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6,704 results for “report”
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>
Dataset di dichiarazioni ambientali vaghe da report di sostenibilità
<p><strong>Descrizione del dataset</strong></p> <p>Questo dataset consiste in un campione di 100 frasi estratte come concordanze da un corpus di report di sostenibilità di aziende italiane, utilizzando lo strumento <em>Sketch Engine</em>. Le frasi sono state selezionate sulla base di parole chiave scelte per individuare potenziali dichiarazioni ambientali vaghe (greenwashing) all'interno del corpus. L'obiettivo dell'annotazione di queste frasi è verificare tale ipotesi e fornire un dataset iniziale per l'addestramento di modelli in grado di riconoscere automaticamente possibili casi di greenwashing. Inoltre, per arricchire l'annotazione, sono state identificate, ove possibile, alcune caratteristiche linguistiche legate all'uso della vaghezza, suddivise in cinque categorie semantiche: <em>degree</em>, <em>quantity</em>, <em>category</em>, <em>time</em>, e <em>softening stance-taking</em>.</p> <p>Il dataset è fornito in formato CSV (separato da ";") e include le seguenti colonne:</p> <ul> <li><strong>n</strong>: un identificativo numerico per ogni frase.</li> <li><strong>enunciato</strong>: la frase estratta oggetto di annotazione.</li> <li><strong>documento</strong>: il riferimento al documento da cui è stata estratta la frase, ottenuto combinando il nome dell'azienda e l'anno di pubblicazione del report.</li> <li><strong>azienda</strong>: il nome dell'azienda che ha redatto il report.</li> <li><strong>anno</strong>: l'anno di pubblicazione del report.</li> <li><strong>ricerca</strong>: la parola chiave cercata che ha permesso di individuare la frase.</li> </ul> <p>Le seguenti colonne indicano l'annotazione, con valori 1 o 0, per segnalare se la frase rientra o meno nelle rispettive categorie:</p> <ul> <li><strong>ambientale</strong>: la frase collega l'azienda a pratiche che suggeriscono un impatto positivo sull'ambiente.</li> <li><strong>vaghezza</strong>: la frase presenta elementi non espressi chiaramente, dovuti a indeterminatezza lessicale (espressioni vaghe, approssimazioni) o a omissioni sintattiche.</li> <li><strong>quantity</strong>: utilizzo di aggettivi o avverbi ambigui, o termini il cui significato dipende fortemente dal contesto e richiederebbe ulteriori spiegazioni.</li> <li><strong>degree</strong>: espressioni che indicano approssimazioni o quantità non specificate.</li> <li><strong>time</strong>: espressioni temporali generiche che mancano di precisione, omettendo la durata o la frequenza esatta delle azioni descritte.</li> <li><strong>category</strong>: espressioni che rimandano a una categoria generica di entità piuttosto che a oggetti specifici.</li> <li><strong>stance</strong>: espressioni che attenuano la presa di posizione, rendendo l'affermazione meno diretta e più negoziabile.</li> </ul> <p><strong>Nota</strong></p> <p>L'annotazione delle frasi è stata eseguita da un solo annotatore e rappresenta un lavoro preliminare nell'ambito di un progetto di dottorato. Pertanto, il dataset deve essere considerato come una base iniziale per ulteriori studi e sviluppi nel campo dell'analisi automatica delle dichiarazioni vaghe nei report di sostenibilità aziendale.</p>
United States tornado reports in landfalling tropical cyclones used in Paredes et al. (2021)
<p>These data include all tropical cyclone tornado reports used in Paredes et al. (2021) plus an additional year (e.g., 2020). These data will not be updated regularly. For the latest version, users should refer to https://www.spc.noaa.gov/misc/edwards/TCTOR/ or contact roger.edwards@noaa.gov.<br> <br> Each specific tropical cyclone tornado record has been extracted from the broader Storm Prediction Center tornado database, for all Atlantic and Gulf of Mexico tropical cyclones to affect the continental United States from 1995–2020. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. Unlike other climatologies previously published in the literature, the chosen time period for this examination essentially covers only the full national deployment of the WSR-88D radar network in the United States. This permits consistent comparisons of a very large sample size of tropical cyclone tornado events (>1600) during the era of modernized National Weather Service warning and verification practices.</p>
Dataset accompanying Nölke et al. 2022. The choice of the white clover population alters overyielding of mixtures with perennial ryegrass and chicory and underlying processes. Scientific Reports
<p>This repository contains biomass and nitrogen yield data as well as data on diversity effects used by Nölke et al. in an article published in Scientific Reports (2022).</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original research article.</p>
Raw data supporting "mScarlet fluorescence lifetime reports lysosomal pH quantitatively"
<p>Original dataset and processing code supporting the preprint (scientific publication) "mScarlet fluorescence lifetime reports lysosomal pH quantitatively."</p> <p>Publication Abstract: The lysosome maintains a highly acidic pH, which is critical for successful lysosomal catabolism. Lysosomal pH (pHlys) is difficult to measure because of the simultaneous need for a sensor with large dynamic range, genetic targetability, low pKa, and a quantitative readout. Here, we demonstrate that the fluorescence lifetime of the mScarlet-LAMP1 fusion protein quantitatively reports lysosomal pH, exhibiting a large dynamic range and a pKa well-tuned for the lysosome. Because fluorescence lifetime is an intrinsic property, pH measurements can be achieved in a single fluorescence channel. mScarlet-LAMP1 lifetime allows for individual lysosome-resolved recordings, a critical advance in describing and understanding pHlys heterogeneity. Using this biosensor, we quantify heterogeneity of pHlys in cultured cells at rest and over time during drug treatment. We anticipate that mScarlet-LAMP1 will enable new insights into the diversity of lysosomal physiology and ionic milieu.</p>
LoGov Switzerland Interview Report n°3
<p>The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Switzerland. To access the full transcription of this interview, the other interview reports on Switzerland, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.</p>
LoGov Switzerland Interview Report n°4
<p>The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Switzerland. To access the full transcription of this interview, the other interview reports on Switzerland, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.</p>
LoGov Switzerland Interview Report n°2
<p>The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Switzerland. To access the full transcription of this interview, the other interview reports on Switzerland, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.</p>
LoGov Poland Interview Report n°7
<p>The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Poland. To access the full transcription of this interview, the other interview reports on Poland, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.</p>
Thesis: Supplementary Tables and reports
<p>Contains Supplementary information for my Thesis</p> <p>Files:</p> <p>model_perfs_and_motifs_top5_annot.mht: MHTML table containing top5 motifs, B1H alignments and dataset quality annotations along with other information. Download and open in Google Chrome browser</p> <p>all_reports.tar.gz : All TF Modisco reports bundled together which are linked in the Table as a standalone resource. Contains every motif, submotif , motif hit distribution with respect to the peak summits and alignments to B1H recognition-code</p> <p>model_archive.tar.gz : All models</p> <p>models_info.tsv: Maps model name to ENCODE ids and dataset related information.</p> <p>Datasets used other than ENCODE use these keys: </p> <p>HughesNB:</p> <p>Najafabadi, Hamed S., et al. "C2H2 zinc finger proteins greatly expand the human regulatory lexicon." <em>Nature biotechnology</em> 33.5 (2015): 555-562.</p> <p> </p> <p>HughesGR:</p> <p>Schmitges, Frank W., et al. "Multiparameter functional diversity of human C2H2 zinc finger proteins." <em>Genome research</em> 26.12 (2016): 1742-1752.</p> <p>ChipExo:</p> <p>Imbeault, Michaël, Pierre-Yves Helleboid, and Didier Trono. "KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks." <em>Nature</em> 543.7646 (2017): 550-554.</p> <p> </p> <p> </p> <p> </p>
Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"
<p>Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing.</p> <p>Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025)</p> <p>A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a></p>
Washover morphometry: lidar-derived and reported in literature
<p>This portfolio includes three sets of data, used and explained in Lazarus, Williams & Goldstein (2022, <a href="https://doi.org/10.1029/2022GL100098">https://doi.org/10.1029/2022GL100098</a>):</p> <ol> <li>washover morphometry measured from lidar-derived topographic change along the coastline of New Jersey, USA, following Hurricane Sandy (2012) ('NJ_Sandy_metrics.csv');</li> <li>the geospatial data layers used to generate those measurements ('WashoverGIS.zip');</li> <li>and a compilation of washover morphometry reported in the literature ('washover_LAV_literature_examples.csv').</li> </ol> <p> </p> <p><strong>Washover morphometry datasets</strong></p> <ul> <li><strong>NJ_Sandy_metrics.csv</strong> – The lidar-derived washover morphometry dataset includes: deposit width (m), intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), deposit perimeter (m), built fraction, the storm event (Sandy 2012), and a general location note.</li> <li><strong>washover_LAV_literature_examples.csv</strong> – Also included here are 35 measurements of washover morphometry reported in the literature by six different studies, sampling different storm events in different coastal barrier settings (Carruthers et al., 2013; Williams, 2015; Jamison-Todd et al., 2020; Rodriguez et al. 2020; Hansen et al., 2021; Williams & Rains, 2022). The literature-based dataset includes: intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), the reference (dataset) in which the measurements were reported, and additional notes.</li> </ul> <p> </p> <p><strong>Geospatial data layers ('WashoverGIS' [zipped])</strong></p> <p>The lidar data underpinning the geospatial data layers here are available from the NOAA Digital Coast Data Viewer (<a href="https://coast.noaa.gov/dataviewer/#/">https://coast.noaa.gov/dataviewer/#/</a>): "2012 USGS EAARL-B Lidar: Pre-Sandy" (pre-storm), and "2012 USGS EAARL-B Lidar: Post-Sandy" (post-storm).</p> <p>Geospatial analysis was done in QGIS version 3.22.5. We masked both the pre- and post-storm surfaces to isolate only positive elevations, and subtracted the pre-storm surface from the post-storm surface to calculated the difference between them; we then retained only the positive differences in the resulting surface to isolate sites of sediment deposition. We manually digitized the perimeters of depositional forms we interpreted as washover, corroborated by aerial imagery (<a href="https://storms.ngs.noaa.gov/">https://storms.ngs.noaa.gov/</a>).</p> <p>Basic geometric characteristics (perimeter, area) were taken directly from the washover polygons; washover length and width were taken from oriented minimum bounding boxes around each polygon. Volume for each washover polygon was measured using the Volume Calculation Tool (version 0.4) plugin for QGIS (<a href="https://github.com/REDcatch/Volume_calculation_for_QGIS3">https://github.com/REDcatch/Volume_calculation_for_QGIS3</a>). In built settings, each washover deposit was associated with a locally estimated built fraction (Lazarus et al., 2021). Elements of the built environment (i.e., buildings) were isolated by creating a binary mask of the pre-storm surface, such that all elevations ³5 m were set to a value = 1, and all elevations <5 m set to zero. Minimum enclosing circles were drawn around each washover polygon, and the total built area (masked value = 1) within each circle summed using the QGIS Zonal Statistics tool. Here, local built fraction is the total built area within a minimum enclosing circle divided by the area of that circle.</p> <p>Geospatial files here include:</p> <ul> <li><strong>NJ_north_wash_metrics.shp</strong> // <strong>NJ_south_wash_metrics.shp </strong>– shapefiles of the digitized washover deposits, with morphometric characteristics compiled in their attribute tables</li> <li><strong>NJ_north_BBs.shp</strong> // <strong>NJ_south_BBs.shp</strong> – oriented bounding boxes to determine deposit intrusion length & width</li> <li><strong>NJ_north_MECs.shp</strong> // <strong>NJ_south_MECs.shp</strong> – minimum enclosing circles, used for calculating local built fraction</li> <li><strong>NJ_north_dSandy_POS.tif</strong> // <strong>NJ_south_dSandy_POS.tif </strong>– positive [post-storm - pre-storm] elevation differences</li> <li><strong>NJ_north_rooftops_th05.tif</strong> // <strong>NJ_south_rooftops_th05.tif </strong>– binary mask based on the pre-storm lidar layer ("2012 USGS EAARL-B Lidar: Pre-Sandy") used for calculating built fraction, in which all topographic elements >= 5 m are set = 1, and all < 5 m are set = 0</li> </ul> <p> </p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Sweden
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Portugal
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Lithuania
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
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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.