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809 results for “complement”
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 – Switzerland
<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: Federal Food Safety and Veterinary Office</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 – Croatia
<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: Croatian Veterinary Institute</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 – Portugal
<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: Instituto Nacional de Investigação Agrária e Veterinária</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 – Hungary
<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: National Food Chain Safety Office</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 – Norway
<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: Norwegian Veterinary Institute</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 – Slovakia
<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: State Veterinary and Food Institute</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 – Ireland
<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: The Food Safety Authority of Ireland</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 – The United Kingdom
<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: Animal and Plant Health Agency</p>
Data complementing the Avian influenza overview December 2021 – March 2022
<p>The Annexes contain data and information on highly pathogenic avian influenza outbreaks in poultry and detections in wild birds in Europe</p> <p>Annex A – Characteristics of the HPAI A(H5Nx)-positive poultry establishments</p> <p>Annex B – Applied prevention and control measures on avian influenza</p> <p>Annex C – Data on wild birds</p>
Datasets for the publication " Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil"
<p>This repository contains the datasets for the publication "Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil".</p> <ul> <li><strong>Bias correction</strong></li> <li>Technical data of existing farms (ABBEólica) </li> <li>Bias correction factors at the farm level</li> </ul> <p> </p> <ul> <li><strong>Demand</strong></li> <li>Simulated wind and solar power</li> <li>Biomass, nuclear, and small hydropower generation in 2019</li> <li>Raw demand data</li> <li>Updated demand </li> </ul> <p> </p> <ul> <li><strong>Hydropower time series</strong></li> <li>Affluent Natural energy of run-of-rivers (fio d'água, in Portuguese) and reservoirs (reservatórios, in Portuguese), installed capacity, and maximal storage</li> </ul> <p> </p> <ul> <li><strong>Offshore wind farms</strong></li> <li>Locations, coordinates, water depth, available areas, water depth, distance to shore, technology,and maximal capacity;</li> <li>Code to estimate offshore wind farm capex and opex.</li> </ul> <p> </p> <ul> <li><strong>Results of Calliope model </strong></li> <li>capacity</li> <li>carrier_prod (power generation)</li> <li>storage</li> <li>costs</li> <li>emissions</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Different taxonomic and functional indices complement the understanding of herb-layer community assembly patterns in a southern-limit temperate forest
<p><span>The efficient conservation of vulnerable ecosystems in the face of global change requires a complete understanding of how plant communities respond to various environmental factors. We aim to demonstrate that a combined use of different approaches, traits, and indices representing each of the taxonomic and functional characteristics of plant communities will give complementary information on the factors driving vegetation assembly patterns. We analyzed variation across an environmental gradient in taxonomic and functional composition, richness, and diversity of the herb-layer of a temperate beech-oak forest that was located in northern Spain. We measured species cover and four functional traits: leaf dry matter content (LDMC), specific leaf area (SLA), leaf size, and plant height. We found that light is the most limiting resource influencing herb-layer vegetation. Taxonomic changes in richness are followed by equivalent functional changes in the diversity of leaf size but by opposite responses in the richness of SLA. Each functional index is related to different environmental factors even within a single trait (particularly for LDMC and leaf size). To conclude, each characteristic of a plant community is influenced by different and even contrasting factors or processes. Combining different approaches, traits, and indices simultaneously will help us understand how plant communities work.</span></p>
Dataset: Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation
<p>This repository contains the files associated with the following article:</p> <p>Jesús Sandoval-Martínez, Ernesto I. Badano, Francisco A. Guerra-Coss, Jorge A. Flores Cano, Joel Flores, Sandra Milena Gelviz-Gelvez, Felipe Barragán-Torres, “Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation”, submitted to <em>Journal of Environmental Management</em>.</p> <p><strong>Supplementary material 01 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. A file correspond to <em>Vachellia pennatula</em> and the another file correspond to <em>Prosopis laevigata</em>. In both files, the first spreadsheet shows the occurrence data (latitude and longitude) used to calibrate the distribution model (SDM) of the corresponding species, the current values of the 19 bioclimatic variables associated with these coordinates and the Spearman correlation coefficients used to select the variables included in the SDM (selected variables are indicated in green). The second spreadsheet shows the current habitat occupancy probabilities of the target species estimated with the SDM at the geographic coordinates of occurrence points, while the table on the side shows the fraction of true presences dropping at the following probability categories: (1) habitat occupancy probabilities below 0.1 = unsuitable spatial units for the species, (2) habitat occupancy probabilities between 0.1 and 0.4 = barely suitable spatial units for the species, (3) habitat occupancy probabilities between 0.4 and 0.7 = moderately suitable spatial units for the species, and (4) habitat occupancy probabilities above 0.7 = highly suitable spatial units for the species. The third spreadsheet shows the one-thousand random geographic coordinates and the corresponding current and future habitat occupancy probabilities of each species. Future habitat occupancy probabilities are provided for three time periods (2041-2060, 2061-2080 and 2081-2100) at four radiative forcing levels each (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>).</p> <p><strong>Supplementary material 02 </strong>is a compressed file that contains a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>. Each of these folders contains the summaries of the MaxEnt outputs that support the results of the corresponding SDM.</p> <p><strong>Supplementary material 03 </strong>is a compressed Keyhole Markup Language file (KMZ) that contains interactive maps that are optimized for the desktop version of Google Earth. To accelerate visualization of maps, we recommend installing this software in a computer meeting the following requirements: CPU Intel Core i5 9<sup>th</sup> generation or higher, CPU clock speed 1.8 GHz or higher, random-access memory (RAM) 8 GB or higher, and video random access memory (VRAM) 1 GB or higher. Otherwise, opening this file may take several minutes. These maps are organized in a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>, which must be expanded for accessing the following information (click on the arrow on the left of folders to expand them):</p> <ul> <li><strong>Current climate </strong>– Activating this folder (click the fox on the left of the folder) display the map of habitat occupancy probabilities of species across Mexico under the current climate.</li> <li><strong>Period 2041-2060, 2061-2080 and 2081-2100 </strong>– Expanding each of these folders (click on the arrow on the left of folders) shows four subfolders that correspond to different radiative forcing levels (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>). Activating each of these sub folders (click the fox on the left of subfolders) display the map of habitat occupancy probabilities of species across Mexico expected on the corresponding time period and radiative forcing level. These maps also show the areas classified as climatically unsuitable in the multivariate environmental similarity surface (MESS) analysis. Clicking on the names of subfolders displays a figure showing the relationship between current and future habitat occupancy probabilities of the species on the corresponding time period and radiative forcing level. In these figures, the red line is the empirical relationship between these variables and the solid blue line is the theoretical relationship with intercept = 0 and slope = 1. The statistical results that support these relationships are also shown in these figures.</li> </ul> <p><strong>Supplementary material 04 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. the file labeled as “Microclimate data” contains two spreadsheets, which correspond to the temperature and rainfall values measured in controls under the current climate and climate change simulation plots located of the field experiments. The file levelled as “Seedling emergence and survival” contains a spreadsheet for <em>Vachellia pennatula</em> and another one for <em>Prosopis laevigata</em>, which contains the data used to estimate the seedling emergence and survival rates in controls and climate change simulation plots.</p>
Tables, Figures and Country Datasets complementing the European Union Summary Report on Zoonoses and Food-borne Outbreaks 2016
<p>All summary tables and figures produced for the European Union Summary Report on Zoonoses and Food-borne Outbreaks 2016 are provided.</p> <p>The Appendix file (Excel file) allows the user to filter by chapter the corresponding summary tables and figures with their abbreviated file name and titles.</p> <p>Lastly, all country data are published as supporting information to this report</p>
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - North Macedonia
<p>This dataset contains ASF laboratory analytical results in domestic pigs and wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2023_MK - Food and Veterinary Agency (FVA)</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)*</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)</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>
Scans of leaves dissected in phyllotactic order: complementation of ago7 mutant A. thaliana plants with truncated promoter transgenes
<p>This transgenic complementation experiment is described in a paper by Hoyer et al. (2019): <a href="https://doi.org/10.1002/pld3.102">https://doi.org/10.1002/pld3.102</a></p> <p>Adaxial (ad) and abaxial (ab) surfaces of rosettes were photographed in between removal of leaves, to enable checking that leaves were removed in the correct phyllotactic order.<strong> </strong>A manifest file with file SHA-1 sums is included.</p> <p>Metadata and LeafJ measurements have been made available separately, to facilitate updates -- see <a href="https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata">https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata</a>, archived as <a href="https://doi.org/10.5281/zenodo.1340636">https://doi.org/10.5281/zenodo.1340636</a></p> <p>Seed was plated and growth started on 2016-08-26. Leaves were dissected and scanned 33 and 35 days post-stratification (2016-09-28 and 30).</p>
Time-lapse photograph dataset: complementation of ago7 mutant A. thaliana plants with truncated promoter transgenes
<p>This transgenic complementation experiment is described in a paper by Hoyer et al. (2019): <a href="https://doi.org/10.1002/pld3.102">https://doi.org/10.1002/pld3.102</a></p> <p>Time-stamped photographs are provided in twelve directories by camera (twelve overlapping fields of view) and a manifest file with file SHA-1 sums is included.</p> <p>Metadata have been made available separately, to facilitate updates -- see <a href="https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata">https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata</a>, archived as <a href="https://doi.org/10.5281/zenodo.1340636">https://doi.org/10.5281/zenodo.1340636</a></p> <p>Seed was plated and growth started on 2016-08-26.</p>
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility [complement to Supplementary Material 2]
<p>3D micro surface data processed in ConfoMap v7.4.8633 (a derivative of MountainsMap Imaging Topography developed by Digital Surf, Besançon, France).</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Tables, Figures and Country Datasets complementing the European Union Summary Report on Zoonoses and Food-borne Outbreaks 2017
<p>All summary tables and figures produced for the European Union Summary Report on Zoonoses and Food-borne Outbreaks 2017 are provided.</p> <p>The Appendix file (Excel file) allows the user to filter by chapter the corresponding summary tables and figures with their abbreviated file name and titles.</p> <p>Lastly, all country data are published as supporting information to this report.</p>
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility [complement to Supplementary Material 4]
<p>Python script and results of the Bayesian Multi-factor ANOVA.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Isotopes complement morphology: Niche partitioning among greenbuls in the Afrotropical lowland forest
<p><a name="_Hlk153960869"></a>Biodiversity plays a vital role in ecosystem functioning, so understanding how species coexist is a cornerstone of ecology. However, despite decades of research, our current knowledge is incomplete due to methodological limitations and sampling bias, particularly in the species-rich tropics. In this study, we combined bill and body morphological traits with stable isotopes in feathers to quantify niche differentiation among six co-occurring greenbul taxa, a diverse group of frugivorous and insectivorous passerines with remarkable similarities in body shape, in the lowland rainforests of Mt. Cameroon, West Central Africa. Our results showed that the greenbul’s niche space was primarily differentiated by variations in body morphology, with Yellow-lored Bristlebill <em>Bleda notatus</em> and Eastern Bearded Greenbul <em>Criniger chloronotus</em> occupying ecological niches distinct from the remaining four taxa, while bill morphology indicated substantial overlap between the taxa. In addition, isotopic composition of the feathers revealed a separation of Western Greenbul <em>Arizelocichla tephrolaema</em> from the other taxa. Our results show that the integration of morphological and isotopic data can provide robust estimates of niche overlaps, providing evidence for the differentiation of ecological roles. This highlights the importance of integrating variable traits to improve our understanding of how animals exploit the multidimensional niche space that enables their coexistence.</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.