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1,921 results for “Incidence”
Incidence of Ticks and Tick Bites at Harvard Forest since 2006
In an effort to determine exposure to ticks and to determine appropriate preventative measures to reduce the occurrence of Lyme Disease in summer research interns at Harvard Forest, data are collected about where students work at Harvard Forest; the time spent in the field; the number of ticks students find on themselves after each trip to the field; and the number of ticks that actually bite and embed themselves in the skin. From these data, we estimate areas of high tick densities and estimate time of summer during which ticks are most prevalent. The results are used to develop recommendations for appropriate precautions that students can take to avoid tick-borne diseases.
In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data
<p>The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s.</p> <p> </p> <p> </p> <p>The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters:</p> <p> </p> <ul> <li> <p>X-ray wavelength: 0.6888 nm</p> </li> <li> <p>Sample detector distance: 809 mm</p> </li> <li> <p>Incidence angle: 0.5 deg.</p> </li> <li> <p>Detector model: XRD 1621 CN3 EHS</p> </li> <li> <p>Acquisition rate : 10 frames per second (10 Hz)</p> </li> <li> <p>Direct beam position (pixels): 545, 222</p> </li> </ul>
SMOS Brightness Temperatures at 40° incidence angle Arctic
<p>This is a data set of polarised brightness temperatures (TBs) at 40° incidence angle from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture and Ocean Salinity (SMOS) mission. The data set was produced to enable a consistent combination of TBs from SMOS with those measured by the SMAP (Soil Moisture Active Passive) satellite.</p> <p>It is based on the version v620 SMOS L1C brightness temperatures, which are not corrected with respect to solar and cosmic radiation or atmospheric effects. A fitting function is applied to the daily multi-angular SMOS data (see Zhao et al., 2015 and Schmitt and Kaleschke, 2018) to obtain brightness temperature values at the SMAP incidence angle of 40°. The data are gridded to a north polar EASE-grid 2.0 (Brodzik et al. 2012) with a grid size of 12.5 km. The data were produced within the framework of the EU Horizon2020 project SPICES and therefore only covers the period from the first available SMAP data to the end of the project (1 April 2015 to 31 May 2018).</p> <p>Within SPICES, SMOS and SMAP data were combined to a homogenized data set, which was then used to estimate sea ice thickness. For details see Schmitt and Kaleschke (2018) and the related data sets of SMAP TBs and SMOS/SMAP sea ice thickness.</p> <p>The files contain the following data fields:<br> <strong>Tbv</strong> - brightness temperatures at vertical polarisation at 40° incidence angle<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation at 40° incidence angle<br> <strong>RMSE_v</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>RMSE_h</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>nmp</strong> - number of incidence angles used for the fit<br> <strong>dataloss</strong> - fraction of discarded data</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
Sub-National COVID-19 Incidence and Determinants Dataset
<p>The Sub-National COVID-19 Incidence and Determinants Dataset contains rich sub-national data on COVID-19 cases and deaths combined with data on factors associated with the spread and severity of COVID-19 outbreaks in 2020. The data covers 503 sub-national areas (NUTS-2 level and equivalents) of 46 countries in five continents (Europe, Asia, North America, South America and Oceania). Indicators were mostly gathered weekly, with the exception of some variables that are monthly and yearly. The dataset was compiled to study the determinants of COVID-19 outbreaks with a focus on the effects of international airline travel. However, the data are useful to investigate also other questions on the sub-national diffusion of COVID-19. The information used to build this dataset was drawn from a variety of sources in order to cover four major areas of interest: health outcomes of the pandemic (COVID-19 cases and deaths), international air travel (number of incoming air passengers, centrality of local airports in the global airline network and air travel limitation policies), population mixing and government policy responses, and pre-pandemic area characteristics (socioeconomic, demographic, public health and co-morbidity). A complete list of sources can be found in the “Data sources” Pdf document attached.</p> <p>Please cite as: Recchi, E., A. Ferrara, A. Rodríguez Sánchez, E. Deutschmann, L. Gabrielli, S. Iacus, L. Bastiani, S. Spyratos & M. Vespe. 2022. The Impact of Air Travel on the Precocity and Severity of Covid-19 Deaths in Sub-National Areas across 45 Countries. Scientific Reports 12: 16522. https://doi.org/10.1038/s41598-022-20263-y</p> <p> </p>
Global Dataset of Cyber Incidents V.1.2
<p>The dataset contains data on 2889 cyber incidents between 01.01.2000 and 02.05.2024 using 60 variables, including the start date, names and categories of receivers along with names and categories of initiators. The database was compiled as part of the <strong><a href="https://eurepoc.eu">European Repository of Cyber Incidents (EuRepoC)</a> </strong>project.</p> <p><br>EuRepoC gathers, codes, and analyses publicly available information from over 200 sources and 600 Twitter accounts daily to report on dynamic trends in the global, and particularly the European, cyber threat environment.<br><br>For more information on the scope and data collection methodology see: <a href="https://eurepoc.eu/methodology">https://eurepoc.eu/methodology</a><br><br><strong>Codebook available <a href="https://eurepoc.eu/wp-content/uploads/2023/07/EuRepoC_Codebook_1_2.pdf">here</a><br><br>Information about each file:</strong></p> <p><strong>Global Database (csv or xlsx):<br></strong>This file includes all variables coded for each incident, organised such that one row corresponds to one incident - our main unit of investigation. Where multiple codes are present for a single variable for a single incident, these are separated with semi-colons within the same cell.</p> <p><strong>Receiver Dataset (csv):<br></strong>In this file, the data of affected entities and individuals (receivers) is restructured to facilitate analysis. Each cell contains only a single code, with the data "unpacked" across multiple rows. Thus, a single incident can span several rows, identifiable through the unique identifier assigned to each incident (incident_id). </p> <p><strong>Attribution Dataset (csv):</strong><br>This file follows a similar approach to the receiver dataset. The attribution data is "unpacked" over several rows, allowing each cell to contain only one code. Here too, a single incident may occupy several rows, with the unique identifier enabling easy tracking of each incident (incident_id). In addition, some attributions may also have multiple possible codes for one variable, these are also "unpacked" over several rows, with the attribution_id enabling to track each attribution.<br><br><strong>eurepoc_global_database_1.2 (json):</strong><br>This file contains the whole database in JSON format. </p>
Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"
<p>Images are from the LROC-NAC and have been cartographically controlled to each other and the <em>Apollo 11</em> landing site as described in Supporting Information Text S1. Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures. The images are provided as .png files with .pgw ("PNG World"). The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name). Additionally, the three <em>e</em> > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest.</p> <p>Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher. File names have the exact same name as the image file that was used to map crater data, with two differences: The initials of the author are appended, and the file extension is "csv" instead of "png". The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers). Crater data are entirely in one .zip file.</p>
Phylogenetic and epidemiologic data relating to age-specific HIV incidence and transmission in Rakai, Uganda, 2003-2018.
<p>This repository contains the data for the analyses presented in the paper Growing gender inequity in HIV infection in Africa: sources and policy implications by M. Monod, A. Brizzi, R. Galiwango, R. Ssekubugu, Y. Chen, X. Xi et al. available in the pre-print <a href="https://doi.org/10.1101/2023.03.16.23287351">https://doi.org/10.1101/2023.03.16.23287351</a> </p> <p>We thank all contributors, program staff and participants to the Rakai Community Cohort Study; all members of the PANGEA-HIV consortium, the <a href="https://www.rhsp.org/index.php">Rakai Health Sciences Program</a>, and CDC Uganda for comments on an earlier version of the manuscript.</p> <p>We also extend our gratitude to the <a href="https://doi.org/10.14469/hpc/2232">Imperial College Research Computing Service</a> and the <a href="https://www.bdi.ox.ac.uk/about/biomedical-research-computing">Biomedical Research Computing Cluster</a> at the University of Oxford for providing the computational resources to perform this study. Additionally, we thank the Office of Cyberinfrastructure and Computational Biology at the <a href="https://www.niaid.nih.gov/">National Institute for Allergy and Infectious Diseases</a> for data management support; and Zulip for sponsoring team communications through the Zulip Cloud Standard chat app. </p> <p>All analysis code is available from <a href="https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender">https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender</a>.</p>
Free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz
<p>This dataset contains calibration data of the free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz. Each of the four channels consisted of a ¼'' externally polarized free-field measurement microphone of the condenser type GRAS 40 BF, a ¼'' preamplifier GRAS 26AC, a power module GRAS 12AQ and an FFT analyzer Ono Sokki CF-9400. The calibration data was acquired in the laboratory of the Physikalisch-Technische Bundesanstalt (PTB).</p>
Mutual extinction and transparency of multiple incident light waves
<p>The basic publication is:<br> A. Lagendijk, A.P. Mosk, and W.L. Vos<br> Europhys. Lett., 130, 34002 (2020)<br> "Mutual extinction and transparency of<br> multiple incident light waves"<br> <br> We have uploaded to the Zenodo database all data enabling everyone to reuse our data, and<br> to reproduce all the figures of our paper</p> <p>The upload contains the file "readme.txt" explaining the content of the upload</p>
Incident at TRANSALLOYS weatherstation PRÉMA project
<p>An incident report and video files relevant to activities of work package 2 of the PRÉMA project.</p> <p>The incident involved the theft and attempted theft of solar panels for the weather station collecting atmospheric data for the PRÉMA project</p>
BeBOD estimates of incidence, prevalence, and years lived with disability for 57 cancer types, 2004-2021
<p><strong>Belgian National Burden of Disease Study</strong></p><p><strong>Estimates of the morbidity burden of disease for 57 cancer sites</strong></p><p><i>Incidence</i></p><p>Data on new cancer cases in Belgium are collected by the <a href="https://kankerregister.org/Annual%20Tables">Belgian Cancer Registry</a> (BCR). For the current study, we selected 80 ICD-10 (C00.0-96.9 and chronic myeloid neoplasms) codes resulting in 57 cancer sites. Data were extracted by year (from 2004 to 2021), age group (5-years), sex and region (N=3). We excluded "Respiratory system and intrathoracic organs, NOS (not otherwise specified)" from further analyses because of too few cases.</p><p><i>Prevalence</i></p><p>Prevalence estimates were estimated using the above-described incidence estimates and the survival estimates also provided by BCR, derived from linkage with the Belgian Crossroads Bank for Social Security. We used a 10-year prevalence perspective meaning that from the year 2013 onwards, we were able to define the prevalence in a given year as the sum of person-months spent in the different health states. Specifically, we used a microsimulation approach to simulate future health states for each year-, age-, sex-, region- and cancer-specific cohort of incident cases.</p><p>See for more details: <a href="https://doi.org/10.1186/s12885-021-09109-4">https://doi.org/10.1186/s12885-021-09109-4</a></p><p><i>Years Lived with Disability</i></p><p>Years Lived with Disability (YLDs) were calculated using both an incidence and prevalence perspective as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p>
Regional Models of Canine Cancer Incidences
<p>The dataset consists of the variables implemented in the regional models of canine cancer incidences (dogCancerModel.txt), the adjacency matrix determining the regions (kNearestNeighbour.txt), and the Swiss municipal boundaries (SwissMunicipalities_2015.shp).</p>
Model of Canine Cancer Incidence (v 2.0)
<p>The table presents the canine cancer incidence and explanatory factors computed within Swiss municipal units. In detail, the table attributes are the following.</p> <ul> <li><strong>localityNumber</strong> — the unique identifier of Swiss municipal units in 2013 according to the Swiss Federal Office of Statistics.</li> <li><strong>canineCancer_count</strong> — the canine cancer incidence per Swiss municipal unit in 2008 retrieved from Swiss Canine Cancer Registry (SCCR) data.</li> <li><strong>caninePopulation_count</strong> — the count of dogs per Swiss municipal unit in 2008 retrieved from Swiss dog census data.</li> <li><strong>canineCancer_ratio</strong> — the canine cancer incidence ratio in per thousand per Swiss municipal unit in 2008 combining Swiss Canine Cancer Registry data and Swiss dog census data.</li> <li><strong>femaleRatio_percent </strong>— the ratio of female dogs in percent per Swiss municipal unit in 2008 retrieved from Swiss dog census data.</li> <li><strong>ageAverage_years</strong> — the average age of dogs in years per Swiss municipal unit in 2008 retrieved from Swiss dog census data.</li> <li><strong>mixedBreed_percent</strong> — the ratio of mixed breed dogs in percent per Swiss municipal unit in 2008 retrieved from Swiss dog census data.</li> <li><strong>IncomeTaxProCapita_swissFrancs</strong> — the income tax pro capita per Swiss municipal unit in 2008 retrieved from Swiss Federal Tax Administration data.</li> <li><strong>veterinaryCare_distanceMunicipality</strong> —distance to the closes veterinary practice in km computed using the areal extent of Swiss municipal units for 2013 computed using Swiss Yellow Page data.</li> <li><strong>veterinaryCare_distanceDasymetric</strong> — distance to the closes veterinary practice in km computed using the dasymetrically refined areal extent of Swiss municipal units for 2013 computed using Swiss Yellow Page data.</li> <li><strong>humanDensity_municipality</strong> — human population density in 1,000 people/km2 computed using the areal extent of Swiss municipal units in 2008 computed using Swiss Federal Statistical Office data.</li> <li><strong>humanDensity_dasymetric</strong> — human population density in 1,000 people/km2 computed using the dasymetrically refined areal extent of Swiss municipal units in 2008 computed using Swiss Federal Statistical Office data.</li> </ul>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
Incidences of community onset severe sepsis, Sepsis-3 sepsis, and bacteremia in Sweden – a prospective population-based study.
<p>Sepsis epidemiology study 2011-2012 Sweden</p> <p>Ljungström, Lars; Andersson, Rune; Jacobsson, Gunnar</p> <p> </p> <p>Data collected during the prospective "Sepsis Skaraborg study" performed 2011-2012 in the western region of Sweden. Adult patients admitted to the emergency department for suspicion of a community-onset sepsis were evaluated. The study was approved by the Regional Ethical Review Board of Gothenburg (376-11). The file includes data for patient characteristics, vital signs, biomarker measurements, cases of bacteremia, and patient classifications using Sepsis-2 and Sepsis-3 criteria.</p>
Predicting COVID-19 Incidence Through Spatiotemporal Human Interactions
<p>This repository contains data (features) necessary to run STXGB model. STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Data Release of Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations
<p>Dataset of the paper "Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations".</p> <p> </p> <p>All the necessary code to deal with these data can be found in: https://github.com/IvanMuro/agn_frac_data_release</p>
Incidence and Characteristics of Adverse Events in Paediatric Inpatient Care: a Systematic Review and Meta-Analysis
<p>This is the open data repository for the connected systematic review and meta-analysis.</p> <p>Data sets for the meta-analysis.</p> <p>Data collection file with all the information extracted from the included studies.</p> <p>QAT file with the information from the quality assessment tool (QAT) for all included studies.</p> <p>ReadMe with information on data sets and updates.</p> <p>Codebooks for data sets.</p> <p>R Code for the analysis</p>
Repository of Raw Datasets for the Study of Anticoagulation and the Incidence of Stroke and Other Outcomes in Patients with Left Ventricular Thrombus
<p>The optimal duration of anticoagulation in patients with left ventricular thrombus (LVT) is unknown. The data package herein presented contains the data used to assess the effect of duration of anticoagulation in the incidence of stroke in patients with left ventricular thrombus (LVT) in a tertiary hospital. These data includes clinical and demographic information, treatment choices (vitamin K antagonists [VKA] versus direct oral anticoagulants [DOAC]), duration of treatment, reason for interruption of treatment, occurrence of stroke, acute myocardial infarction, bleeding events, thrombus resolution and recurrence, and death.<br> The raw dataset is available upon request to the corresponding author.</p>
HawaiiCoast_GT: Curated AIS for Hawaii's coast correlated with ground truth incidents
<p>Because of the high-risk nature of emergencies and illegal activities at sea, it is critical that algorithms designed to detect anomalies from maritime traffic data be robust. However, there exist no publicly available maritime traffic datasets with real-world labelled anomalies. As a result, most anomaly detection algorithms for maritime traffic are validated without ground truth. We introduce the HawaiiCoast_GT dataset, the first ever publicly available automatic identification system dataset with a large corresponding set of true anomalous incidents. This dataset—cleaned and curated from Bureau of Ocean Energy Management (BOEM) and National Oceanic and Atmospheric Administration (NOAA) automatic identification system (AIS) data--covers Hawaii’s coastal waters for four years (2017-2020) and contains 88,749,176 AIS points for a total of 2,622 unique vessels. 208 tracks are labelled corresponding to 154 labelled real-world incidents. The codebase used to curate the original AIS data is being made openly available on GitHub.</p>
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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.