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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: EFSA; ECDC;

    All tables produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2022: - Campylobacter - E. coli - MRSA - Salmonella - ESBL Annexes A to F are also included. EU; PDF; biohaw@efsa.europa.eu

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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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/
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    WORKING VERSION. All layers will be visible in this linked webgis app along with estimated error. <== To Be Updated in new final version, not yet so. The layers available in this dataset are in a WGS84 geographic coordinate reference system (EPSG:4326) where latitude and longitude coordinates at 0.0008983 degrees ground sampling distance per cell, which corresponds to about 1 ha, i.e. ~100 m x ~100 m at the equator, but decreases in area with increasing latitude as the coordinate system is not equal-area, e.g. ~70 m west-est direction at 45° latitude and ~50 m west-est direction at 60° latitude. A_Aspect.tif, A_slope.tif and A__elevation.tif - Earth surface height above sea level and morphology. Prefix was added to group them in Zenodo. biomass.tif - Biomass values at year 2020, Mg/ha. canopyBulkDensity.tif - Amount of canopy biomass per volume of canopy (kg/m3) canopyBaseHeight.tif - Height of canopy start measured from the ground (m) canopyCover.tif - percent of canopy coverage - resampled and updated from the map of Hansen et al. 2005 canopyHeight.tif - Total height of canopy from the ground (m) - resampled from the map of Lang, N., et al. (2023) Fuel Model FuelModelClasses_ScottBurgan.tif - the category of Fuel Model according to Scott&Burgan 2005 FuelModelClasses_Aragonese.tif - the category of Fuel Model according to Aragonese et al. 2023 DOI: 10.5194/essd-15-1287-2023 - values are from 1 to 24, with a Look Up Table for correspondence (values are ordered matching the order in table 1 of the article) . FuelModelClasses_ScottBurgan.clr/qml CLR/QML - style file for QGIS FuelModelClasses_Aragonese.clr/qml CLR/QML - style file for QGIS FuelModelPercent.tif - the percent of fuel model category belonging to that pixel, values range between 50 and 100. Allows to spot pixels with expected mixed fuel categories.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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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: EFSA; Di Piazza, Giulio; Dujardin, Bruno; Levorato, Sara; +4 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.  EU; xlsx; mese@efsa.europa.eu;

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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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: EFSA BIOHAZ Panel; Koutsoumanis, Konstantinos; Allende, Ana; Alvarez-Ordonez, Avelino; +23 Authors

    The European Food Safety Authority (EFSA) asked the Panel on Biological Hazards (BIOHAZ) to deliver a scientific Opinion on the maintenance of the list of qualified presumption of safety (QPS) biological agents. The QPS approach was developed by the EFSA Scientific Committee to provide a harmonised generic pre-evaluation to support safety risk assessments of biological agents intentionally introduced into the food and feed chain, in support of the concerned scientific Panels and Units in the frame of market authorisations. The taxonomic identity, body of knowledge, the safety concerns in relation to pathogenicity and virulence, and the safety for the environment of those microbiological agents are assessed. Safety concerns identified for a respective taxonomic unit (TU) are, where possible and reasonable in number, reflected as ‘qualifications’ that are assessed at the strain level by the EFSA’s scientific Panels. The “list of microorganisms with QPS status” first established in 2007, has been revised and updated annually until 2014 via QPS Opinions; since 2014 the updates are carried out and published every 3 years. If new information is retrieved from extended literature searches (ELS) that would change the QPS status of a TU or its qualifications, this is also published in the Panel Statement covering the previous 6-months period. The ELS protocol can be found at https://zenodo.org/doi/10.5281/zenodo.3607188 and the Search strategies are available at: https://doi.org/10.5281/zenodo.3607192. The QPS Panel Statement also includes the evaluation of microbiological agents notified to EFSA within the 6-month period for an assessment for feed additives, food enzymes, food additives and flavourings, and novel foods or plant protection products for a possible QPS status. The new QPS status recommendations are incorporated into the 2022 updated “list of microorganisms with QPS status” is available in this upload. The list of “Microbiological agents as notified to EFSA” from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment can be found at https://doi.org/10.5281/zenodo.3607183. Useful links on EFSA QPS EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps Link to the virtual issue on QPS on Wiley Online Library: https://efsa.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1831-4732.QPS Versions history: Versions 1 and 2 were substituted by version 3 – all are associated with the QPS Panel statement EFSA 7: suitability of taxonomic units notified to EFSA until September 2017: https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2018.5131 Version 4 is associated with the QPS Panel statement EFSA 8: suitability of taxonomic units notified to EFSA until March 2018: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2018.5315 Versions 5 and 6 were substituted by version 7 – all are associated with the QPS Panel statement EFSA 9: suitability of taxonomic units notified to EFSA until September 2018: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5555 Version 8 is associated with the QPS Panel statement EFSA 10: suitability of taxonomic units notified to EFSA until March 2019: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5753 Version 9 is associated with the QPS Panel statement EFSA 11: suitability of taxonomic units notified to EFSA until September 2019: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5965 and with the Scientific Opinion on the update of the list of QPS-recommended biological agents intentionally added to food or feed as notified to EFSA (2017-2019): https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5966 Version 10: updates version 9 following the update of the qualification of Bacillus velezensis as: ‘absence of toxigenic potential and absence of aminoglycoside production ability’ - applied in the QPS Statement part 11 (ON-5965) and in the 2019 Scientific Opinion (ON-5966). Version 11: updates version 10 following the addition of the Bacillus circulans ‘for production purposes only’ - applied in the QPS Statement part 13 (ON-6377) and in the 2019 Scientific Opinion (ON-5966). Version 12 updates version 11 following the addition of the Bacillus paralicheniformis with the qualifications 'absence of toxigenic activity’ and ‘absence of genetic information to synthesize bacitracin'; and adding Schizochytrium limacinum, which is a synomym for Aurantiochytrium limacinum, - applied in the QPS Statement part 14 (ON-6689) and in the 2019 Scientific Opinion (ON-5966). Version 13 updates version 12 following the addition of Haematococcus lacustris synonym Haematococcus pluvialis, recommended for QPS status with the qualification ‘for production purposes only’. Some other changes related to taxonomy and qualifications are described in the QPS Statement part 15 (ON-7045). Version 14, update of version 13, is related to QPS Statement part 16 (ON-7408). Version 15 is related to QPS Statement part 17 (ON-7746). Version 16 is related to QPS Statement part 18 (ON-8092). Version 17 is related to QPS Statement part 19 (ON-8517). Version 18 is also related to QPS Statement part 19 (ON-8517) with a small correction. Note: As of January 2022 the updated list is provided only as excel file format. EU; XLSX; biohaz@efsa.europa.eu

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
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    Authors: Bancheri, Marialaura;

    The database contains the hydrological properties of the soils within the regions of interest presented in the article on the LandSupport Best practice tool

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    Following a request from the European Commission, in 2018 EFSA released a renovated database of host plant species of Xylella spp. (including both species X. fastidiosa and X. taiwanensis) together with a scientific report (EFSA, 2018). EFSA was tasked to maintain and update this database periodically. The mandate now covers the period 2021-2026 and EFSA is requested to release an update of the database twice per year. In December 2023 EFSA released the ninth update of the Xylella spp. host plant database (VERSION 9) with information retrieved from literature search up to June 2023 and recent Europhyt outbreak notifications (EFSA, 2023). The protocol applied for the extensive literature review, data collection and reporting, as well as results and lists of host plants are described in detail in the related scientific report (EFSA, 2023). The overall number of Xylella spp. host plants determined with at least two different detection methods or positive with one method (between: sequencing, pure culture isolation) reaches now 439 plant species, 200 genera and 69 families (category A – see section 2.4.2 of EFSA (2023)). Such numbers rise to 696 plant species, 307 genera and 88 families if considered regardless of the detection method applied (category E, see section 2.4.2 of EFSA (2023)). The Excel files here attached represent the VERSION 9 of the Xylella spp. host plants database. For a detailed description of the information included in the database, please consult the related scientific report (EFSA, 2023). The Excel file "Xylella spp. host plants database – VERSION 9" contains several sheets: the LEGENDA (with extensive description of each table), the full detailed raw data of the Xylella spp. host plant database (sheet "observation") and several examples of data extraction. Additional Excel files contain the lists of host plant species of X. fastidiosa (subsp. unknown (i.e. not reported), fastidiosa, multiplex, pauca, morus, sandyi, tashke, fastidiosa/sandyi) and X. taiwanensis infected naturally, artificially and in not specified conditions, and according to different categories (A, B, C, D, E – see section 2.4.2 of EFSA (2023)). The Excel file "new_host_plant_species_v9" contain the list of new host plant species added to the database in this new update. Question number: EFSA-Q-2022-00815 Output number: EN-8478 Contacts: plants@efsa.europa.eu Bibliography: EFSA (European Food Safety Authority), 2018. Scientific report on the update of the Xylella spp. host plant database. EFSA Journal 2018;16(9):5408, 87 pp. https://doi.org/10.2903/j.efsa.2018.5408 EFSA (European Food Safety Authority), Gibin, D., Gutierrez Linares, A., Fasanelli, E., Pasinato, L., & Delbianco, A. (2023). Update of the Xylella spp. host plant database – systematic literature search up to 30 June 2023. EFSA Journal, 21(12), e8477. https://doi.org/10.2903/j.efsa.2023.8477 EU; xlsx; plants@efsa.europa.eu

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    Authors: Fassò, Alessandro; Rodeschini, Jacopo; Fusta Moro, Alessandro; Shaboviq, Qendrim; +7 Authors

    The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the AgrImOnIA project. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. The building process of the dataset is detailed in the companion paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. SCIENTIFIC DATA, 1-19. available here. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. The files in the record, renamed according to their version (es. .._v_3_0_0), are: Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software. Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied. Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor. Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area. The Agrimonia dataset can be reproduced using the code available at the GitHub page: https://github.com/AgrImOnIA-project/AgrImOnIA_Data UPDATE 31/05/2023 - NEW RELEASE - V 3.0.0 A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable WE_rh_min, WE_rh_mean and WE_rh_max have been recomputed due to some bugs. In addition, two new columns are added, they are LI_pigs_v2 and LI_bovine_v2 and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation. A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered. UPDATE 11/03/2023 - NEW RELEASE - V 2.0.2 A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable WE_tot_precipitation have been recomputed due to some bugs. A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable WE_precipitation_t is corrected. UPDATE 24/01/2023 - NEW RELEASE - V 2.0.1 minor bug fixed UPDATE 16/01/2023 - NEW RELEASE - V 2.0.0 A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed: Added values for LA_land_use variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv) Deleted incorrect values for LA_soil_use variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv) Fixed duplicate sensors corresponding to the same pollutant within the same station (in Metadata_monitoring_network_registry_v_2_0_0.csv)

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    Authors: Fassò, Alessandro; Rodeschini, Jacopo; Fusta Moro, Alessandro; Shaboviq, Qendrim; +6 Authors

    The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the AgrImOnIA project. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. The building process of the dataset is detailed in the companion paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. SCIENTIFIC DATA, 1-19. available here. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. The files in the record, renamed according to their version (es. .._v_3_0_0), are: Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software. Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied. Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor. Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area. The Agrimonia dataset can be reproduced using the code available at the GitHub page: https://github.com/AgrImOnIA-project/AgrImOnIA_Data UPDATE 31/05/2023 - NEW RELEASE - V 3.0.0 A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable WE_rh_min, WE_rh_mean and WE_rh_max have been recomputed due to some bugs. In addition, two new columns are added, they are LI_pigs_v2 and LI_bovine_v2 and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation. A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered. UPDATE 11/03/2023 - NEW RELEASE - V 2.0.2 A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable WE_tot_precipitation have been recomputed due to some bugs. A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable WE_precipitation_t is corrected. UPDATE 24/01/2023 - NEW RELEASE - V 2.0.1 minor bug fixed UPDATE 16/01/2023 - NEW RELEASE - V 2.0.0 A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed: Added values for LA_land_use variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv) Deleted incorrect values for LA_soil_use variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv) Fixed duplicate sensors corresponding to the same pollutant within the same station (in Metadata_monitoring_network_registry_v_2_0_0.csv)

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    ZENODO
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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2022
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    ZENODO
    Dataset . 2023
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      ZENODO
      Dataset . 2023
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      ZENODO
      Dataset . 2023
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      ZENODO
      Dataset . 2023
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      ZENODO
      Dataset . 2022
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      ZENODO
      Dataset . 2023
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      Data sources: Datacite
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    Authors: Stefanini, Federico M.;

    Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-scale disease prediction by incorporating the biological mechanisms of the disease process and the different phenological stages of the vines. In this study, we developed a Bayesian model to predict the probability of Plasmopara viticola infection in grapevines, considering various disease management approaches. To aid decision-making, we introduced a multi-attribute utility function that incorporated a sustainability index for each strategy. The data used in this study were derived from trials conducted during the production years 2018-2020, involving the application of five disease management strategies: conventional Integrated Pest Management (IPM), conventional organic, IPM with substantial fungicide reduction combined with host-defense inducing biostimulants, organic management with biostimulants, and the use of biostimulants only.

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    Dataverse UNIMI
    Dataset . 2023
    Data sources: Dataverse UNIMI
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      Dataverse UNIMI
      Dataset . 2023
      Data sources: Dataverse UNIMI
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    Authors: El-Hokayem, Léonard; De Vita, Pantaleone; Usman, Muhammad; Link, Andreas; +1 Authors

    Groundwater dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat and sustaining human livelihoods. A novel multicriteria framework helps to identify areas where potential groundwater dependent vegetation (pGDV) occurs in the Mediterranean biome. Globally-available datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks are combined in a weighted, easy-to-use index, composed of eleven thematic layers. Input layers for the index calculation are available in the data collection: 1) pre-processed (rasterised and clipped to the Mediterranean) and 2) harmonised and reclassified. All input data was extracted globally. Either, directly from the respective studies or through the data catalogue in the Google Earth Engine. All datasets were acquired and processed in 2022 and 2023. Time series data for potential inflow dependency and Normalized Difference Vegetation Index (NDVI) were extracted for the period 2003-2021. Finally, the mean value was calculated over this period. All other data sets, however, mark a fixed point in time. Ground truth vegetation data was used to calculate layer weightings with a Random Forest. 10 m * 10 m vegetation plots were collected in July and August 2021 and 2022 in southern Italy (Campania region) inside the 'Cilento, Vallo di Diano and Alburni National Park'. 236 vegetation plots are available, containing general information on the vegetation (habitat, species number, stratification), mean indicator values, plant life forms, leaf anatomy as well as a calculated ecohydrological potential for the presence of GDV. The potential was calculated based on the coverage of phreatophyte species and the moisture value of non-phreatophyte species. The final pGDV maps including different weightings of the eleven thematic layers are compiled at a resolution of 500 m in WGS1984 (EPSG 4326). Finally, five pGDV classes (very low to very high potential) were derived and the share of high pGDV was calculated for level 8 HydroBASINS in the Mediterranean. Results support prioritisation of areas for essential regional high-resolution identification of GDV, to ensure sustainable groundwater management and in turn protect GDV as local biodiversity hotspots.

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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    Authors: EFSA; ECDC;

    All tables produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2022: - Campylobacter - E. coli - MRSA - Salmonella - ESBL Annexes A to F are also included. EU; PDF; biohaw@efsa.europa.eu

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
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      ZENODO
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    WORKING VERSION. All layers will be visible in this linked webgis app along with estimated error. <== To Be Updated in new final version, not yet so. The layers available in this dataset are in a WGS84 geographic coordinate reference system (EPSG:4326) where latitude and longitude coordinates at 0.0008983 degrees ground sampling distance per cell, which corresponds to about 1 ha, i.e. ~100 m x ~100 m at the equator, but decreases in area with increasing latitude as the coordinate system is not equal-area, e.g. ~70 m west-est direction at 45° latitude and ~50 m west-est direction at 60° latitude. A_Aspect.tif, A_slope.tif and A__elevation.tif - Earth surface height above sea level and morphology. Prefix was added to group them in Zenodo. biomass.tif - Biomass values at year 2020, Mg/ha. canopyBulkDensity.tif - Amount of canopy biomass per volume of canopy (kg/m3) canopyBaseHeight.tif - Height of canopy start measured from the ground (m) canopyCover.tif - percent of canopy coverage - resampled and updated from the map of Hansen et al. 2005 canopyHeight.tif - Total height of canopy from the ground (m) - resampled from the map of Lang, N., et al. (2023) Fuel Model FuelModelClasses_ScottBurgan.tif - the category of Fuel Model according to Scott&Burgan 2005 FuelModelClasses_Aragonese.tif - the category of Fuel Model according to Aragonese et al. 2023 DOI: 10.5194/essd-15-1287-2023 - values are from 1 to 24, with a Look Up Table for correspondence (values are ordered matching the order in table 1 of the article) . FuelModelClasses_ScottBurgan.clr/qml CLR/QML - style file for QGIS FuelModelClasses_Aragonese.clr/qml CLR/QML - style file for QGIS FuelModelPercent.tif - the percent of fuel model category belonging to that pixel, values range between 50 and 100. Allows to spot pixels with expected mixed fuel categories.

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    ZENODO
    Dataset . 2024
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    ZENODO
    Dataset . 2024
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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2024
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    ZENODO
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      ZENODO
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      ZENODO
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      ZENODO
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    Authors: EFSA; Di Piazza, Giulio; Dujardin, Bruno; Levorato, Sara; +4 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.  EU; xlsx; mese@efsa.europa.eu;

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    ZENODO
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      ZENODO
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    Authors: EFSA BIOHAZ Panel; Koutsoumanis, Konstantinos; Allende, Ana; Alvarez-Ordonez, Avelino; +23 Authors

    The European Food Safety Authority (EFSA) asked the Panel on Biological Hazards (BIOHAZ) to deliver a scientific Opinion on the maintenance of the list of qualified presumption of safety (QPS) biological agents. The QPS approach was developed by the EFSA Scientific Committee to provide a harmonised generic pre-evaluation to support safety risk assessments of biological agents intentionally introduced into the food and feed chain, in support of the concerned scientific Panels and Units in the frame of market authorisations. The taxonomic identity, body of knowledge, the safety concerns in relation to pathogenicity and virulence, and the safety for the environment of those microbiological agents are assessed. Safety concerns identified for a respective taxonomic unit (TU) are, where possible and reasonable in number, reflected as ‘qualifications’ that are assessed at the strain level by the EFSA’s scientific Panels. The “list of microorganisms with QPS status” first established in 2007, has been revised and updated annually until 2014 via QPS Opinions; since 2014 the updates are carried out and published every 3 years. If new information is retrieved from extended literature searches (ELS) that would change the QPS status of a TU or its qualifications, this is also published in the Panel Statement covering the previous 6-months period. The ELS protocol can be found at https://zenodo.org/doi/10.5281/zenodo.3607188 and the Search strategies are available at: https://doi.org/10.5281/zenodo.3607192. The QPS Panel Statement also includes the evaluation of microbiological agents notified to EFSA within the 6-month period for an assessment for feed additives, food enzymes, food additives and flavourings, and novel foods or plant protection products for a possible QPS status. The new QPS status recommendations are incorporated into the 2022 updated “list of microorganisms with QPS status” is available in this upload. The list of “Microbiological agents as notified to EFSA” from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment can be found at https://doi.org/10.5281/zenodo.3607183. Useful links on EFSA QPS EFSA topic on QPS: https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps Link to the virtual issue on QPS on Wiley Online Library: https://efsa.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1831-4732.QPS Versions history: Versions 1 and 2 were substituted by version 3 – all are associated with the QPS Panel statement EFSA 7: suitability of taxonomic units notified to EFSA until September 2017: https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2018.5131 Version 4 is associated with the QPS Panel statement EFSA 8: suitability of taxonomic units notified to EFSA until March 2018: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2018.5315 Versions 5 and 6 were substituted by version 7 – all are associated with the QPS Panel statement EFSA 9: suitability of taxonomic units notified to EFSA until September 2018: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5555 Version 8 is associated with the QPS Panel statement EFSA 10: suitability of taxonomic units notified to EFSA until March 2019: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5753 Version 9 is associated with the QPS Panel statement EFSA 11: suitability of taxonomic units notified to EFSA until September 2019: https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5965 and with the Scientific Opinion on the update of the list of QPS-recommended biological agents intentionally added to food or feed as notified to EFSA (2017-2019): https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5966 Version 10: updates version 9 following the update of the qualification of Bacillus velezensis as: ‘absence of toxigenic potential and absence of aminoglycoside production ability’ - applied in the QPS Statement part 11 (ON-5965) and in the 2019 Scientific Opinion (ON-5966). Version 11: updates version 10 following the addition of the Bacillus circulans ‘for production purposes only’ - applied in the QPS Statement part 13 (ON-6377) and in the 2019 Scientific Opinion (ON-5966). Version 12 updates version 11 following the addition of the Bacillus paralicheniformis with the qualifications 'absence of toxigenic activity’ and ‘absence of genetic information to synthesize bacitracin'; and adding Schizochytrium limacinum, which is a synomym for Aurantiochytrium limacinum, - applied in the QPS Statement part 14 (ON-6689) and in the 2019 Scientific Opinion (ON-5966). Version 13 updates version 12 following the addition of Haematococcus lacustris synonym Haematococcus pluvialis, recommended for QPS status with the qualification ‘for production purposes only’. Some other changes related to taxonomy and qualifications are described in the QPS Statement part 15 (ON-7045). Version 14, update of version 13, is related to QPS Statement part 16 (ON-7408). Version 15 is related to QPS Statement part 17 (ON-7746). Version 16 is related to QPS Statement part 18 (ON-8092). Version 17 is related to QPS Statement part 19 (ON-8517). Version 18 is also related to QPS Statement part 19 (ON-8517) with a small correction. Note: As of January 2022 the updated list is provided only as excel file format. EU; XLSX; biohaz@efsa.europa.eu

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      ZENODO
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    Authors: Bancheri, Marialaura;

    The database contains the hydrological properties of the soils within the regions of interest presented in the article on the LandSupport Best practice tool

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    ZENODO
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      ZENODO
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    Following a request from the European Commission, in 2018 EFSA released a renovated database of host plant species of Xylella spp. (including both species X. fastidiosa and X. taiwanensis) together with a scientific report (EFSA, 2018). EFSA was tasked to maintain and update this database periodically. The mandate now covers the period 2021-2026 and EFSA is requested to release an update of the database twice per year. In December 2023 EFSA released the ninth update of the Xylella spp. host plant database (VERSION 9) with information retrieved from literature search up to June 2023 and recent Europhyt outbreak notifications (EFSA, 2023). The protocol applied for the extensive literature review, data collection and reporting, as well as results and lists of host plants are described in detail in the related scientific report (EFSA, 2023). The overall number of Xylella spp. host plants determined with at least two different detection methods or positive with one method (between: sequencing, pure culture isolation) reaches now 439 plant species, 200 genera and 69 families (category A – see section 2.4.2 of EFSA (2023)). Such numbers rise to 696 plant species, 307 genera and 88 families if considered regardless of the detection method applied (category E, see section 2.4.2 of EFSA (2023)). The Excel files here attached represent the VERSION 9 of the Xylella spp. host plants database. For a detailed description of the information included in the database, please consult the related scientific report (EFSA, 2023). The Excel file "Xylella spp. host plants database – VERSION 9" contains several sheets: the LEGENDA (with extensive description of each table), the full detailed raw data of the Xylella spp. host plant database (sheet "observation") and several examples of data extraction. Additional Excel files contain the lists of host plant species of X. fastidiosa (subsp. unknown (i.e. not reported), fastidiosa, multiplex, pauca, morus, sandyi, tashke, fastidiosa/sandyi) and X. taiwanensis infected naturally, artificially and in not specified conditions, and according to different categories (A, B, C, D, E – see section 2.4.2 of EFSA (2023)). The Excel file "new_host_plant_species_v9" contain the list of new host plant species added to the database in this new update. Question number: EFSA-Q-2022-00815 Output number: EN-8478 Contacts: plants@efsa.europa.eu Bibliography: EFSA (European Food Safety Authority), 2018. Scientific report on the update of the Xylella spp. host plant database. EFSA Journal 2018;16(9):5408, 87 pp. https://doi.org/10.2903/j.efsa.2018.5408 EFSA (European Food Safety Authority), Gibin, D., Gutierrez Linares, A., Fasanelli, E., Pasinato, L., & Delbianco, A. (2023). Update of the Xylella spp. host plant database – systematic literature search up to 30 June 2023. EFSA Journal, 21(12), e8477. https://doi.org/10.2903/j.efsa.2023.8477 EU; xlsx; plants@efsa.europa.eu

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    ZENODO
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    ZENODO
    Dataset . 2021
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      Dataset . 2018
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    Authors: Fassò, Alessandro; Rodeschini, Jacopo; Fusta Moro, Alessandro; Shaboviq, Qendrim; +7 Authors

    The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the AgrImOnIA project. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. The building process of the dataset is detailed in the companion paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. SCIENTIFIC DATA, 1-19. available here. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. The files in the record, renamed according to their version (es. .._v_3_0_0), are: Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software. Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied. Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor. Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area. The Agrimonia dataset can be reproduced using the code available at the GitHub page: https://github.com/AgrImOnIA-project/AgrImOnIA_Data UPDATE 31/05/2023 - NEW RELEASE - V 3.0.0 A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable WE_rh_min, WE_rh_mean and WE_rh_max have been recomputed due to some bugs. In addition, two new columns are added, they are LI_pigs_v2 and LI_bovine_v2 and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation. A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered. UPDATE 11/03/2023 - NEW RELEASE - V 2.0.2 A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable WE_tot_precipitation have been recomputed due to some bugs. A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable WE_precipitation_t is corrected. UPDATE 24/01/2023 - NEW RELEASE - V 2.0.1 minor bug fixed UPDATE 16/01/2023 - NEW RELEASE - V 2.0.0 A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed: Added values for LA_land_use variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv) Deleted incorrect values for LA_soil_use variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv) Fixed duplicate sensors corresponding to the same pollutant within the same station (in Metadata_monitoring_network_registry_v_2_0_0.csv)

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    Authors: Fassò, Alessandro; Rodeschini, Jacopo; Fusta Moro, Alessandro; Shaboviq, Qendrim; +6 Authors

    The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the AgrImOnIA project. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. The building process of the dataset is detailed in the companion paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. SCIENTIFIC DATA, 1-19. available here. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. The files in the record, renamed according to their version (es. .._v_3_0_0), are: Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software. Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied. Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor. Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area. The Agrimonia dataset can be reproduced using the code available at the GitHub page: https://github.com/AgrImOnIA-project/AgrImOnIA_Data UPDATE 31/05/2023 - NEW RELEASE - V 3.0.0 A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable WE_rh_min, WE_rh_mean and WE_rh_max have been recomputed due to some bugs. In addition, two new columns are added, they are LI_pigs_v2 and LI_bovine_v2 and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation. A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered. UPDATE 11/03/2023 - NEW RELEASE - V 2.0.2 A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable WE_tot_precipitation have been recomputed due to some bugs. A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable WE_precipitation_t is corrected. UPDATE 24/01/2023 - NEW RELEASE - V 2.0.1 minor bug fixed UPDATE 16/01/2023 - NEW RELEASE - V 2.0.0 A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed: Added values for LA_land_use variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv) Deleted incorrect values for LA_soil_use variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv) Fixed duplicate sensors corresponding to the same pollutant within the same station (in Metadata_monitoring_network_registry_v_2_0_0.csv)

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    Authors: Stefanini, Federico M.;

    Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-scale disease prediction by incorporating the biological mechanisms of the disease process and the different phenological stages of the vines. In this study, we developed a Bayesian model to predict the probability of Plasmopara viticola infection in grapevines, considering various disease management approaches. To aid decision-making, we introduced a multi-attribute utility function that incorporated a sustainability index for each strategy. The data used in this study were derived from trials conducted during the production years 2018-2020, involving the application of five disease management strategies: conventional Integrated Pest Management (IPM), conventional organic, IPM with substantial fungicide reduction combined with host-defense inducing biostimulants, organic management with biostimulants, and the use of biostimulants only.

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    Dataverse UNIMI
    Dataset . 2023
    Data sources: Dataverse UNIMI
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      Dataverse UNIMI
      Dataset . 2023
      Data sources: Dataverse UNIMI
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    Authors: El-Hokayem, Léonard; De Vita, Pantaleone; Usman, Muhammad; Link, Andreas; +1 Authors

    Groundwater dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat and sustaining human livelihoods. A novel multicriteria framework helps to identify areas where potential groundwater dependent vegetation (pGDV) occurs in the Mediterranean biome. Globally-available datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks are combined in a weighted, easy-to-use index, composed of eleven thematic layers. Input layers for the index calculation are available in the data collection: 1) pre-processed (rasterised and clipped to the Mediterranean) and 2) harmonised and reclassified. All input data was extracted globally. Either, directly from the respective studies or through the data catalogue in the Google Earth Engine. All datasets were acquired and processed in 2022 and 2023. Time series data for potential inflow dependency and Normalized Difference Vegetation Index (NDVI) were extracted for the period 2003-2021. Finally, the mean value was calculated over this period. All other data sets, however, mark a fixed point in time. Ground truth vegetation data was used to calculate layer weightings with a Random Forest. 10 m * 10 m vegetation plots were collected in July and August 2021 and 2022 in southern Italy (Campania region) inside the 'Cilento, Vallo di Diano and Alburni National Park'. 236 vegetation plots are available, containing general information on the vegetation (habitat, species number, stratification), mean indicator values, plant life forms, leaf anatomy as well as a calculated ecohydrological potential for the presence of GDV. The potential was calculated based on the coverage of phreatophyte species and the moisture value of non-phreatophyte species. The final pGDV maps including different weightings of the eleven thematic layers are compiled at a resolution of 500 m in WGS1984 (EPSG 4326). Finally, five pGDV classes (very low to very high potential) were derived and the share of high pGDV was calculated for level 8 HydroBASINS in the Mediterranean. Results support prioritisation of areas for essential regional high-resolution identification of GDV, to ensure sustainable groundwater management and in turn protect GDV as local biodiversity hotspots.

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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/
    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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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/
      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/
      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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