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8,460 results for “MONITORING”
Figure 1 in Non-invasive genetic study and population monitoring of the brown bear (Ursus arctos) (Mammalia: Ursidae) in Kastoria region - Greece
Figure 1. The study area in Kastoria region and capture locations (red dots) for the 75 living bears.
Greenland Peregrine Falcon eggshell thickness monitoring data 1972 - 2019
<p>The Peregrine Falcon (Falco peregrinus tundrius) population in Greenland has been monitored in different survey areas in South and West Greenland since 1972. At visits to Peregrine Falcon nests, eggshell fragments from hatched eggs as well as addled (dead) eggs left behind have been collected with the aim of monitoring the thickness of the eggshells as well as analysing the whole eggs for contaminants. The shell thickness serves as a proxy for the falcons’ exposure to certain persistant organic pollutants, in particular DDT and its breakdown products (see summaries in Cade et al. 1988).</p> <p>This data set contains the raw data on 6665 eggshell thickness measurements of:<br> 1. Whole eggs from South Greenland 1986-2015<br> 2. Eggshell fragments from the study area in South Greenland 1981-2019<br> 3. Eggshell fragments from the study area around Kangerlussuaq in West Greenland 1972-1989</p> <p>The data set contains a mix of measurements of shell thickness including or excluding the eggshell membranes from the same clutch of eggs. Based on those measurements the average membrane thickness is 0.071 mm (SD=0.013) – a figure confirmed by other studies – and this ’membrane factor’ can be added or subtracted for comparisons with other data sets.</p> <p>Further details regarding the sampling areas, measurement methods and the results of trends analyses of changes in shell thickness are provided in Falk et al. (2006 and 2018).</p> <p>The file named <em>1_Data_Eggshell_Thickness_1972-2019.csv</em> contains the raw measurements data and the file <em>2_ReadMe_Eggshell_Thickness_1972-2019.txt</em> specifies the content. </p> <p>The file named <em>3_Rscript_Eggshell_Thickness_1972-2019.R</em> provides an R script for summarizing and plotting the data as shown in the file <em>4_Plot_Eggshell_Thickness_1972-2019.pdf</em></p>
Water temperature and discharge at monitoring station along the Rhine
<p>We provide daily average water temperatures and discharge at following Rhine monitoring stations:<br> Basel; Worms; Koblenz; Cologne.<br> We provide the yearly heat input from Nuclear power plants between following stations:<br> upstream Basel; between Basel and Worms; between Worms and Koblenz; between Koblenz and Cologne.<br> We provide the calculated catchment wide air temperature, described in the paper https://doi.org/10.5194/hess-2019-518<br> The data is connected to the publication:<br> Anthropogenic Influence on the Rhine water temperatures<br> https://doi.org/10.5194/hess-24-5027-2020</p>
Data for Wang and Kent, GRL, 2021, "RESET: A method to monitor thermoremanent alteration in Thellier-series paleointensity experiments"
<p>This zip file contains data presented in the GRL paper “RESET: A method to monitor thermoremanent alteration in Thellier-series paleointensity experiments” by Wang and Kent, 2021. </p> <p>Folder “Figure2e” contains a subfolder that contains all the raw hysteresis measurements (with heated temperatures in degree Celsius in file names) and an Excel spreadsheet that summarizes the hysteresis parameters, which are used in plotting the Day diagram as shown in Figure 2e.</p> <p>Folder “Figure2f-s” contains raw FORC data of specimens GA79.5y and GA84.6y measured after each heating treatment (with heated temperatures in degree Celsius in file names) that are used in plotting the FORC diagrams as shown in Figure 2f to 2s.</p>
Sodium [18F]Fluoride PET Can Efficiently Monitor In Vivo Atherosclerotic Plaque Calcification Progression and Treatment
<p>Dataset for the research article entitled "Sodium [<sup>18</sup>F]Fluoride PET Can Efficiently Monitor <em>In Vivo </em>Atherosclerotic Plaque Calcification Progression and Treatment" published in the MDPI journal Cells.</p>
Difference in effect of pheromone for monitoring the European spruce bark beetle
<p>In recent decades there have been an increasing number of outbreaks of the European spruce bark beetle (<i>Ips typographus</i>) in Europe. A large amount of sanitary felling has taken place, with significant economic and ecological consequences. In order to anticipate such large-scale outbreaks, an effective monitoring system should be set up. One important aspect of monitoring is the decision on which pheromone to use. We suggest a framework for selecting an effective pheromone with few side effects and implemented it on five different pheromones under different disturbance conditions: Pheroprax, IT Ecolure, Ipstyp, Ipsowit and Typosan. We set 50 traps in two areas with sites that were disturbed and undisturbed by wind storms. We collected bark beetles from traps every one to two weeks from the end of March until the end of September in 2019. We investigated the number of bark beetles caught, bark beetle dynamics, amount of bycatch and predators, the taxonomic groups of the bycatch and the overall costs of the monitoring system. We found that Pheroprax, IT Ecolure and Ipsowit caught the most bark beetles and best showed the population dynamics. There was a low amount of bycatch (less than 6% of the total catch) and predators (a few individuals), but some groups seem to prefer certain pheromones. The cost of the pheromones increased with their effectiveness. However, pheromone costs are low relative to the personnel costs involved in setting traps and collecting bark beetles. The framework and the results will help professionals to decide which pheromones to purchase for their bark beetle monitoring system.</p>
Data for: A Comparison of the Influence of Different Multi-Core Processors on the Runtime Overhead for Application-Level Monitoring
<p>Application-level monitoring is required for continuously operating software systems to maintain their performance and availability at runtime. Performance monitoring of software systems requires storing time series data in a monitoring log or stream. Such monitoring may cause a significant runtime overhead to the monitored system.</p> <p>In this paper, we evaluate the influence of multi-core processors on the overhead of the Kieker application-level monitoring framework. We present a breakdown of the monitoring overhead into three portions and the results of extensive controlled laboratory experiments with microbenchmarks to quantify these portions of monitoring overhead under controlled and repeatable conditions. Our experiments show that the already low overhead of the Kieker framework may be further reduced on multi-core processors with asynchronous writing of the monitoring log.</p> <p>Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Data for: A Benchmark Engineering Methodology to Measure the Overhead of Application-Level Monitoring
<p>Application-level monitoring frameworks, such as Kieker, provide insight into the inner workings and the dynamic behavior of software systems. However, depending on the number of monitoring probes used, these frameworks may introduce significant runtime overhead. Consequently, planning the instrumentation of continuously operating software systems requires detailed knowledge of the performance impact of each monitoring probe.</p> <p>In this paper, we present our benchmark engineering approach to quantify the monitoring overhead caused by each probe under controlled and repeatable conditions. Our developed MooBench benchmark provides a basis for performance evaluations and comparisons of application-level monitoring frameworks. To evaluate its capabilities, we employ our benchmark to conduct a performance comparison of all available Kieker releases from version 0.91 to the current release 1.8.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Data for: Performance Benchmarking of Application Monitoring Frameworks
<p>Application-level monitoring of continuously operating software systems provides insights into their dynamic behavior, helping to maintain their performance and availability during runtime. Such monitoring may cause a significant runtime overhead to the monitored system, depending on the number and location of used instrumentation probes. In order to improve a system’s instrumentation and to reduce the caused monitoring overhead, it is necessary to know the performance impact of each probe.<br /> While many monitoring frameworks are claiming to have minimal impact on the performance, these claims are often not backed up with a detailed performance evaluation determining the actual cost of monitoring. Benchmarks can be used as an effective and affordable way for these evaluations. However, no benchmark specifically targeting the overhead of monitoring itself exists. Furthermore, no established benchmark engineering methodology exists that provides guidelines for the design, execution, and analysis of benchmarks.<br /> This thesis introduces a benchmark approach to measure the performance overhead of application-level monitoring frameworks. The core contributions of this approach are 1) a definition of common causes of monitoring overhead, 2) a general benchmark engineering methodology, 3) the MooBench micro-benchmark to measure and quantify causes of monitoring overhead, and 4) detailed performance evaluations of three different application-level monitoring frameworks. Extensive experiments demonstrate the feasibility and practicality of the approach and validate the benchmark results. The developed benchmark is available as open source software and the results of all experiments are available for download to facilitate further validation and replication of the results.</p> <p>This dataset supplements the thesis and contains the results of all experiments, including the raw result data, the results of additional experiments, and the configuration of our benchmarks.</p>
SNSF Open Access Monitoring: Data Publications 2013-2014
<p>This spreadsheet contains supplemental data to the report: "Open Access to Publications: SNSF monitoring report 2013 - 2015" (http://doi.org/10.5281/zenodo.584131), where it was found that at least 39% of publications out of SNSF funded grants are either Gold or Green Open Access.</p> <p>The initial data is based on the reported publications to the Swiss National Science Foundation via the grant administration system mySNF as of <strong>11. September 2015.</strong> This data is also available as open data on p3.snf.ch. Please be aware, that grant beneficiaries can update the metadata to the reported publications, including the Open Access status anytime during and after the end of a grant.</p> <p>Prior to validation, the actual publication date of publications with “in press/accepted” status was verified insofar as possible, and the details updated (approx. 3'000 publications). For further processing only publications with <strong>publication date 2013-2014</strong> were considered. Publications with the status “in press/accepted” status were excluded. Duplicates, i.e. publications cited as output for more than one project (approx. 2'500) are also filtered out. Where identifiable as such, pure abstracts (e.g. in journal supplements), working papers and pre-prints were also excluded.</p> <p><br> The basic goal was to locate a DOI for the publication and update the metadata. 78% of the 17'420 publications scrutinised have in the meantime been assigned a DOI. Using the DOI and the linked metadata available in CrossRef, unique searches could be performed in additional sources such as DOAJ, Pubmed and Pubmed Central, OpenAIRE (repository aggregator) and ADS (Astrophysics Data System). After the initial search for open access full text in these external sources, a search was made in Google Scholar using the DOI of the remaining publications still to be validated and links to any open access full text were extracted. Where even the Google Scholar search failed to turn up open access full text, the status was set to “closed access”. The OA status of 4'212 publications from the period 2013 to 2014 remained unclear, but it is more likely that they are in closed rather than open access mode.</p>
Printing Unit Condition Monitoring
<p>This data set contains raw sensor signals of four analogue sensors and five features derived from them. These are used to monitor the condition of a printing unit in a demonstrator application and to detect a sensor defect, which is simulated in the data set.</p> <p>The demonstrator is used to simulate the <em>wiping </em>process of an <em>Intaglio printing</em> process. Intaglio is the major printing process to produce security prints like banknotes. Engraved structures in the printing plates, which are mounted on a rotating <em>plate cylinder</em>, are filled with ink, which is transferred onto the printing substrate under high pressure. A second cylinder denoted by <em>wiping cylinder</em>, which is working in the printing unit, is lubricated with a solvent to wipe off surplus ink from the printing plates by rotating in the direction opposite to the plate cylinder. This process is crucial as wiping errors immediately lead to print errors.</p> <p>The printing unit demonstrator contains models of the two cylinders, which are turned by electric drives. Pressure between the wiping cylinder having a rubber surface and the steel-surfaced plate cylinder is freely adjustable.<br /> A set of four analogue sensors (contact force, solid-bourne sound, electric current of wiping and plate cylinder drives) continuously acquire data during operation to monitor the process.<br /> The sensors each output a continuous voltage signal in the range of [-10,10] V, which is proportional to the respective quantity the sensor is observing. Thus, each signal's unit is irrelevant and abandoned as changes of the original quantity of interest are reflected also in the respective voltage signal.<br /> All output time-domain signals are synchronously and equidistantly sampled at a frequency of 20 kHz and quantised with a resolution of 16 bit.</p> <p>The acquired data is then split into non-overlapping batches of 50000 samples (corresponding to 2.5 sec of operation), respectively. The length of the time frame was chosen to ensure that 3 revolutions of the plate cylinder are captured in each signal data batch. The solid-bourne sound signal is treated by the FFT to determine its frequency spectrum per signal batch. Altogether, 5 features per plate cylinder revolution are extracted. This results in 15 feature values per signal data batch. The extracted features are:</p> <ul> <li>contact force mean: arithmetic mean of the contact force,</li> <li>solid-bourne sound intensity: root mean square of the solid-bourne sound,</li> <li>solid-bourne sound maxPowFreqInd: index of the frequency component with largest power,</li> <li>motor current wiping cylinder mean: arithmetic mean of the wiping cylinder motor current,</li> <li>motor current plate cylinder mean: arithmetic mean of the plate cylinder motor current.</li> </ul> <p>Each plate cylinder revolution is represented by one instance in the feature data sets. That is, every instance in the data set is described by a vector of 5 feature values.</p> <p>The raw data and feature data sets are divided into two parts, each containing data of one of the two experiments under different operation conditions:</p> <ul> <li><strong>Static printing unit demonstrator operation:</strong><br /> The static experiment observes the printing unit demonstrator during 20:13 min of operation. The printing unit demonstrator was started immediately before the data acquisition began. No additional manipulations or events occurred during the experiment. Therefore, only data representing the demonstrator's normal condition is contained in the data set. It contains 10,000,000 raw signal samples resulting in 600 instances (plate cylinder revolutions), which are in summary described by 3,000 feature values.</li> <li><strong>Manipulated printing unit demonstrator operation:</strong><br /> The printing unit demonstrator was started ca. 23:00 min before the data acquisition began. During this 10:31 min long experiment, the demonstrator application was intentionally manipulated. In addition, the solid-bourne sound sensor signal was manipulated through low-pass filtering in order to simulate a defect of this sensor. An unintended incident also occurred during this experiment. Therefore, data representing both the demonstrator's normal and abnormals conditions are contained in the data set. The sequence of events along with an objective classification of the demonstrator condition by the human experimenter is summarised in the file <em>PrintingUnit_manip_events.txt</em>. The data set contains 5,950,000 raw signal samples, which are in summary described by 1,785 feature values.</li> </ul> <p><em><strong>File name conventions and contents</strong></em></p> <p>The files in the <em>data set</em> are organised such that each row represents a data set instances, columns represent the respective sensor or feature:</p> <ul> <li><strong>PrintingUnitData*.csv:</strong> These files contain raw sensor signals.</li> <li><strong>PrintingUnitFeatures*.csv:</strong> These files contain the features extracted from the sensor signals.</li> </ul> <p><em>Additional files</em> contain information about</p> <ul> <li><strong>*_condition.csv:</strong> The condition if the printing unit is indicated by 'n' (normal condition) or 'a' (abnormal condition). These are the labels of the data set instances.</li> <li><strong>*_filter.csv:</strong> The solid-bourne sound filter status is indicated by '1' (filter activated) or '0' (filter deactivated).</li> <li><strong>*_time.csv:</strong> The relative time, at which the respective instance of the data set was determined. It is represented as the number of days from January 0, 0000 as is returned from MATLAB's datenum function (cf. http://www.mathworks.com/help/matlab/ref/datenum.html for details).</li> </ul> <p>The <em>operation conditions</em> (with respect to the experiment, cf. above) are distinguished by</p> <ul> <li><strong>*_static*.csv:</strong> Static printing unit demonstrator operation.</li> <li><strong>*_manip*.csv:</strong> Manipulated printing unit demonstrator operation.</li> </ul>
Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "
<p>Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots.</p> <p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.</p>
Individual work of Bachelor students monitored using a dynamic assessment dashboard
<p>This document explain how data were generated and how to interpret them. </p> <p><strong>LICENSE: CC0</strong><br> But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license. <br> Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.</p> <p> </p> <p><strong>HOW DATA WERE COLLECTED</strong><br> Data provided in this dataset were collected in Fall 2016 as part of the Bachelor course 'Formation des usagers en bibliothèques' taught at the University of applied sciences Geneva (Information Sciences Department). <br> Data were generated by the interaction of the students with DAD, the Dynamic assessment dashboard, software to be released in 2017 (https://github.com/grolimur/DAD). Only data related to individual activities are published in this set. They were then cleaned and anonymised.</p> <p>The activities.csv file is provided for information purpose.</p> <p>Data are provided in 2 formats:</p> <p>* Comma-spearated values (submissions.csv)<br> * SQLite (submissions.sqlite)</p> <p>CSV is not correctly read by Excel, it's recommended to convert it into an .xslx file before using it. <br> SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).</p> <p> </p> <p><strong>CODEBOOK</strong><br> Here is the name, the meaning and the possible values of the columns (name - description [possible values]).</p> <p>ID - database row ID [(database unique row ID)]<br> ActivityID - unique activity ID [1, 2, 3, 4, 10, 14, 16, 21, 22, 23, 41]<br> StudentID - unique anonymous student ID [#50 to #72]<br> date - recorded by the database [timestamp]<br> year - year extracted from date [2016, 2017]<br> month - month extracted from date [01, 11, 12]<br> day - day extracted from date [1 to 31]<br> weekday - weekday calculated from date [1, 2, 3, 4, 5, 6, 7]<br> hour - hour extracted from date - 00 to 23]<br> minute - minute extracted from date [00 to 59]<br> second - second extracted from date [00 to 59]<br> gr - group activity or not [0]<br> validated - was the submission validated or not [-1 or 1]<br> week - week number [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, H1, H2]</p> <p><em>ActivityID meaning</em><br> Activities are described in activities.csv. This file conatins only individual activities description. <br> The 2 activities iwth the bonus particularity are not present in the data because students didn't need to apply for them. A bonus was automatically added when activity 1 AND 2 AND 3 AND 4 (bonus = activity 5) as well as when activities 21 AND 22 AND 23 (activity 24) were completed.</p> <p><em>Date format</em><br> The date is formatted like YYYY-MM-DD hh:mm:ss.</p> <p><em>Weekday meaning</em><br> 1 stands for Monday, 2 for Tuesday and so on until 7 for Sunday.</p> <p><em>Group meaning</em><br> 0 stands for 'individual activity' and 1 would stand for group activity'. <br> Only entries with gr="0" appears in this dataset.</p> <p><em>Validated meaning</em><br> -1 stands for 'rejected' and 1 for 'validated'.</p> <p><em>Week meaning</em><br> The course lasts 10 weeks. 1 stands for the 1st week and so on until 10 that stands for the 10th week. <br> H1 and H2 stand for the Christmas holiday weeks that took place between week 8 and 9. <br> No submission were sent in weeks 1, 2 and 10.</p> <p> </p>
Data and results for manuscript "Flow dynamics in hyper-saline aquifers: hydro-geophysical monitoring and modeling"
<p>The paper presents a general methodology that will help understand how freshwater and saltwater may interact in natural porous media, with a particular view at practical applications such as the storage of freshwater underground in critical areas such as semi-arid zones around the Mediterranean sea. The methodology is applied to a case study in Sardinia and shows how a mix of advanced monitoring and mathematical modeling tremendously advance our understanding of these systems.</p> <p>This package contains the raw cross-hole time-lapse ERT data, additional field data, the ERT inversion results of the field data as well as the modeling data in terms of the concentration distribution of the density-dependent flow and transport model and the inverted synthetic ERT monitoring results.</p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Northern Ireland results from the monitoring of pesticide residues in food
<p>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.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> 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.</p> <p>If a sample is <strong>contains quantifiable residues</strong> 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)</p> <p>A sample is considered <strong>non-compliant</strong> 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.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Department of Agriculture, Environment and Rural Affairs (DAERA)</p> <p>MOPER_2022 - Department of Agriculture, Environment and Rural Affairs (DAERA)</p> <p>MOPER_2021 - Department of Agriculture, Environment and Rural Affairs (DAERA)</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Swedish results from the monitoring of pesticide residues in food
<p>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.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> 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.</p> <p>If a sample <strong>contains quantifiable residues</strong> 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)</p> <p>A sample is considered <strong>non-compliant</strong> 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.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Swedish Food Agency</p> <p>MOPER_2022 - Swedish Food Agency</p> <p>MOPER_2021 - Swedish Food Agency</p> <p>MOPER_2020 - Swedish Food Agency</p> <p>MOPER_2019 - Swedish Food Agency</p> <p>MOPER_2018 - Swedish Food Agency</p> <p>MOPER_2017 - Swedish Food Agency</p> <p>MOPER_2016 - Swedish Food Agency</p> <p>MOPER_2015 - Swedish Food Agency</p> <p>MOPER_2014 - Swedish Food Agency</p> <p>MOPER_2013 - Swedish Food Agency</p> <p>MOPER_2012 - Swedish Food Agency</p> <p>MOPER_2011 - Swedish Food Agency</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Spanish results from the monitoring of pesticide residues in food
<p>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.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> 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.</p> <p>If a sample <strong>contains quantifiable residues</strong> 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)</p> <p>A sample is considered <strong>non-compliant</strong> 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.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2022 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2021 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2020 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2019 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2018 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2017 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2016 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2015 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2014 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2013 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2012 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p>MOPER_2011 - Agencia Española de Consumo, Seguridad Alimentaria y Nutrición</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Slovenian results from the monitoring of pesticide residues in food
<p>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.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> 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.</p> <p>If a sample <strong>contains quantifiable residues</strong> 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)</p> <p>A sample is considered <strong>non-compliant</strong> 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.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2022 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2021 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2020 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2019 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2018 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2017 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2016 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2015 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2014 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2013 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2012 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p>MOPER_2011 - Ministry for agriculture, forestry and food, Health Inspectorate of the Republic of Slovenia</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Slovakian results from the monitoring of pesticide residues in food
<p>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.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> 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.</p> <p>If a sample <strong>contains quantifiable residues</strong> 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)</p> <p>A sample is considered <strong>non-compliant</strong> 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.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2022 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2021 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2020 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2019 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2018 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2017 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2016 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2015 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2014 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2013 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2012 - State Veterinary and Food Administration of the Slovak Republic</p> <p>MOPER_2011 - State Veterinary and Food Administration of the Slovak Republic</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.