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557 results for “data reporting”
Excel mapping tools for 2018 zoonoses data reporting
<p>The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.</p> <p>You can choose between the dynamic or the manual version of the tool.</p>
Supporting publication for 'Guidelines for reporting 2018 prevalence sample-based data in accordance with SSD2 data model'
<p>These two Excel documents help you to map terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer you examples on how prevalence data can be reported using SSD2</p>
Excel mapping tool for 2019 avian influenza data reporting
<p>Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.</p> <p>Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.</p> <p>The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.</p>
High Definition Modular Multilevel Converter (Final Report and Data)
<p>These are the raw data generated in the 2do Joint Experiments sponsored by IRP Wind EU project</p>
Underlying data of the project "Are CONSORT checklists submitted by authors adequately reflecting what information is actually reported in published papers?"
<p><em><strong>Supplementary file 3.xlsx</strong></em> contains the evaluations for the 12 papers included in the study.</p>
Data for publication 'Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape' (Scientific Reports 2019)
<p>Data on vessel tracks and underwater noise levels presented in the publication Hermannsen, L., Mikkelsen, L., Tougaard, J., Beedholm, K., Johnson, M. and P. T. Madsen, "Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape", Scientific Reports 9:15477 (<a href="https://doi.org/10.1038/s41598-019-51222-9">https://doi.org/10.1038/s41598-019-51222-9</a>).</p>
Data from the article "Short-interval wildfire and drought overwhelm boreal forest resilience" Whitman et al., Scientific Reports, 2019
<p>Field data collected in the Northwest Territories and Wood Buffalo National Park, as well as plot locations, for the study "Short-interval wildfire and drought overwhelm boreal forest resilience" by Whitman et al. in Scientific Reports, 2019.</p> <p>For metadata or assistance please contact the authors. If you intend to publish research using these data, please contact and inform the authors, and cite the source article.</p>
Data for the Reproducibility of the Report: Collaboration between IRCC Candiolo and OSR TIGET, 2024
<p>Collection of processed <code>.Robj</code> files (primarily Seurat spatial transcriptomics datasets) and the original publicly available data for reproducibility of the report generated in collaboration between OSR-TIGET and IRCC Candiolo (academic year 2024). For complete reproduction, visit: https://github.com/carloelle/Report_OSR_Candiolo_2024 .</p> <p>All data provided here is publicly available, and no data leakage has occurred.</p>
Data reported in: Effects of ozone isotopologue formation on the clumped-isotope composition of atmospheric O2
<p>Tropospheric <sup>18</sup>O<sup>18</sup>O is an emerging proxy for past tropospheric ozone and free-tropospheric temperatures. The basis of these applications is the idea that isotope-exchange reactions in the atmosphere drive <sup>18</sup>O<sup>18</sup>O abundances toward isotopic equilibrium. However, previous work used an offline box-model framework to explain the <sup>18</sup>O<sup>18</sup>O budget, approximating the interplay of atmospheric chemistry and transport. This approach, while convenient, has poorly characterized uncertainties. To investigate these uncertainties, and to broaden the applicability of the <sup>18</sup>O<sup>18</sup>O proxy, we developed a scheme to simulate atmospheric <sup>18</sup>O<sup>18</sup>O abundances (quantified as ∆<sub>36</sub> values) online within the GEOS-Chem chemical transport model. These results are compared to both new and previously published atmospheric observations from the surface to 33 km. Simulations using a simplified O<sub>2</sub> isotopic equilibration scheme within GEOS-Chem show quantitative agreement with measurements only in the middle stratosphere; modeled ∆<sub>36</sub> values are too high elsewhere. Investigations using a comprehensive model of the O-O<sub>2</sub>-O<sub>3</sub> isotopic photochemical system and proof-of-principle experiments suggest that the simple equilibration scheme omits an important pressure dependence to ∆<sub>36</sub> values: the anomalously efficient titration of <sup>18</sup>O<sup>18</sup>O to form ozone. Incorporating these effects into the online ∆<sub>36</sub> calculation scheme in GEOS-Chem yields quantitative agreement for all available observations. While this previously unidentified bias affects the atmospheric budget of <sup>18</sup>O<sup>18</sup>O in O<sub>2</sub>, the modeled change in the mean tropospheric ∆<sub>36</sub> value since 1850 C.E. is only slightly altered; it is still quantitatively consistent with the ice-core ∆<sub>36</sub> record, implying that the tropospheric ozone burden increased less than ~40% over the twentieth century.</p>
Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol
<p><strong>Aim:</strong> To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements, and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high quality absences from OR based on questionnaires sent to observers.</p> <p><strong>Location:</strong> Both datasets cover the same large area (Sweden) and time period (2000 -2013).</p> <p><strong>Methods:</strong> Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data were assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR.</p> <p><strong>Results:</strong> For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species-environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found.</p> <p><strong>Main Conclusions:</strong> Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist.</p>
Literature Survey of Electrostatic Incidents- Fire Protection Research Foundation report data
<p>The attached spreadsheet includes 89 incidents gathered as part of a FPRF report (report number FPRF-2021-07) "Static Electricity Incident Review" produced in August 2021. The report is available at: <a href="https://www.nfpa.org/News-and-Research/Data-research-and-tools/Electrical/Static-Electricity-Incident-Review ">https://www.nfpa.org/News-and-Research/Data-research-and-tools/Electrical/Static-Electricity-Incident-Review </a></p> <p>The incidents gathered are on electrostatic incidents found in the literature or public domain, and categorized in the following 10 columns:</p> <ol> <li>Incident Date</li> <li>Incident Location</li> <li>Incident Type</li> <li>Summary of Incident</li> <li>Potential NFPA 77 technical item that was breached</li> <li>Link/references to incident (if available)</li> <li>Number of Casualties</li> <li>Date last accessed</li> <li>Conclusions</li> <li>Why the incident happened.<br> </li> </ol>
Chemistry data identification report
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Data for Pelgrim et al. (2021) Scientific Reports
<p>Dataset for Pelgrim et al. (2021). Functional connectivity (FC) matrices were created by computing Pearson correlation coefficients between the mean time course of 105 regions of interest (ROIs) of the functional MRI images, then converted to normally distributed <em>Z</em>-scores using Fisher's r-to-<em>Z</em>-transformation.</p> <p>- 40 connectivity matrices of 22q11 deletion syndrome patients<br> - 76 connectivity matrices of healthy controls <br> - subject data<br> - names of 105 ROIs</p> <p> </p> <p>22q11 deletion syndrome, psychosis, functional MRI, network analysis, graph theory, functional connectivity</p>
Reported funding data for open infrastructure
<p>Reported funding data for open infrastructure projects, focused primarily on the services in the <a href="https://investinopen.org/blog/funding-open-infrastructure-a-survey-of-available-data-sources/">pilot for Invest in Open Infrastructure's funding landscape research</a>, as well as other notable providers in the SCOMCat index.</p>
Excel mapping tool for 2022 avian influenza data reporting
<p>Data collection is an important task of the European Food Safety Authority (EFSA) and a fundamental component of risk assessment (Articles 22 and 23 of Regulation (EC) No 178/2002). EFSA receives a large volume of data from Member States (MSs) that is used in support of its risk assessment mission.</p> <p>Council Directive 2005/94/EC and Commission Decision 2010/367/EU lay down the guidelines for monitoring and reporting avian influenza surveillance data on poultry and wild birds by European Union (EU) MSs. EFSA has been assigned the task of collating, validating, analysing, and summarising in an annual report the results from the avian influenza surveillance programmes in poultry and wild birds. For the reporting of data, EFSA provides a Data Collection Framework that allows data providers to submit data in eXtensible Markup Language (XML) format through a web interface or a web service. Here, a data model describing the format, and the content requested when submitting data through the DCF, is provided.</p> <p>The data model supports the reporting of laboratory testing results for avian influenza in both wild birds, and poultry. The use of this reporting standard ensures that reported results are comparable between reporting countries. Data reported in this format will be used to generate epidemiological updates on the avian influenza surveillance testing carry out in Europe, and to provide scientific advice to the European Commission.</p>
Preprocessed EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains intermediate preprocessing files for the above-named paper. Preprocessing scripts are stored at: <a href="http://osf.io/fndk5/">https://osf.io/fndk5/</a></p> <p>For the raw EEG data, please see <a href="http://zenodo.org/record/6992085">https://zenodo.org/record/6992085</a>.</p> <p>The intermediate preprocessing files are *_ica.rds (R datasets saved after running ICA), and *_prepro.rds (R datasets saved after individual preprocessing complete, before combination with other participants). To use the data with the preprocessing scripts, download and save it in a folder called prepro_EEG_data as explained in the preprocessing script.</p>
Prevalence data complementing the European Union One Health 2021 Zoonoses Report
<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 is: 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 2021 Zoonoses Report
<p>This dataset includes animal population aggregated data under the framework of Directive 2003/99/EC.</p>
Food and waterborne outbreaks data complementing the European Union One Health 2021 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. </p>
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>
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.