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6,410 results for “results”
Greg Kolodziejzyk's 13-year associative remote viewing experiment results (session-level data)
<p>This dataset is a transformation of Greg Kolodziejzyk's remote viewing data (see Related datasets below). Greg used a "rapid-fire" technique whereby several short free-response remote viewing trials were completed in a single session. The trial-level data was transformed by Adrian Ryan into session-level Z-scores by exact binomial, in order that the data could be combined with those from other experiments, for the analyses reported here:</p> <ul> <li>Ryan, A., & Spottiswoode, J. (2015). Variation of ESP by Season, Local Sidereal Time, and Geomagnetic Activity. Extrasensory Perception, 377-394.</li> </ul> <p>Three files are included:</p> <ul> <li>Data file</li> <li>Code book</li> <li>Transform Procedure: The R script used to transform the original trial-level data into session-level data.</li> </ul>
FEA result
<p>This dataset includes the stress and position (x, y, z) data at all inspection points along the key curves mentioned in the five finite element analysis (FEA) models presented in the study. The data is provided in an Excel file and contains detailed relationships between stress values and their corresponding spatial coordinates. This comprehensive dataset facilitates replication of results and further analysis by interested researchers.</p>
LEX4BIO Results
<h4>LEX4BIO project’s animated video featuring in a visually fascinating way the need for the research conducted within the action, the solutions it offers through its achieved results, impact in the context of circular economy and Green Deal as well as considerations for the way ahead.</h4> <h4>“Soil degradation and environmental pollution due to unsustainable use of natural resources are major drivers of the climate and biodiversity crises. Research on nutrient recovery and innovation in the EU has made significant advancements in recent years, aiming to enhance nutrient use efficiency in agriculture, and reduce nutrient losses to the environment”.</h4>
GNN Models and results for the paper "Band-gap regression with architecture-optimized message-passing neural networks"
<p>Contains files with model parameters for random search and reference models, as well as the converted AFLOW dataset, in graphs form. Corresponds to results in <a href="https://arxiv.org/pdf/2309.06348.pdf">https://arxiv.org/pdf/2309.06348.pdf</a>.</p> <p>Model predictions along with AUID identifiers are located in result_combined.zip, band gap (egap) and formation energy (ef) predictions are from the PaiNN ensemble, band gap classification is done by MPEU model.</p> <p>New results include PaiNN NAS models.</p> <p>Compatible source code can be found at <a href="https://github.com/tisabe/jraph_MPEU/tree/v1.0.0">jraph_MPEU GitHub repository</a>.</p>
Evaluation Results - Semantic Zoom With Immersive Detail View for ExplorViz
<p>This archive contains the evaluation results of the master thesis 'Semantic Zoom With Immersive Detail View for ExplorViz'.</p> <p>The evaluation is divided into a user evaluation of usability and user performance and a rendering performance evaluation.</p> <p>The evaluation compares the version of ExplorViz with Semantic Zoom and without Semantic Zoom.</p> <p>The complete user survey can be viewed in the PDF: 'Printed version of the survey - ExplorViz with Semantic Zoom.pdf'.</p> <p><br>- The file 'survey_archive_277626.lsa' is exported from LimeSurvey and contains the survey and the responses.<br>- results-survey277626.csv' contains the results in csv format.<br>- The file 'results-statistics.pdf' is a pdf that contains statistics about the survey results.<br>- The file 'results-all-answers-ExplorViz with Semantic Zoom.pdf' lists all the participants' answers in text format.<br>- The file 'allChartImages.zip' displays the results data in graphs.</p> <p><br>As part of a performance evaluation of the frontend, a Python script using Selenium was used.<br>The results can be found in the csv files:<br>- 'performance_RendertimeTracegen - XXXL world with high communication2024-11-19--22-13-36-SZLongTerm'<br>- 'performance_RendertimeTracegen - XXXL world with high communication2024-11-19--22-09-24-NoSZLongTerm'</p> <p>The Python script is split into two files:<br>- 'selenium_test.py'<br>- 'helpers.py'</p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Norway
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_NO: Norwegian Veterinary Institute (NVI)</li> <li>TSE_2022_NO: Norwegian Veterinary Institute (NVI)</li> <li>TSE_2021_NO: Norwegian Veterinary Institute (NVI)</li> <li>TSE_2020_NO: Norwegian Veterinary Institute (NVI)</li> <li>TSE_2019_NO: Norwegian Veterinary Institute (NVI)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Latvia
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_LV: Food and Veterinary Service (PVD)</li> <li>TSE_2022_LV: Food and Veterinary Service (PVD)</li> <li>TSE_2021_LV: Food and Veterinary Service (PVD)</li> <li>TSE_2020_LV: Food and Veterinary Service (PVD)</li> <li>TSE_2019_LV: Food and Veterinary Service (PVD)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Luxembourg
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection:</strong></p> <ul> <li>TSE_2023_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2022_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2021_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2020_LU: Veterinary Services Administration (ASV)</li> <li>TSE_2019_LU: Veterinary Services Administration (ASV)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Bosnia and Herzegovina
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> <li>TSE_2022_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> <li>TSE_2021_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> <li>TSE_2020_BA: Food Safety Agency of Bosnia and Herzegovina (FSA)</li> </ul> <p>For Bosnia and Herzegovina, 2020 is the first year reporting TSE surveillance results.</p>
Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces
<p>The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas.</p> <p>For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>.</p> <p>During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> <p><em><strong>This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).</strong></em></p>
Interim Data and Results for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Interim exposure data and results for the published case study. External raw data is available <a href="https://zenodo.org/records/14260630">here</a> and the remaining raw data is generated via code. This is too large to share on Zenodo. There are stochastic aspects in going from raw exposure to interim exposure data and interim exposure data to results, which is why we make the specific interim exposure data and results of the published study available here. Other interim data is not stochastic and can be reproduced following the code reproduction instructions (see https://github.com/abpoll/j40_gc). Please see the underlying study for more details about the methods. The data here can be generally reproduced (not bit-wise) following the code and instructions at this repository: https://zenodo.org/records/14261361.</p> <p> </p> <p>The interim exposure data is named "exp" and the results data is named "results."</p>
Making predictions for geoscience and climate policy workshop results
<p>This provides the data files and basic plotting and interpretation for results for predictions made in a series of workshops run in 2024, with predictions made for 2024, 2050, 2100 and 5o years after net zero. The workshop results are found in the files "EGU_Prediction-making workshop questions cleaned.csv" and "RGS_...", the code expects these to be in a folder denoted "data". More details can be found in the README.txt. Updates to this codebase can be found in <a href="https://github.com/Rlamboll/ClimatePredictionsWorkshop">https://github.com/Rlamboll/ClimatePredictionsWorkshop.</a></p> <p> </p>
ConnectionChecker: Results for Cultural Heritage Datasets
<p>ConnectionChecker: Results for Cultural Heritage Datasets. We have evaluated two datasets of CH domain:</p> <p>1.A subset of the dataset Persons of National Library of Netherlands with 3,000,000 triples, 698,527 entities, and 535,407 owl:sameAs relationships.</p> <p>2.The dataset World War I as LOD from the Universities of Aalto and Helsinki, Finland with 47,176 triples, 11,339 entities and 547 owl:sameAs relationships.</p> <p>ne can download the triples of these datasets, their metadata in CSV and VoID format, the provenance of entities and the inferred sameAs relationships. Moreover, it contains screenshots showing the produced visualizations for these datasets.</p> <p><a href="https://demos.isl.ics.forth.gr/ConnectionChecker">https://demos.isl.ics.forth.gr/ConnectionChecker</a></p>
2024 Philadelphia Marathon Results (with splits)
<p>This dataset includes the results of the 2024 Philadelphia Marathon. These unofficial results were sourced from the Philly Marathon website on Friday, 11/29/2024.</p> <p>These results were initially collected as part of an analysis that was published on <a href="https://runningwithrock.com/2024-philly-marathon-data/">Running with Rock</a> and in <br><a href="https://medium.com/runners-life/the-2024-philadelphia-marathon-by-the-numbers-f08243aa8ee4">Runner's Life</a>.</p> <p>The dataset includes 12,381 results with the following information:</p> <ul> <li>Bib number</li> <li>Name (First Last)</li> <li>City</li> <li>State</li> <li>Age (0 indicates unknown)</li> <li>Gender (Men, Women, Non-Binary)</li> <li>Overall Place</li> <li>Gender Place</li> <li>Age Place (within gender and age group)</li> <li>Finish Time (H:MM:SS)</li> <li>Split at 5k (MM:SS)</li> <li>Split at 15k (H:MM:SS)</li> <li>Split at Half (H:MM:SS)</li> <li>Split at 30k (H:MM:SS)</li> <li>Split at 40k (H:MM:SS)</li> </ul> <p>Note that there was no field indicating a runner's country. However, by analyzing the state column, you can infer whether a runner is from the United States or another country.</p>
Results Data: Understanding the distributional effects of recurrent floods in the Philippines
<p>This dataset provides all the files required to visualize the main Figures of the paper:</p> <p>Sauer, Inga and Walsh, Brian James and Frieler, Katja and Bresch, David N. and Otto, Christian, Understanding the Distributional Effects of Recurrent Floods in the Philippines (May 27, 2024).</p> <p>The filenames indicate the figures for which the dataset serve as input. The name "haz" indicates a file that provides flooded areas, the final number indicates the admin1 region.</p> <p>keff-> capital stock damage</p> <p>cons_sm-> consumption loss smoothed with savings</p> <p>wb_sm-> well-being loss smoothed with savings</p>
Data and results for the paper: "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC"
<div> <div>We provide the following files used in the study "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC".</div> <div>The paper has been accepted for presentation in the research track of the IEEE/ACM International Conference on Software Engineering (ICSE) 2025 and will be included in the conference proceedings.</div> <div>The preprint is available on <a href="http://arxiv.org/abs/2501.15181" target="_blank" rel="noopener">arXiv</a>.</div> <br> <div><strong>User stories e-commerce.xlsx</strong></div> <div>307 real-world user stories from 3 different eCommerce projects.</div> <br> <div><em>Project A</em> defines a complete set of requirements for a B2C focused onlineshop of a publishing house who aims do sell his own publications directly.</div> <div><em>Project B</em> contains a partial set of requirements for a B2C focused onlineshop of a bookseller.</div> <div><em>Project C</em> includes a subset of B2C and B2B requirements for an online bookstore, supplemented by an eProcurement module designed to provide information and automation for industrial customers.</div> <div>Most of the user stories come with additional acceptance criteria, written in unstructured natural language.</div> <br> <div>The user stories have been anonymized and the merchant's real names were replaced with neutral terms.</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned across all projects</div> <div>- Project: user story belongs to project A, B or C</div> <div>- Connextra: user story in connextra pattern</div> <div>- Acceptance Criteria: acceptance criteria that came with the user story</div> <br> <div><strong>User stories CMS.xlsx</strong></div> <div>34 CMS related user stories from a dataset that was originally created by</div> <div>*Lucassen, G., Dalpiaz, F., van der Werf, J.M.E., Brinkkemper, S.: Visualizing user</div> <div>story requirements at multiple granularity levels via semantic relatedness. In: Con-</div> <div>ceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November</div> <div>14-17, 2016, Proceedings 35. pp. 463–478. Springer (2016)*</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned</div> <div>- Connextra: user story in connextra pattern</div> <br> <div><strong>Issues e-commerce.xlsx</strong></div> <div>54,396 issues, we harvested from seven different issue trackers between June 2011 and July 2024</div> <div>- magento2 (https://github.com/magento/magento2/issues)</div> <div>- nopCommerce (https://github.com/nopSolutions/nopCommerce/issues)</div> <div>- OpenCart (https://github.com/opencart/opencart/issues)</div> <div>- PrestaShop (https://github.com/PrestaShop/PrestaShop/issues)</div> <div>- Shopware5 (https://issues.shopware.com/?products=SW-5)</div> <div>- Shopware6 (https://issues.shopware.com/?products=SW-6)</div> <div>- WooCommerce (https://github.com/woocommerce/woocommerce/issues)</div> <br> <div>Columns</div> <div>- id: unique ID we have assigned</div> <div>- Issue Tracker: issue tracker this issue originates from</div> <div>- Title: title of the original issue</div> <div>- Body: body / description of the original issue</div> <div>- Preprocessed: result of preprocessing the issue as described in the paper</div> <div>- Sample: issue was part of our 3,500 sample issues we used to evaluate CrUISE-AC</div> <br> <div><strong>Issues CMS.xlsx</strong></div> <div>64,500 issues, we harvested from two different issue trackers between April 2002 and August 2024</div> <div>- Moodle (https://github.com/magento/magento2/issues)</div> <div>- Umbraco (https://github.com/nopSolutions/nopCommerce/issues)</div> <br> <div>Columns are the the same as for "Issues e-commerce.xlsx"</div> <br> <div><strong>trivia-trainingdata.csv</strong></div> Manually labelled dataset to train the trivia classifier. <div>The dataset contains 1916 phrases with an even distribution of 958 trivia and 958 non-trivia phrases.</div> <br> <div>- Label = 1: this sentence is trivia</div> <div>- Label = 0: this sentence is not considered trivia</div> <br> <div>Any source code was replaced by [CODE] to simplify the classification process. Source code in markdown could be identified easily as it is enclosed by a special character https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks</div> <br><strong>Prompts</strong><br> <div><em>prompt_match.txt</em>: prompt we used across all LLMs to assess, if an issue potentially might affect a given user story</div> <em>prompt_generate.txt</em>: GPT4-turbo prompt to convert an issue text into gherkin-style acceptance criteria for a given user story<br> <div><em>prompt_evaluate.txt</em>: GPT4-turbo prompt to assess the usefulness of a newly generated acceptance criteria for a given user story</div> <br> <div><strong>Evaluation e-commerce.xlsx</strong></div> issue / user story pairs, generated acceptance criteria and result of manual evaluation.<br> <div> </div> <div>Columns</div> <div>- StoryID: unique ID of the user story (refer to User stories e-commerce.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues e-commerce.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- Existing AC: acceptance criteria that originally came with the user story</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E2: evaluation result by expert 2 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E3: evaluation result by expert 3 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 3/4 majority: did at least 3 experts assess this AC as relevant (1 = yes; 0 = no)</div> <br> <div><strong>Evaluation CMS.xlsx</strong></div> <div>- StoryID: unique ID of the user story (refer to User stories CMS.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues CMS.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E5: evaluation result by expert 5 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 2/3 majority: did at least 2 experts assess this AC as relevant (1 = yes; 0 = no)</div> </div>
FoodLAND - Dataset on producers' socio-economic conditions, operational and technological states, and behavioural experimental results
<p>This submission derives from Work Package 3 "Producers' behaviours, agrobiodiversity, and food diversity" of the H2020 project FoodLAND "Food and Local, Agricultural and Nutritional Diversity" (2020-2025). It consists of two datasets, and two survey questionnaires used to gather these data (English version). The datasets include information about small crop and fish farmers, respectively, sampled in so-called Food Hubs (i.e., local production regions) in Morocco, Kenya, Tanzania, Tunisia, and Uganda. The crop farmers' data were collected in two Food Hubs in each country, while the fish farmers' data were collected in one Food Hub in each of Kenya and Uganda, plus a limited number of observations in Tanzania, in all the cases using standardised survey questionnaires. In one location in each of Morocco, Kenya, Tanzania and Tunisia, lab-in-the-field experiments were run with crop farmers, while in one location in Uganda the same experiments were run with fish farmers. The experimental protocols have been submitted separately. The datasets are provided as Excel Workbooks, while the questionnaires are provided in PDF format. Each dataset includes one sheet about "Conditions" (one row per farmer: 4,529 observations for crop farmers and 927 for fish farmers), one sheet about "Production" (one row per farmer and per up to three crops: 10,668 observations for crop farmers and 1,245 for fish farmers), and one sheet with the results of the behavioural experiments (one row per farmer: 1,987 observations for crop farmers and 406 for fish farmers).</p>
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0- Part 1: System description and preliminary experimental results
<table> <tbody> <tr> <td> <p><span>DA_BC</span></p> </td> <td> <p><span>Assimilating only BC surface observations while excluding operational meteorological observations</span></p> </td> </tr> <tr> <td> <p><span>DA_MET</span></p> </td> <td> <p><span>Assimilating only operational meteorological observations while excluding BC surface observations</span></p> </td> </tr> <tr> <td> <p><span>DA_MET_then_BC</span></p> </td> <td> <p><span>First assimilating operational meteorological observations, then assimilating BC surface observations</span></p> </td> </tr> <tr> <td> <p><span>DA_MET_BC_simult</span></p> </td> <td> <p><span>Assimilating both operational meteorological and BC surface observations </span><span>simultaneously</span></p> </td> </tr> <tr> <td>code.zip</td> <td>Including black carbon observation preprocessing codes, black carbon observation operator codes, black carbon module tangent and adjoint interface codes, the CMA-GFS-AERO 4D-Var main program,and drawing script.</td> </tr> </tbody> </table>
Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system
<p>These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system</p>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Croatia
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Croatia. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken.</p> <p>Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation.</p> <p>Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2023 – Croatian Food Agency</p> <p>VMPR_2022 – Croatian Food Agency</p> <p>VMPR_2021 – Croatian Food Agency</p> <p>VMPR_2020 – Croatian Food Agency</p> <p>VMPR_2019 – Croatian Food Agency</p> <p>VMPR_2018 – Croatian Food Agency</p> <p>VMPR_2017 – Croatian Food Agency</p>
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