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3,788 results for “indices”
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – 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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 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 2020/2021 – Belgium
<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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection SCIENSANO</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 2020/2021 – France
<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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: French Agency for Food, Environmental and Occupational Health & Safety</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 2020/2021 – Austria
<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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Austrian Agency for Health and Food Safety</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 2020/2021 – Lithuania
<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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: National Food and Veterinary Risk Assessment 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 2020/2021 – Romania
<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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Institute of Hygiene and Veterinary Public Heath and Institute for Diagnosis and Animal Health</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 2020/2021 – Germany
<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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods, as well as the voluntary reporting of and Whole Genome Sequencing (WGS) data are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Federal Institute for Risk Assessment</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 2020/2021 – 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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 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 2020/2021 – 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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods, as well as the voluntary reporting of and Whole Genome Sequencing (WGS) data are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Finnish Food Authority</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 2020/2021 – Luxembourg
<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 (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Administration des services vétérinaires</p>
Data from: Quantifying neighbour effects on tree growth: are common "competition" indices biased?
<p>1. Interactions among neighbouring plants are key determinants of plant growth. To characterise the cumulative effect of all neighbours on the growth of a focal plant, neighbourhoods are often described by 'competition' indices. Common competition indices calculate the summed size of neighbour plants (focal-independent index) whilst others include the summed ratio of the neighbour size relative to focal plant size (focal-dependent). A frequently overlooked statistical artifact is that focal-dependent indices may lead to biased estimates of neighbourhood effects on plant growth when growth is size-dependent.</p> <p>2. Here, we conduct a literature search to determine the most common index types used to explain neighbour effects on tree growth. We then assess the ability of two common index types – focal-dependent and focal-independent – to correctly infer neighbourhood effects in (1) observations of tree growth in an experimental forest in south-east Tasmania, Australia, and (2) an artificially created dataset where tree growth is unrelated to the neighbourhood.</p> <p>3. Both indices detected the competitive neighbourhood effect on tree growth observed in our own dataset but differed in their conclusion regarding neighbour effects in the simulated data. Despite the simulated dataset being generated so there was no relationship between tree growth and their neighbourhood, the focal-dependent index detected strong, competitive neighbourhood effects when intrinsic growth was incorrectly related to tree size. In contrast, when we considered the focal-independent index as the neighbourhood metric, we correctly did not detect any neighbourhood effects in the simulated data regardless of how size-dependent growth was described.</p> <p>4. <em>Synthesis</em>. 'Competition' indices are a useful method to characterise the cumulative neighbourhood effect on plant growth, however, we demonstrate that indices which include the size of the focal plant in their calculation can be biased by an inherent relationship between tree growth and initial size. Whilst this bias typically overstates the strength of competition in determining focal tree growth, we show that it can be mitigated by correctly describing intrinsic growth. We discuss the limitations of both index types, provide recommendations for performing statistical modelling, and outline how to check for accurate neighbour inference.</p>
Greenspace and bluespace quality indicators
<p>Data extracted from literature on green and blue space quality indicators. Protocol for data extraction found here:</p> <pre>https://doi.org/10.5281/zenodo.6594870</pre> <p>Funded by Rural and Environment Science and Analytical Services Division (JHI-C6-1)</p>
Data for: Morphological covariance and onset of foot prehensility as indicators of integrated evolutionary dynamics in the herons (Ardeidae)
<p>The ultimate form an organism attains is based, in part, on the rate and timing of developmental trajectories and on compensatory relationships between morphological traits. For example, there is often an inverse correlation between the relative size of an organism's head and the length of its legs. Avian examples with disproportionately small heads and long legs include ostriches (Struthionidae), flamingos (Phoenicopteridae), cranes (Gruidae), stilts (Recurvirostridae), and storks (Ciconiidae). To determine whether a possible compensatory relationship exists between relative head size and hind-limb length in a typically long-legged family of birds—the Ardeidae—we measured skull dimensions (length, width, and height of cranium, and total skull length, including culmen) and skeletal hind-limb dimensions (femur, tibiotarsus, and tarsometatarsus) of the 12 North American species (north of Mexico) and of 12 additional taxa, including the morphologically divergent Agamia and Cochlearius. Our analyses reveal a negative allometric relationship between head size and leg length. For example, <em>Ardea</em> species exhibit the smallest relative head sizes and the longest legs, while <em>Butorides</em>, <em>Nycticorax</em>, <em>Nyctanassa</em>, and <em>Cochlearius</em> have among the largest heads relative to hind-limb length. Furthermore, both positive and negative allometries occur in paired comparisons between the three hind-limb bones, resulting in tall morphotypes having disproportionately short femurs while small morphotypes exhibit long femurs; this relationship has implications for foraging behavior. Moreover, long legs of <em>Ardea</em> apparently derive from an extended growth period, or hypermorphosis, while relatively short legs of <em>Butorides</em> result from growth truncation. The latter are thus morphologically paedomorphic features that, paradoxically, permit a functional precociality of the hind limbs: early onset of prehensile ability of the feet for grasping branches, which nestlings retain into adulthood, later expressed in foraging mode. This developmentally accelerated prehensile function in small species may be attributed, in part, to selection for predator avoidance in the early nestling stage.</p>
Measures of urban form and mobility energy use indices for each census tract in the United States
<p>This dataset contains data on urban form (the configuration of the built environment) for each census tract in the United States, encompassing density (destination access), land use diversity (entropy), road network properties, road network capacity relative to the surrounding population, and public transit access. Metrics are measured around the centroid of each census tract in multiple given radii. The data also contain other publicly available metrics for each census tract that may be helpful, such as each tract's associated city, zipcode, and county name, area and water area, and centroid coordinates. Certain measures resemble those available in the U.S. Environmental Protection Agencies' Smart Location database or were derived from them, while others were compiled using additional data sources and the statistical model presented in the associated main article. Specifically, the data presented here contain travel energy use indices for each census tract, reflecting the estimated difference in daily land-based mobility energy use per capita relative to the baseline (the U.S. average) as a result of that environment's particular urban form. </p>
Raw_data_Combining several indicators to assess the effectiveness of tailor-made health plans in pig farms
<p>Raw data for a paper submitted to Peer Community In Animal Science. </p>
Data from: Rhizodeposition through root senescence and root exudation of atmospheric C and N by legumes is controlled by traits indicative of resource acquisition and root development
<p><span>Legume crop production has many benefits for agricultural systems. Through the rhizodeposition process, they release a significant amount of C and N into the soil, increasing soil organic C and reducing the use of N fertilizer. Rhizodeposition is known as a dynamic process influenced by many factors. </span></p> <p><span>The aim of this study was to study the contribution of root exudation and root senescence to the rhizodeposition of atmospheric C and N during vegetative and reproductive growth in annual and perennial legumes and to understand how this is linked to the fixation capacities of C and N and root functional traits.</span></p> <p><span>An original approach that combined <sup>13</sup>CO<sub>2</sub> labeling and the <sup>15</sup>N dilution method was developed to measure the rhizodeposition of atmospheric C and N throughout plant growth by two annual grain legumes (pea and faba bean) and two perennial forage legumes (white and crimson clovers).</span></p> <p><span>C rhizodeposition was found to increase proportionally with N rhizodeposition during reproductive development and the differences observed between species were related to the C and N fixation abilities. The use of root traits such as specific root length, root tissue density, and root dry matter content suggests a strong contribution of root exudation to C rhizodeposition at vegetative growth and a strong contribution of root senescence to both C and N rhizodeposition during reproductive growth.</span></p> <p><span><em><strong>Synthesis:</strong></em> Both C and N rhizodeposition appeared to be controlled by traits indicative of resource acquisition and root development. </span></p>
Comparing Finnish universities' publication profiles using multidimensional field-normalized indicators - dataset
<p>This dataset from the VIRTA Publication Information Service consists of the metadata of 241,575 publications of Finnish universities (publication years 2016–2021) merged from yearly datasets downloaded from <a href="https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot">https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot</a>.</p> <p>The dataset contains following information:</p> <ul> <li>Organisation: name of the university</li> <li>Publication year: the year of publication</li> <li>Subfield: one of 66 fields of science based on Statistics Finland field of science classification (in Finnish), see the classification in English: <a href="https://www2.stat.fi/en/luokitukset/tieteenala/">https://www2.stat.fi/en/luokitukset/tieteenala/</a></li> <li>Peer-reviewed: 1=peer-reviewed publications, 0=not peer-reviewed publications</li> <li>Science communication: 1=publications aimed at professional and general audiences, 0=peer-reviewed and not peer-reviewed publications aimed at academic audience.</li> <li>Bibliodiversity: 1=peer-reviewed book publications (chapters, monographs and edited volumes) and conference articles, 0=peer-reviewed journal articles.</li> <li>Multilingualism: share of peer-reviewed publications in languages other than English (Finnish, Swedish and other languages).</li> <li>Domestic publishing: 1=peer-reviewed publications in journals and books published in Finland, 0=peer-reviewed publications in journals and books published outside Finland.</li> <li>Domestic collaboration: 1=peer-reviewed publications with co-authors from more than one Finnish university, 0=peer-reviewed publications without co-authors from more than one Finnish university.</li> <li>International collaboration: 1=share of peer-reviewed publications with co-authors affiliated with foreign institutions, 0=share of peer-reviewed publications without co-authors affiliated with foreign institutions.</li> <li>Research performance: 1=peer-reviewed outputs in JUFO levels 2 (“leading”) and 3 (“top”) publication channels, 0=peer-reviewed outputs in JUFO levels 1 (“basic”) and 0 (“other”) publication channels.</li> <li>Open access: 1=peer-reviewed open access publications, including gold, hybrid and green OA, 0=peer-reviewed closed publications.</li> </ul>
The species coalescent indicates possible bat and pangolin origins of the COVID-19 pandemic
<p>A consensus species tree is reconstructed from 11 gene trees for human, bat, and pangolin beta coronaviruses from samples taken early in the pandemic (prior to April 1, 2020). Using coalescent theory, the shallow (short branches relative to the hosts) consensus species tree provides evidence of recent gene flow events between bat and pangolin beta coronaviruses predating the zoonotic transfer to humans. The consensus species tree was also used to reconstruct the ancestral sequence of human SARS-CoV-2, which was 2 nucleotides different from the Wuhan sequence. The time to most recent common ancestor was estimated to be Dec 8, 2019, with a bat origin. Some human, bat, and pangolin coronavirus lineages found in China are phylogenetically distinct, a rare example of a class II phylogeography pattern (Avise et al. in Ann Rev Eco Syst 18:489–422, 1987). The consensus species tree is a product of evolutionary factors, providing evidence of repeated zoonotic transfers between bat and pangolin as a reservoir for future zoonotic transfers to humans.</p>
ARISE-SAI_1.5: UKESM1 Extreme Temperature and Precipitation Indices
<p>The UKESM1 ARISE experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere. The injections occur at four different latitudes: 15 degrees N & S and 30 degrees N & S, at an altitude of approximately 20 km.</p> <p>The simulations are based on the medium-emissions CMIP6 scenario ssp245 and cover the years 2035 to 2070. They form a 5-member ensemble with initial conditions taken from the corresponding five members of the ssp245 simulations upon which they were based. Data for the UKESM1 ARISE-SAI-1.5 simulations is available at https://data.ceda.ac.uk/badc/deposited2022/arise and the data for the UKESM1 SSP2-4.5 simulations is available on the Earth System Grid Federation database.</p> <p>The files included here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022).</p> <p><strong>Precipitation Indices</strong></p> <p>PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT</p> <p><strong>Temperature Indices</strong></p> <p>TNN, TNX, FD, TR, TN90, TN10, TXX, TXN, ID, SU, TX90, TX10</p>
Data World Bank Indicators
<p>The Dataset consist of the indicators which are used to select datasets using the library:<br> import world_bank_data as wb # https://github.com/mwouts/world_bank_data<br> method wb.get_series<br> <br> indicators_education.csv<br> indicators_gender.csv<br> indicators_health.csv<br> indicators_worlddevelopment.csv<br> <br> The files are combined together in the corresponding csv files:<br> wb_education.csv', <br> wb_gender.csv', <br> wb_health.csv', <br> wb_worlddev.csv'<br> <br> <br> <br> They have the following licenses:<br> https://datacatalog.worldbank.org/public-licenses#cc-by</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.