Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Black Carbon as residuals of monsoon clouds

This data set is obtained from an aircraft campaign Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) conducted over the Indian subcontinent to measure cloud and aerosol properties. Data presented in the paper Black Carbon as residuals of monsoon clouds can be found. The data consist of in-cloud and ambient atmosphere Black Carbon measurements and cloud properties.  The data set consists of the following: 1. Mean Aerosol Size Distribution (#/cm**3 ) below cloud base 2. Temperature (°C ), Total Droplet Concentration (#/cm*3 ), Refrectrory Black Carbon (rBC) concentration (#/cm*3 ) 3. rBC mixing state data 4. Data for Figure1, rBC inside the cloud and ambient atmosphere (#/cm**3 ). 5. Mean Relative Humidity (%) and coating thickness (nm) with standard deviations. 6. Scattering Inacasdence Ratio- Scattering Incasdance Time_ Coating thickness 7. Statistics of small drop, mid and large drop concentrations (#/cm**3 )

opencc-by-4.0Apr 2024View details →

Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data

This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information. See 'Title' tab for contents.

opencc-by-4.0Jun 2024View details →

Region-specific sourcing of lignocellulose residues as renewable feedstocks for a net-zero chemical industry

data_1_lignocellulose_residue_grid_all_years_scenarios – datasets presenting the theoretical, ecological, and available potential of various lignocellulose residues on the GLOBIOM grid level (200 km × 200 km) data_2_lignocellulose_feedstock_potential_impacts_country_level_all_scenarios – datasets presenting the theoretical, ecological, and available potential of various lignocellulose residues on the country level and their corresponding climate-change impacts, water stress, and land-use-related biodiversity loss impacts LUID_CTY – shapefile for the global map with GLOBIOM grids

opencc-by-4.0Aug 2019View details →

EC 5th Framework ENPOWER austenitic edge welded beam contour cut metrology for assessing residual stress

Data from contour method cut surfaces collected from an autogenously edge-welded AISI 316H stainless steel beam produced as part of ENPOWER. Surfaces were generated as part of a slitting experiment and then subsequently measured with a coordinate measurement machine. This dataset forms the basis for <a href="https://doi.org/10.1115/1.4004626">"Slitting and Contour Method Residual Stress Measurements in an Edge Welded Beam" Hosseinzadeh et al. (2012)</a>, and further information on the specimen background and diffraction based results can be found in <a href="https://doi.org/10.1115/PVP2008-61339">"A statistical framework for analysing weld residual stresses for structural integrity assessment" Nadri et al. (2008)</a>. Datasets are in the form of lists of x,y.z coordinates, with one point per line, whitespace delimited in millimeters. The *Perimeter1.txt file coincides with *Surface1.txt, with the former an outline identifying the cut surface periphery, and the latter points lying on the surface. The same format is employed for the other side of the cut.

opencc-by-4.0Aug 2024View details →

CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample

<h3>Dataset Description</h3> This dataset contains high-resolution CT-scan images that capture the residual fracture surface within a core sample of Molasse de Villarlod Sandstone. The core sample was extracted after conducting a radial hydraulic fracture experiment on a 25 × 25 × 25 cm cubic block of sandstone (M03 sample). The experiment was designed to simulate fluid-driven fracture propagation and closure, and the resulting fracture path was preserved in the core sample. Core Location in the M03 Cube Sample: <ul> <li>Z: 12.5 cm</li> <li>South-North: 12.5 cm</li> <li>West-East: 11.5 cm to 1.36 cm (Coring direction)</li> </ul> This spatial information specifies the exact location and orientation of the core extraction within the M03 cube sample. <h4>CT-scan instrument details:</h4> The M03 sample was analyzed using an X-ray micro-CT scanner (RX-Solutions Ultratom) under consistent scanning protocols and parameters. A reflective 230 kV microfocus X-ray source (Hamamatsu L10801) equipped with a 0.2 mm thick copper filter, a tungsten cathode, and a tungsten target was employed for the imaging process. The scans were conducted with a voltage of 120 kV and a current intensity of 80 mA. The volume data acquisition was performed in continuous helical mode, ensuring complete coverage of the sample’s height. For sample M03, 6 full rotations were executed, with 1312 projections captured for each 360° rotation, allowing for highly precise volume reconstruction. The X-ray beam attenuation was recorded by an XL Varex Paxscan 2530HE plane detector with a resolution of 2176 x 1792 pixels, and an exposure time of 0.50 seconds per projection. The acquired projections were processed using RX-Solutions X-act software with Filtered Backprojection to reconstruct a corrected volume. This reconstruction yielded approximately 9000 slices in 16-bit TIFF format, with voxel dimensions of 10 x 10 x 10 microns, providing detailed insights into the internal structure of the sample. <h4>Key Features:</h4> <ul> <li> Fracture Characteristics: The fracture observed in the CT-scans represents a residual opening that remains post-fracturation. It is entirely contained within the core, showcasing the internal fracture geometry resulting from the hydraulic fracturing process. </li> <li> CT-Scan Details: The CT-scans were taken perpendicular to the fracture surface, offering a detailed cross-sectional view of the fracture at different depths. This orientation is critical for accurately capturing the fracture morphology and allows for the reconstruction of the fracture surface in 3D. </li> <li> Material Information: The core sample is composed of Molasse de Villarlod Sandstone, a sedimentary rock which is porous (18% porosity) and permeable. This material choice is relevant for studying fracture closure subjected to the leak-off of the fluid inside the porous medium. </li> <li> Experimental Context: The radial hydraulic fracture experiment aimed to simulate the propagation of hydraulic fracture and its closure due to the leakage of fluid inside fracture into the porous medium. The dataset provides valuable insights into fracture propagation patterns, surface roughness, and the effects of fluid-driven fractures in porous media. </li> </ul> <h4>Applications:</h4> This dataset is particularly valuable for researchers and engineers involved in: <ul> <li>Fracture mechanics and surface characterization</li> <li>3D reconstruction and visualization of fracture surfaces</li> <li>Surface roughness analysis</li> <li>Hydraulic fracturing studies</li> <li>Geomechanical modeling</li> </ul> <h4>File Structure:</h4> The dataset is organized into zip-folder contains .tif images corresponding to different depths within the core. Each tif-image is a CT-scan for that specific depth, labeled according to their position along the fracture path. <h4>Processing code:</h4> Follow the URL repository in the software section to access to the code for processing these images and reconstructing the fracture surfaces. Acknowledgment: We would like to extend our deepest thanks to Gary Perrenoud, Albert Taureg, and Lionel Pittet, the technical specialists of the PIXE platform at École Polytechnique Fédérale de Lausanne (EPFL). Their expertise and support in operating the CT-scan machine were important to the success of this research. We greatly appreciate their dedication and the high-quality work they provided. Contact and Support: Email: Brice Lecampion: brice.lecampion@epfl.ch Mohsen Talebkeikhah: m.talebkeikhah@gmail.com

opencc-by-4.0Jun 2021View details →

ATOM3D: Residue Identity (RES) Dataset

Residue Identity (RES) dataset from the <a href="http://www.atom3d.ai">ATOM3D project</a>. This upload includes three zipped data directories: <ol> <li>Full, unsplit dataset in LMDB format</li> <li>Split datasets, with each in LMDB format</li> <li>Text files containing train, validation, and test indices used to split raw dataset</li> <li>README containing dataset details</li> </ol>

opencc-by-4.0Oct 2022View details →

DATASET: Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale

This upload contains data for the manuscript "Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale." It contains kinetics data, UV-vis data, surface calculations, and activity assays for the systems described in the manuscript.

opencc-by-4.0Jan 2023View details →

Residual dynamics resolves recurrent contributions to neural computation

This repository contains all the neural and simulated datasets used in the above publication. All the data are stored as .mat files. This repository is organized into three main folders. For ease of use, we recommend extracting the contents of each of the three folders into a single folder titled ‘data’. <ol> <li>neuraldata –Raw and preprocessed neural data. Organized into the following folders: <ol> <li>array_TDR_dotsTask_data – contains the non-processed neural data of each session for both monkeys</li> <li>array_dotsTask_beh_xx – contains the behavioral data of each session for both monkeys (xx = monkey)</li> <li>array_reppasdotsTask_datasegmented_binsize=xxms – contains the pre-processed neural data (session wise) of both monkeys for a specified bin size (xx = bin size)  </li> </ol> </li> <li>simulations – Data from the simulated models. Organized into the following folders: <ol> <li>toymodels – Simulated data from all models of decisions/movement. Also includes simulations of the augmented line attractor and rotational dynamics models.</li> <li>twoarearnn – Simulated data from the two types of (feedback and nofeedback) two-area RNN models, along with the impulse response simulations.  </li> </ol> </li> <li>analyses – contains processed data files from various stages of the analysis pipeline. Only the most important files are listed below: <ol> <li>xx_aligned_reppasdotsTask_binsize=45ms.mat – session-aligned neural data for a specified monkey (xx = monkey)</li> <li>xx_ndim=8_lag=3_alpha=200_50_allconfigsresdynresults.mat – contains the residual dynamics for a specified monkey.</li> <li>xx_aligned_hankelagdimCV_reppasdotsTask_binsize=45ms.mat and xx_aligned_smoothnessCV_reppasdotsTask_binsize=45ms.mat – results of cross-validation of the residual dynamics pipeline for a specified monkey (xx = monkey)</li> </ol> </li> </ol>

opencc-by-4.0Nov 2022View details →

Pesticide residue monitoring in South Tyrol (Italy)

<ul> <li>Pesticide residue monitoring programm in South Tyrol (Province Bolzano, Italy)</li> <li>The programm is organized and conducted by the Department of Public Health, Servizio Igiene e Sanità Pubblica—Azienda Sanitaria dell’Alto Adige, Bolzano, Italy</li> <li>Further despription and data available at: <ul> <li>https://www.sabes.it/gesundheitsvorsorge/umweltmedizin.asp</li> <li>https://umwelt.provinz.bz.it/pestizide-suedtirol.asp</li> </ul> </li> <li>Residue concentrations of grass samples per sampling site and sampling date can be found in the file "Pesticide_monitoring_SouthTyrol_2018_2021.csv "</li> <li>Available period: 2018 to 2021</li> <li>All sampling sites were public sites</li> <li>Names of active agents are in Italian</li> <li>Residue concentartions are given in "mg kg-1"</li> <li>"n.d." means "not detected"</li> <li>Lower limit of quantification (LOQ) was by default 0.01 mg kg-1</li> <li>GPS coordinates of the sampling sites are can be found in thefile "Coordinates_sampling_sites.csv"</li> </ul>

opencc-by-4.0Jun 2023View details →

Doodleverse/Segmentation Gym Residual Unet models for 2-class (alluvial sediment, other) segmentation of RGB aerial orthomosaic imagery

Doodleverse/Segmentation Gym Residual Unet models for 2-class (alluvial sediment, other) segmentation of RGB aerial orthomosaic imagery This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify subaerial alluvial sediment (sand, gravel, etc) in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here: Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC. Classes: {0=other, 1=sediment}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference File descriptions 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally,  1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU 2. sample_images.zip contains a few example input files, for model testing References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0Jun 2023View details →

High-quality RNA residues: RNA2023

Introduction -------------------------------------------------------------------------------- This is the RNA2023 dataset by the Richardson Lab at Duke University These are high-quality residues from high-quality, low-redundancy RNA chains in the PDB. For a similar set of quality-filtered protein residues, see the top2018 datasets at: <a href="https://doi.org/10.5281/zenodo.4626149">https://doi.org/10.5281/zenodo.4626149</a> <a href="https://doi.org/10.5281/zenodo.5115232">https://doi.org/10.5281/zenodo.5115232</a>   Corresponding authors -------------------------------------------------------------------------------- dcrjsr at kinemage.biochem.duke.edu christopher.sci.williams at gmail.com Usage recommendations -------------------------------------------------------------------------------- RNA residues that fail the filtering criteria described below have been removed from the files.  As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. Files already contain hydrogens added by Reduce in the context of the original full models. Two datasets are provided.  The standard dataset is rna2023_pruned.  We recommend this version as the default.  The RNA backbone conformational space is highly diverse, and some real conformations fall below the statistical threshold for recognition as suites.  Therefore we do not recommend excluding suite outliers from the dataset except in specialty cases.  We also provide a rna2023_nosuiteout dataset.  In this case, no residues having "!!" outlier suite identifications are permitted.  This set may be useful in specialist cases where suite outliers are undesireable or where losing some real conformations is an acceptable sacrifice for maximal filtering. Each dataset also has a mmCIF version. Note: Chains are named based on author chain ids, except for 8b0x, chain a.  To avoid conflicts with 8b0x chain A in file systems that do not support case-sensitive file names, 8b0x chain a has been renamed to chain AB, matching its PDB/mmCIF designation. Additional files -------------------------------------------------------------------------------- rna2023_pdbmetadata.csv contains information on release date, resolution, title, authors, etc for each source pdb. rna2023_chain_list contains a list of all included chains, plus statistics on the number residues from the original chain passed the quality filters. rna2023_suitename_table.csv and rna2023_suitename_table_nosuiteout.csv contain suitename identifications of rotameric RNA backbone conformations (1a, 1c, 2u, 6d, etc) precomputed for convenience. Filtering criteria: Chain level -------------------------------------------------------------------------------- The chain list was derived from http://rna.bgsu.edu/rna3dhub/nrlist, version 3.150 as of 2020/10/28, with a 1.9Å resolution cutoff. We added 6ugg chain A and two recent EM ribosome structures: 8a3d and 8b0x After residue-level filtering, chains with no complete suites were removed. Filtering criteria: Residue level -------------------------------------------------------------------------------- Even excellent structures usually contain some poorly-resolved regions.  Residue-level filtering helps avoid including these regions in otherwise high-quality data Residues are required to meet the following validation quality contain: No sugar pucker outliers No steric overlaps or "clashes", as per Probe >= 0.5Å No covalent bond or angle geometry outliers Optionally, no !! suite outliers Residues from xray structures are required for meet the following fit-to-map criteria: Average of worst 2 atoms' 2Fo-Fc map values >= 1.2 Average of worst 2 atoms' RSCC scores >= 0.7 No atoms modeled at partial occupancy Residues from em structures are required for meet the following fit-to-map criteria: RSCC >= 0.7 Residue inclusion fraction = 1.0 or >= 0.95, depending on structure No atoms modeled at partial occupancy Filtering is documented in each pruned file. See USER  DOC lines in .pdb and data_rna2023_dataset loops in .cif Version history -------------------------------------------------------------------------------- Version 1.0 Jun 30, 2023 Initial version

opencc-by-4.0Aug 2023View details →

DATASET: Exploring the Residue-Level Interactions between the R2ab Protein and Polystyrene Nanoparticles

This file contains the raw hydrogen-deuterium exchange data used in the manuscript. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD044469. 202308

opencc-by-4.0Aug 2020View details →

Residual stresses in clad pressure vessel steel measured by contour method

Data from contour method cut surfaces of low alloy steel plates clad in stainless steel. Two plates, each measuring 300 mm (length) x 200 mm (width) x 20 mm (thickness), were extracted from the outer cylindrical structure of a nuclear steam generator. The material was forged 18MND5 (French designation equivalent to A 508 Gr.3 Cl. 1). Stainless steel beads were then deposited, by submerged arc strip cladding, on the plates. One plate was clad in a single layer of AISI 309L, the second one was clad with a double layer, 309L followed by 308L. The datasets are in the form of lists of x, y, z coordinates, with one point per line, whitespace delimited in millimetres. Each cut has four files associated to it, two for each cut surface. For each surface, there is an outline file identifying the cut surface periphery and a points file containing the points lying on the surface. Two .mat files have also been uploaded, with the results from the analyses on the single and double layer clad plates. These measurements are part of a broader experimental investigation to better understand the role of residual stresses in underclad cracking. The contour method was used to characterise residual stresses in conjunction with neutron diffraction measurements. The details of the experimental procedure and other information will be found in the paper “Internal stresses in a clad pressure vessel steel during post-weld heat treatment and their relevance to underclad cracking" Cattivelli et al., soon to be published.

opencc-by-4.0Apr 2023View details →

Northern Ireland results from the monitoring of pesticide residues in food

Swedish results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Swedish Food Agency MOPER_2022 - Swedish Food Agency MOPER_2021 - Swedish Food Agency MOPER_2020 - Swedish Food Agency MOPER_2019 - Swedish Food Agency MOPER_2018 - Swedish Food Agency MOPER_2017 - Swedish Food Agency MOPER_2016 - Swedish Food Agency MOPER_2015 - Swedish Food Agency MOPER_2014 - Swedish Food Agency MOPER_2013 - Swedish Food Agency MOPER_2012 - Swedish Food Agency MOPER_2011 - Swedish Food Agency   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

opencc-by-4.0Jul 2018View details →

Spanish results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2022 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2021 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2020 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2019 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2018 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2017 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2016 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2015 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2014 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2013 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2012 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición MOPER_2011 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

Slovenian results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2022 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2021 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2020 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2019 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2018 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2017 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2016 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2015 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2014 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2013 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2012 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia MOPER_2011 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

Slovakian results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - State Veterinary and Food Administration of the Slovak Republic MOPER_2022 - State Veterinary and Food Administration of the Slovak Republic MOPER_2021 - State Veterinary and Food Administration of the Slovak Republic MOPER_2020 - State Veterinary and Food Administration of the Slovak Republic MOPER_2019 - State Veterinary and Food Administration of the Slovak Republic MOPER_2018 - State Veterinary and Food Administration of the Slovak Republic MOPER_2017 - State Veterinary and Food Administration of the Slovak Republic MOPER_2016 - State Veterinary and Food Administration of the Slovak Republic MOPER_2015 - State Veterinary and Food Administration of the Slovak Republic MOPER_2014 - State Veterinary and Food Administration of the Slovak Republic MOPER_2013 - State Veterinary and Food Administration of the Slovak Republic MOPER_2012 - State Veterinary and Food Administration of the Slovak Republic MOPER_2011 - State Veterinary and Food Administration of the Slovak Republic   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

opencc-by-4.0Jul 2018View details →

Romanian results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - National Sanitary Veterinary and Food Safety Authority MOPER_2022 - National Sanitary Veterinary and Food Safety Authority MOPER_2021 - National Sanitary Veterinary and Food Safety Authority MOPER_2020 - National Sanitary Veterinary and Food Safety Authority MOPER_2019 - National Sanitary Veterinary and Food Safety Authority MOPER_2018 - National Sanitary Veterinary and Food Safety Authority MOPER_2017 - National Sanitary Veterinary and Food Safety Authority MOPER_2016 - National Sanitary Veterinary and Food Safety Authority MOPER_2015 - National Sanitary Veterinary and Food Safety Authority MOPER_2014 - National Sanitary Veterinary and Food Safety Authority MOPER_2013 - National Sanitary Veterinary and Food Safety Authority MOPER_2012 - National Sanitary Veterinary and Food Safety Authority MOPER_2011 - National Sanitary Veterinary and Food Safety Authority   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

Portuguese results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Direcção-Geral de Alimentação e Veterinária MOPER_2022 - Direcção-Geral de Alimentação e Veterinária MOPER_2021 - Direcção-Geral de Alimentação e Veterinária MOPER_2020 - Direcção-Geral de Alimentação e Veterinária MOPER_2019 - Direcção-Geral de Alimentação e Veterinária MOPER_2018 - Direcção-Geral de Alimentação e Veterinária MOPER_2017 - Instituto Nacional de Investigação Agrária e Veterinária MOPER_2016 - Instituto Nacional de Investigação Agrária e Veterinária MOPER_2015 - Instituto Nacional de Investigação Agrária e Veterinária MOPER_2014 - Instituto Nacional de Investigação Agrária e Veterinária MOPER_2013 - Instituto Nacional de Investigação Agrária e Veterinária MOPER_2012 - Instituto Nacional de Investigação Agrária e Veterinária MOPER_2011 - Instituto Nacional de Investigação Agrária e Veterinária   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14