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87 results for “one health”
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Croatia
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - the United Kingdom (Northern Ireland)
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Finland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Luxembourg
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Animal disease data complementing the European Union One Health 2021 Zoonoses Report
<p>This dataset contains the mandatory annual data reported for bovine tuberculosis and for bovine and ovine and caprine brucellosis based on Directive 2003/99.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Ireland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Can Massage During One Year Improve Health in Health-care Providers Working in Hospital
ClinicalTrials.gov study NCT05555082. IPD Sharing: NO. Countries: 1. Publications: 0.
Optimizing Health From Pregnancy Through One Year Postpartum
ClinicalTrials.gov study NCT03069690. IPD Sharing: NO. Countries: 1. Publications: 0.
Addressing Health Disparities in Childhood Obesity, One Summer at a Time
ClinicalTrials.gov study NCT03595332. IPD Sharing: NO. Countries: 1. Publications: 2.
Genomic epidemiology of Escherichia coli: antimicrobial resistance through a One Health lens in sympatric humans, livestock and peri-domestic wildlife in Nairobi, Kenya
Open the record for dataset details and reuse information.
Datasets of the study: "Describing variability in pig genes involved in coronavirus infections: towards a One Health perspective in conservation of animal genetic resources"
<p><strong>Dataset description</strong></p> <p>Sequencing data (*.bam files) of four pig genes (<em>ACE2</em>, <em>ANPEP</em>, <em>DPP4</em> and <em>TMPRSS2</em>)<em> </em>that can serve as receptors or protease for priming the infection of coronaviruses.</p> <p>The datasets are related to 22 European pig breeds and wild boars (Alentejana, AL; Apulo-Calabrese, AC; Basque, BA; Bísara, BI; Black Slavonian, BS; Casertana, CA; Cinta Senese, CS; Gascon, GA; Krškopolje, KR; Lithuanian Indigenous Wattle, LIW; Lithuanian White Old Type, LWOT; Majorcan Black, MB; Mora Romagnola, MR; Moravka, MO; Nero Siciliano, NS; Sarda, SA; Schwäbisch-Hällisches Schwein, SHS; Swallow-Bellied Mangalitsa, SBMA; Turopolje, TU; Italian Duroc, IDU; Italian Large White, ILW; Italian Landrace, ILA; Wild Boar, WB). This work took advantage of a study design developed within the Horizon 2020 TREASURE project.</p> <p>Each folder contains *.bam files and the related indexes *.bai. The name of the investigated breed and gene is part of the file name (e.g. ILW.ACE2.bam identifies the sequencing data related to the ACE2 gene in the Italian Large White pig breed). Details of sequencing and the bioinformatic pipeline are below reported.</p> <p><strong>Sequencing data</strong></p> <p>A total of 22 DNA pools were constructed from the European pig breeds and one DNA pool was constructed from European wild boars, including in each pool 30 or 35 individual DNA samples pooled at equimolar concentration. For the 22 DNA pools, libraries were prepared and fed into an Illumina HiSeq X Ten sequencer for paired-end sequencing, obtaining 150 bp length reads. The wild boar DNA pool was sequenced from 250 bp fragment libraries, with 100 bp long paired-end reads, on the BGISeq 500 platform, following the provider’s procedures.</p> <p><strong>Data processing</strong></p> <p>Reads that were obtained from the sequenced libraries were cleaned by removing adapter sequences and filtering out sequences presenting more than 10% unknown bases (N) and/or containing low-quality bases (Q ≤ 5) over 50% of the total sequenced bases. Then, filtered high-quality reads were mapped on the latest version of the <em>Sus scrofa</em> reference genome (Sscrofa11.1; https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/003/025/GCF_000003025.6_Sscrofa11.1/GCF_000003025.6_Sscrofa11.1_genomic.fna.gz) using the BWA-MEM algorithm v.0.7.17 and the parameters for paired-end data. Picard v.2.1.1 (https://broadinstitute.github.io/picard/) was used to remove duplicated reads. Whole sequence data are available in the EMBL-EBI European Nucleotide Archive (ENA) repository (http://www.ebi.ac.uk/ena), under the study accession PRJEB36830. </p> <p>Reads covering the four genes (ACE2: NC_010461.5:12094853-12156275; ANPEP: NC_010449.5:55346083-55378881; DPP4: NC_010457.5:68655849-68748818; TMPRSS2: NC_010455.5:204871561-204907561) were extracted with samtools v.1.7 and exported as aligned, sorted and indexed *.bam files. Gene length includes UTRs and flanking regions of 5 kbp upstream [flanking (5’-UTR)] and downstream [flanking (3’-UTR)].</p>
A cross-sectional survey on bat human interaction in Pakistan: one health perspective
<p><b>Background and Aim</b></p> <p class="MDPI17abstract">Serologic evidence has identified <i>Lyssavirus</i> exposure among bats in the Indian subcontinent. This study aims to investigate the perception of people regarding bats and the frequency of bat human interaction with One Health implications.</p> <p class="MDPI17abstract"><b>Material and Method </b></p> <p class="MDPI17abstract">A cross-sectional study was conducted using a structured questionnaire among individuals (n=1466) in two distinct topographic residential backgrounds (Mountainous and Plain regions) in Punjab and Khyber Pakhtunkhwa province in Pakistan.</p> <p class="MDPI17abstract"><b>Result </b></p> <p class="MDPI21heading1"><span><span><span>A considerable number of respondents (28%) reported bat's left fruits in their gardens. People who saw bat left fruits in their garden had also reported more bat conflict incidences (32%) as compared to those who had not seen bat left fruit (16%). A higher proportion of respondents from the mountainous districts (23%) reported bat conflict incidents, compared to the plain region respondents (17%). Univariate analysis model also highlighted that topographic residential background (mountainous vs plain residential area) had significance (p<0.05) in describing bat conflict and suspected human rabies-related deaths, in comparison with provincial residential background (Punjab vs Khyber Pakhtunkhwa). </span></span></span></p> <p class="MDPI17abstract"><b>Conclusion </b></p> <p class="MDPI17abstract">Our findings indicate the necessity of a One Health comprehensive surveillance system for emerging and re-emerging zoonotic pathogens, including wildlife as a potential Lyssavirus reservoir, within a context of increased public health education efforts targeted at bats. Epidemiologic and ecologic investigations, as well as laboratory characterization of the virus responsible for human deaths in the mountainous regions, in Pakistan, will provide essential information required to develop strategies for zoonotic pathogen prevention and control.</p>
Food and waterborne outbreaks data complementing the European Union One Health 2020 Zoonoses Report
<p>Food and waterborne outbreaks data reported under the framework of Directive 2003/99/EC and in accordance with the update of the technical specifications for harmonised reporting of FBOs through the EU reporting system in accordance with Directive 2003/99/EC. This dataset includes the number of outbreaks, as well as the number of human cases, hospitalisations and deaths, per causative agent. In addition, other information can include data on causative agents, food vehicles, and the factors in food preparation and handling that contributed to the food-borne outbreaks. Reporting countries can also provide information on the nature of the evidence supporting the suspicion of the food vehicle. This evidence can be epidemiological, microbiological, descriptive environmental, or based on product tracing investigations. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: PubliFBO2020_20211109: >></p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Croatia
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Animal population data complementing the European Union One Health 2020 Zoonoses Report
<p>This dataset includes animal population aggregated data under the framework of Directive 2003/99/EC. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: Animal_p20_20211109: >></p>
One Health EJP MATRIX video about the One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP)
<p>This video was recorded during the webinar about the <strong>One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP) </strong>as part of the OHEJP MATRIX Webinar Series 2022 <em>Solutions for One Health Surveillance in Europe</em>. The webinar took place on November 10<sup>th</sup> from 14:00 to 15:15 CET and was hosted by the <a href="https://onehealthejp.eu/jip-matrix/">One Health EJP project MATRIX.</a></p> <p>The One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP) is a community resource supporting the adoption of the OH paradigm. The OHS Codex/KIP comprises five high-level “action principles”, which respectively support: i) planning and management; ii) collaboration; iii) knowledge exchange; iv) data interoperability; v) reporting and dissemination. These principles are applicable to any sector-specific or cross sectorial surveillance activity.<br> Under each of these principles, the OHS Codex/KIP provides the users a collection of resources (e.g. tools, technical resources, guidance documents and experiences) that address specific OH-problems in the context of the corresponding principle. As an open community framework, it is continuously updated by and for the community. More information is available <a href="https://oh-surveillance-codex.readthedocs.io/en/latest/">here.</a></p> <p>The OHS Codex/KIP has been initially devolped in <a href="https://onehealthejp.eu/jip-orion/">One Health EJP ORION</a> and was expanded and promoted within <a href="https://onehealthejp.eu/jip-matrix/">One Health EJP MATRIX.</a></p> <p>The MATRIX and ORION projects are part of the One Health European Joint Programme (OHEJP).<br> They received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.</p> <p>Leading MATRIX partner of the OHS Codex/KIP: German Federal Institute for Risk Assessment (BfR). Contact: Matthias Filter (Matthias.Filter@bfr.bund.de)</p> <p><em><strong>Webinar agenda:</strong></em></p> <p><strong>Welcome and general overview of the MATRIX solutions for One Health Surveillance</strong><br> Guido Benedetti, Statens Serum Institut (SSI), Denmark</p> <p><strong>One Health Surveillance CODEX: Knowledge Integration Platform – ORION perspective and MATRIX extension</strong><br> Matthias Filter, German Federal Institute for Risk Assessment (BfR), Germany</p> <p><strong>Live Demo and special features of the One Health Surveillance CODEX: Knowledge Integration Platform</strong><br> Yvonne Mensching, German Federal Institute for Risk Assessment (BfR), Germany</p>
Supplementary material 1 from: Scaccia N, Günther T, Lopez de Abechuco E, Filter M (2021) The Glossaryfication Web Service: an automated glossary creation tool to support the One Health community. Research Ideas and Outcomes 7: e70183. https://doi.org/10.3897/rio.7.e70183
The Glossaryfication Web Service: an automated glossary creation tool to support the One Health community
Efficiency of an All-in-one Health Monitoring Device in Elderly Residential Setting
ClinicalTrials.gov study NCT05302895. IPD Sharing: YES. Countries: 1. Publications: 1.
Chronic Malnutrition and Oral Health Status in Children Aged One to Five Years
ClinicalTrials.gov study NCT03529500. IPD Sharing: YES. Countries: 1. Publications: 19.
A One Health Study of Monkeypox Human Infection
ClinicalTrials.gov study NCT05058898. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.