period: a cross-sectional study of a cohort of 1,713 university students
<p>Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterisations of the microstructural architecture of white matter, and they are increasingly utilised to study white matter changes during development and ageing. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18 to 26. We assessed the age and sex dependence of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in the white matter regions as defined itein the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterised by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. These findings emphasize the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.</p>
Mini Talks/Videos about Open Science from OLS-2 Cohort
<p><strong>We aim to present important topics in Open Science (e.g. open Licence, open review, open access, open sources, agile, open protocols, ...) within 10-15 minutes videos with subtitles and translations. </strong></p> <p>All resources are cited from the <strong>Open Life Science (OLS-2) </strong>and available on <a href="https://www.youtube.com/channel/UCs12-ZgnDJOWIWN3Vo1XHXA">Open LifeSci YouTube channel</a>. This project was initiated by <a href="https://twitter.com/talarify?lang=en">Talarify</a> and <a href="https://twitter.com/OpenSciSaudi">Open Science Community in Saudi Arabia</a> to facilitate learning of Open science practices to <strong>novice learners</strong> and to make OLS videos and captions accessible, re-useable, and encourage further translation in other languages beyond English and Arabic.</p> <p><strong>Individual videos can be downloaded from the GitHub repository:</strong></p> <ul> <li><a href="https://www.youtube.com/watch?v=Zj8EWGq5Wkk&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=1">Introduction to Open Life Sciences</a></li> <li><a href="https://www.youtube.com/watch?v=gQx-au72h04&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=2">Open Canvas for Project Strategy</a></li> <li><a href="https://www.youtube.com/watch?v=YKCKDAJ1RDU&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=3">Roadmapping for Open Projects</a></li> <li><a href="https://www.youtube.com/watch?v=2xGFF6qHOb8&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=4">A Primer on Open Licenses</a></li> <li><a href="https://www.youtube.com/watch?v=kodsPukJcE0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=5">README for Open Projects</a></li> <li><a href="https://www.youtube.com/watch?v=XXuy9suO4Kw&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=6">Contributing Guidelines and Codes of Conduct for Open Projects</a></li> <li><a href="https://www.youtube.com/watch?v=VLpJTBhuotM&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=7">Agile and Iterative Project Management Methods</a></li> <li><a href="https://www.youtube.com/watch?v=RMeGH8AnEIU&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=8">Open Scientific Code in Research</a></li> <li><a href="https://www.youtube.com/watch?v=xDDdJsHa078&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=9">Open Data</a></li> <li><a href="https://www.youtube.com/watch?v=3er0NjjHGHE&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=10">Open Education and Training</a></li> <li><a href="https://www.youtube.com/watch?v=Fw6B3kdy5Ow&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=11">Preprint in the Context of Open Science</a></li> <li><a href="https://www.youtube.com/watch?v=hva-oTapSWU&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=12">Open Protocols</a></li> <li><a href="https://www.youtube.com/watch?v=llp1KD7T93s&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=13">Making training materials FAIR in 10 steps</a></li> <li><a href="https://www.youtube.com/watch?v=nqvDU-skyoQ&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=14">FAIR research software</a></li> <li><a href="https://www.youtube.com/watch?v=9Z4CGxpN4pY&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=15">Universal theme</a></li> <li><a href="https://www.youtube.com/watch?v=4H9YDPwaHls&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=16">Career Guidance in Academia</a></li> <li><a href="https://www.youtube.com/watch?v=EOAJeq-q6W0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=17">Career Path and Guidance</a></li> <li><a href="https://www.youtube.com/watch?v=gY64DenZBM0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=18">Open Leadership</a></li> <li><a href="https://www.youtube.com/watch?v=gY64DenZBM0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=18">Persona and Pathways</a></li> <li><a href="https://www.youtube.com/watch?v=Mh3r7wZiDyI&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=20">Mountain of Engagements</a></li> <li><a href="https://www.youtube.com/watch?v=_mLXXyx6nyk&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=21">Inclusion can't be an afterthought</a></li> </ul> <p>They are also available on a <a href="https://www.youtube.com/watch?v=Zj8EWGq5Wkk&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=1">playlist on YouTube</a>.</p> <p><strong>Contributing</strong> 💝</p> <p>We welcome all contributions to improve this project especially first-timers to expand the translation!</p> <p><strong>You don't need to know git to start contributing, we use <a href="https://crowdin.com/project/ols2">Crowdin localisation</a>, which enables you to translate strings of SRT files while watching the video and adding content to your translation. More details are added to <a href="https://github.com/open-life-science/ols2-cohort-talks-and-transcripts/blob/main/CONTRIBUTING%E2%80%8B.md">our Contribution Guide</a>.</strong></p>
Mini Talks/Videos about Open Science from OLS-1 Cohort
<p><strong>We aim to present important topics in Open Science (e.g. open Licence, open review, open access, open sources, agile, open protocols, ...) within 10-15 minutes videos with subtitles and translations. </strong></p> <p>All resources are cited from the <strong>Open Life Science (OLS-1) </strong>and available on <a href="https://www.youtube.com/channel/UCs12-ZgnDJOWIWN3Vo1XHXA">Open LifeSci YouTube channel</a>. This project was initiated by <a href="https://twitter.com/talarify?lang=en">Talarify</a> and <a href="https://twitter.com/OpenSciSaudi">Open Science Community in Saudi Arabia</a> to facilitate learning of Open science practices to <strong>novice learners</strong> and to make OLS videos and captions accessible, re-useable, and encourage further translation in other languages beyond English and Arabic.</p> <p><strong>Individual videos can be downloaded from the GitHub repository:</strong></p> <ul> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Life Sciences Introduction</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Canvas for Project Strategy</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Roadmapping for Open Projects: Open by design</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Project Structure: Open Licensing</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">README's for Open Projects</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Contributing Guidelines and Codes of Conduct for Open Projects</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Agile & Iterative Project Management Methods</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Science Hardware</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Software in Research</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Data in the Life Sciences</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Citizen Science using your own personal data</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Education and Training</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Preprints and open peer review</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Protocols: creating, editing, and getting credit for scientific methods</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Inclusion can't be an afterthought</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Mountain of engagement:Discovering pathways & patterns of engagement in your work</a></li> <li><a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main">Open Life Sciences Tools</a></li> </ul> <p>They are also available on <a href="https://www.youtube.com/watch?v=COEeFEEHpkc&list=PL1CvC6Ez54KCReA22ayb_1nbUTueJT_Cx">a playlist on YouTube</a>.</p> <p><strong>Contributing</strong> 💝</p> <p>We welcome all contributions to improve this project especially first-timers to expand the translation!</p> <p><strong>You don't need to know git to start contributing, we use <a href="https://crowdin.com/project/ols1">Crowdin localisation</a>, which enables you to translate strings of SRT files while watching the video and adding content to your translation. More details are added to our <a href="https://github.com/open-life-science/ols1-cohort-talks-and-transcripts/blob/main/CONTRIBUTING%E2%80%8B.md">Contribution Guide</a>.</strong></p> <p> </p>
SASC: A Simple Approach to Synthetic Cohorts. Applying COVID-19 clinical data to generate longitudinal observational patient cohorts and comparison with alternative synthetic cohort approaches as well as real patient data
<p>Subset from COVID-19 Dataset from https://zenodo.org/record/3766350#.YVcfyTFBxgA. Used as reference for a publication dealing with synthetic patient cohort generation.</p>
The CHASING COVID Cohort Study: A national, community-based prospective cohort study of SARS-CoV-2 pandemic outcomes in the USA
<p>The Communities, Households and SARS-CoV-2 Epidemiology (CHASING) COVID Cohort Study is a community-based prospective cohort study launched during the upswing of the USA COVID-19 epidemic. The objectives of the cohort study are to: (1) estimate and evaluate determinants of the incidence of SARS-CoV-2 infection, disease and deaths; (2) assess the impact of the pandemic on psychosocial and economic outcomes and (3) assess the uptake of pandemic mitigation strategies. 6740 people are enrolled in the cohort, including participants from all 50 US states, the District of Columbia, Puerto Rico and Guam. Participants are contacted regularly to complete study assessments, including interviews and dried blood spot specimen collection for serologic testing.</p> <p>Datasets are provided in CSV and sas7bdat (with formatting script) file formats.</p>
Metagenome assembled genome database of a human cohort and fecal reactors
<p><strong>HumanCohort_annotations.tsv.zip:</strong> This is the custom MAG database (n=2447 MAGs) and corresponding annotations that were used in Borton 2022: "Targeted curation of the gut microbial gene content modulating human cardiovascular disease". The citation will be updated upon publication of the manuscript. Metagenome assembled genomes were generated from fecal metagenomes derived from a 54 person cohort and anoxic methylated amine enrichments. </p> <p><strong>HumanCohortmetabolism_summary.xlsx.zip: </strong> This is the annotation summary for 2447 MAGs in the cohort database. </p> <p><strong>Quality_Abundance_CohortMAGs.xlsx: </strong>This is a genome inventory of the 2447 MAGs in the cohort database including genome statistics and relative abundance. </p> <p><strong>orig_1D_NMR_fids.zip: </strong>NMR data derived from anoxic methylated amine enrichments. </p>
Dataset from: Leveraging open tools to realize the potential of self-archiving: A cohort study in clinical trials
<p>This record includes the data associated with the study "Leveraging open tools to increase the potential of self-archiving to increase discoverability: A cohort study in clinical trials". The code used to generate these data is available under an open license in GitHub (<a href="https://github.com/delwen/oa-archiving-permissions">https://github.com/delwen/oa-archiving-permissions</a>). The deposit includes:</p> <p>- `intovalue.csv`: download of the IntoValue dataset (IntoValue 1 and IntoValue 2), which is actively maintained in GitHub (<a href="https://github.com/maia-sh/intovalue-data">https://github.com/maia-sh/intovalue-data</a>). The data was downloaded on 17 December 2022. More information on the generation of this dataset can be found at: <a href="https://doi.org/10.5281/zenodo.5141343">https://doi.org/10.5281/zenodo.5141343</a>. This data corresponds to the start of the trial screening flow diagram in the manuscript (n = 3,788).</p> <p>- `oa-unpaywall.csv`: dataset containing the results of the Unpaywall API query (query date: 17 December 2022). The dataset queried includes the following adaptations from `intovalue.csv`:</p> <ul> <li>As updated registry data had been downloaded on 1 November 2022, the IntoValue inclusion criteria were re-applied: <ul> <li>Interventional</li> <li>Study completion date between 2009 and 2017</li> <li>Complete based on study status</li> <li>Conducted by a German university medical center.</li> </ul> </li> <li>The dataset was further limited to: <ul> <li>Unique trials (trials from IntoValue 2 were preserved)</li> <li>Unique publications with a DOI</li> </ul> </li> </ul> <p>- `oa-syp-permissions.csv`: dataset containing the results of the Shareyourpaper API query (query date: 17 December 2022). The dataset queried is the same as in `oa-unpaywall.csv`.</p> <p>- `oa-merged-data.csv`: dataset containing the merged Unpaywall and Shareyourpaper data for all clinical trial results publications considered in this study. The dataset was further limited to journal articles that resolved in Unpaywall and were published between 2010 - 2020 (based on the publication date in Unpaywall). This is the main dataset underlying the analyses in the manuscript.</p>
Heel and cord blood datasets for Bangladesh and Zambia cohorts
<div> <div> <div> <div> <p><strong>Background</strong>: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low-income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data.</p> <p><strong>Methods</strong>: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound.</p> <p><strong>Results</strong>: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh).</p> <p><strong>Conclusions</strong>: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.</p> </div> </div> </div> </div>
The effect of Vitamin D levels on the course of COVID-19 in hospitalized patients – a 1-year prospective cohort study
<p>Background: The aim of the current study was to assess the patients with COVID-19 and the impact of vitamin D supplementation on the course of COVID-19.<br> Methods: This prospective cohort study included patients hospitalized due to COVID-19 between December 2020 and December 2021. Patients' demographic, clinical, and laboratory parameters were analysed. <br> Results: 301 participants were enrolled in the study. 46 (15,3%) had moderate, and 162 (53,8%) had severe COVID-19. 14 (4,7%) patients died, and 30 (10,0%) were admitted to the ICU due to disease worsening. The majority needed oxygen therapy (n=224; 74,4%). Average vitamin 25(OH)D3 levels were below optimal at the admittance, and vitamin D deficiency was detected in 205 individuals. More male patients were suffering from vitamin D deficiency. Patients with the more severe disease showed lower levels of vitamin 25(OH)D3 in their blood. The most severe group of patients had more symptoms that lasted significantly longer with progressing disease severity. This group of patients also suffered from more deaths, ICU admissions, and treatments with dexamethasone, remdesivir, and oxygen.<br> Conclusion: Patients with the severe course of COVID-19 were shown to have increased inflammatory parameters, increased mortality, and higher incidence of vitamin D deficiency. The results suggest that the vitamin D deficiency might represent a significant risk factor for a severe course of COVID-19.</p> <p> </p>
GWAS results of selected binarized neurocognitive task performances from Philadelphia Neurodevelopmental Cohort
<p>This archive contains analysis results associated with the publication:</p> <p>Shraddha Pai, Shirley Hui, Philipp Weber, Soumil Narayan, Owen Whitley, Peipei Li, Viviane Labrie, Jan Baumbach, Anne L Wheeler, Gary D Bader. (2023). Multi-scale systems genomics analysis predicts pathways, cell types and drug targets involved in normative variation in peri-adolescent human cognition. Cerebral Cortex.</p> <p>-----------------------------------------------------------<br> *loco.mlma files: Univariate GWAS summary statistics for binarized performance of computerized neurocognitive tests in the Philadelphia Neurodevelopmental Cohorts. GWAS was performed on individuals ascertained to be of European genetic ancestry. For methods, please refer to the publication above.</p> <p>Phenotype codes:<br> lnb_tp2 Working memory LNB: Number of Correct Responses to 2-Back Trials (TP)<br> pcpt_t_tp Attention PCPT: Total of Correct Responses to Number Trials (TP) and Letter Trials (TP)<br> peit_cr Emotion identification PEIT: Total Correct Responses for All Test Trials, by genus<br> pfmt_ifac_tot Face memory PFMT: Total Correct Responses for All Test Trials<br> plot_tc Spatial reasoning PLOT: Total Correct Responses for All Test Trials, by genus<br> pmat_pc Nonverbal reasoning PMAT: Percent of Correct Responses for All Test Trials, by genus<br> pvrt_cr Verbal reasoning PVRT: Total Correct Responses for All Test Trials, by genus<br> pwmt_kiwrd_tot Word memory PWMT: Total Correct Responses for All Test Trials<br> volt_svt Object memory VOLT: Total Correct Responses for All Test Trial</p>
Data and Codebook for: Post-recovery relapse of children treated with a simplified, combined nutrition treatment protocol in Mali : a prospective cohort study
<p>Data and Codebook to recreate analyses for the study. </p> <p>Abstract: The present study aimed to determine the 6-month incidence of relapse and associated factors among children who recovered following mid-upper arm circumference (MUAC) based simplified combined treatment using the ComPAS protocol. A prospective cohort of 420 children who had reached a MUAC ≥125 mm for two consecutive measures was monitored between December 2020 and October 2021. Children were seen at home by study enumerators fortnightly for 6 months. Relapse was defined as developing a MUAC <125 mm or edema. The overall 6-month cumulative incidence of relapse [95%CI] was 26.1% [21.7;30.8] and the incidence rate per 100 child-months was 4.8 [4.0;5.9]. Relapse was similar among children initially admitted to treatment with a MUAC<115mm or oedema and among those with a MUAC≥115mm but <125mm. Relapse was predicted by lower anthropometry both at admission to and discharge from treatment, and higher number of illness episodes per month of follow-up. Having a vaccination card, using an improved water source, having agriculture as the main source of income and increases in caregivers workload during follow-up all protected from relapse. Children discharged recovered following treatment remain at risk of relapsing into acute malnutrition. To achieve reduction in relapse, recovery criteria may need to be revised.</p>
Stability and change in male fertility patterns by cognitive ability across 32 birth cohorts
<p>The relationship between cognitive ability (CA) and childbearing remains unsettled. Using Norwegian administrative registers with population coverage, we study how male lifetime fertility patterns differ across cognitive score groups, and how these changed across the 1950–1981 birth cohorts, covering a period characterized by rapid social and economic change. The analyses reveal systematic differences in fertility and fertility timing across CA groups, with high-scoring males having delayed but ultimately higher fertility than lower-scoring males. This pattern remains stable over time despite strong trends towards delayed and reduced fertility. The overall positive relationship between CA and fertility is primarily driven by high rates of childlessness in the lowest-scoring group, with low-scoring males showing higher rates of parity progression conditional on having children.</p>
Data files for manuscript "Elucidating the clinical and molecular spectrum of SMARCC2-associated NDD in a cohort of 65 affected individuals"
<p># 2023-06-28<br> # Data files for manuscript "Elucidating the clinical and molecular spectrum of SMARCC2-associated NDD in a cohort of 65 affected individuals"<br> # Summary<br> This ZIP-file contains the supplementary files of our SMARCC2 study "Elucidating the clinical and molecular spectrum of SMARCC2-associated NDD in a cohort of 65 affected individuals". <br> Suppl. File S2 contains comprehensive clinical data<br> Suppl. File S3 contains comprehensive genetic data<br> Suppl. File S4 contains files of the SMARCC2 N-terminal homology model</p> <p># Folder structure<br> ./ (parent directory containing this README file and all subfolders)<br> ./Files/ (contains Excel Suppl. File S2 and Suppl.File S3, and ZIP Suppl.File S4)</p> <p># Files and checksums<br> Algorithm Hash Path<br> --------- ---- ----<br> MD5 003A879AA75CF8B5C4E3E05F76C4EB4C SMARCC2-Supplementary\Files\FileS2_cases_clinical-table.xlsx<br> MD5 73B4F9E83419A8404345101CAA4D2205 SMARCC2-Supplementary\Files\FileS3_variants-and-domains.xlsx<br> MD5 B0A12F36B4801EB6C4D21BA02B4270BB SMARCC2-Supplementary\Files\FileS4_SMARCC2 N-terminal homology model.zip</p>
Procedure of a cohort study
<p>Procedure of a cohort study for two arms/groups:</p> <p>One group is exposed, with a negative-impact risk factor (e.g., smoking, environmental exposure, etc.) or a preventive-impact factor (e.g., exercise, vaccination, diet, support, etc. ). The second group is non-exposed. Prospectively, the frequency with which participants in the two cohorts become ill or do not become ill (outcome) is determined. The risk of disease/non-disease is reported as an absolute frequency for both cohorts.</p>
Summary statistics of cis-pQTLs for plasma proteins measured using Olink Explore I and II technology in the KARMA cohort.
<p>This data set contains summary statistics for cis regions (+/- 1 Mb around the protein coding gene) for proteins measured by the Olink Explore I and II technology in pre-diagnostic plasma samples from 299 Breast Cancer cases and 299 Breast Cancer free controls from the KARMA cohort. Only proteins detected in at least 25% of individuals are included in this data set. Data set from <a href="https://www.researchsquare.com/article/rs-2749047/v1">Evaluation of Circulating Plasma Proteins in Breast Cancer: A Mendelian Randomization Analysis | Research Square</a></p>
Development of Continuous Glucose Monitoring System Cohort for Personalized Diabetes Prevention and Management Platform
ClinicalTrials.gov study NCT04369833. IPD Sharing: NO. Countries: 1. Publications: 11.
The Risk of Venous Thromboembolism in Systemic Inflammatory Disorders: a United Kingdom (UK) Matched Cohort Study
ClinicalTrials.gov study NCT03835780. IPD Sharing: YES. Countries: 1. Publications: 2.
Spatio-temporal variation in diet among age and sex cohorts of a model generalist bird species, the Great Tit Parus major: new insights revealed by DNA metabarcoding
Open the record for dataset details and reuse information.
Identification of infectious agents in early marine Chinook and Coho salmon associated with cohort survival
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ScienceDex guides
Understand access before you commit
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.