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12,574 results for “health IT”
Health assessment of plantations based on LiDAR canopy spatial structure parameters
<p>The Yellow River Delta (YRD) has China's largest artificial <em>Robinia pseudoacacia</em> forest, which was planted in the late 1970s and suffered extensive dieback in the 1990s. The health grade of the <em>R.pseudoacacia</em> forest (named canopy vigor grade, CVG) could be achieved by using high-resolution images and canopy vigor indicators (CVIs). However, a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass (AGB) of <em>R.pseudoacacia</em> forest. Therefore, this study aims to construct forest health indicators (FHIs) based on canopy spatial structure parameters extracted from LiDAR. The FHIs included Weibull_α (the scale parameter of the Weibull density function that reflects the shape of the tree canopy), VCI (vertical complexity index), sdCC (the standard deviation of canopy cover), H<sub>99</sub> (the 99th percentile height) and cvLAD (the coefficient of variation of leaf area density), and could significantly distinguish three forest health grades (FHG) (<em>p</em> < 0.05). The FHG was positively correlated with forest AGB (<em>r<sub>s</sub></em> = 0.51, <em>p</em> = 0.004), and the similarity value with CVG was 63.33%. The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity, and distinguish forest health status without prior classification information.</p>
Global and regional health and food security under strict conservation scenarios.
<p>Data supporting Nature Sustainability paper 'Global and regional health and food security under strict conservation scenarios'. Zipped folder contains LandSyMM output used to generate results. The tif files are the biodiversity prioritisation areas used in the 50% and 30% strict protection scenarios generated as per the methods in Jung, M., Arnell, A., de Lamo, X. <em>et al.</em> Areas of global importance for conserving terrestrial biodiversity, carbon and water. <em>Nat Ecol Evol</em> <strong>5, </strong>1499–1509 (2021). https://doi.org/10.1038/s41559-021-01528-7. </p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Norway
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Luxembourg
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Animal disease data complementing the European Union One Health 2020 Zoonoses Report
<p>This dataset contains the mandatory annual data reported for bovine tuberculosis and for bovine and ovine and caprine brucellosis based on Directive 2003/99 that cites in Recital 7 the Council Directives 64/432/EEC and 91/68/EEC. The relevant Commission Decisions relating to the officially free (OF) MS and MS' regions and related reporting are: Decision 2003/467/EC for bovine tuberculosis and bovine brucellosis, and Decision 93/52/EC for sheep and goat brucellosis (B. melitensis). REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: Disease_status_data_2020_20211109: >></p>
Prevalence data complementing the European Union One Health 2020 Zoonoses Report
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: Prev_data_2020 >></p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - the United kingdom
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Finland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Sweden
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - METC Subset
<p><strong>Overview:</strong> This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209 </a>- data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Note #1:</strong> we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.</p> <p><strong>Application: </strong>The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).</li> <li>Resolution: 640x480</li> <li>Number of videos: 212</li> <li>Number of annotation files: 212</li> </ul> <p>Movement codes (in JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Note #2: </strong>The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.</p> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset
<p><strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.</p> <p>This dataset is part on three dataset series all following the same format:</p> <ul> <li><a href="https://zenodo.org/record/4537209">https://zenodo.org/record/4537209</a> - data collected in Pauls Stradins Clinical University Hospital</li> <li><a href="https://zenodo.org/record/5808764">https://zenodo.org/record/5808764</a> - data collected in Jurmala Hospital</li> <li><a href="https://zenodo.org/record/5808789">https://zenodo.org/record/5808789</a> - data collected in the Medical Education Technology Center (METC) of Riga Stradins University</li> </ul> <p><strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff.</p> <p><strong>Statistics:</strong></p> <ul> <li>Frame rate: 30 FPS</li> <li>Resolution: 320x240 and 640x480</li> <li>Number of videos: 2427</li> <li>Number of annotation files: 4818</li> </ul> <p>Movement codes (both in CSV and JSON files):</p> <ul> <li>1: Hand washing movement — Palm to palm</li> <li>2: Hand washing movement — Palm over dorsum, fingers interlaced</li> <li>3: Hand washing movement — Palm to palm, fingers interlaced</li> <li>4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked</li> <li>5: Hand washing movement — Rotational rubbing of the thumb</li> <li>6: Hand washing movement — Fingertips to palm</li> <li>7: Turning off the faucet with a paper towel</li> <li>0: Other hand washing movement</li> </ul> <p><strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.</p> <p><strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project:</p> <ul> <li> <p>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. <a href="https://doi.org/10.3390/data6040038">https://doi.org/10.3390/data6040038</a></p> </li> </ul> <p><strong>Contact information: </strong>atis.elsts@edi.lv</p>
Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"
<p>Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.</p>
Family Type, Mental and Physical Health of in-school Female Adolescents in Ado-Ekiti, Nigeria
<p>This dataset is based on a cross-sectional survey conducted in Ado-Ekiti, Nigeria among in-school female adolescents. The general objective of the study was to examine the influence of family type on the physical and mental health of in-school female adolescents. The data were obtained in 2017 from in-school female adolescents aged 10-18 years old. The respondents were randomly selected from four purposively selected secondary schools. Two schools were selected from the government-owned public schools and two from schools owned by individuals or private organizations. Nigeria operates a six-year secondary education; three years at the junior secondary school (JSS 1-3) and three years at the senior secondary (SS 1-3). The population for this study excluded students in SS3 because they had just concluded their terminal examination at the time of this study. Using the formula proposed by (Krejcie & Morgan, 1970) for deriving a small sample when the population is known, a sample size of 383 was derived from a population of 1656 female students in the four schools. With 10% added to adjust for non-response, a total of 421 students were involved in the study. The number of respondents allocated to each school was proportional to the size of the female students' population in each school. In each school, respondents were assigned to classes proportional to the size, and the particular respondents were identified using a random number. Data were collected with a self-administered structured questionnaire which was piloted before the actual survey. The data is saved in Stata format. </p> <p>A scale for measuring self-reported mental well was designed using questions adapted from Ross, Mirowsky, & Goldsteen (1990) and (Langton & Berger, 2011). The scale reliability coefficient was 0.85. The students were asked to state how often they experience 22 conditions such as feeling sad, discouraged, lonely, hopeless, worthless, wishing you were dead, having trouble concentrating, having difficulty sleeping, crying, and worried among others. </p> <p>Physical health was measured using a scale generated with questions adapted from (Langton & Berger, 2011; Ross et al., 1990). The students were asked to state the frequency of experiencing seven conditions in the month before the survey: feeling sick, tired, dizzy, having chest pain, a headache, muscle or joint pain, and stomach ache. The response options were every day, more than two times, two times, once, and never, graded 1-5.</p> <p>Family type was categorised in four ways: 1) two-parent and single parent. Two parents comprised of couples who were legally married and those who were living together in a consensual union whereas single parents comprised never married, widowed, divorced, and separated mother or father. 2) two-parent (monogamous), two-parent (polygynous), and single parent 3) two-parent, single father, and single mother 4) two-parent, never married, widowed, divorced, and separated</p>
Response to COVID-19: Clients' perspectives on the utilization of reproductive, maternal and child health care services in Nigeria
<p>This dataset was a part of a cross-sectional descriptive study in 320 rural communities in 32 Local Government Areas (LGAs) across the Federal Capital Territory (FCT), Abuja, and 9 out of the 36 Nigerian States in 2020. The dataset contains views of women who used primary health care centres in the selected LGAs for maternal and child health care, and family planning services before, during, and after the COVID-19 pandemic lockdown in Nigeria. The sample size was determined using the Yamane sample size formula. The estimated sample size per State was 384 (3,840 for nine States and FCT), with a 10% adjustment for non-response, the total sample size was 422 per State or location (4220 for the nine States and FCT). The data are stored in SPSS format. <br> The study was an initiative of UNFPA Nigeria. They coordinated it under the One UN Basket fund to respond to COVID-19 and implemented it under the supervision of three national NGOs: The Women’s Health and Action Research Centre (WHARC), Education as a Vaccine (EVA), and the Planned Parenthood Federation of Nigeria (PPFN). </p> <p> </p>
Modelled results for Potential Health and Economic Impacts of Shifting Manufacturing
<p>Files include modelled 100-year annual mean aerosol concentrations, zonal wind and meridional wind in the baseline and sensitivity simulations.</p>
Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health
<p>Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely-related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modeled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2 °C hotter in a fossil-fueled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.</p>
CitieS-Health Barcelona Panel Study Results
<p>This dataset contains the data collected for the Barcelona epidemiological research study within the framework of the CitieS-Health project. Data were collected from September 2020 to March 2021 in Barcelona (Spain) and aim to assess the association between short-term exposure to air pollution and mental health. A scientific paper based using this dataset is under review. </p>
A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool
<p>Datasets relating to the Wellcome Open Research publication of the same name.</p>
Food Inflation and Child Health
<p>Data Repository for</p> <p> </p> <p>Woldemichael, A., Kidane, D., and Shimeles, A. 2022. Food Inflation and Child Health. World Bank Economic Review. https://doi.org10.1093/wber/lhac009</p>
Influence of conspiracy theories and distrust of community health volunteers on adherence to COVID-19 guidelines and vaccine uptake in Kenya
<p>This cross-sectional study collected data between 25 May –27 June 2021 n=447. It involved all registered community health volunteers (CHVs) who had participated in the COVID-19 vaccine hesitancy study. This data was collected as part of an Epidemic Ethics/WHO initiative that FCDO/Wellcome Grant 214711/Z/18/Z has supported. WHO’s specific grant number was 2020/1077878-0). The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. No authors received a salary from the funders.</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.