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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Nguyen, Hong Anh; MULLER, BERTRAND; Martre, Pierre;

    The dataset comprises estimates of six major parameters of the root system architecture model ArchiSimple for 14 bread wheat cultivars grown in several experiment experiments carried out two high-throughput root phenotyping platforms (4MPI at INRAE, UMR AgroEcologie and RootPheAir at UCL) with different growth media and non-destructive imaging systems together with a conventional set-up in the green house in the Montpellier Plant Phenotyping Platforms (M3P, INRAE, UMR LEPSE) with a solid growth medium. Data for the two to three replicate plants in each experiments are reported. The dataset also include daily weather data in each experiment. A detailed description of the plant growth conditions and Methods can be found in theassociated publication.

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    ZENODO
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      ZENODO
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    Authors: Sundermann, Esther M.; Nauta, Maarten; Swart, Arno;

    Figure 3 The probability of illness and infection for the human population after consumption of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the challenge studies dataset with 1000 various mean doses (the so-called ChallengeVarMeanDoses simulation).

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    Authors: Di Piazza, Giulio; Dujardin, Bruno; Levorato, Sara; Medina, Paula; +3 Authors

    Aiming at accelerating the implementation of cumulative risk assessment to pesticide residues, EFSA performed a two‐step prioritisation analysis, on individual pesticides and on target organ systems, that allows to identify (i) low‐priority substances expected to have a marginal contribution to cumulative risk, and (ii) high priority organ systems to be addressed in future cumulative risk assessments. The analysis encompassed 350 substances and 36 raw primary commodities of plant origin surveyed in the monitoring cycle 2019–2021, carried out in 30 population groups, covering 3 age classes, and 17 EU countries. Probabilistic exposure calculations, for chronic and acute effects, were executed on the occurrence and consumption data by a two‐dimensional procedure, modelling variability and uncertainty. The input data for the prioritsation of chronic and acute exposure risk assessment to pesticide residues and the output data produced are reported in the following annexes: Annex A.1 – Input data for the prioritisation of chronic cumulative risk assessment to pesticide residues;Annex A.2 – Input data for the prioritisation of acute cumulative risk assessment to pesticide residues;Annex B.1 – Output data from the chronic exposure assessment of pesticides that have an EU-agreed health-based guidance value;Annex B.2 – Output data from the chronic exposure assessment of pesticides that have a tentative health-based guidance value;Annex B.3 – Output data from the acute exposure assessment of pesticides that have an EU-agreed health-based guidance value;Annex B.4 – Output data from the acute exposure assessment of pesticides that have a tentative health-basedguidance value;Annex C.1 – Output data from the combined exposure assessment of pesticides that may have chronic effects on theinvestigated target organ systems;Annex C.2 – Output data from the combined exposure assessment of pesticides that may have acute effects on theinvestigated target organ systems Detailed information on methodology, results and conclusions are included in the full report 'Prioritisation of pesticides and target organ systems for dietary cumulative risk assessment based on the 2019–2021 monitoring cycle', DOI: 10.2903/j.efsa.2024.8554.

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    Authors: EFSA CONTAM Panel; Dieter, Schrenk; Bignami, Margherita; Bodin, Laurent; +27 Authors

    Annexes to the Risk to human health related to the presence of perfluoroalkyl substances in food - available at: https://www.efsa.europa.eu/en/efsajournal/pub/6223 Annex A - Occurrence and exposure data Annex B - Distribution of analytical results Annex C - Comparison of PFOA and PFOS occurrence and exposure data with previous assessment (EFSA CONTAM Panel, 2018). Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields).

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      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    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) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. Reporting authorities contributing to 2022 AMR data collection: Netherlands Food and Consumer Product Safety Authority

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    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) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. Reporting authorities contributing to 2022 AMR data collection: Icelandic Food and Veterinary Authority

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
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    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) 2020/1729. 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.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    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) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. Reporting authorities contributing to 2022 AMR data collection: Ministry for Agriculture, Fisheries and Animal Rights

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    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) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. Reporting authorities contributing to 2022 AMR data collection: Food and Veterinary Service of the Republic of Latvia

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    Authors: European Food Safety Authority;

    The Data Collection Framework (DCF) application is constituted of an interactive web-based application that aims at facilitating data exchange, data extraction, and data reusability. A harmonized terminology is used to collect and analyse data in a coherent way with the aim to support scientific research. DCF_catalogues file contains all the valid catalogues published in the DCF. Catalogue format is compatible with Catalogue Browser (java application available here: https://github.com/openefsa/catalogue-browser/wiki) Catalogue_list file contains the list of all catalogues, each with its scopenote, explaining the content of the catalogue itself.

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    Authors: Nguyen, Hong Anh; MULLER, BERTRAND; Martre, Pierre;

    The dataset comprises estimates of six major parameters of the root system architecture model ArchiSimple for 14 bread wheat cultivars grown in several experiment experiments carried out two high-throughput root phenotyping platforms (4MPI at INRAE, UMR AgroEcologie and RootPheAir at UCL) with different growth media and non-destructive imaging systems together with a conventional set-up in the green house in the Montpellier Plant Phenotyping Platforms (M3P, INRAE, UMR LEPSE) with a solid growth medium. Data for the two to three replicate plants in each experiments are reported. The dataset also include daily weather data in each experiment. A detailed description of the plant growth conditions and Methods can be found in theassociated publication.

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    Authors: Sundermann, Esther M.; Nauta, Maarten; Swart, Arno;

    Figure 3 The probability of illness and infection for the human population after consumption of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the challenge studies dataset with 1000 various mean doses (the so-called ChallengeVarMeanDoses simulation).

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    Authors: Di Piazza, Giulio; Dujardin, Bruno; Levorato, Sara; Medina, Paula; +3 Authors

    Aiming at accelerating the implementation of cumulative risk assessment to pesticide residues, EFSA performed a two‐step prioritisation analysis, on individual pesticides and on target organ systems, that allows to identify (i) low‐priority substances expected to have a marginal contribution to cumulative risk, and (ii) high priority organ systems to be addressed in future cumulative risk assessments. The analysis encompassed 350 substances and 36 raw primary commodities of plant origin surveyed in the monitoring cycle 2019–2021, carried out in 30 population groups, covering 3 age classes, and 17 EU countries. Probabilistic exposure calculations, for chronic and acute effects, were executed on the occurrence and consumption data by a two‐dimensional procedure, modelling variability and uncertainty. The input data for the prioritsation of chronic and acute exposure risk assessment to pesticide residues and the output data produced are reported in the following annexes: Annex A.1 – Input data for the prioritisation of chronic cumulative risk assessment to pesticide residues;Annex A.2 – Input data for the prioritisation of acute cumulative risk assessment to pesticide residues;Annex B.1 – Output data from the chronic exposure assessment of pesticides that have an EU-agreed health-based guidance value;Annex B.2 – Output data from the chronic exposure assessment of pesticides that have a tentative health-based guidance value;Annex B.3 – Output data from the acute exposure assessment of pesticides that have an EU-agreed health-based guidance value;Annex B.4 – Output data from the acute exposure assessment of pesticides that have a tentative health-basedguidance value;Annex C.1 – Output data from the combined exposure assessment of pesticides that may have chronic effects on theinvestigated target organ systems;Annex C.2 – Output data from the combined exposure assessment of pesticides that may have acute effects on theinvestigated target organ systems Detailed information on methodology, results and conclusions are included in the full report 'Prioritisation of pesticides and target organ systems for dietary cumulative risk assessment based on the 2019–2021 monitoring cycle', DOI: 10.2903/j.efsa.2024.8554.

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    Authors: EFSA CONTAM Panel; Dieter, Schrenk; Bignami, Margherita; Bodin, Laurent; +27 Authors

    Annexes to the Risk to human health related to the presence of perfluoroalkyl substances in food - available at: https://www.efsa.europa.eu/en/efsajournal/pub/6223 Annex A - Occurrence and exposure data Annex B - Distribution of analytical results Annex C - Comparison of PFOA and PFOS occurrence and exposure data with previous assessment (EFSA CONTAM Panel, 2018). Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields).

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    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) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. Reporting authorities contributing to 2022 AMR data collection: Netherlands Food and Consumer Product Safety Authority

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    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) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included. Reporting authorities contributing to 2022 AMR data collection: Icelandic Food and Veterinary Authority

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    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) 2020/1729. 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.

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