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7,378 results for “case”
Data from: Linking land use and the nutritional ecology of herbivores: a case study with the Senegalese locust
1) Access to high-quality food is a main driver of population dynamics. For herbivores protein and carbohydrates are key nutrients that are notoriously variable in plants and are affected by land use. However, few studies have linked foraging decisions and performance in the laboratory to the nutritional landscape available in the field. 2) <i>Oedaleus senegalensis</i> is a nonmodel locust, a grass-feeder, and the main pest of millet, a subsistence crop in the Sahel. In this study, we examined dietary preference and locust performance across a range of protein:carbohydrate ratios using the Geometric Framework methodology. We then applied a fitness landscape approach to visualize these results with the plant nutrient contents available across four land-use types: millet, groundnut, fallow, and grazed fields. Finally, we contrasted our results with locust distribution in the field. Several locust species (<i>O. senegalensis</i> included) exhibit density dependent color polymorphism thus we also reported individual coloration (brown or green). 3) We found that <i>O. senegalensis</i> preferred moderately carbohydrate-biased food 1:1.6 protein: carbohydrate ratio. All traits recorded (mass gain, development time, growth rate, molt success, and performance index) were best near that ratio and declined on either side presenting a "hump-shape". Fallow fields contained more plants, particularly grasses, that were both abundant and closer to the optimal protein:carbohydrate ratio recorded from the lab experiments. 4) When we surveyed <i>O. senegalensis</i> abundance and proportion, we found that they were more numerous in the fallow fields. Brown morph individuals, the ones associated with high density, were proportionally more abundant in fallow fields than green individuals. 5) Our study provides evidence that variation in nutritional landscapes—relative to an herbivore's optimal nutrient balance—is a key driver of herbivore population distribution and abundance, and can be used to predict bottom-up effects on herbivore species. protein:carbohydrate ratio recorded from the lab experiments.
SmartUpLab- Co-Creation in sustainable mobility research - Systematic Reviews and Case Studies
<p>The current dataset presents the results of systematic reviews and case studies about co-creation tools best practice for sustainable mobility, carried out in the context of the research project SmartUpLab (funded by EFRE).</p>
DAFNE Basemap for the Zambezi River Basin case study
<p>Spatial dataset derived from many different open data repositories and cropped on the Zambezi River Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study.</p> <p><strong>zambezi.gpkg</strong>: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap:</p> <ul> <li><em>basin</em>: Hydrological basin of the Zambezi river (case study boundary)</li> <li><em>subbasin</em>: Basins of the main tributaries of the Zambezi River</li> <li><em>waterbodies</em>: Natural lakes and reservoirs boundaries</li> <li><em>rivers</em>: River network</li> <li><em>stations</em>: Streamflow, Water level and Rainfall Gauging stations</li> <li><em>dams</em>: Existing dams</li> <li><em>wetlands</em>: Wetlands</li> <li><em>protected_areas</em>: Protected areas</li> <li><em>aei_pct_cells</em>: Area equipped for irrigation, expressed as percentage of total area.</li> <li><em>roads</em>: Main roads network</li> <li><em>cities</em>: Main cities in the riparian countries</li> <li><em>countries</em>: Administrative borders of riparian countries</li> <li><em>markers</em>: DAFNE model components location, with existing and planned dams and power plants, irrigation schemes, environmental target areas.</li> </ul> <p><strong>zambezi_raster.zip</strong>: raster dataset including the following layers:</p> <ul> <li><em>DEM_1km: </em>Digital Elevation Model</li> <li>Global Surface Water: <ul> <li><em>change</em>: Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2015</li> <li><em>extent: </em>Maximum Water Extent shows all the locations ever detected as water over period 1984-2015</li> <li><em>occurence</em>: Occurrence shows where surface water occurred between 1984 and 2015 and provides information concerning overall water dynamics.</li> <li><em>recurrence</em>: Recurrence provides information concerning the inter-annual behaviour of water surfaces and captures the frequency with which water returns from year to year.</li> <li><em>seasonality</em>: Seasonality map provides information concerning the intra-annual behaviour of water surfaces for a single year (2015) and shows permanent and seasonal water and the number of months water was present.</li> <li><em>transitions</em>: Transitions map provides information on the change in surface water seasonality between the first and last years (between 1984 and 2015) and captures changes between the three classes of not water, seasonal water and permanent water.</li> </ul> </li> </ul> <p>Original data sources includes:</p> <ul> <li><a href="http://www.fao.org/aquastat/en/">AQUASTAT</a>, the FAO global information system on water resources and agricultural water management;</li> <li> <p><a href="https://www.naturalearthdata.com/">Natural Earth, </a> a public domain map dataset available at different scales;</p> </li> <li> <p><a href="https://www.protectedplanet.net/en">Protected Planet, </a> the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN;</p> </li> <li> <p><a href="https://www.openstreetmap.org/">OpenStreetMap</a>, a collaborative project to create a free editable map of the world;</p> </li> <li> <p><a href="http://zamwis.wris.info/">ZAMWIS</a>, a management information system for the Zambezi River Basin;</p> </li> <li> <p>NASA's Shuttle Radar Topography Mission (<a href="http://srtm.csi.cgiar.org/">SRTM</a>) Digital Elevation Database;</p> </li> <li> <p><a href="https://global-surface-water.appspot.com/.">Global Water Surface</a>, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.</p> </li> </ul>
DAFNE Basemap for the Omo-Turkana Basin case study
<p>Spatial dataset derived from many different open data repositories and cropped on the Omo-Turkana Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study.</p> <p><strong>omo-turkana.gpkg</strong>: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap:</p> <ul> <li><em>basin</em>: Hydrological basin of the Omo-Turkana river (case study boundary)</li> <li><em>subbasin</em>: Basins of the main tributaries</li> <li><em>waterbodies</em>: Natural lakes and reservoirs boundaries</li> <li><em>rivers</em>: River network</li> <li><em>dams</em>: Existing dams</li> <li><em>protected_areas</em>: Protected areas</li> <li><em>aei_pct_cells</em>: Area equipped for irrigation, expressed as percentage of total area.</li> <li><em>roads</em>: Main roads network</li> <li><em>cities</em>: Main cities in the riparian countries</li> <li><em>countries</em>: Administrative borders of riparian countries</li> <li><em>markers</em>: DAFNE model components location, with existing and planned dams and power plants, irrigation schemes, environmental target areas.</li> </ul> <p><strong>zambezi_raster.zip</strong>: raster dataset including the following layers:</p> <ul> <li><em>srtm_90m: </em>Digital Elevation Model</li> <li>Global Surface Water: <ul> <li><em>change</em>: Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2015</li> <li><em>extent: </em>Maximum Water Extent shows all the locations ever detected as water over period 1984-2015</li> <li><em>occurence</em>: Occurrence shows where surface water occurred between 1984 and 2015 and provides information concerning overall water dynamics.</li> <li><em>recurrence</em>: Recurrence provides information concerning the inter-annual behaviour of water surfaces and captures the frequency with which water returns from year to year.</li> <li><em>seasonality</em>: Seasonality map provides information concerning the intra-annual behaviour of water surfaces for a single year (2015) and shows permanent and seasonal water and the number of months water was present.</li> <li><em>transitions</em>: Transitions map provides information on the change in surface water seasonality between the first and last years (between 1984 and 2015) and captures changes between the three classes of not water, seasonal water and permanent water.</li> </ul> </li> </ul> <p>Original data sources include:</p> <ul> <li><a href="http://www.fao.org/aquastat/en/">AQUASTAT</a>, the FAO global information system on water resources and agricultural water management;</li> <li> <p><a href="https://www.naturalearthdata.com/">Natural Earth,</a> a public domain map dataset available at different scales;</p> </li> <li> <p><a href="https://www.protectedplanet.net/en">Protected Planet, </a> the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN;</p> </li> <li> <p><a href="https://www.openstreetmap.org/">OpenStreetMap</a>, a collaborative project to create a free editable map of the world;</p> </li> <li> <p>NASA's Shuttle Radar Topography Mission (<a href="http://srtm.csi.cgiar.org/">SRTM</a>) Digital Elevation Database;</p> </li> <li> <p><a href="https://global-surface-water.appspot.com/.">Global Water Surface</a>, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.</p> </li> </ul>
PharmaCoNER corpus: gold standard annotations of Pharmacological Substances, Compounds and proteins in Spanish clinical case reports
<p><strong>Intro:</strong></p><p>The PharmaCoNER corpus (divided into train, dev and test) is a Gold Standard manually annotated dataset used for the the PharmaCoNER shared task posed at BIONLP-ST (at EMNLP). In addition, we include here the PharmaCoNER background set. It contains the train, development and test sets of the two subtasks (subtask-1 and subtask-2) with Gold Standard annotations. In addition, it contains the documents of the background set, without annotations.</p><p>The PharmaCoNER corpus consists of:</p><ul><li>Manually classified clinical case sections derived from Open access Spanish medical publications, named the Spanish Clinical Case Corpus (SPACCC).</li><li>It was manually selected by a practicing oncologist and revised by a clinical documentalist to assure that records were relevant/representative and resembled structure and content relevant to process clinical records.</li><li>The final corpus: 1000 clinical cases 16,504 sentences</li><li>The corpus contains a total of 396,988 words, with an average of 396.2 words per clinical case.</li><li>It covers a range of medical disciplines including oncology, urology, cardiology, pneumology or infections diseases, etc.</li><li>The corpus has been annotated at the mention level by experts in medicinal chemistry and pharmacology following a granular annotation scheme covering four mention types:<ul><li><i>Entity type 1 (NORMALIZABLES)</i>: mentions of chemicals that can be manually normalized to a unique concept identifier (primarily SNOMED-CT).</li><li><i>Entity type 2 (NO_NORMALIZABLES)</i>: mentions of chemicals that could not be normalized manually to a unique concept identifier.</li><li><i>Entity type 3 (PROTEINAS)</i>: mentions of proteins/genes following an adaptation of the BioCreative GPRO track annotation guidelines (includes peptides, peptide hormones & antibodies).</li><li><i>Entity type 4 (UNCLEAR )</i>: cases of general substance class mentions of clinical relevance, including certain pharmaceutical formulations, general treatments, chemotherapy programs, and vaccines.</li><li>Mentions class "<i>UNCLEAR</i>" (not evaluated for the PharmaCoNER track)</li></ul></li></ul><p> </p><p> </p><p><strong>Please, cite: </strong></p><p>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</p><p> </p><p><strong>Annotation quality</strong></p><p>Inter-annotator agreement: 93% for annotation, 73% for mapping.</p><p>For more information, see the <a href="https://paperswithcode.com/paper/pharmaconer-pharmacological-substances">paper</a>.</p><p> </p><p><strong>Format</strong></p><p>For subtask 1 annotations are distributed in <i>Brat</i> format. (More info at Brat webpage https://brat.nlplab.org/standoff.html)</p><p>For subtask-2, codes are associated with each document are given in a <i>TSV</i> file with the following columns: </p><blockquote><p>filename code</p></blockquote><p> </p><p><strong>Shared task goal:</strong></p><p>In the two subtasks, the goal is to predict the annotations of the test files (either the ANN files or the TSV with the codes) given only the plain text files. </p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/pharmaconer/"><strong>Web</strong></a></li><li><a href="https://www.aclweb.org/anthology/D19-5701.pdf"><strong>Citation</strong></a><strong>: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li><li><a href="https://doi.org/10.5281/zenodo.4271908"><strong>Silver Standard corpus</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li><li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li><li><a href="https://www.youtube.com/watch?v=B3ZzJl5OMkY"><strong>Youtube video(general setting)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/pharmaconer-pharmacological-substances-compounds-and-proteins-named-entity-recognition-track-at-bionlpost-workshop-november-4-skycity-rm-2-hong-kong-emnlp2019"><strong>Slides PharmacoNER overview talk at BIONLP-ST / EMNLP </strong></a></li></ul><p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p><p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p><p> </p><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p> </p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in PharmaCoNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST</a> (Corpus of symptoms, signs and findings mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul>
5-day WRF case study for the Southern Alps of New Zealand (hourly output)
<p>Full output file from the Weather and Research Forecasting (WRF) model for a 2 km resolution domain centered over the Southern Alps of New Zealand. Modelling period is 02-Feb-2011 00:00:00 UTC to 07-Feb-2011 00:00:00 UTC (5 days) with hourly output frequency. The data-set was generated in the framework of a case study to investigate the mesoscale influence of an atmospheric river on the mass balance of Brewster Glacier (Southern Alps) during a summertime melt event. For the related publication refer to <a href="https://doi.org/10.1029/2020JD034217">https://doi.org/10.1029/2020JD034217</a>.</p>
Novel Coronavirus (COVID-19) Cases in The Netherlands
<p>On 27 February 2020, the first case of COVID-19 disease was confirmed in The Netherlands by RIVM (National Institute for Public Health and the Environment). In the weeks after, thousands of people were diagnosed with the infectious disease. Data on COVID-19 case counts are important for research and applications on various topics like epidemiology and statistics.</p> <p>This dataset contains reported case counts derived from official sources like RIVM (National Institute for Public Health and the Environment), LCPS (National Coordination Center for Patient Distribution), and NICE (National Intensive Care Evaluation). Data from these sources are collected, standardized, and published in various formats on a daily basis.</p> <p>The README document in this repository provides an overview of the available datasets, their file location(s), and codebooks. Copies of the original data are stored in the folder named 'raw_data'. Scripts to process the raw data into standardized files can be found in the folder workflows.</p>
MEDDOCAN corpus: gold standard annotations for Medical Document Anonymization on Spanish clinical case reports
<p><strong>Intro:</strong></p> <p>Meddocan shared task dataset (divided in train, dev and test). In addition, we include here the Meddocan background set.</p> <p>It contains the training, development and test sets of the Meddocan shared task with Gold Standard annotations.</p> <p>In addition, it contains the documents of the background set, without annotations.</p> <p> </p> <p><strong>Annotation quality</strong></p> <p>Inter-annotator agreement: 98% </p> <p>For more information, see the <a href="http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf">paper</a>. </p> <p> </p> <p><strong>Format:</strong></p> <p>Annotations are distributed in Brat format. See <a href="https://brat.nlplab.org/standoff.html">Brat webpage</a> for more information.</p> <p>In addition, annotations are also distributed in XML format (based on i2b2 XML format).</p> <p>In the <a href="https://temu.bsc.es/meddocan/index.php/resources/">Meddocan webpage</a>, there is a script to convert between MEDDOCAN-Brat, MEDDOCAN-XML, and i2b2 formats.</p> <p> </p> <p><strong>Shared task goal:</strong></p> <p>In the three subtasks, the goal will be to predict the annotations given only the plain text files. </p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/meddocan/">Web</a></strong></li> <li><strong>Citation: </strong>Montserrat Marimon et al. “Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.” In: IberLEF@ SEPLN. 2019, pp. 618–638.</li> <li><strong>Silver Standard corpus</strong></li> <li><a href="https://doi.org/10.5281/zenodo.4279337"><strong>Annotation guidelines</strong></a></li> </ul> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/meddocan/">https://temu.bsc.es/meddocan/</a> or email us at encargo-pln-life@bsc.es</p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital (SEAD)</p>
Pretrained Spec2Vec model (case study for MS2Query)
<p>The model was trained for 20 iterations on a subset of the "AllPositive" dataset, created from GNPS positive ionmode mass spectra (subset of 91320 spectra out of 95320 spectra after filtering). It is an example model to run the MS2query app: https://github.com/iomega/ms2query.</p>
Supplementary Information for "The Peculiar Case of the Hyperthermostable Pyrimidine Nucleoside Phosphorylase from Thermus thermophilus"
<p>This is the external Supplementary Information for our publication "The Peculiar Case of the Hyperthermostable Pyrimidine Nucleoside Phosphorylase from <em>Thermus thermophilus</em>".</p> <p>The .pdf file contains the Supplementary Information: author contributions, experimental procedures and supplementary items.</p> <p>The .zip file contains the raw data and metadata for all items (supplementary and main text) and the calculations.</p> <p>The Python code used for spectral unmixing is available on this platform (10.5281/zenodo.3243376) and has been described in our previous publications (10.3390/mps2030060, 10.5281/zenodo.3333469, 10.1002/cbic.202000204, 10.5281/zenodo.3723806 ). Further applications of this method can be found in our recent article in <em>Adv. Synth. Catal.</em> (10.1002/adsc.201901230) and its supporting material (10.5281/zenodo.3568858).</p>
Three-systems for visual numerosity: A single case study
<p>MOT TASK</p> <p>Each file refers to a session with a particular condition. It is spelt out in the file name the number of objects to track as well as the total amount of objects on the screen</p> <p>Within each file it is found a variable called “MatriceRisultati”. Which contains:</p> <ol> <li>Number of targets to follow</li> <li>Number of correct answers</li> <li>Number of trials at the condition</li> <li>Percent correct responses (i.e. value_2 / value_3)</li> </ol> <p> </p> <p>NECKLACE – DISTANCE TASK</p> <p>Each file contains raw data for each session. All the data are store in a variable called RESP, which contains parameters for each trial.</p> <p>The crucial columns are</p> <ol> <li>Inter dot distance in the reference (in pixels – typically 1 pixel =~0.03 cm)</li> <li>Interdot distance in the test (in pixels)</li> <li>Subject choice to the question “which contains closer dots”</li> </ol> <p>For analysis one has to draw a psychometric curve (i.e. a cumulative gaussian) that fits the data of column 3, as a function of interdot distance (column 2). Varinat may include dividing column 2 by column 1 (so to have the ratio between test and reference) and run the psychometric curve on such normalized dimension</p> <p> </p> <p>Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.</p> <p> </p> <p>Numerosity discrimination</p> <ul> <li>The relevant columns in the matrix ‘a’ contain the following information:</li> <li>1<sup>st</sup>: Numerosity</li> <li>2<sup>nd</sup>: Log10 Numerosity</li> <li>3<sup>rd</sup>: Response </li> </ul> <p> </p> <p>Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.</p> <p> </p> <p>Numerosity estimation</p> <ul> <li>The relevant columns in the matrix ‘ContengoRisultati’ contain the following information:</li> <li>1<sup>st</sup>: Numerosity</li> <li>2<sup>nd</sup>: Response</li> </ul> <p> </p> <p>Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.</p> <p> </p> <p> </p>
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates
<p>EFSA was requested to assess the impact on effectiveness of <em>post-mortem</em> inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals.</p> <p>The following information is indicated for each disease/condition in the disease map:</p> <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at <em>ante mortem </em>inspection;</li> <li>The lesions associated with the disease that could be detected at <em>post mortem</em> inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> <p>From the disease map, the list of organs to be considered at <em>post mortem</em> inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.</p>
Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon
<p>This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present. </p>
FIG. 6 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 6. — Triangle plot presenting variations in the frequency of the three main species between the different archaeological features on the site of Villeneuve-Saint-Germain.
FIG. 7 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 7. — Plan of the southern ditch sector on the site of Villeneuve-Saint-Germain: A, general plan of the site of Villeneuve-Saint-Germain (from Pion 1996); B, southern ditch sector on the site of Villeneuve-Saint-Germain (from Ruby & Auxiette 2010). Scale bar: 10 m.
FIG. 2 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 2. — Development of herd composition during the Iron Age. BCP, Berry-au-Bac " le Chemin de la Pêcherie "; BGM, Bucy-le-Long " le Grand Marais "; LLF, Limé " les Fussis "; MDV = Menneville " Derrière le Village "; BFM, Bucy-le-Long " le Fond du Petit Marais "; LLP, Limé " La Prairie "; BAR, Baranton; BET, Bétheny " les Equiernolles "; BVC, Bazoches-sur-Vesles " les Chantraînes "; CLB, Ciry-Salsogne " le Bruy "; MNV, Mont-Notre-Dame " Vaudigny "; DRF, Damary " le Ruisseauds Fayau "; CSS, Condé-sur-Suippe; RD, Reims-Durocortorum; AC, Acy-Romance; SFR, Sermoise " les Fausses Rues "; VSE, Villeneuve-Saint-Germain " les Etomelles "; VSG, Villeneuve-Saint-Germain.
FIG. 8 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 8. — Simplified chart of butchery cuts from bovinae according to observations carried out on the site of Villeneuve-Saint-Germain.
FIG. 2 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 2. — Development of herd composition during the Iron Age. BCP, Berry-au-Bac " le Chemin de la Pêcherie "; BGM, Bucy-le-Long " le Grand Marais "; LLF, Limé " les Fussis "; MDV = Menneville " Derrière le Village "; BFM, Bucy-le-Long " le Fond du Petit Marais "; LLP, Limé " La Prairie "; BAR, Baranton; BET, Bétheny " les Equiernolles "; BVC, Bazoches-sur-Vesles " les Chantraînes "; CLB, Ciry-Salsogne " le Bruy "; MNV, Mont-Notre-Dame " Vaudigny "; DRF, Damary " le Ruisseauds Fayau "; CSS, Condé-sur-Suippe; RD, Reims-Durocortorum; AC, Acy-Romance; SFR, Sermoise " les Fausses Rues "; VSE, Villeneuve-Saint-Germain " les Etomelles "; VSG, Villeneuve-Saint-Germain.
FIG. 7 in Socio-economic changes and their implication in the consumption and trade of meat during the La Tène period in Northern France: the cases of the Villeneuve-Saint-Germain and Condé-sur-Suippe (Aisne) oppida
FIG. 7. — Plan of the southern ditch sector on the site of Villeneuve-Saint-Germain: A, general plan of the site of Villeneuve-Saint-Germain (from Pion 1996); B, southern ditch sector on the site of Villeneuve-Saint-Germain (from Ruby & Auxiette 2010). Scale bar: 10 m.
ScienceDex guides
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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.
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DANDI Archive for NWB datasets
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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.