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Dataset results
809 results for “complement”
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Germany
<p>This dataset contains ASF laboratory analytical results in wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_DE - Federal Research Institute for Animal Health, Friedrich-Loeffler-Institute (FLI)</li> <li>ASF2023_DE - Federal Research Institute for Animal Health, Friedrich-Loeffler-Institute (FLI)</li> <li>ASF2022_DE - Federal Research Institute for Animal Health, Friedrich-Loeffler-Institute (FLI)*</li> <li>ASF2022_DE - Federal Research Institute for Animal Health, Friedrich-Loeffler-Institute (FLI)</li> </ul> <p> </p> <p> </p> <p>*This version of the ASF laboratory data has been republished with the subunit identification code (sampUnitIds.subUnitId) column empty due to data protection reasons</p>
Pig population data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - Czechia
<p>This dataset contains swine population data.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2024_POP_EXTRACTION_CZ - State Veterinary Administration (SVA)</li> <li>ASF2023_POP_EXTRACTION_CZ - State Veterinary Administration (SVA)</li> <li>ASF2022_POP_EXTRACTION_CZ - State Veterinary Administration (SVA)*</li> <li>ASF2022_POP_EXTRACTION_CZ - State Veterinary Administration (SVA)</li> </ul> <p> </p> <p> </p> <p>*This version of the animal population data has been republished with the establishment and subnit identification code (estabId, subUnitId) columns empty due to data protection reasons</p>
Animal disease data complementing the European Union One Health 2022 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 2022 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 2022 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 2022 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>
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Sweden
<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 2022 Zoonoses Report - Norway
<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 2022 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>
Sample based prevalence data complementing the European Union One Health 2022 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>
Prevalence data complementing the European Union One Health 2022 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>
Food and waterborne outbreaks data complementing the European Union One Health 2022 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>
Negative Complement of a Set of Vulnerability-Fixing Commits: Supplementary Material
<div> <div><span>EASE 2024 - Industry track</span></div> <br> <div><span>This archive contains accompanying materials for the paper: </span></div> <br> <div><span>Rocío Cabrera Lozoya, Antonino Sabetta, Tommaso Aiello. "Negative Complement of a Set of Vulnerability-Fixing Commits: Method and Dataset" </span></div> <br> <div><span>submitted to the Industry track at EASE 2024 - https://conf.researchr.org/track/ease-2024/ease-2024-industry</span></div> <br> <div><span>It contains the following folders and files:</span></div> <br> <div><span>-</span><span> data</span></div> <div><span> </span><span>-</span><span> commit_pairs_final.csv : Dataset of 534 commit pairs corresponding to a positiive (security-relevant) commit and a negative sample obtained by the approach described in the paper.</span></div> <div><span> </span><span>-</span><span> single_positive.csv : Contains a single positive instance (taken from the MSR2019 dataset) which can be used to test the generate_negative_complement.py script.</span></div> <div><span>-</span><span> scripts</span></div> <div><span> </span><span>-</span><span> generate_negative_complement.py : Takes in a single .java file and obfuscates its developer-defined identifiers.</span></div> <div><span> </span><span>-</span><span> obfuscate_java_file.py : Generates a negative complement for a security-relevant dataset. The ouput is written to couple_dataset.csv.</span></div> <div><span> </span><span>-</span><span> requirements.txt : Requirements needed in the virtual environment to successfully run the previous scripts.</span></div> </div>
Leech-derived iDNA complements traditional surveying methods, enhancing species detections for rapid biodiversity sampling in the tropics
<p>Deforestation, exploitation, and other drivers of biodiversity loss in Madagascar leave its highly endangered and predominantly endemic wildlife at risk of extinction. Decreasing biodiversity threatens to compromise ecosystem functions and vital services provided to people. New, economical, and diverse methods of biodiversity monitoring can help to establish reliable baseline and long-term records of species richness. Metabarcoding with invertebrate-derived DNA (iDNA) has emerged as a promising new biosurveillance tool. An unexpected wet forest fragment tucked in the dry cliffs of Madagascar's southcentral plateau, the Ivohibory Protected Area (IPA), hosts a unique mosaic of species diversity, featuring both dry and wet forest species. Recently elevated to protected status, the IPA has been surveyed for flora and fauna with a range of inventory methods over three years and six expeditions (2016, 2017, & 2019). We collected 1,451 leeches over 12 days from the IPA to supplement known species richness and to compare results against current records. With iDNA, we pooled tissues, and isolated, and amplified bloodmeal DNA with five sets of primers. We detected 20 species of which four are species of frogs previously undetected and three of which are previously unknown to exist in this region. iDNA surveys can provide complementary data to traditional surveying methods like camera traps, line transects, and bioacoustic methods.</p>
Data to "Cardiopulmonary exercise testing complements both spirometry and nuclear imaging for assessing sarcoidosis disease stage and for monitoring disease activity"
<p>This record contains analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Torregiani, C., Reale, M., Confalonieri, M., Dore, F., Crisafulli, C., Baratella, E., ... & Maiello, G. (2024). Cardiopulmonary exercise testing complements both spirometry and nuclear imaging for assessing sarcoidosis stage and for monitoring disease activity. <em>Sarcoidosis, Vasculitis, and Diffuse Lung Diseases</em>, 41(1), e2024017-e2024017. https://doi.org/10.36141/svdld.v41i1.15125</p>
Dataset related to article "Amnion epithelial cells are an effective source of factor H and prevent kidney complement deposition in factor H deficient mice"
<p>File excel with raw data of parameters (column) for any single animal (line) of each experimental group</p>
Dataset: DNA barcodes and microsatellites: how they complement for species identification in the complex genus Tamarix (Tamaricaceae)
<p class="CuerpoA">DNA barcoding allows the identification of an organism by comparing the sequence of selected DNA regions (barcodes) with a previously compiled database, and it can be useful for taxonomic identification of species in complex genera, such as <i>Tamarix</i>. Many species of this genus show convergent morphology, which leads to frequent errors in their identification. Highly variable genetic markers, such as microsatellites or short sequence repeats (SSR), could be used to differentiate species where DNA barcodes fail. Here, we tested the ability of both, five different marker regions (<i>rbcL</i>, <i>matK</i>, ITS, <i>trnH</i>-<i>psbA</i>, and <i>ycf1</i>), and 14 microsatellites, to properly identify <i>Tamarix</i> species, especially those from the Mediterranean Basin, and compared the pros and cons of the different analytical methods for species identification. DNA barcoding allows the genetic identification of certain species in <i>Tamarix</i>. The two-locus barcodes <i>matK</i>+ITS and ITS+<i>ycf1</i> were the best-performing combinations, allowing up to 69% and 70%, respectively, correct identification. However, DNA barcoding failed in phylogenetically close groups, such as many Mediterranean species. The use of SSR can aid the identification of species, and the combination of both types of data (DNA barcoding and SSR) improved the success. The combination of data was especially relevant in detecting the presence of hybridization processes, which are common in the genus. However, caution must be exercised when choosing the clustering methods for the SSR data, since different methods can lead to very different results.</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Albania
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Institute of Food Safety and Veterinary</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Estonia
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Veterinary and Food Board</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Finland
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Finnish Food Authority</p>
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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.