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1,958 results for “surveillance”
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - The Netherlands
<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 - Slovakia
<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 - Norway
<p>This dataset contains the results of the 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 - Iceland
<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>
Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021 – monthly maps
<p>Here you can find the monthly maps referred to in the 'Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021' by EFSA. Figure legend for all figures: Monthly observations and samples from wild birds on the EFSA list of target species for 2021 by NUTS3 region. The green colour scale represents the number of wild bird observations from the target species, as per data provided by the EuroBirdPortal project. The black dots represent the number of wild bird samples from target species tested within the countries' avian influenza passive surveillance programmes. Wild bird samples reported at NUTS2 level are not shown on these maps.</p>
Dataset - CORE-MD Post-Market Surveillance Tool
<p>WP3 of CORE-MD investigated how to aggregate and extract maximal value for post-market surveillance from medical device registries, big data, clinical practices and experience, and the internet. This data collection was created by the Task 3.2 of the CORE-MD project, as the result of the proposed methodological framework to transform unstructured and dispersed publicly available safety information (Field Safety Notices, recalls, alerts) into a standardized and harmonized database. The databases includes 137,720 historical safety notices (updated to February 2024) safety notices published by different competent national authorities (16 EU Member States and 5 extra EU jurisdictions).</p>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
A Twitter Dataset for Spatial Infectious Disease Surveillance
<p>Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.</p>
Italian COVID-19 Integrated Surveillance Dataset (v42.0.0)
<p><strong>Abstract</strong></p> <p>COVID-19 integrated surveillance data provided by the <a href="http://www.iss.it/">Italian National Institute of Health</a> and processed via <a href="https://github.com/InPhyT/UnrollingAverages.jl">UnrollingAverages.jl</a> to deconvolve the weekly simple moving averages.</p> <p><strong>Overview</strong> </p> <p>Every week the National Institute for Nuclear Physics (<a href="https://home.infn.it/it/">INFN</a>) imports an anonymous individual-level dataset from the Italian National Institute of Health (<a href="https://www.iss.it/">ISS</a>) and converts it into an incidence time series data organized by date of event and disaggregated by sex, age and administrative level with a consolidation period of approximately two weeks. The information available to the <a href="https://home.infn.it/it/">INFN</a> is summarised in the following <a href="https://covid19.infn.it/iss/campi-iss.pdf">meta-table</a>.</p> <p><strong>Output Data </strong></p> <p>The output data has been stored <a href="https://github.com/InPhyT/COVID19-Italy-Integrated-Surveillance-Data/tree/main/3_output/data">here</a> and contain the following information:</p> <ul> <li>Reconstructed daily time series of <strong>confirmed cases by date of diagnosis</strong> stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>symptomatic cases by date of symptoms onset</strong> stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>ordinary hospital admissions</strong> by date of admission stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>intensive hospital admissions</strong> by date of admission stratified by sex and age at the regional level;</li> <li>Reconstructed daily time series of <strong>deceased cases by date of death</strong> stratified by sex and age at the regional level.</li> </ul>
Escape from NK cell tumor surveillance by NGFR-induced lipid remodeling in melanoma
<p>Metabolomics Data used in the publication</p> <p>1) raw files obtained by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>):</p> <p>QCpools (technical replicate of all sample pooled: p2947_o5292_SOP3_DDA5_pos_QCpool_cells</p> <p>Sample of M010817 CMVTOEV cells not induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_noninduced_sample</p> <p>Sample of M010817 CMVTOEV cells induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_induced_sample</p> <p>Sample of M010817 CMVTONGFR cells not induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_noninduced_sample</p> <p>Sample of M010817 CMVTONGFR cells induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_induced_sample</p> <p>2) method description LC-MS</p> <p>LC-MS-based lipidomic analysis was performed with M010817 CMVTOEV and CMVTONGFR cells pretreated with 1 μg/ml doxycycline for 24 h. Cells were detached with PBS 2 mM EDTA, washed and resuspended in 1-butanol/methanol (1:1). Cells were vortexed for 15 sec and subsequently sonicated on ice with a pulse of 3x 10 sec and 1x 30 sec at an amplitude of 15% using a SONOPULS HD 2070 Ultrasonic Homogenizer (Bandelin). Cell extracts were centrifuged at 16’000 g, 20°C for 10 min to remove macromolecules and subsequently diluted (1:3) with water/methanol (1:2) solution. The dilution was vortexed and centrifuged (16,000 x g, 20 °C, 10 min). 100 μl of the supernatant was transferred to a glass vial with narrowed bottom (Total Recovery Vials, Waters) for LC-MS injection. <br> Lipids were separated on a nanoAcquity UPLC (Waters) equipped with a HSS T3 capillary column (150 μm x30mm, 1.8 μm particle size, Waters), applying a gradient of 5 mM ammonium acetate in water/acetonitrile 95:5 (A) and 5 mM ammonium acetate in isopropanol/acetonitrile 90:10 (B) from 5% B to 100% B over 10 min. The following 5 min conditions were kept at 100% B, followed by 5 min reequilibration to 5% B. The injection volume was 1 μL. The flow rate was constant at 2.5 μl/min. The UPLC was coupled to QExactive mass spectrometer (Thermo) by a nanoESI source. MS data was acquired using positive polarization and data-dependent acquisition (DDA). Full scan MS spectra were acquired in profile mode from 80-1200 m/z with an automatic gain control target of 1e6, an Orbitrap resolution of 70`000, and a maximum injection time of 200 ms. The 5 most intense charged (z = +1 or +2) precursor ions from each full scan were selected for collision induced dissociation fragmentation. Precursor was accumulated with an isolation window of 0.4 Da, an automatic gain control value of 5e4, a resolution of 17`500, a maximum injection time of 50 ms and fragmented with a normalized collision energy of 20, 30 and 40 (arbitrary unit). Generated fragment ions were scanned in the linear trap. Minimal signal intensity for MS2 selection was set to 500.</p> <p> </p> <p>3) csv result files were generated by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>)</p> <p>adducts found: adducts_p2947_o5292_new_10K_20210910</p> <p>identifications found: identifications_p2947_o5292_new_10K_20210910</p> <p>abundances of features: measurements_p2947_o5292_new_10K_20210910</p> <p>Metaboanalyst file statistics: Metabo_p2947_o5292_10k_cells_CVcleaned_20210916</p> <p>Metaboanalyst file enrichment: Metabo_Enrichment_p2947_o5292_10k_cells_20210910 _p75</p> <p>4) method description data analysis</p> <p>Data sets were evaluated with Progenesis QI software (Nonlinear Dynamics), which aligns the ion intensity maps based on a reference data set, followed by a peak picking on an aggregated ion intensity map. Detected ions were identified based on accurate mass, detected adduct patterns and isotope patterns by comparing with entries in the LipidMaps Data Base (LM) and KEGG database. Considered adducts were M+H, M+NH4, 2M+H M+H-H2O. A mass accuracy tolerance of 5 ppm was set for the searches. Fragmentation patterns were considered for 600 the identifications of metabolites. Putative identifications were further ranked based on Mass error (observed mass – exact mass), isotope similarity (observed versus theoretical).</p> <p> </p>
Dissemination of information in event-based surveillance, a case study of Avian Influenza - dataset
<p>This dataset contains a set of tables corresponding to the manual analysis of outbreak-related reports detected by two event-based surveillance tools, PADI-web and HealthMap, supporting the submitted article "Dissemination of information in event-based surveillance, a case study of Avian Influenza".</p> <p>The reports were published between 1 July 2018 and 30<sup>st</sup> June 2019 and described one or several avian influenza outbreaks. We collected 337 reports from PADI-web and 115 from HealthMap. Two epidemiologists identified all the reported events in the news, and classified them as official (notified to the World Organization for Animal Health) or non-official.</p> <p>In order to trace back the source of the event’s information, the epidemiologist manually traced the information pathway of all events mentioned in the PADI-web and HealthMap news. The pathway was deducted from the sources cited in the news. When a source was cited with a hyperlink, we followed the hyperlink to retrace the information pathway as far as possible to the primary source. For each cited source, we created a pair of emitter <em>S<sub>E</sub></em> and receptor sources <em>S<sub>R</sub></em>. We labelled each new source with its type (e.g. online news source, national veterinary authority, etc.). We also recorded their geographical focus (local, national or international) and their specialization in the animal health news coverage (general or specialized).</p> <p>The script for data analyses is available at https://github.com/SarahVal/EBS-network.</p>
AI results complementing the 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 2019, 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 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Slovakia
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, 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 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Norway
<p>This dataset contains the results of the surveillance activities conducted in 2019, 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 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Slovenia
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, 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 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - The United Kingdom
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, 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> <p>Disclaimer: Data collected by the UK at national level prior to 31/01/2020 has been used in EFSA data reports as is related to a period in which this country was still a European Union Member State.</p>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Malta
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, 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> </ul>
AI results complementing the 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 2019, 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 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Ireland
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, 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 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union- The Netherlands
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, 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>
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.