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CitieS-Health Barcelona Survey Results
<p>This dataset contains the data collected using an online survey on knowledge, perceptions and preferences on topics to be investigated around the theme of air pollution and health in Barcelona, Spain. The data collected are for the CitieS-Health project in Barcelona. A scientific paper based on the online survey results is under review. </p>
E-Health Dataset Thailand
<p>E-Health Data set Thailand </p> <p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em></p>
Preliminary table of Citizen Science initiatives for monitoring soil health
<p>This matrix is the result of collaborative work for Deliverable 1.1 (WP1; T1.1) of the ECHO project. It facilitated the creation of an overview of the current state of the art in projects, initiatives, or activities that have already involved citizens in monitoring soil health, from both inside and outside the European Union. From this, strategic recommendations for ECHO were derived, ensuring that this project not only makes a valuable contribution to the field of soil health monitoring but also sets a precedent for future citizen science endeavors.</p>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></p>
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
Source Data and ambient ozone dataset generated in "Substantially underestimated global health risks of current ozone pollution"
<p>Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden. Our model demonstrates strong performance, achieving a mean bias of less than -1.5 parts per billion against in-situ measurements. We estimate that 66.2% of the global population is exposed to excess ozone for short term (> 30 days per year), and 94.2% suffers from long-term exposure. Furthermore, severe ozone exposure levels are observed in Cropland areas, particularly over Asia. Importantly, the all-cause ozone-attributable deaths significantly surpass previous recognition from specific diseases worldwide. Notably, mid-latitude Asia (30°N) and the western United States show high mortality burden, contributing substantially to global ozone-attributable deaths. Our study highlights current significant global ozone-related health risks and may benefit the ozone-exposed population in the future.</p>
EJPSOIL ARTEMIS on-farm monitoring of soil health and ecosystems services (meta)data
<p>This database includes the data and metadata from the initial on-farm monitoring od soil health and soil related ecosystem services of the EJPSOIL ARTEMIS project. </p>
Supplementary Datafile for "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe"
<p>The dataset contains the results of the study "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe" by Marco Springmann, Eugenia Dinivitzer, Florian Freund, Jørgen Dejgård Jensen, and Clara G Bouyssou. </p> <p>It contains VAT rates on foods across Europe, as well as the results of reforming VAT rates for foods, including increasing rates for meat and dairy and reducing rates for fruits and vegetables. The set of results include changes in food demand, changes in environmental impacts (greenhouse gas emissions, land use, water use, and eutrophication potential), changes in diet-related mortality, changes in costs (revenues, climate change costs, costs of illness). </p>
Long term effects of payment for performance on maternal and child health outcomes– evidence from Tanzania
<p>These are the datasets underpinning the paper entitled: <strong>Long term effects of payment for performance on maternal and child health outcomes– evidence from Tanzania.</strong></p> <p><strong>The datasets are provided in csv and Stata 16 format along with variable descriptions, and the Stata do file used for analysis.</strong></p>
Needs of plant health laboratories and applicability of the horizontal proficiency testing approach based on the questionnaire answers
<p>Data collected in the framework of work package 5 of the Valitest project. They correspond to the needs and expectation concerning proficiency assessment expressed by plant health laboratories during a survey conducted online in 2019.</p>
CADD-SV - A framework to score the effects of structural variants in health and disease
<p>Required annotation data-set to run the CADD-SV framework; a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Pre-scored variants as well as additional information on used features.<br> A webserver for online scoring as well as data downloads is available at: https://cadd-sv.bihealth.org/<br> Source code for CADD-SV is available at GitHub: https://github.com/kircherlab/CADD-SV</p>
GERONTE H2020 Project - GERDAT002 - Composition of the health care professional consortium
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community</strong> <strong>with a dataset of core health care professionals that should be involvoed in the evaluation and treatment trajectories of older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of the GerOnTe care pathway was to determine which health care professionals should be included in the health care professional consortium (HPC) providing care for the patient. Beforehand, we had considered the option of four core members and at least eight other participants depending on the patient’s specificities or profile.</p> <p>Based on clinical experience, we developed a list of 15 potential participants, including general practitioner, one or more oncology specialists (such as surgeons, medical oncologists, radiotherapists), geriatrician, oncology nurse, social worker, clinical pharmacist, physiotherapist, anaesthesiologist, home care nurse, dietician, occupational therapist, spiritual helpers/clerics, psychologist/psychiatrist, palliative care specialist, organ-specific physician(s) such as cardiologist, pulmonologist, nephrologist, rheumatologist etc.</p> <p>This list was presented to the expert panel, and they were asked to determine whether or not these participants should be involved in decision-making and/or the subsequent oncologic care trajectory; experts could specify if these participants should be involved for all patients, only in specific situations/profiles, or did not need to be involved.</p> <p>The results of this expert panel survey and the subsequent composition of the health care professional consortium forms the basis of this dataset.</p>
THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE "HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM" AND ITS IMPACTS
<p><strong>Dataset name:</strong><em> asppl_dataset_v2.csv </em></p> <p><strong>Version: </strong>2.0 </p> <p><strong>Dataset period: </strong>06/07/2018 - 01/14/2022</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>8118</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </p> </li> <li> <p>Brazilian Occupational Classification (CBO) (Brasil, 2022b);</p> </li> <li> <p>National Registry of Health Establishments (CNES) (Brasil, 2022c); </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>asppl_dataset_v2.csv</em> dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical. </p> </td> <td> <p>Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)</p> </td> </tr> <tr> <td> <p><strong>course_progress</strong></p> </td> <td> <p>Percentage of completion of the course. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>Range from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>0, 1, 2, 3, 4, 5 or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). </p> </td> </tr> <tr> <td> <p><strong>CNES</strong></p> </td> <td> <p>The CNES code refers to the health establishment where the participant works.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>CNES Code or NaN.</p> </td> </tr> <tr> <td> <p><strong>health_care_level</strong></p> </td> <td> <p>Identification of the health care network level for which the course participant works.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“ATENCAO PRIMARIA”,</p> <p>“MEDIA COMPLEXIDADE”, </p> <p>“ALTA COMPLEXIDADE”, </p> <p>and their possible combinations.<br> <br> (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE") </p> </td> </tr> <tr> <td> <p><strong>year_enrollment</strong></p> </td> <td> <p>Year in which the course participant registered.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Year (YYYY).</p> </td> </tr> <tr> <td> <p><strong>CBO</strong></p> </td> <td> <p>Participant occupation.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>prison_syphilis_and_population_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>13</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>National Penitentiary Department (DEPEN) (Brasil, 2022d); </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>prison_syphilis_and_population_brazil.csv</em> dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.</p> <p><strong>Table 2:</strong> Description of DEPEN dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2017</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2017</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2018</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2018</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2019</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2019</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2020</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2020</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>pop_2017</strong></p> </td> <td> <p>Prison population in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2018</strong></p> </td> <td> <p>Prison population in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2019</strong></p> </td> <td> <p>Prison population in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2020</strong></p> </td> <td> <p>Prison population in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>students_cumulative_sum.csv</em></p> <p><strong>Dataset period: </strong>2018 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: 7</strong></p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>students_cumulative_sum.csv</em> dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 3:</strong> Description of Students dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region of the course participant. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>Number of students enrolled in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Standardized rate of students in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Standardized rate of students in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Standardized rate of students in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>syphilis_tests_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Brazilian Ministry of Health, through the Outpatient Information System of the Brazilian Health System (SIA/SUS) (Brasil, 2022f);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>syphilis_tests_brazil.csv</em> dataset (see Table 4) originate mainly from the Outpatient Information System of the Brazilian Health System (SIA/SUS). This dataset provides data on the number of tests for syphilis detection by region and year. In addition, it brings a rate that represents the normalized data to compare the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 4:</strong> Description of Syphilis Testes dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region where tests for syphilis were performed. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>2017</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2017</strong></p> </td> <td> <p>Syphilis testing rate in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Syphilis testing rate in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Syphilis testing rate in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Syphilis testing rate in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: <a href="https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114">https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114</a> .</p> <p>Brasil (2022b). Cbo - classificação brasileira de ocupações. Available from: <a href="http://www.mtecbo.gov.br/cbosite/pages/home.jsf">http://www.mtecbo.gov.br/cbosite/pages/home.jsf</a> .</p> <p>Brasil (2022c). Cnes - cadastro nacional de estabelecimentos de saúde. Available from: <a href="http://cnes.datasus.gov.br/">http://cnes.datasus.gov.br/</a> .</p> <p>Brasil (2022d). Departamento penitenciário nacional. levantamento nacional de informações penitenciárias. Available from: <a href="https://www.gov.br/depen/pt-br/servicos/sisdepen">https://www.gov.br/depen/pt-br/servicos/sisdepen</a> .</p> <p>Brasil (2022e). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados">https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados</a> .</p> <p>Brasil (2022f). Ministério da saúde - sistema de informações ambulatoriais do sus (sia/sus). Available from: <a href="https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/">https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/</a> .</p> <p>Valentim, J., Oliveira, E. d. S. G., Valentim, R. A. d. M., Dias-Trindade, S., Dias, A. d. P., Cunha-Oliveira, A., et al. (2021). Data report: “health care of persons deprived of liberty” course from brazil’s unified health system virtual learning environment. Frontiers in Medicine 8. doi:10.3389/fmed.2021.742071.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE “HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM” AND ITS IMPACTS <br> </p> <p><strong>AUTHORS:</strong></p> <p>Janaína L. R. S. Valentim<sup>1,2</sup>, Sara Dias-Trindade<sup>2,3</sup>, Eloiza da S. G. Oliveira<sup>1,4</sup>, José A. M. Moreira<sup>2,5</sup>, Felipe Fernandes<sup>1</sup>, Manoel Honório Romão<sup>1</sup>, Philippi S. G. de Morais<sup>1</sup>, Alexandre R. Caitano<sup>1</sup>, Aline P. Dias<sup>1</sup>, Carlos A. P. Oliveira<sup>1,4,6</sup>, Karilany D. Coutinho<sup>1</sup>, Ricardo B. Ceccim<sup>7</sup>, Ricardo A. M. Valentim<sup>1</sup></p> <p> </p> <p><sup>1</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>2</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Coimbra, Portugal</p> <p><sup>3</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Faculty of Arts and Humanities, Coimbra, Portugal</p> <p><sup>4</sup>Multidisciplinary Institute for Human Development with Technologies, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil</p> <p><sup>5</sup>Open University (Universidade Aberta), Department of Education and Distance Learning (DEED), Lisbon, Portugal</p> <p><sup>6</sup>International Council for Open and Distance Education, Oslo, Norway</p> <p><sup>7</sup>Postgraduate Program in Education, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil</p> <p> </p> <p> </p>
Massive Health Education through Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil
<p><strong>Repository </strong></p> <p><strong>Dataset name: </strong>avasus_syphilis_trail_dataset.csv </p> <p><strong>Version:</strong> 1.0 </p> <p><strong>Dataset period: </strong>05/12/2016 - 01/14/2022 </p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>177732<strong> </strong></p> <p><strong>Number of Attributes: </strong>16 </p> <p><strong>Missing Values: </strong>Yes </p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </p> </li> <li> <p>Brazilian Occupational Classification (CBO) (Brasil, 2022b);</p> </li> <li> <p>National Registry of Health Establishments (CNES) (Brasil, 2022c).</p> </li> </ul> <p><strong>Description:</strong> The data contained in the avasus_syphilis_trail_dataset.csv dataset (see Table 1) originate from AVASUS users who have taken a course on the “Syphilis and other STI” learning path. This dataset provides elemental data to analyze the impact and reach of the trails and the profile of their participants.</p> <p><strong>Table 1: </strong>Description of Dataset Features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Source</strong></p> </td> </tr> <tr> <td> <p><strong>user_id</strong></p> </td> <td> <p>Unique identifier of the user (anonymously).</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Randomly generated integer.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>user_gender</strong></p> </td> <td> <p>Gender of the user. </p> </td> <td> <p>Categorical. </p> </td> <td> <p>Feminino / Masculino / Não Informado. (In English: Female, Male or Uninformed)</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>user_occupation</strong></p> </td> <td> <p>User occupation</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_cnes</strong></p> </td> <td> <p>The CNES code refers to the health establishment where the user works.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>CNES Code or NaN.</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_level_attention</strong></p> </td> <td> <p>Identification of the health care network level for which the user works.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“ATENCAO PRIMARIA”,</p> <p>“MEDIA COMPLEXIDADE”, </p> <p>“ALTA COMPLEXIDADE”, </p> <p>and their possible combinations.</p> <p>(In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")</p> <p>Or NaN.</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_region</strong></p> </td> <td> <p>Brazilian region in which the user resides.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). Other options: “Exterior” or “Não Informado” (In English: Outside or Not informed).</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_id</strong></p> </td> <td> <p>Unique identifier of the course performed by the avasus user.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Code list according to AVASUS.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_name</strong></p> </td> <td> <p>Name of the course taken by the avasus user.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>Course name according to AVASUS.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_workload</strong></p> </td> <td> <p>Course timetable.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Range from 0 to 120.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_creation_date</strong></p> </td> <td> <p>Course creation date.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_id</strong></p> </td> <td> <p>Unique identification of the enrollment carried out by the student in some course of the trail.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Randomly generated single integer.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_creation</strong></p> </td> <td> <p>Date the student registered.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_completion_date</strong></p> </td> <td> <p>Date the student completed the course.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_current_progress</strong></p> </td> <td> <p>Student progress regarding course completion.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Range from 0 to 100.</p> <br> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_text_evaluation</strong></p> </td> <td> <p>Comment made by the student about the course.</p> </td> <td> <p>Categorial. </p> </td> <td> <p>Free text or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>0, 1, 2, 3, 4, 5 or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> </tbody> </table> <p><br> <br> <br> </p> <p><strong>References </strong></p> <p>[1] Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114 . </p> <p>[2] Brasil (2022b). Classificação brasileira de ocupações - CBO. Available from: http://www.mtecbo.gov.br/cbosite/pages/home.jsf . </p> <p>[3] Brasil (2022c). Cadastro nacional de estabelecimentos de saúde - CNES. Available from: http://cnes.datasus.gov.br/ .</p> <p><strong>Article: </strong>Massive Health Education with Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil</p>
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
Dataset for "Authorship concentration in health sciences journals from Latin America and the Caribbean"
<p>Authorship concentration indexes and other data for journals in the LILACS (Latin American and the Caribbean Literature on Health Sciences) bibliographic database, from 2015 to 2019 (FONTENELLE, 2022). These data are read and created by <a href="https://doi.org/10.5281/zenodo.6127497">analytic code in Zenodo</a>.</p> <ul> <li><em>authorship_concentration.csv</em> - dataset derived in Fontenelle (2022) from raw data exported from LILACS. This is the main file, and it's CC-BY because other researchers might have curated the raw data differently and thus derived different data. CSV file encoded with ASCII.</li> <li><em>authorship_concentration_datadictionary.csv</em> - data dictionary for the previous file. This file is actually CC0. CSV file encoded with ASCII.</li> <li><em>journals.csv</em> - dataset about the journals indexed in LILACS between 2015 and 2019. As a result of simply converting and filtering the original TITLE database, this is actually CC0 by the Pan American Health Organization (PAHO). CSV file encoded with UTF-8.</li> <li><em>journal_subjects.csv</em> - DeCS descriptors for the journals identified by the ISSN. CC0 by the Pan American Health Organization (PAHO), as above. CS file encoded with ASCII.</li> </ul>
Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland – a multicentre cohort study
<p>For details, see publication</p>
Data bases for Measuring impacts of oral health promotion interventions on health inequities: the example of New Caledonia
<p>Extract from the New Caledonian (NC) epidemiological survey database for identifying the determinants and risk factors explaining the presence of untreated dental caries and to compare the prevalence and severity of dental caries between 2012 and 2019, in order to identify potential changes that occurred in NC.</p>
Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors
<p>This is the dataset and stata code for the paper "Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors” submitted to Plos One May 1st, 2024.</p>
SFB1451 - key mechanisms of motor control in health and disease
<p>Note: this dataset has been deprecated in favour of <a href="https://github.com/sfb1451/all-datasets" target="_blank" rel="noopener">sfb1451/all-datasets</a>; see <a href="https://doi.org/10.5281/zenodo.11119424">10.5281/zenodo.11119424</a><br><br>This dataset indexes projects of the Collaborative Research Centre 1451 (CRC1451; dt: Sonderforschungsbereich, SFB1451). This collaborative research centre brings together neuroscientists investigating genetic factors, cellular, and synaptic as well as systems/neural network processes underlying motor control in animals and humans, in both health and neuropsychiatric diseases. All investigators are committed to the CRC's multi-faceted, iterative, and integrative agenda with the long-term goal of identifying the essential mechanisms underlying normal and pathological motor control.</p>
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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.