Resultados totales (Incluyendo duplicados): 34260
Encontrada(s) 3426 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285101
Dataset. 2022

CONTENTS OF SOIL ORGANIC MATTER FRACTIONS AS AFFECTED BY WARMING AND RAIN EXCLUSION AT A SEMIARID MEDITERRANEAN SITE

  • Plaza de Carlos, César
  • Maestre, Fernando T.
Data and metadata of total, free, intra-aggregate, and mineral-associated organic C and N contents of soils from a dryland ecosystem warming experiment established in Aranjuez, Central Spain., European Commission: BIODESERT - Biological feedbacks and ecosystem resilience under global change: a new perspective on dryland desertification (647038) VULCAN - Vulnerability of soil organic carbon to climate change in permafrost and dryland ecosystems (654132), Peer reviewed

DOI: http://hdl.handle.net/10261/285101
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285101
HANDLE: http://hdl.handle.net/10261/285101
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285101
PMID: http://hdl.handle.net/10261/285101
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285101
Ver en: http://hdl.handle.net/10261/285101
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oai:digital.csic.es:10261/285101

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285142
Dataset. 2021

H2020 PROJECT CAPTOR: RAW DATA COLLECTED BY LOW-COST MOX OZONE SENSORS IN A REAL AIR POLLUTION MONITORING NETWORK

  • Barceló-Ordinas, José María
  • Ferrer-Cid, Pau
  • García Vidal, Jorge
  • Viana, Mar
  • Ripoll, Anna
The H2020 CAPTOR project deployed three testbeds in Spain, Italy and Austria with low-cost sensors for the measurement of tropospheric ozone (O3). The aim of the H2020 CAPTOR project was to raise public awareness in a project focused on citizen science. Each testbed was supported by an NGO in charge of deciding how to raise citizen awareness according to the needs of each country. The data presented here correspond to the raw data captured by the sensor nodes in the Spanish testbed using SGX Sensortech MICS 2614 metal-oxide sensors. The Spanish testbed consisted of the deployment of twenty-five nodes. Each sensor node included four SGX Sensortech MICS 2614 ozone sensors, one temperature sensor and one relative humidity sensor. Each node underwent a calibration process by co-locating the node at a reference station, followed by a deployment in a non-urban area in Catalonia, Spain. All nodes spent two to three weeks co-located at a reference station in Barcelona, Spain (urban area), followed by two to three weeks co-located at three non-urban reference stations near the final deployment site. The nodes were then deployed in volunteers' homes for about two months and, finally, the nodes were co-located again at the non-urban reference stations for two weeks. All data presented in this repository are raw data taken by the sensors that can be used for scientific purposes such as calibration studies using machine learning algorithms, or once the concentration values of the nodes are obtained, they can be used to create tropospheric ozone pollution maps with heterogeneous sources (reference stations and low-cost sensors)., Peer reviewed

Proyecto: EC/H2020/688110
DOI: http://hdl.handle.net/10261/285142
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285142
HANDLE: http://hdl.handle.net/10261/285142
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285142
PMID: http://hdl.handle.net/10261/285142
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285142
Ver en: http://hdl.handle.net/10261/285142
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oai:digital.csic.es:10261/285142

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285150
Dataset. 2020

DATA FROM: MEGAFAUNA BIOGEOGRAPHY EXPLAINS PLANT FUNCTIONAL TRAIT VARIATION IN THE TROPICS

  • Dantas, Vinícius de L.
  • Pausas, J. G.
[Methods] We compiled data on the presence of spines, SLA, WD, HMax and presence of spines for Afrotropical and Neotropical savanna and forest woody species (trees and shrubs), from literature and herbarium sources (a list of the data sources is found in Appendix 1). We first compiled SLA and WD data from the primary literature and calculated species means at the biogeographic scale. Then, for Afrotropical species, we searched for HMax and spine data in JSTOR Global Plant (http://plants.jstor.org/) and in the African Plant Database of the Conservatoire et Jardin botaniques de la Ville de Genève and South African National Biodiversity Institute, using the list of species for which we obtained SLA and WD data as reference. For Neotropical species we obtained HMax and presence of spine information from the NeoTropTree dataset (Oliveira-filho, 2017) and Flora do Brasil (2020) for all the available species recorded in Brazilian savanna (Cerrado) and forest (Amazon and Atlantic forest) biomes. For spinescence, we only considered species with detailed descriptions of stem and branch features. We classified species as savanna, forest, or generalist (occurring in both savanna and forest) species, based on the study site descriptions reported in the literature sources from which the data were acquired, and on Mendonça et al. (2008). We only considered species that were consistently classified as forest or savanna, excluding species reported to occur in both ecosystem types, to better pinpoint the patterns and simplify the results. We also classified species according to the biogeographic region in which they occur as Afrotropical or Neotropical species (based on the data reference sources). Introduced species were also excluded using occurrence information from the flora websites and datasets used to compile height and spine data. We obtained data for number and proportion of African geoxylic plants from Maurin et al., (2014). This data is based on the flora of the Zambesian region, a savanna dominated region that includes 12 African countries. Maurin et al., (2014) present two datasets, a sampled dataset, with 53 geoxyles out of the 1400 woody species, and a provisional list of 266 African geoxylic suffrutices taxa occurring south of the Equator. We did not use the latter because an accurate quantification of the southern African flora was not provided. However, a preliminary estimate based on Germishuizen et al. (2003) indicates a total of 8169 woody taxa for southern Africa (including trees, shrubs, dwarf shrubs, subshrubs and suffrutex, but excluding scrumbers, as the proportion of woody stems was not reported). Based in these figures, we found that the first and second datasets represent a similar proportion of geoxyles for African woody species (4 and 3 %, respectively), and would provide very similar results in the statistical analyses. Thus, we only report the results for the sampled species of Maurin et al., (2014). For the Neotropical savanna region, we searched for information on stem and underground organs for subshrub species in the checklist of Mendonça et al. (1998). The list comprises 6429 savanna plant species from the Cerrado region (the largest Neotropical savanna-dominated region) and represents an older version of a more recent checklist with almost twice the number of plant species (but more difficult to work with because only the printed version is available; Mendonça et al., 2008). We then searched for information in plant species descriptions compiled by the Rio de Janeiro Botanical garden and publicly available in Portuguese at the Flora do Brasil (2020) website. We only considered information for species containing detailed descriptions of aerial and underground structures. We found information of this sort for 220 subshrubs out of the 816 subshrub species in the checklist, of which 101 were geoxyles (according to the definition used by Maurin et al., 2014). Based in the observed proportion (46%), we estimated the number of geoxyles among the 816 subshrubs to be 376 species, from a total of 3599 woody species. Thus, the comparison is between savanna regions, not actual savanna or forest vegetation (unlike the comparison for other traits), and only includes subshrub geoxyles, to match the criteria used by Maurin et al., (2014). [Environmental Data] We obtained decimal geographic coordinates for the species for which we obtained WD, SLA and HMax in GBIF.org (28 February 2020; see reference list for doi) and the R package “rgbif”. In order to exclude very close occurrences and, thus, match the resolution of the available satellite-derived environmental data (see below), we rounded the decimal coordinates to include only three decimal digits and then remove repeated species occurrences. We also excluded coordinates falling outside Africa, South and Central Americas, and outside the following biomes (according to Dinerstein et al. 2017): Tropical and Subtropical Moist and Dry Broadleaf Forests; Tropical and Subtropical Grasslands, Savannas and Shrublands; Montane Grasslands and Shrublands; Tropical and Subtropical Coniferous Forests; and, Deserts and Xeric Shrublands. This was directed at minimizing errors, standardizing the latitude ranges and biomes considered for each biogeographic regions, and to exclude flooded ecosystems in which plant relationships with climate and soil are likely different. Thus, from the initial approximately 2,8 million occurrences, we only used 87,739 occurrences, and the number of coordinates per species varied from 1 to 1432. Based on these coordinates, we obtained climate, soil and fire data for each occurrence location from global datasets. We obtained climate data from WorldClim 2 (Fick & Hijmans, 2017), soil data from SoilGrid (250 m of spatial resolution; Hengl et al. 2017), and fire data from the MODIS product MCD14ML collection 6 v.3 (Giglio et al., 2018). We used mean annual precipitation and temperature, as well as rainfall seasonality for the years 1970-2000, as climate variables; cation exchange capacity, organic carbon content, weight percentages of clay (<0.0002 mm), silt (0.0002–0.05 mm), and sand particles (0.05–2 mm), as well as the volumetric percentage of coarse fragments (>2 mm), as soil variables; and fire count per area (as a proxy for fire frequency) and radiative power (a proxy for fire intensity) as fire variables. Soil variables were the averages between two depth, 0.05 and 2 m. Fire data was obtained from a circular area of 5 km centered on the occurrence coordinates between the years 2000 and 2019 (both included). For each species and biogeographic region, we calculated the overall means as an indicator of species habitat preferences as defined by their average position in environmental niche space. [Usage Notes] The dataset that is made available here cosists of two files in .csv format. The first is the complete trait dataset for specific leaf area (sla; mm2.mg-1), wood density (woo; g.cm-3), HMax (m) and Spines (yes/no). The list of reference sources for trait data is presentes in the end of this note. Other abreviations in this file are: ref.sla: reference sources for sla data; ref.woo: reference sources for wood density data; ref.hmax: reference sources for hmax data; mat: mean annual temperature; map: mean annual precipitation; rs: rainfall seasonality; nfires5: number of fires per 5 km area (our proxy for fire frequency); avgfrp: average fire radiative power (our proxy for fire intensity); cec: soil cation exchange capacity; orc: soil organic carbon content; cly: weight percentage of clay particles (<0.0002 mm) in the soil; slt: weight percentage of silt particles (0.0002–0.05 mm) in the soil; snd: weight percentage of the sand particles (0.05–2 mm) in the soil; crf: volumetric percentage of coarse fragments (>2 mm) in the soil. The second file attached is a dataset of Geoxyle species (geox; y(yes)/n(no)) for a subset of the Brazilian Cerrado species., [Aim] Biomes can diverge substantially in plant functional traits and disturbance regimens among regions. Given that Neotropical and Afrotropical regions have contrasting histories of the megafauna (because of the Holocene megafaunal extinction in the Neotropics), we hypothesize that they should harbour plants with different traits in relationship to herbivory and fire, especially in savannas. We predicted that herbivory resistance traits should be more prominent in Afrotropical savanna plants and fire resistance in Neotropical savanna plants., [Location] Tropics., [Time period] Not applicable., [Major taxa studied] Angiosperms (woody)., [Methods] We compiled data for five key plant functional traits (wood density, specific leaf area, maximum tree height, spinescence and proportion of geoxyles) for forest and savanna woody species from the two distant regions (Afrotropics and Neotropics). We related these data to climate, soil and fire variables and tested predictions for megafauna selection., [Results] Spines and high wood density were more common among Afrotropical than Neotropical savanna species and species from the two forests. Moreover, the Neotropical savanna region contained more geoxyles than the Afrotropical savanna region. Finally, Afrotropical species were taller than Neotropical species. These differences were consistent with our predictions for trait selection by the megafauna, and these patterns did not change when considering climate, soil and fire regimens in the models., [Main conclusions] Our results highlight the great potential of these traits for summarizing disturbance strategy axes in tropical woody species and suggest that global variation in plant traits is unlikely to be understood fully without consideration of historical factors, especially the direct and indirect impacts of megafauna., Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (Finance Code 001), Award: 88887.311538/2018-00., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285150
HANDLE: http://hdl.handle.net/10261/285150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285150
PMID: http://hdl.handle.net/10261/285150
Digital.CSIC. Repositorio Institucional del CSIC
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Ver en: http://hdl.handle.net/10261/285150
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oai:digital.csic.es:10261/285150

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285155
Dataset. 2020

COMPARATIVE PROTEOMICS ANALYSIS OF ANISAKIS SIMPLEX S. S. – EVALUATION OF THE RESPONSE OF INVASIVE LARVAE TO IVERMECTIN

  • Polak, Iwona
  • Lopieńska-Biernat, Elzbieta
  • Stryiński, Robert
  • Carrera, Mónica
  • Mateos, Jesús
Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285155
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285155
HANDLE: http://hdl.handle.net/10261/285155
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285155
PMID: http://hdl.handle.net/10261/285155
Digital.CSIC. Repositorio Institucional del CSIC
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Ver en: http://hdl.handle.net/10261/285155
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oai:digital.csic.es:10261/285155

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285331
Dataset. 2022

WEED VEGETATION, CROP YIELD AND QUALITY AND MANAGEMENT OF 26 WHEAT FIELDS OF MEDITERRANEAN SPAIN

  • Hernández-Plaza, Ana
  • Bastida, F.
  • Gibson, David J.
  • Barro Losada, Francisco
  • Giménez, María J.
  • Pallavicini, Yésica
  • Izquiedo, Jordi
  • González-Andújar, José Luis
[Method] The study was carried out in 2013–2014 across 26 wheat fields (Triticum aestivum L. and Triticum turgidum subsp. durum Desf.) under a Mediterranean climate in Spain. Fields were either organically or conventionally managed and were located in cereal areas in four Spanish regions: Andalusia (15 fields), Madrid (3 fields), Castilla-La Mancha (4 fields) and Catalonia (4 fields). In each field, ten (1 m × 1 m) plots were established during crop tillering and maintained until harvest. Weeds were sampled at the end of the crop vegetative period (April with dates slightly varying depending on field location). In each plot we recorded the weed species present, and we visually assessed the cover (in cm2) of each weed species and the crop. We also measured plant height (cm). Immediately before crop harvest, we sampled the plots again to obtain crop yield. In each plot we counted the number of wheat stems with ears and cut 30 of them. We determined the dry weight (after 48 h at 65 °C) of the 30 stems, threshed the ears and weighed the grain for each sample. In doing this we obtained an average value for the grain weight of an ear. We calculated crop grain weight in each plot multiplying mean grain weight of each sample by the total number of fertile stems in the plot. In each plot, we obtained two measures of grain quality: the percentage of total dry protein content determined using the Kjeldahl standard method at the Laboratorio Agroalimentario de Córdoba (Córdoba, Spain) and the glutenin to gliadin ratio. The amount of glutenin and gliadin in each plot, was obtained from 100 mg of wheat flour. First, we ground the wheat grains of each sample using a ball mill to obtain flour of a 100 µm particle size. The extraction of gliadin and glutenin proteins was done using a modified classical Osborne procedure based on protein solubility. The method is described in detail in Wieser et al. (1998) and Pistón et al. (2011). Gliadin protein was extracted stepwise three times, samples were centrifuged, and the supernatans were collected and pooled. The insoluble material from the previous step was used to obtain the glutenin fraction in a similar manner. Then, each of the extracts were filtered and they were applied to a 300SB-C8 reverse phase analytical column using a 1200 Series Quaternary LC System liquid chromatograph (Agilent Technologies) with a DAD UV-V detector. Absorbance was monitored with the DAD UV-V module at 210 nm. The amounts of both fractions were determined using bovine serum albumin as protein standard. Both fractions were expressed as μg/mg flour. The glutenin to gliadin ratio was calculated by dividing the amount of glutenins by gliadin content. For each field we also recorded the crop species and the variety, and collected data on management practices: sowing date (month), fertilization rate (kg N/ha), preceding crop (legume, fallow, sunflower or cereal; categories depending on the nutrient demand of the crop), type of tillage and herbicides used. We also obtained data on the average monthly temperature (ºC) and total precipitation (mm) during crop growth season from the nearest meteorological stations. [Methods for processing the data] Different plant community diversity indices were computed from data. Statistical analyses were performed to understand the relationship between crop quality and weeed community diversity as explained in: Paper accepted for publication., Weed community structure, including composition and taxonomic and functional diversity, may explain variability in crop quality, adding to the variability accounted for by management, climatic and genetic factors. Focusing on Mediterranean rainfed wheat crops, we sampled weed communities from 26 fields in Spain that were either organically or conventionally managed. Weed communities were characterized by their abundance and taxonomic, compositional and trait-based measures. Grain protein concentration and the glutenin-to-gliadin ratio were used as indicators of wheat grain quality. Linear mixed-effects models were used to analyze the relationship between crop quality and weed community variables, while accounting for environmental factors. Nitrogen fertilization, previous crop and precipitation explained an important part of wheat grain protein concentration (R2marginal = 0.39) and composition (R2marginal = 0.26). Weed community measures had limited effects on grain quality (increasing R2marginal of models by 1% on average). The weed effects were related to the composition and the functional structure of their communities but not to their abundance. Environmental conditions promoting higher protein concentration also selected for weed species with competitive attributes, whereas the role of weed functional diversity depended on the functional trait and on the resource limiting crop grain quality. Understanding the mechanisms of weed effects on crop quality could aid in designing sustainable weed management practices., Grants AGL2012-33736 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”., Peer reviewed

DOI: http://hdl.handle.net/10261/285331, https://doi.org/10.20350/digitalCSIC/14863
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285331
HANDLE: http://hdl.handle.net/10261/285331, https://doi.org/10.20350/digitalCSIC/14863
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285331
PMID: http://hdl.handle.net/10261/285331, https://doi.org/10.20350/digitalCSIC/14863
Digital.CSIC. Repositorio Institucional del CSIC
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Ver en: http://hdl.handle.net/10261/285331, https://doi.org/10.20350/digitalCSIC/14863
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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285449
Dataset. 2020

DATA FROM: GENETIC ADMIXTURE INCREASES PHENOTYPIC DIVERSITY IN THE NECTAR YEAST METSCHNIKOWIA REUKAUFII

  • Álvarez-Pérez, Sergio
  • Dhami. Manpreet K.
  • Pozo, María I.
  • Crauwels, Sam
  • Verstrepen, Kevin
  • Herrera, Carlos M.
  • Lievens, Bart
  • Jacquemyn, Hans
Table S5.xlsx -- Pairwise correlations between phenotypic traits of Metschnikowia reukaufii. Table S6.xlsx -- Detailed results obtained in tests of phylogenetic signal for different phenotypic traits and indices of overall performance of Metschnikowia reukaufii. Table S7.xlsx -- Detailed model fitting results obtained for phenotypic traits and indices of overall performance of Metschnikowia reukaufii. mronlyvcf-renamed.vcf -- High coverage SNPs obtained from whole genome mapping of 73 Metschnikowia reukaufii strains to diploid reference (mean coverage = 47.9×, range 23 – 116×). MR_phenotypes.xlsx -- Phenotypic data obtained for 73 Metschnikowia reukaufii strains., Raw data and supplementary files for the manuscript "Genetic admixture increases phenotypic diversity in the nectar yeast Metschnikowia reukaufii"., European Commission: PHENOGENYEAST - Exploring the phenotypic landscape of nectar yeasts in relation to their genetic background (327635)., Peer reviewed

Proyecto: EC/FP7/327635
DOI: http://hdl.handle.net/10261/285449
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285449
HANDLE: http://hdl.handle.net/10261/285449
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285449
PMID: http://hdl.handle.net/10261/285449
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285449
Ver en: http://hdl.handle.net/10261/285449
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oai:digital.csic.es:10261/285449

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285459
Dataset. 2020

RISK ASSESSMENT ON GLYCOALKALOIDS IN FEED AND FOOD: OCCURRENCE DATA IN FOOD AND FEED SUBMITTED TO EFSA AND DIETARY EXPOSURE ASSESSMENT FOR HUMANS

  • EFSA CONTAM Panel
  • Schrenk, Dieter
  • Bignami, Margherita
  • Bodin, Laurent
  • Chipman, James Kevin
  • Del Mazo, Jesús
  • Hogstrand, Christer
  • Hoogenboom, Laurentius (Ron)
  • Leblanc, Jean-Charles
  • Nebbia, Carlo Stefano
  • Nielsen, Elsa
  • Ntzani, Evangelia
  • Petersen, Annette
  • Sand, Salomon
  • Schwerdtle, Tanja
  • Vleminckx, Christiane
  • Wallace, Heather
  • Brimer, Leon
  • Cottrill, Bruce
  • Dusemund, Birgit
  • Mulder, Patrick
  • Vollmer, Günter
  • Binaglia, Marco
  • Ramos Bordajandi, Luisa
  • Riolo, Francesca
  • Roldan-Torres, Ruth
  • Grasl-Kraupp, Bettina
[UPDATE to version 2 of this upload] 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). [Description - Version 1] Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow) Table A.5. European Starch Association data on feed and potatoes for starch Table A.6. Details acute assessment across surveys (consumption days only) Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285459
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285459
HANDLE: http://hdl.handle.net/10261/285459
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285459
PMID: http://hdl.handle.net/10261/285459
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285459
Ver en: http://hdl.handle.net/10261/285459
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oai:digital.csic.es:10261/285459

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285463
Dataset. 2020

OCCURRENCE DATA ON NICKEL IN FOOD

  • EFSA CONTAM Panel
  • Schrenk, Dieter
  • Bignami, Margherita
  • Bodin, Laurent
  • Chipman, James Kevin
  • Del Mazo, Jesús
  • Grasl-Kraupp, Bettina
  • Hogstrand, Christer
  • Hoogenboom, Laurentius (Ron)
  • Leblanc, Jean-Charles
  • Nebbia, Carlo Stefano
  • Ntzani, Evangelia
  • Petersen, Annette
  • Sand, Salomon
  • Schwerdtle, Tanja
  • Vleminckx, Christiane
  • Wallace, Heather
  • Guérin, Thierry
  • Massanyi, Peter
  • van Loveren, Henk
  • Baert, Katleen
  • Gergelova, Petra
  • Nielsen, Elsa
Contains the raw (no data cleaning applied to it) occurrence dataset on nickel as extracted from EFSA DWH on 7 February 2020 in food samples presented in the opinion as described in its section 3.2.1. The data 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)., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285463
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285463
HANDLE: http://hdl.handle.net/10261/285463
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285463
PMID: http://hdl.handle.net/10261/285463
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285463
Ver en: http://hdl.handle.net/10261/285463
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oai:digital.csic.es:10261/285463

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285464
Dataset. 2020

ANNEXES TO THE UPDATE OF THE RISK ASSESSMENT OF NICKEL IN FOOD AND DRINKING WATER

  • EFSA CONTAM Panel
  • Schrenk, Dieter
  • Bignami, Margherita
  • Bodin, Laurent
  • Chipman, James Kevin
  • Del Mazo, Jesús
  • Grasl-Kraupp, Bettina
  • Hogstrand, Christer
  • Hoogenboom, Laurentius (Ron)
  • Leblanc, Jean-Charles
  • Nebbia, Carlo Stefano
  • Ntzani, Evangelia
  • Petersen, Annette
  • Sand, Salomon
  • Schwerdtle, Tanja
  • Vleminckx, Christiane
  • Wallace, Heather
  • Guérin, Thierry
  • Massanyi, Peter
  • van Loveren, Henk
  • Baert, Katleen
  • Gergelova, Petra
  • Nielsen, Elsa
Annex A – Benchmark dose analysis The Annex is provided as a separate pdf file containing the detailed results of the benchmark dose analyses from which no reference point was selected. Annex B – Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment The Annex is provided as a separate Excel file containing the dietary surveys per country and age group. Annex C – Occurrence data on nickel in food and drinking water The Annex is provided as a separate Excel file containing summary statistics on occurrence data on nickel. Annex D – Chronic and acute dietary exposure to nickel and the contribution of different food groups to the dietary exposure The Annex is provided as a separate Excel file containing the chronic and acute dietary exposure to nickel per survey and the contribution of different food groups to the dietary exposure., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285464
Digital.CSIC. Repositorio Institucional del CSIC
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Digital.CSIC. Repositorio Institucional del CSIC
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Digital.CSIC. Repositorio Institucional del CSIC
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Ver en: http://hdl.handle.net/10261/285464
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Digital.CSIC. Repositorio Institucional del CSIC
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Dataset. 2020

RISK ASSESSMENT OF OCHRATOXIN A IN FOOD - SUMMARY STATISTICS ON OCCURRENCE AND CONSUMPTION DATA AND EXPOSURE ASSESSMENT RESULTS - FINAL OCCURRENCE DATA

  • EFSA CONTAM Panel
  • Schrenk, Dieter
  • Bodin, Laurent
  • Chipman, James Kevin
  • Del Mazo, Jesús
  • Grasl-Kraupp, Bettina
  • Hogstrand, Christer
  • Hoogenboom, Laurentius (Ron)
  • Leblanc, Jean-Charles
  • Nebbia, Carlo Stefano
  • Nielsen, Elsa
  • Ntzani, Evangelia
  • Petersen, Annette
  • Sand, Salomon
  • Schwerdtle, Tanja
  • Vleminckx, Christiane
  • Wallace, Heather
  • Alexander, Jan
  • Dall'Asta, Chiara
  • Mally, Angela
  • Metzler, Manfred
  • Binaglia, Marco
  • Horvath, Zsuzsanna
  • Steinkellner, Hans
  • Bignami, Margherita
Annex: Summary statistics on occurrence and consumption data and exposure assessment results Table 1 Number of analytical results excluded from the initial dataset during data cleaning, and justification for exclusion Table 2 Occurrence values of OTA (µg/kg) in food as reported in the cleaned database Table 3 Occurrence values of OTA (µg/kg) in food as used for the exposure assessment Table 4 Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment Table 5 Results of chronic dietary exposure assessment on OTA (ng/kg bw per day) Table 6 Main contributing food categories to the mean LB exposure assessments to OTA across European dietary surveys and population groups Table 7 Distribution of LOQ values among the different food categories Table 8 All contributing food categories to the mean LB and UB exposure to OTA across European dietary surveys and population groups FormattedDATA_OchratoxinA.zip: Occurrence data on OTA Contains the occurrence data of OTA on 73,891 food samples presented in the opinion as described in its section 4.7 Occurrence data., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/285465
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285465
HANDLE: http://hdl.handle.net/10261/285465
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285465
PMID: http://hdl.handle.net/10261/285465
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285465
Ver en: http://hdl.handle.net/10261/285465
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/285465

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