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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 – Poland
<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 Veterinary Research 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 – The Netherlands
<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: Netherlands Food and Consumer Product Safety 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 2019/2020 – Italy
<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: Istituto Zooprofilattico Sperimentale delle Regioni Lazio e Toscana</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 – North Macedonia
<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: Faculty of Veterinary Medicine - Skopje.</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 – Iceland
<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: Icelandic Food and Veterinary 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 2019/2020 – Slovenia
<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: Ministry for Agriculture, Forestry and Food</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 – Denmark
<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 Institute - The Technical University of Denmark</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 – Latvia
<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: Assessment and Registration Agency of Food and Veterinary Service of Latvia</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 – 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>
Future Heat Stress Indicators for Johannesburg and Ekurhuleni
<p>This dataset is part of the scientific paper: Souverijns, N., De Ridder, K., Veldeman, N., Lefebre, F., Kusambiza-Kiingi, F., Memela, W., Jones, N.K.W., 2022. Urban heat in Johannesburg and Ekurhuleni, South Africa: A meter-scale assessment and vulnerability analysis. Urban Climate, 46, 101331. <a href="https://doi.org/10.1016/j.uclim.2022.101331">https://doi.org/10.1016/j.uclim.2022.101331</a></p> <p>Heat stress indicators for present and future climates for the cities of Johannesburg and Ekurhuleni (South Africa).at 30m spatial resolution calculated with UrbClim (De Ridder et al., 2015) for the following scenarios:</p> <p>- 2001-2020: present time (ERA5 input data)</p> <p>- 2021-2040 RCP4.5 (CMIP5 ensemble)</p> <p>- 2021-2040 RCP8.5 (CMIP5 ensemble)</p> <p>- 2041-2060 RCP4.5 (CMIP5 ensemble)</p> <p>- 2041-2060 RCP8.5 (CMIP5 ensemble)</p> <p>For each of the 23 indicators, an overview image is available at high resolution. Furthermore, georeferenced GeoTiffs are available to visualize & analyse the indicators on the users preferred system. The indicators are available in EPSG4326/WGS84. A description of each indicator is given:</p> <p>- Cooling degree hours: Annual number of hours during which the temperatures rises over 25°C, multiplied by the number of degrees the temperature rises above 25°C. This is an international standard to estimate the energy demand for air conditioning use.</p> <p>- CSDI: Cold spell duration index. Annual number of days with at least six consecutive days when the night temperature < 10th percentile</p> <p>- Heatwave days: Number of heatwave days per year calculated following the definition of the South African Weather Service. If the maximum temperature at a particular town is expected to meet or exceed 5 degrees C above the average maximum temperature of “the hottest month” for that particular place (this is 32°C for Johannesburg & Ekurhuleni), as well as persisting in that mode for 3 days or more (https://www.weathersa.co.za/home/weatherques)</p> <p>- T2M_daily_mean_max: Average daily maximum temperature</p> <p>- T2M_daily_mean_max_topography: Daytime Urban Heat Island corrected for topography</p> <p>- T2M_daily_mean_min: Average daily minimum temperature</p> <p>- T2M_daily_mean_min_topography: Nighttime Urban Heat Island corrected for topography</p> <p>- T2M_dayover25: Annual number of days attaining a maximum temperature > 25°C</p> <p>- T2M_dayover25_duetourban: Annual number of days attaining a maximum temperature > 25°C that are caused by urban canopy</p> <p>- T2M_dayover30: Annual number of days attaining a maximum temperature > 30°C</p> <p>- T2M_dayover30_duetourban: Annual number of days attaining a maximum temperature > 30°C that are caused by urban canopy</p> <p>- T2M_max: Absolute maximum temperature</p> <p>- T2M_mean: Absolute average temperature</p> <p>- T2M_min: Absolute minimum temperature</p> <p>- T2M_nightover20: Number of nights attaining a minimum temperature not dropping below 20°C</p> <p>- T2M_nightover25: Number of nights attaining a minimum temperature not dropping below 25°C</p> <p>- TN10P: Annual number of days when the night temperature < 10th percentile</p> <p>- TN90P: Annual number of days when the night temperature > 90th percentile</p> <p>- TN_max: Annual minimum nighttime warmest temperature</p> <p>- TX10P: Annual number of days when the day temperature < 10th percentile</p> <p>- TX90P: Annual number of days when the day temperature > 90th percentile</p> <p>- TX_min: Annual maximum daytime coolest temperature</p> <p>- WSDI: Warm Spell Duration Index. Annual number of days with at least six consecutive days when daytime temperatues > 90th percentile</p>
Quality of care and performance indicators of mental health supported accommodation services in England
<p class="MsoNormal"><span>This dataset includes data from Mental Health supporting accommodation services in England. It includes information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript published in Plos One: "Almeda, N., García-Alonso, C. R., Killaspy, H., Gutiérrez-Colosía, M. R., & Salvador-Carulla, L. (2022). The critical factor: The role of quality in the performance of supported accommodation services for complex mental illness in England. Plos One, 17(3), e0265319. https://doi.org/10.1371/journal.pone.0265319"</span></p> <p class="MsoNormal"><span>The research associated with the present data focused on developing an analytical process for assessing the performance of the Mental health (MH) supporting accommodation services from 14 different regions of England considering the effect of the quality-of-care indicators in the performance. For doing so every service was classified in Residential Care (move on and non-move on oriented), Supported Housing or Floating Outreach. Then, information about the quality-of-care was collected from each domain of the instrument QuIRC-SA. Finally, a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence was used.</span></p> <p class="MsoNormal"><span>The main results of the analyses pointed out that the incorporation of quality domains as variables (outputs) in DEA had a neutral-positive or positive global impact on the performance of MH-supported accommodation services.</span></p>
IIb-RAD-seq coupled with random forest classification indicates regional population structuring and sex-specific differentiation in salmon lice (Lepeophtheirus salmonis)
<p><span>The aquaculture industry has been dealing with salmon lice problems forming serious threats to salmonid farming. Several treatment approaches have been used to control the parasite. Treatment effectiveness must be optimized, and the systematic genetic differences between sub-populations must be studied to monitor louse species and enhance targeted control measures. We have used IIb-RAD sequencing in tandem with a random forest classification algorithm to detect the regional genetic structure of the Norwegian salmon lice and identify important markers for sex differentiation of this species. We identified 19428 single nucleotide polymorphisms (SNPs) from 95 individuals of salmon lice. These SNPs, however, were not able to distinguish differential structure of lice populations. Using the random forest algorithm, we selected 91 SNPs important for geographical classification and 14 SNPs important for sex classification. The geographically important SNP data substantially improved the genetic understanding of the population structure and classified regional demographic clusters along the Norwegian coast. </span><span>We also uncovered SNP markers that could help determine the sex of the salmon louse. </span><span>A large portion of the SNPs identified to be under directional selection were also ranked highly important by random forest. According to our findings, there is a regional population structure of salmon lice associated with the geographical location along the Norwegian coastline.</span></p>
SiEUGreen_Dataset_for_Monitoring_the_contribution_of_urban_agriculture_to_urban_sustainability:_an_indicator-based_framework
<p>The data was collected for scientific publication: Tapia, C., Randall, L., Wang, S.; Borges, L. A. (2021): Monitoring the contribution of urban agriculture to urban sustainability: an indicator-based framework. <em>Sustainable Cities and Society</em>. In press, <a href="https://doi.org/10.1016/j.scs.2021.103130">https://doi.org/10.1016/j.scs.2021.103130</a></p> <p>This dataset includes the data from the survey in Brabrand Fallaesgartneriet, which is the study case reported in the article.</p>
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Allen Brain Atlas
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OpenNeuro
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