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2,008 results for “presence”
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Germany
<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>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <ul> <li>TSE_2023_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2022_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2021_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2020_DE: Federal Ministry of Food and Agriculture (BMEL)</li> <li>TSE_2019_DE: Federal Ministry of Food and Agriculture (BMEL)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - the Netherlands
<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_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2022_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2021_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2020_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2019_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Slovenia
<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_SI: Administration of the Republic of Slovenia for Food Safety, Veterinary and Plant Protection (UVHVVR)</li> <li>TSE_2022_SI: Administration of the Republic of Slovenia for Food Safety, Veterinary and Plant Protection (UVHVVR)</li> <li>TSE_2021_SI: Administration of the Republic of Slovenia for Food Safety, Veterinary and Plant Protection (UVHVVR)</li> <li>TSE_2020_SI: Administration of the Republic of Slovenia for Food Safety, Veterinary and Plant Protection (UVHVVR)</li> <li>TSE_2019_SI: Administration of the Republic of Slovenia for Food Safety, Veterinary and Plant Protection (UVHVVR)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Romania
<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_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2022_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2021_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2020_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> <li>TSE_2019_RO: National Sanitary Veterinary and Food Safety Authority (ANSVSA)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Greece
<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_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2022_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2021_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2020_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2019_EL: Ministry of Rural Development and Food (MINAGRIC)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Lithuania
<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_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2022_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2021_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2020_LT: State Food and Veterinary Service (VMVT)</li> <li>TSE_2019_LT: State Food and Veterinary Service (VMVT)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Portugal
<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_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2022_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2021_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2020_PT: Portuguese National Authority for Animal Health (DGAV)</li> <li>TSE_2019_PT: Portuguese National Authority for Animal Health (DGAV)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Italy
<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_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2022_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2021_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2020_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2019_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Malta
<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_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2022_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2021_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2020_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> <li>TSE_2019_MT: Ministry for Agriculture, Fisheries and Animal Rights</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Finland
<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_FI - Finnish Food Authority (Evira)</li> <li>TSE_2022_FI - Finnish Food Authority (Evira)</li> <li>TSE_2021_FI - Finnish Food Authority (Evira)</li> <li>TSE_2020_FI - Finnish Food Authority (Evira)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - United Kingdom in respect of Northern Ireland
<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_XI - Department of Agriculture, Environment and Rural Affairs</li> <li>TSE_2022_XI - Department of Agriculture, Environment and Rural Affairs</li> <li>TSE_2021_XI - Department of Agriculture, Environment and Rural Affairs</li> </ul> <p> </p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - North Macedonia
<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_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2022_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2021_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2020_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2019_MK: Food and Veterinary Agency (FVA)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Cyprus
<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_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2022_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2021_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2020_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2019_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> </ul>
Presence of Echinococcus multilocularis and Taenia taeniaformis in muskrats (Ondatra zibethicus)
<p>This dataset describes the presence of <em>Echinococcus multilocularis</em> and <em>Taenia taeniaformis </em>in muskrats (<em>Ondatra zibethicus</em>) in Flanders, Belgium.</p> <p>From 1994 onward, muskrats from Flanders have been autopsied with the aim of understanding their demography. In 2008, we first detected EM in a muskrat from Lessines (Wallonia), just across the border with Flanders. Therefore, from 2009 onward, all available muskrats from Flanders and neighbouring regions were collected and examined.</p> <p>The dataset contains the following variables:</p> <ul> <li><code>date</code>: YYYY-MM-DD</li> <li><code>province</code>: Flemish province where the muskrat was captured, for locations outside of Flanders only a municipality is provided</li> <li><code>municipality</code>: the municipality where the muskrat was captured</li> <li><code>weight</code>: the weight of the animal in grams</li> <li><code>sex</code>: either M: male or F: female</li> <li><code>taen</code>: Infection with <em>Taenia taeniaformis</em>, scored as: <ul> <li>0: No <em>T. taeniaformis </em>present</li> <li>1: Few <em>T. taeniaformis </em>present</li> <li>2: Multiple <em>T. taeniaformis </em>present</li> <li>3: A high number of <em>T. taeniaformis </em>present</li> </ul> </li> <li><code>EM</code>: Infection with <em>Echinococcus multilocularis</em>, scored as: <ul> <li>N: negative, no parasites present</li> <li>P: positive, parasite present</li> <li>?: unsure</li> </ul> </li> </ul>
Dataset of handling noise for "Assessing the relevance of perceptually driven objective metrics in the presence of handling noise"
<p>This dataset contains the handling noise chunks used in [1] under the<br> folders "tapping" and "rustle" and the csv files used to compute the<br> results in section 5 of [1] under the "csv" folder. These chunks have<br> been extracted from seven in-house recordings and twelve of the sixteen<br> recordings from [2]. The twelve recordings from [2] have first been<br> converted from the .wma to the .wav format and resampled from 44100 Hz<br> to 48000 Hz before the extraction of the chunks. The sample rate of the<br> in-house recordings is natively 48000 Hz.</p> <p><br> [1] Angonin, C., Chourdakis E. T., and Åeng, R. A. "Assessing the<br> relevance of perceptually driven objective metrics in the presence of<br> handling noise", in 152nd Audio Engineering Society Convention,<br> Netherlands, 2022.</p> <p>[2] Kentric, P. Jackson, I. R., Fazenda, B. M, Cox, T. J., and Li, F. F.<br> "Microphone handling noise: Measurements of perceptual threshold and effects<br> on audio quality." PloS one, 10(10), 2015.</p> <p> </p>
Presence-Absence Points for Tree Species Distribution Modelling for Europe
<p>The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by <a href="https://zenodo.org/record/5524611">Heisig and Hengl (2020)</a> and absence data from the <a href="https://ec.europa.eu/eurostat/web/lucas">LUCAS</a> (in-situ source) dataset.</p> <p>A set of <strong>50</strong> different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by <a href="https://zenodo.org/record/4725429">Parente et al. (2021)</a>. We overlaid the points with the probability maps for the classes:</p> <ul> <li>311: Broad-leaved forest,</li> <li>312: Coniferous forest,</li> <li>313: Mixed forest,</li> <li>323: Sclerophyllous forest,</li> <li>324: Transitional woodland-shrub,</li> <li>333: Sparsely vegetated area.</li> </ul> <p>Points were included in the dataset only if the probability value extracted for at least one of the above classes was <strong>≥ 50%</strong> for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets.</p> <p>The final dataset contains <strong>4,359,999</strong> observations for and a total of <strong>630 </strong>columns. <br> <br> The first <strong>8 </strong>columns of the dataset contain metadata information used to uniquely identify the points:</p> <ul> <li><strong>id</strong>: unique point identifier,</li> <li><strong>year</strong>: year of observation,</li> <li><strong>postprocess</strong>: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,</li> <li><strong>Tile_ID</strong>: contains the tile id from the eu_tiling_system (30 km grid),</li> <li><strong>easting</strong>: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>northing</strong>: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>Atlas_class</strong>: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,</li> <li><strong>lc1</strong>: contains original LUCAS land cover class or NULL if it's a presence point.</li> </ul> <p>The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of <strong>16 target species </strong>for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (<em>i.e.</em> <a href="http://link.springer.com/article/10.1023/A:1010933404324">Random Forest</a>, <a href="http://dl.acm.org/doi/abs/10.1145/2939672.2939785">XGBoost</a>, <a href="https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2344614">GLM</a>), which served as input to train a linear meta-model (<em>i.e.</em> <a href="http://papers.nips.cc/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">Logistic regression classifier</a>), responsible for predicting the final probability of presence for each species.</p> <p>The <em>RDS </em>file is created from a data.table object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p>We provide <em>RDS </em>files for a 30km tile as an example containing raster stacks at 30m resolution of all the covariates included in the regression matrix. You can find the specific geographical location of the tile in Europe using the attached <em>GeoPackage </em>("eu_tiling_system_30km"): open it in QGIS and filter by "ID".</p> <p>In our approach we considered both static and dynamic covariates: dynamic covariates are calculated as averages of a 4 years time window (example: 2004 contains averages from 2002 to 2006). To get the predictions for a specific year, covariates contained in the <em>static</em> RDS file need to be bound with the respective year.</p> <p>To access our predictions (probabilities and uncertainties) produced for the target species access:</p> <ul> <li><strong>Open Data Science Europe viewer: <a href="https://maps.opendatascience.eu">https://maps.opendatascience.eu</a></strong></li> <li>Check the <strong>Related identifiers </strong>section of this repository to access each species individually</li> </ul> <p>If you instead would like to know more about the creation of this dataset and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"
<p>Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"</p> <p>This package contains an anonymized packet of 802.11 probe requests captured in Lloret de Mar during the Indoor Positioning and Indoor Navigation 2021 conference. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
Data for: Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass
<p>This repository contains data for the paper: Bijleveld, A. I. et al. (2018) Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass. Scientific Reports 8, 3039, doi:10.1038/s41598-018-21285-1.</p>
Winter cumulated water presence of "Marais Breton nord" calculated with the WIW
<p><span>The WIW was calculated for all SENTINEL 2 cloudless images available within the period from January to April from 2017 to 2024. All WIW rasters were then added to provide the cumulated water presence ranging from 1 to 40 corresponding to the number of dates related to available cloudless images at that period. </span>The table provides the dates of the images used. EPSG 3857</p>
GATEMAN project, GNSS raw data in presence of spoofing
<p>GNSS raw data generated during the in-lab validation activities of spoofing detection and localization performed in the frame of the <strong>GATEMAN</strong> project. These files are grouped for each type of validation scenario defined. A word file describing the test setup is included.</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.