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34,778 results for “diseases”
Building a data curation pipeline for complex diseases: the case of Major Depression - Supplementary Material
<p>This entry contains the data generated by the study "Building a data curation pipeline for complex diseases: the case of Major Depression".</p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
Peripheral MC1R activation modulates immune responses and confers neuroprotection in a mouse model of Parkinson's disease
<p>Raw data sets for the manuscripts</p> <p>This work was supported by NIH grants R01NS102735 and R01NS110879, the Farmer Family Foundation Initiative for Parkinson’s Disease Research and the MJFF and ASAP [ASAP-000312].</p>
Atrophy Pattern Maps of Alzheimer's Disease, Mild Cognitive Impairment, Parkinson's Disease, and Frontotemporal Dementia
<p>The files contain voxel-wise t-statistics maps contrasting deformation based morphometry (DBM) measurements of Alzheimer's disease (AD), Parkinson's disease (PD), mild cognitive impairment (MCI), and fronto-temporal dementia (FTD) patients against matched normal controls.</p> <p>AD and MCI maps are based on ADNI data, available at:</p> <p>PD map is based on PPMI data, available at:</p> <p>FTD map is based on NIFD data, available at:</p> <p>For more information regarding the participants and method details, see:</p> <p>Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." <em>Neurobiology of aging</em> 111 (2022): 54-63.</p>
Epidemiology and Disease Burden of Neurocritical Disorders: A Cohort Study - Data Sharing
<p>Dataset of a Neurocritical Brazil cohort study whose summary is described below.</p> <p><strong>Abstract</strong></p> <p><strong>Objective:</strong> To describe a cohort of neurocritical patients and their differences based on primary neurological diagnoses and identify predictors of mortality and unfavorable outcome along with the disease burden of each neurological condition on intensive care unit (ICU) admission. <strong>Methods:</strong> Prospective cohort study including patients admitted to 36 ICUs in Brazil and followed up for 30 days. <strong>Results:</strong> Of 4245 patients admitted to the participating ICUs during the study period, 1194 (28.1%) were neurocritical patients and were included in the study. Neurocritical patients had a mean mortality rate 1.7 times higher than non-neurocritical patients admitted to the same ICUs (17.21% versus 10.1%, respectively). The most frequent primary neurological diagnoses on ICU admission were postoperative care of elective neurosurgery, traumatic brain injury, ischemic stroke, and encephalopathy. The estimated total disability-adjusted life-years (DALYs) were 4482.94 in the overall cohort, and the diagnosis with the highest DALYs was traumatic brain injury (1634.42). DALYs were significantly impacted by the patients’ primary neurological diagnosis, sex, age group, and number of secondary neurological injuries. <strong>Conclusion:</strong> We accurately described the epidemiology of neurocritical patients and estimated their overall and relative disease burden. The findings of this study are important to direct policies regarding education, prevention, and treatment of severe neurocritical diseases.</p> <p> </p>
Transcriptomic analyses of normal-appearing CNS white matter from multiple sclerosis donors reveal subtype-specific molecular signatures of disease (REVISED)
<p>Datasets of bulk RNA-sequencing of NAWM from MS donors + supplementary images of RNA deconvolution of cell trajectories</p>
Dataset for "Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey"
<p>Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology</p> <p><strong>Summary of study</strong></p> <p>An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).</p> <p>Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.</p> <p>The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.</p> <p>Sixty-five individuals participated in the survey.</p> <p>See the published article for full study details.</p> <p><strong>Description of uploaded files</strong></p> <p>1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.</p> <p>2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. </p> <p>3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.</p> <p>4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.</p> <p>5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.</p>
Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"
<p><strong>Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"</strong></p> <p> </p> <p>Includes:</p> <ul> <li>NIAID Dataset schema</li> <li>NIAID ComputationalTool schema</li> <li>Crosswalk between NIAID schemas and common schemas</li> <li>Survey of Schema.org-compliant repositories</li> </ul> <p><br> The open access movement and scientific reproducibility concerns have led the biomedical research community to embrace efforts to make scientific datasets openly accessible. While many datasets are now available, there are still challenges in ensuring that they are Findable, Accessible, Interoperable, and Reusable (FAIR). To improve the FAIRness of datasets, we evaluated dataset repositories for compliance with Schema.org standards – a collection of standards developed to increase metadata searchability across the internet. Adoption of the Schema.org Dataset standard was highly variable in biomedical research datasets, and the standard omitted many desirable metadata fields. We customized the Schema.org Dataset standard to catalog datasets collected across a Systems Biology research consortium consisting of 15 Centers. We developed a reusable process for creating a schema which is interoperable with other standards, but still extendable and customizable to a particular context. Here, we describe our process along with the associated gains in FAIRness, and discuss ongoing challenges with dataset discoverability – the first step to ensure that the vast amount of open data published by the research community is reused to its maximum value.</p>
Coronavirus disease (COVID-19) case data - South Africa
<p>COVID 19 Data for South Africa created, maintained and hosted by <a href="https://dsfsi.github.io/">DSFSI research group</a> at the University of Pretoria</p> <p><strong>Disclaimer:</strong> We have worked to keep the data as accurate as possible. We collate the COVID 19 reporting data from NICD and South Africa DoH. We only update that data once there is an official report or statement. For the other data, we work to keep the data as accurate as possible. If you find errors let us know. </p> <p>See original GitHub repo for detailed information <a href="https://github.com/dsfsi/covid19za">https://github.com/dsfsi/covid19za</a></p>
Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model
<p>Amyloid-β plaques and neurofibrillary tau tangles are the neuropathologic hallmarks of Alzheimer’s disease (AD), but the spatiotemporal cellular responses and molecular mechanisms underlying AD pathophysiology remain poorly understood. Here we introduce STARmap PLUS to simultaneously map single-cell transcriptional states and disease marker proteins in brain tissues of AD mouse models at a voxel size of 95 95 350 nm. This high-resolution spatial transcriptomics map revealed a core-shell structure where disease-associated microglia (DAM) closely contact amyloid-β plaques, whereas disease-associated astrocyte-like cells (DAA-like) and oligodendrocyte precursor cells (OPC) are enriched in the outer shells surrounding the plaque-DAM complex. Hyperphosphorylated tau emerged mainly in excitatory neurons in the CA1 region accompanied by infiltration of oligodendrocyte subtypes into the axon bundles of hippocampal alveus. The integrative STARmap PLUS method bridges single-cell gene expression profiles with tissue histopathology at subcellular resolution, providing an unprecedented roadmap to pinpoint the molecular and cellular mechanisms of AD pathology and neurodegeneration.</p>
Social contact patterns relevant for infectious disease transmission in Cambodia
<p>Social contact data from a community-based survey conducted in Cambodia in 2012. </p>
Data for: Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease.
<p>Summary of Bivariate GWAS scan results reported in:<br> <a href="https://pubmed.ncbi.nlm.nih.gov/30525989/">Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease. </a>Siewert KM, Voight BF. Circ Genom Precis Med. 2018 Dec;11(12):e002239. doi: 10.1161/CIRCGEN.118.002239.</p> <p>PMID: 30525989 </p>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p>This section highlights the various methods used for this study. It covered study setting, study design, study approach, study population, sampling techniques, sample size calculation, inclusion and exclusion criteria, ethical consideration, data collection, data management and data analysis<strong>. </strong></p> <p> </p> <p><strong>Study Setting</strong></p> <p>The study was carried out at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi between February to August, 2022. The study covered all the six (6) Colleges in the University.</p> <p> </p> <p><strong>Study Design</strong></p> <p>This was a cross sectional study to ascertain health check practices among university lecturers.</p> <p> </p> <p><strong>Study Approach</strong></p> <p>The study employed quantitative approach in which data was collected using questionnaires with both closed- and open-ended questions.</p> <p><strong>Study Population</strong></p> <p>The study population involved 838 Lecturers across the six Colleges at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi. A study of the lecturer population per college revealed that Colleges of Health Sciences (highest) and Agric /Natural resources (lowest) were the outliers (Quality Assurance and Planning Office, 2020).</p> <p> </p> <p><strong>Sampling Technique </strong></p> <p> </p> <p>Simple probability technique was used to select the name of a college and the day/date to visit. Two sets of papers were folded with names of colleges (set 1) and day/date of visit (set 2). A picker picked one folded paper from each set and the name of the college and the day/date to visit was matched. In this case, the ordering of date and visit gave 1<sup>st</sup> College of Humanities & Social Sciences, 2<sup>nd</sup> College of Agric and Natural Resources, 3<sup>rd</sup> College of Art & Built Environment, 4<sup>th</sup> College of Engineering, 5<sup>th</sup> College of Science and 6<sup>th</sup> College of Health Sciences. We then used the ‘walk in’’ system to select the study participants. Within the days to visit a college, any lecturer we meet in his/ her office was a potential study participant.</p> <p> </p> <p> </p> <p> </p> <p><strong>Sample Size Calculation</strong></p> <p>The sample size was obtained using Yamane, 1967 formulae as shown below:</p> <p> </p> <p> </p> <p>Where n= is the population of Lecturers in at KNUST</p> <p>E= is the level of precision</p> <p>Therefore: n= 838</p> <p> 1+838 (0.0025)</p> <p>n = 838</p> <p>1+ 2.098</p> <p> </p> <p>838</p> <p>3.095</p> <p> </p> <p> n=270 </p> <p>However, due to logistical constrains, 205 participants were contacted across the 6 Colleges at Kwame Nkrumah University of Science and Technology. We then applied simple proportions to get the number of lecturers to be consulted in each college.</p> <p> </p> <p><strong>Inclusion and Exclusions Criteria</strong></p> <p>Inclusion criteria was made up of all Lecturers on KNUST campus who are in active service and consented to participate. All other staff not within this category were excluded from this research.</p> <p> </p> <p><strong>Ethical Considerations</strong></p> <p>Ethical approval was sought from the CHRPE, KNUST with approval reference no: CHRPE/AP/581/21. The aim of the research was explained to participants. Those who consented to participate in the research were given consent forms to sign and date. Again, participants were assured of confidentiality. Participants were told that, they were free to withdraw from the study in the cause of time. In other words, study participants were not coerced into the study.</p> <p> </p> <p><strong>Data Collection Tool</strong></p> <p>Data was collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues on physical inactivity and tobacco use. Aside these four main risk factors of NCDs, the questionnaire also captured frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-up, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank and lecturer’s colleges (categorized into binary variable; COHS /COS/COE and CABE/CANR/COHSS), family history of NCDs and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p> <p> </p>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p><strong>Data Collection Tool</strong></p> <p>Data were collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues with physical inactivity, and tobacco use. Aside from these four main risk factors of NCDs, the questionnaire also captured the frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-ups, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of the job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges' effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank, and lecturer’s colleges (categorized into binary variables; Colleges, family history of NCDs, and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p>
Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson's disease
<p>Parkinson’s Disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s Disease, currently affecting more than 10 million people worldwide. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. We explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional network identified by a recent Lavin et al. 2020 (<a href="https://doi.org/10.3389/fphys.2020.00653">https://doi.org/10.3389/fphys.2020.00653)</a> study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis. VCF files were generated using AIDD (Plonski et al., 2020) (<a href="https://doi.org/10.1186/s12859-020-03888-6">https://doi.org/10.1186/s12859-020-03888-6</a>).</p>
PheKnowLator Human Disease Knowledge Graph Benchmarks -- v1.0.0
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v1.0.0)</strong></p><p><strong>Build Date: September 03, 2019</strong></p><p>The KG Benchmark Builds can also be downloaded from Zenodo:<br>👉 <strong>KGs:</strong> <a href="https://doi.org/10.5281/zenodo.7030200">https://doi.org/10.5281/zenodo.7030200</a><br>👉 <strong>Embeddings:</strong> <a href="https://zenodo.org/record/7030189">https://zenodo.org/record/7030189</a></p><p> </p><p><strong>Required Input Documents</strong></p><ul><li>resource_info.txt</li><li>class_source_list.txt</li><li>instance_source_list.txt</li><li>ontology_source_list.txt</li></ul><p> </p><p><strong>Data</strong></p><p><strong>Data Download Date:</strong> November 30, 2018</p><p><i><strong>Ontologies</strong></i></p><ul><li><a href="http://purl.obolibrary.org/obo/go.owl">Gene Ontology</a></li><li><a href="http://purl.obolibrary.org/obo/hp.owl">Human Phenotype Ontology</a></li></ul><p><i><strong>Classes</strong></i></p><ul><li><a href="http://purl.obolibrary.org/obo/doid.owl">Human Disease Ontology</a></li><li><a href="http://geneontology.org/gene-associations/goa_human.gaf.gz">Gene Ontology: gene associations</a></li><li><a href="https://reactome.org/download/current/gene_association.reactome">Reactome: gene associations</a></li><li><a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_genes_to_phenotype.txt">Human Phenotype Ontology: all source annotations - genes to phenotypes</a></li><li><a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastSt">Human Phenotype Ontology: all source annotations - diseases to genes to phenotypes</a></li></ul><p><i><strong>Instances</strong></i></p><ul><li><a href="http://ctdbase.org/reports/CTD_chem_gene_ixns.tsv.gz">CTD: chemicals-genes</a></li><li><a href="http://ctdbase.org/reports/CTD_chem_pathways_enriched.tsv.gz">CTD: chemicals-pathways</a></li><li><a href="http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz">CTD: chemicals-diseases</a></li><li><a href="http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz">CTD: genes-pathways</a></li><li><a href="http://ctdbase.org/reports/CTD_diseases_pathways.tsv.gz">CTD: diseases-pathways</a></li><li><a href="https://stringdb-static.org/download/protein.links.v10.5/9606.protein.links.v10.5.txt.gz">STRING DB: Proteins</a></li><li><a href="https://string-db.org/mapping_files/entrez_mappings/entrez_gene_id.vs.string.v10.28042015.tsv">String DB: entrez gene mappings</a></li></ul><p> </p><p><strong>Knowledge Graphs</strong></p><p><strong>Knowledge Representation</strong><br>We worked with a PhD-level biologist to develop a knowledge representation (see the figure below) that modeled mechanisms underlying human disease.</p><p> </p><p>To do this, we manually mapped all possible combinations of the following six node types:</p><ul><li>Humans Diseases</li><li>Human Phenotypes</li><li>Human Genes</li><li>Gene Ontology concepts</li><li>Reactome Pathways</li><li>Chemicals</li></ul><p>As shown in the figure above, the <a href="http://basic-formal-ontology.org/">Basic Formal Ontology</a> and <a href="https://github.com/oborel/obo-relations/">Relation Ontology</a> ontologies were then used to create edges between the node types.</p><p> </p><p>As shown in this figure, the following edge-types were created:</p><ul><li><strong>Phenotypes-Genes:</strong> The <a href="http://purl.obolibrary.org/obo/hp.owl">Human Phenotype Ontology (HP)</a> provides <a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_genes_to_phenotype.txt">phenotype-Entrez gene annotations</a> that were used to map 6,651 HP classes to 120,288 Entrez genes.</li><li><strong>Phenotypes-Diseases:</strong> The <a href="http://purl.obolibrary.org/obo/hp.owl">HP</a> provides <a href="http://compbio.charite.de/jenkins/job/hpo.annotations.monthly/lastStableBuild/artifact/annotation/ALL_SOURCES_ALL_FREQUENCIES_diseases_to_genes_to_phenotypes.txt">HP-DOID-Gene annotations</a> that were used to map 5,438 HP concepts to 43,817 DOID concepts.</li><li><strong>Biological processes, Molecular Functions, and Cellular Locations-Genes:</strong> The <a href="http://purl.obolibrary.org/obo/go.owl">Gene Ontology (GO)</a> provides <a href="http://geneontology.org/gene-associations/goa_human.gaf.gz">GO-Gene annotations</a> that were used to map 17,505 GO concepts to 265,002 Entrez genes.</li><li><strong>Biological processes, Molecular Functions, and Cellular Locations-Pathways-Pathways:</strong> <a href="https://reactome.org/">Reactome</a> provides <a href="https://reactome.org/download/current/gene_association.reactome">GO-Gene links</a> that were used to map 17,906 pathways to 1,910 biological processes, molecular functions, and cellular locations.</li><li><strong>Chemicals-Pathways:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chem_pathways_enriched.tsv.gz">Chemical-pathway links</a> that were used to map 8,886 MESH concepts to 711,043 Reactome pathways.</li><li><strong>Chemicals-Genes:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chem_gene_ixns.tsv.gz">Chemical-Gene links</a> that were used to map 8,881 MESH concepts 410,379 Entrez genes.</li><li><strong>Chemicals-Diseases:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz">Chemical-Disease links</a> that were used to map 14,238 MESH concepts 1,216,900 DOID concepts.</li><li><strong>Genes-Genes:</strong> The<a href="https://string-db.org/">STRING Database</a> provides <a href="https://stringdb-static.org/download/protein.links.v10.5/9606.protein.links.v10.5.txt.gz">Gene-Gene links</a> that were used to create 594,100 gene-gene interactions. When generating these mappings, only the inferred protein-protein relationships considered to be high confidence were used (score of 700 or better).</li><li><strong>Genes-Disease:</strong> Mappings between genes and diseases were retrieved from <a href="http://www.disgenet.org/web/DisGeNET/menu">DisGeNet</a> via SPARQL endpoint and used to map 6,051 Entrez genes to 20,452 DOID concepts.</li><li><strong>Genes-Pathways:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz">Gene-Pathway links</a> that were used to map 110,370 Entrez genes to 107,029 Reactome pathways.</li><li><strong>Pathways-Disease:</strong> The <a href="http://ctdbase.org/">Comparative Toxicogenomics Database (CTD)</a> provides <a href="http://ctdbase.org/reports/CTD_diseases_pathways.tsv.gz">Pathway-Disease links</a> that were used to map 1,818 Reactome pathways to 106,727 DOID concepts.</li></ul><p> </p><p><strong>Knowledge Graph</strong><br>The knowledge graph represented above was built using the following steps: Merge Ontologies: Merge ontologies using the <a href="https://github.com/owlcollab/owltools/wiki">OWL Tools API</a><br>Express New Ontology Concept Annotations: Create new ontology annotations by asserting a relation between the instance and an instance of the ontology class. For example to assert the following relations:</p><blockquote><p><a href="https://www.ncbi.nlm.nih.gov/mesh/68009020">Morphine</a> --> <a href="https://www.ebi.ac.uk/ols/ontologies/ro/properties?iri=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_0002606">is substance that treats</a> --> <a href="https://hpo.jax.org/app/browse/term/HP:0002076">Migraine</a></p><p>We would need to create two axioms:</p><ul><li>isSubstanceThatTreats(Morphine, x1)</li><li>instanceOf(x1, Migraine)</li></ul></blockquote><p>While the instance of the HP class hemiplegic migraines can be treated as an anonymous node in the knowledge graph, we generate a new international resource identifier for each newly generated instance.</p><p><strong>Deductively Close Knowledge Graph:</strong> The knowledge graph is deductively closed by using the OWL 2 EL reasoner, ELK via Protégé v5.1.1. ELK is able to classify instances and supports inferences over class hierarchies and object properties. inference over disjointness, intersection, and existential quantification (ontology class hierarchies).</p><p><strong>Generate Edge List:</strong> The final step before exporting the edge list is to remove any nodes that are not biologically meaningful or would otherwise reduce the performance of machine learning algorithms and the algorithm used to generate embeddings.</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨Available KG benchmark files are zipped and listed below. For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-3,-2019">here</a>.</p>
KCNA2 IgG Autoimmunity In Neuropsychiatric Diseases
<p>One patient of the cohort (#25) received a temporal lobe biopsy to rule out a malignant disease. Histological and immunohistochemical images of diagnostic sections are shown. From these images regions of interest are shown in Figure 2.</p>
Watkins natural accessions yellow rust disease resistant scores
<p>The file contains the phenotypic information from field trails conducted in Kenya at the Kenya Agriculture and Livestock Research Organisation (KALRO) and the Ethiopian Institute of Agricultural Research (EIAR). Data are separated into the three rusts (yellow rust (Yr), stem rust (Sr) and leaf rust (Lr)), although data is not complete at all locations. When possible both seedling and adult plant data is provided. Adult scores include several observation across the growing season. For scoring rust severity, the modified Cobb scale (Peterson et al. 1948) was used to determine the percentage of tissue infected (0-100%) with rust and infection response (S, MS, MR and R, corresponding to susceptible, moderately susceptible, moderately resistant and resistant). </p> <p>Accession codes relate to Watkins landraces and their country of origin and accession names are indicated. Locations, dates and disease scores are indicated. Missing data is indicated as "-". </p> <p>For more detailed passport data and access to germplasm visit the John Innes Centre Germplasm Resources Unit (<a href="https://www.seedstor.ac.uk/search-browseaccessions.php?idCollection=39">SeedStor</a>). Additional germplasm resources and populations developed from the Watkins accessions can be found here: <a href="https://wisplandracepillar.jic.ac.uk/">https://wisplandracepillar.jic.ac.uk/</a> </p> <p> </p>
Data for “Herbivory damage but not plant disease under experimental warming is dependent on weather for three subalpine grass species”, Rocky Mountain Biological Laboratory, Gothic, Colorado, 2015-2017.
Both theory and prior studies predict that climate warming should increase attack rates by herbivores and pathogens on plants. However, past work has often assumed that variation in abiotic conditions other than temperature (e.g., precipitation) do not alter warming responses of plant damage by natural enemies. Studies over short time periods span low variation in weather, and studies over long-time scales often neglect to account for fine-scale weather conditions. Here, we used a 20+ year field warming experiment to investigate if warming affects herbivory and disease are dependent on variation in ambient weather observed over three years. We studied three common grass species in a subalpine meadow in the Colorado Rocky Mountains, USA. We visually estimated herbivory and disease every two-weeks during the growing season and evaluated weather conditions during the previous two- or four-week time interval (two-week average air temperature, two- and four-week cumulative precipitation) as predictors of the probability and amount of damage. Herbivore attack was 13% more likely and amount of damage was 29% greater in warmed plots than controls across the focal species, but warming treatment had little affect on plant disease. Herbivory presence and damage increased the most with experimental warming when preceded by wetter, rather than drier, fine-scale weather, but preceding ambient temperature did not strongly interact with elevated warming to influence herbivory. Disease presence and damage increased, on average, with warmer weather and more precipitation regardless of warming. The effect of warming over reference climate on herbivore damage is dependent on and amplified by fine-scale weather variation, suggesting more boom-and-bust damage dynamics with increasing climate variability. However, the mean effect of regional climate change is likely reduced monsoon rainfall, for which we predict a reduction in insect herbivore damage. Plant disease was generally unrelated
Age, size, and disease severity of thinleaf alder stems along the Tanana River floodplains, collected in 2006 and 2007.
Thinleaf alder stands on the Tanana River floodplains, sampled in 2006 and 2007: A subsample of thinleaf alder stems were aged using tree-ring analysis, and the age structures of alder populations were estimated from their size structures (DN_alder_stand_structure1), based on the size/age relationships derived from this dataset. Relationships between disease incidence/severity and size/age were also explored. Included in this dataset are: the stem diameter at breast height (dbh), age of stem at ground level, age at breast height, and disease severity.
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.