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Data and analysis for: "Persistent Spatial Clustering and Predictors of Pediatric La Crosse Virus Neuroinvasive Disease Risk in Eastern Tennessee and Western North Carolina, 2003–2020"
<p>This is the initial release of the data and code corresponding to the manuscript submitted to PLoS Neglected Tropical Diseases. <strong>Please refer to the README.md file</strong> for a description of the contents of this repository and how to use them. The README file can be opened with a text editor, or viewed directly in the GitHub repository. The data and code are provided within a project directory with a reproducible R package library for ease and accuracy of reproducibility. </p> <p><strong>Ethics Approval</strong></p> <p>This study was approved by the University of Tennessee, Knoxville Institutional Review Board (UTK IRB-22-07079-XP) and the Tennessee Department of Health Institutional Review Board (TDH IRB 2021-0314). Data provided here is de-identified and aggregated (both temporally and spatially) to protect the privacy of individuals included in the study, in concordance with IRB and Data Use Agreements.</p>
Genome-wide association analyses identify novel Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility
<p>The Brugada syndrome GWAS summary statistics</p> <p>Brugada syndrome is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of <em>SCN5A</em>, encoding the cardiac sodium channel Na<sub>V</sub>1.5, susceptibility genes remain largely unknown. We performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with Brugada syndrome and 10,001 controls.</p> <p> </p>
Summary statistics for "Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's Disease"
<p>These are the burden test results (summary statistics) for the publication:</p> <p>"Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s Disease",</p> <p>Nature Genetics, 2022.</p> <p> </p> <p><em>Format: tab-separated-value.</em></p> <p><em>Fields:</em></p> <ul> <li><em>gene_stable_id: Ensembl gene id</em></li> <li><em>gene_name: standard gene name</em></li> <li><em>pvalue: burden test significance (likelihood ratio test, population structure correction based on 6 PCA components)</em></li> <li><em>cmac_all: sum of minor allele dosages across all contributing samples and variants</em></li> <li><em>group: variant group (LOF, LOF+REVEL>=75, LOF+REVEL>=50, LOF+REVEL>=25, see publication methods for further selection criteria).</em></li> <li><em>beta/se: beta/se of logistic ordinal regression (see publication methods). Positive = risk-increasing. Negative = risk-decreasing.</em></li> </ul> <p> </p>
UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits
<p>Summary-level GWAS data for 53 traits generated by <a href="https://www.genomicsplc.com/">Genomics plc</a> as presented in:</p> <p>Thompson D. et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits (<a href="https://doi.org/10.1101/2022.06.16.22276246">https://doi.org/10.1101/2022.06.16.22276246</a>)</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p><strong>NOTES</strong></p> <p>These analyses were carried out using the full UK Biobank (UKB) imputation data release (v3b). After removal of exclusions and withdrawals, a subset of 337,151 UKB individuals, the White British Unrelated (WBU) subgroup, was defined as the intersection of two sample groups created by Bycroft et al 2018 (Nature 562, 203-209): the ‘White British ancestry’ group (UKB Data Field 22006) and the ‘used in genetic principal components’ group (UKB Data Field 22020), the latter being high quality samples that were filtered to avoid closely related individuals. All GWAS analyses were performed on the WBU subgroup.</p> <p>Phenotypes were defined as described in Supplementary Table 1 ‘Phenotype definitions’ using a combination of Hospital Episode Statistics, Cancer Registry reports (where applicable) and self-report responses, with the exception of coronary artery disease (CAD). GWAS data was generated for both a “narrow” and a “broad” definition of CAD. The former was used as part of the training data for the Enhanced CAD PRS, the latter was used as part of the training data for the Enhanced CVD PRS. The phenotype definitions for “narrow” and a “broad” CAD are as follows:</p> <table> <tbody> <tr> <td>Narrow CAD<br> (includes angina)</td> <td>ICD10 codes (where .X indicates all subcodes) from both hospital and death records: I21, I22, I23, I24.1, I25.2, I20.X. ICD9 codes: 410-412, 42979, 413.X. OPCS-4 codes (K40.1–40.4, K41.1–41.4, K45.1–45.5,K49.1–49.2, K49.8–49.9, K50.2, K75.1–75.4, K75.8–75.9), self-reported heart attack (UKB codes 1075 in field 20002; code 1 in field 6150), self-reported coronary angioplasty (ptca) or coronary artery bypass graft (UKB codes 1070 and 1095 in field 20004), self-reported angina.</td> </tr> <tr> <td>Broad CAD<br> (includes angina and all ischaemic heart disease)</td> <td>As for Narrow CAD, plus ICD10 codes I24.X, I25X, and ICD9 codes 414.X (where .X indicates all subcodes).</td> </tr> </tbody> </table> <p>Note that there is no GWAS for cardiovascular disease (CVD) per se. This is because the UKB training data for the Enhanced CVD PRS consisted of separate GWASs for “narrow” CAD and ischaemic stroke.</p> <p>All analyses included Age at assessment, sex (for non-sex specific traits), genotyping chip, and 10 principal components as covariates.</p> <p>GWAS summary statistics for each trait were generated by applying PLINK 2.0 to the WBU subgroup, using a logistic regression for disease traits, and a linear regression model for quantitative traits. For chromosome X variants males were treated as having 0 or 2 alternative alleles.</p> <p>The results are not adjusted for genomic control.</p> <p><strong>DATA FILE CONTENT DESCRIPTION (DISEASE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size (log odds ratio)</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ncase</td> <td>Number of cases</td> </tr> <tr> <td>ncontrol</td> <td>Number of controls</td> </tr> </tbody> </table> <p><strong>DATA FILE CONTENT DESCRIPTION (QUANTITATIVE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ntotal</td> <td>Total sample size</td> </tr> </tbody> </table> <p><strong>FILE NAMES</strong></p> <p>The following is a list of traits and their corresponding file names.</p> <p><em><strong>DISEASE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age-related macular degeneration</td> <td>amd_strict_UKB_WBU.csv.gz</td> </tr> <tr> <td>Alzheimer's disease</td> <td>alzheimers_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Asthma</td> <td>asthma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Atrial fibrillation</td> <td>atrial_fibrillation_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bipolar disorder</td> <td>bipolar_disorder_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bowel cancer</td> <td>CRC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Breast cancer</td> <td>BC_UKB_WBU_women.csv.gz</td> </tr> <tr> <td>Coeliac disease</td> <td>celiac_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Narrow coronary artery disease</td> <td>NARROW_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Broad coronary artery disease</td> <td>BROAD_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Crohn's disease</td> <td>crohns_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Epithelial ovarian cancer</td> <td>OC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Hypertension</td> <td>HT_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ischaemic stroke</td> <td>IS_stroke_UKB_WBU.csv.gz</td> </tr> <tr> <td>Melanoma</td> <td>melanoma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Multiple sclerosis</td> <td>multiple_sclerosis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Osteoporosis</td> <td>OP_WBU_training.csv.gz</td> </tr> <tr> <td>Prostate cancer</td> <td>PC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Parkinson's disease</td> <td>parkinsons_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Primary open angle glaucoma</td> <td>POAG_WBU_training.csv.gz</td> </tr> <tr> <td>Psoriasis</td> <td>psoriasis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Rheumatoid arthritis</td> <td>rheumatoid_arthritis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Schizophrenia</td> <td>schizophrenia_UKB_WBU.csv.gz</td> </tr> <tr> <td>Systemic lupus erythematosus</td> <td>lupus_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 1 diabetes</td> <td>t1d_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 2 diabetes</td> <td>T2D_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ulcerative colitis</td> <td>ulcerative_colitis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Venous thromboembolic disease</td> <td>VTE_UKB_WBU.csv.gz</td> </tr> </tbody> </table> <p><em><strong>QUANTITATIVE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age at menopause</td> <td>age_at_menopause_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein A1</td> <td>apolipoprotein_a1_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein B</td> <td>apolipoprotein_b_UKB_WBU.csv.gz</td> </tr> <tr> <td>Body mass index</td> <td>bmi_UKB_WBU.csv.gz</td> </tr> <tr> <td>Calcium</td> <td>calcium_UKB_WBU.csv.gz</td> </tr> <tr> <td>Docosahexaenoic acid</td> <td>docosahexaenoic_acid_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated bone mineral density T-score</td> <td>BMD_WBU_training.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (creatinine based)</td> <td>egfr_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (cystatin based)</td> <td>egfr_cys_UKB_WBU.csv.gz</td> </tr> <tr> <td>Glycated haemoglobin</td> <td>hba1c_UKB_WBU_nodiabetes.csv.gz</td> </tr> <tr> <td>High density lipoprotein cholesterol</td> <td>hdl_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Height</td> <td>height_UKB_WBU.csv.gz</td> </tr> <tr> <td>Intraocular pressure</td> <td>iop_WBU_training.csv.gz</td> </tr> <tr> <td>Low density lipoprotein cholesterol</td> <td>ldl_UKB_WBU_nostatins.csv.gz</td> </tr> <tr> <td>Omega-6 fatty acids</td> <td>omega_6_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Omega-3 fatty acids</td> <td>omega_3_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphatidylcholines</td> <td>phosphatidylcholines_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphoglycerides</td> <td>phosphoglycerides_UKB_WBU.csv.gz</td> </tr> <tr> <td>Polyunsaturated fatty acids</td> <td>polyunsaturated_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Resting heart rate</td> <td>resting_heart_rate_UKB_WBU.csv.gz</td> </tr> <tr> <td>Remnant cholesterol (Non-HDL, Non-LDL cholesterol)</td> <td>remnant_cholesterol__UKB_WBU.csv.gz</td> </tr> <tr> <td>Sphingomyelins</td> <td>sphingomyelins_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total cholesterol</td> <td>total_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total fatty acids</td> <td>total_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total triglycerides</td> <td>total_triglycerides_UKB_WBU.csv.gz</td> </tr> </tbody> </table>
Online Appendix for PhD Thesis Titled "Dissecting Causal Relationships and Molecular Mechanisms in Disease using Genetic Risk Profiles"
<p>This repository contains 23 tables and two figures, which are too big to be included in the Appendix section of my thesis document.</p> <p>The second version includes additional summary statistics of metabolite-PGS associations which can be found at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas/.</p>
Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes
<p>This is the dataset for the publication "Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes".</p>
Data and code for 'Connectivity over a disease risk gradient enables recovery of rainforest frogs'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Bell, S.C., Heard, G.W., Berger, L. and Skerratt, L.F. (in review). Connectivity over a disease risk gradient enables recovery of rainforest frogs. </p> <p>The data are provided as a series of .csv files. R code is provided separately for each of the following components:</p> <p>1. A script to collate nightly air temperature data from stream-side temperature loggers, plus daily maximum air temperature data from the nearest Australian Bureau of Meteorology recording station to each site.</p> <p>2. A script to fit a hierarchical linear model to the stream-side air temperature, with the aid of the R2OpenBUGS package.</p> <p>3. A script to fit a hierarchical logistic regression model to the chytrid infection data, with the aid of the R2OpenBUGS package.</p> <p>4. A script to fit logistic regression models to the frog occupancy data, with the aid of the R2OpenBUGS package. </p> <p>These files allow all analyses from the paper to be reproduced. </p>
Valuation of heat related mortality risk and tick-borne diseases
<p>Monetary impacts of premature mortality due to heat waves</p> <p>Preferences for public programmes against spread of ticks due to climate change and a new vaccine against Lyme disease, prevalence of tick-borne diseases and exposure to ticks</p>
A protocol to assess the risk of dementia among patients with coronary artery diseases using CAIDE score-Extended Data
<p>The contents of this extended data file are-<br> 01. Consent form (English & Bengali Version)<br> 02. Interview Questionnaire (English & Bengali Version)<br> These contents will help to address the objectives of the study that attempted to identify the risk of long-term dementia among coronary artery disease patients in Bangladesh.</p>
Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates
<p>EFSA was requested to assess the impact on effectiveness of <em>post-mortem</em> inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals.</p> <p>The following information is indicated for each disease/condition in the disease map:</p> <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at <em>ante mortem </em>inspection;</li> <li>The lesions associated with the disease that could be detected at <em>post mortem</em> inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> <p>From the disease map, the list of organs to be considered at <em>post mortem</em> inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.</p>
A meta-analysis on global change drivers and the risk of infectious disease database and code
<p>Data and code associated with the manuscript "A Meta-analysis on Global change drivers and the risk of infectious disease".</p>
An exposome atlas of serum reveals risk of chronic diseases in Chinese population
<p>Although adverse environmental exposures are considered to be a major cause to chronic diseases, current studies have provided limited knowledge on real-world chemical exposures and related risks. Here, we collected serum samples from 5696 healthy people and patients, including 12 chronic diseases in China, and completed serum biomonitoring containing 267 chemicals using gas and liquid chromatography-tandem mass spectrometry. 74 high-frequently detected exposures were used for exposure characterization and risk analysis. Results showed that region was the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances were associated with multiple chronic diseases, and some of them exceeded safe ranges. Mixture effect models showed significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provided a comprehensive human serum exposure atlas and its disease risk, which could guide subsequent more in-depth cause-and-effect studies between environmental exposures and human health. The R codes and related example data for statistical analysis and figure production have been deposited to the GitHub (https://github.com/youlei2023/ExposomeAtlas).</p>
An epidemiological Study to Assess Household Transmission & Associated Risk Factors for COVID-19 Disease amongst Residents of Delhi, India.
<p><strong><em>Executive summary</em></strong>: Studying the spread and epidemiological characteristics of COVID-19 virus specially in household settings are needed to prepare our self-better in preventing and controlling this epidemic. In this study we proposed a conceptual framework of four level of determinates and tried to understand the transmission dynamics of COVID-19 among household contacts along with clinical, epidemiological and virologic characteristics of the infection. </p> <p><strong>Aims & Objectives:</strong></p> <ol> <li>the proportion of asymptomatic cases and symptomatic cases;</li> <li>the incubation period of COVID-19 and the duration of infectiousness and of detectable shedding;</li> <li>the serial interval of COVID-19 infection; </li> <li>clinical risk factors for COVID-19, and the clinical course and severity of disease; </li> <li>high-risk population subgroups;</li> <li>the secondary infection rate and secondary clinical attack rate of COVID-19 infection among household contacts; and</li> <li>the associations of various factors across four dimensions interaction associated with risk of transmission</li> </ol> <p><strong>Methodology:</strong> This was a case-ascertained study where all susceptible contacts of a laboratory confirmed COVID-19 case were studied prospective for four weeks after their enrolment. It was done in New Delhi, during the end of first wave as well as whole second wave from December 2020 to July 2021. The study team collected the key information by questionnaire along with blood and oro-nasal swab during the household visits. Follow-up was done on day 7, 14 and 28 for observing the disease characteristic and symptomatology along with confirmation by serum and oro-nasal swab testing. Daily characteristics of the infection were noted by the participants on symptoms diary.</p> <p><strong>Results: </strong>We enrolled 99 households, each having one laboratory-confirmed COVID-19 index case along with their 318 susceptible contacts. By the end of the follow-up, secondary infection rate was seen at 55.5%, while seroconversion in 46.6%. Hospitalization and case fatality rate was 3.83% and 1.7% respectively. Among epidemiological characteristics we observed serial interval of 8.0 ± 6.7 days, generation time 3.8 ± 6.4, while secondary attack rate was 54.9%. The predictors of secondary infection among individual contact level were being female (OR:2.13, 95% CI:1.27 - 3.57), age of the household contact (1.01;1.00 - 1.03), symptoms at baseline (3.39; 1.61- 7.12) and during follow-up (3.18; 1.64 - 6.19), while only symptoms during follow-up (3.81: 1.43 - 10.14) and being RT-PCR positive (8.32; 3.22 -21.54) was significantly and independently associated with seroconversion among household contacts. Among index case-level age of the primary case (1.03; 1.01 -1.04) and any symptoms during follow-up (6.29; 1.83-21.63) significantly and independently associated with secondary infection while any symptoms during follow-up was associated with seroconversion among household contacts. Among household-level characteristics having more rooms (4.44; 2.16 - 9.13) independently associated with secondary infection, while more rooms (3.98; 1.23 -12.90) along with overcrowding (0.37; 0.16 - 0.82) associated with seroconversion. Among contact pattern only taking care of the index case (2.02;1.21- 3.38) was significantly and independently associated with secondary infection, while none was associated with seroconversion.</p> <p><strong>Conclusion: </strong>A high secondary cases and secondary attack rate was seen in our study. This highlights the need to adopts strict measure and advocate COVID appropriate behaviours in order to break the transmission chain at household level. The targeted approach at household contacts with higher risk would be efficient in limiting the development of infection among susceptible contacts. </p>
Global predictions for the risk of establishment of Pierce's disease of grapevines
<p>Example to compute the risk of Pierce's Disease establishment from ERA5-Land temperature data. Related to the publication <em><a href="https://www.nature.com/articles/s42003-022-04358-w" target="_blank" rel="noopener">Global predictions for the risk of establishment of Pierce’s disease of grapevines</a></em></p> <p>See <a href="https://github.com/agimenezromero/PierceDisease-GlobalRisk-Predictions" target="_blank" rel="noopener">GitHub</a> repo.</p>
The efficacy of wildlife fences for keeping reindeer outside a chronic wasting disease risk area
<p>Data to</p> <p>"The efficacy of wildlife fences for keeping reindeer outside a chronic wasting disease risk area"</p> <p>accepted in Ecological Solutions and Evidence</p>
High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets
<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>
Literature Review 13/02/2018: Genetic risk of Parkinson's disease dementia due to APOE4 or MAPT
<p>Literature review assessing genetic risk of dementia due to <em>APOE4</em> or <em>MAPT </em>in Parkinson's disease, performed on the 13<sup>th</sup> February 2018. All studies had to fulfil three<em> a priori </em>inclusion criteria:</p> <p>1) Case control studies using clinically diagnosed or pathologically confirmed PD and PDD.</p> <p>2) Time between motor diagnosis and experimental assessment could be defined or estimated.</p> <p>3) Genotype information supplied, allowing the odds ratio (OR) and confidence intervals (CI) to be calculated that aligned with the genotype categories used in this work.</p> <p>For <em>MAPT</em>, a PubMed search for the term “<em>MAPT Parkinson’s dementia</em>” identified 105 potential matches, of which 9 met the inclusion criteria. For <em>APOE4</em>, a PubMed search for the term “<em>APOE Parkinson’s dementia</em>” identified 188 potential matches, of which 19 met the inclusion criteria. Note, the review includes several publications arising from the CamPaIGN cohort; As we were interested in genetic risk as a function of time from diagnosis, we included each unique study time-point. </p>
Fig. 1 in Applying a modified streamlined disease risk analysis framework to a platypus conservation translocation, with special consideration for the conservation of ecto- and endoparasites
Fig. 1. An outline of how parasite conservation can be considered in translocation planning, reproduced from Carlson et al. (2020).
VEClim's initial assessment of Aedes albopictus activity and disease risk in three decades: historical (1980-1990), recent (2010-2020), and future (2090-2100).
<p><span>This is the initial release of VEClim, an early warning decision support system for climate-sensitive vector activity and vector-borne disease risk assessment, comprising an assessment of the seasonal dynamics of <em>Aedes albopictus</em>, the Asian tiger mosquito, and the associated risk of chikungunya virus transmission. This analysis compares three decades, i.e., historical (1980-1990), </span><span>recent <span>(2010-2020), and future (2090-2100), in terms of seasonal and geospatial averages. </span></span></p>
"Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.
<p> "Decision support systems halve fungicide compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.https://doi.org/10.1038/s43247-021-00291-8 | www.nature.com/commsenv</p> <p>Dataset includes the results of 80 independent experiments reported in 22 articles and it has a dimension of 329 rows x 42 columns. Further information in "Description Supplementary Data 1.pdf" file. </p> <p>This dataset was assembled including also the data from the publication Agronomy 2020, 10(4), 560; https://doi.org/10.3390/agronomy10040560.</p>
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.