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Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</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><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
A Curated Gene and Biological System Annotation of Adverse Outcome Pathways Related to Human Health
<p>Adverse Outcome Pathways (AOPs) are multi-scale models of biological mechanisms connecting molecular initiating events to adverse outcomes through measurable key events. AOPs can guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation in chemical safety assessment. Here, we present a comprehensive molecular annotation of AOPs relevant to human health to embed the AOP framework into molecular data interpretation, which supports the development and application of novel AOP-based approaches in biomedical research.</p> <p>Please cite the following publication alongside this Zenodo entry when using the data:</p> <p>Saarimäki, L.A., Fratello, M., Pavel, A. <em>et al.</em> A curated gene and biological system annotation of adverse outcome pathways related to human health. <em>Sci Data</em> <strong>10</strong>, 409 (2023). https://doi.org/10.1038/s41597-023-02321-w</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>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Data for One Health and Veterinary Systems in Africa
<p>One Health Database Africa<br> The “One Health and Veterinary Systems in Africa: Taking stock of current coverage, needs, and opportunities to meet present and changing threats and optimize collaboration” is reviewing current capacity and programmatic status, gaps, and operations in each country and by sub-regions of Africa.</p> <p>This datasets combines multiple sources to produce a single One Health database containing selected indicators. The database encompasses 54 countries across the continent of Africa (according the UN).</p>
Data underlying the publication: "Effects of hatching system on chick quality, welfare and health of young breeder flock offspring"
<p>The aim of the current study was to evaluate effects of two alternative hatching systems (hatchery-feeding and on-farm hatching)<br> compared to conventional hatching systems with respect to chick quality, welfare and health of a young breeder flock.<br> To study the effect of treatments on the competence of the humoral immune response, blood titres after a live attenuated NCD<br> vaccination was assessed.<br> To study differences in disease resilience, the susceptibility to develop tracheal inflammation after infection with a<br> life attenuated infectious bronchitis vaccine virus was assessed by trachea lesion scoring and expression of genes related<br> to epithelial integrity and inflammatory responses.</p>
Ontologizing Health Systems at Scale: Making Translational Discovery a Reality (Recorded talk)
<p>This entry contains the recorded talk that was presented at the 2021 American Medical Informatics Association Virtual Informatics Summit (<a href="https://amia.org/education-events/amia-2021-virtual-informatics-summit">https://amia.org/education-events/amia-2021-virtual-informatics-summit</a>).</p>
OMOP2OBO: Semantic Integration of Standardized Clinical Terminologies to Power Translational Digital Medicine Across Health Systems (Recorded Introduction)
<p>This entry contains the recorded introduction that was presented at the 2020 Observational Health Data Science Initiative Symposium (<a href="https://www.ohdsi.org/events/2020-ohdsi-symposium/">https://www.ohdsi.org/events/2020-ohdsi-symposium/</a>).</p>
Dataset: Tivic Health Systems, Inc. (TIVC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Database used in : Analysis of intermunicipal journeys for cardiac surgery in Brazilian Unified Health System (SUS): an approach based on network theory
<p>Data and scripts referring to the results generated in the article entitled: <strong>Analysis of intermunicipal journeys for cardiac surgery in Brazilian Unified Health System (SUS): an approach based on network theory.</strong></p> <p> </p> <p>To obtain the results of the work the following sequence of database treatment was performed:</p> <p> </p> <p>DATASUS --> BASE_PER_YEAR --> EDGES_BASE --> EDGES_VC_BASE</p> <p>The bases were downloaded from the DATASUS site (link: https://datasus.saude.gov.br/transferencia-de-arquivos/#) in .dbc format separated by month and year; using Tabwin we joined the bases generating a base for each year in csv format. The reformatted bases are gathered together in the file PER_YEAR_BASE.ZIP; from the bases for each year we manually built the files in csv format with the list of the edges with the following fields: "Source", "Target", "Type", "Id", "Label" and "Weight". The "Source" column was filled with data from MUNIC_RES and the "Target" column with data from MUNIC_MOV. The "ID" and "Weight" fields were filled in automatically using Gephi, where the "Weight" column represents the sum of the edge, defined by the pair of Source and Target columns, were repeated throughout the year. This generated the bases containing the list of edges that are grouped in the file EDGES_BASE.ZIP. Each base was filtered to contain only edges related to the city "Vitória da Conquista" and grouped in the file EDGES_VC_BASE.zip</p> <p>INDE BASE --> NODES_BASE</p> <p>To build the list of nodes containing the list of municipalities with their respective geographical locations (in UTM), we used the database of the INDE (available on the link: https://visualizador.inde.gov.br/). The file in shape format was treated in the ArqGis program and the database with the network nodes was created (file NODES_BASE.csv).</p> <p>EDGES_VC_BASE and NODES_BASE --> NETWORK</p> <p>Using the program Gephi we joined the bases referring to the edges (EDGES_VC_BASE) and those referring to the nodes of the network (NODES_BASE) and built the networks for each year studied for the city of "Vitória da Conquista". All networks are in gephi format and compressed in the NETWORKS.zip file.</p> <p>EDGES_VC_BASE and NODES_BASE --> INDICES</p> <p>Using the R script "distance.R" and using as input the files of edges (EDGES_VC_BASE) and nodes (NODES_BASE) we generate files in csv format with the columns: dist_med_in , dist_med_out, Flow_in and flow_out. The indexes dist_med_in and dist_med_out represent the average distance traveled in meters to enter and leave the municipality, respectively; the indexes flow_in and flow_out estimate the quantity of people that entered and left the municipality. All index files are grouped in the compressed file INDICES.zip.</p> <p>The last two digits at the end of all file names represent the year of analysis. </p>
Assessment of Capacity of the primary Health System in the management of noncommunicable Diseases in Ghana
<p>This questionnaire was used to assess the capacity of primary health system in the management of NCDs</p>
Question Answering auf dem Lehrbuch "Health Information Systems" mit Hilfe von unüberwachtem Training eines Pretrained Transformers
Masterthesis, verwendete Datensätze, Skripte und Ergebnisse des praktischen Teils
Antimicrobial Resistance Microbiological Dataset (ARMD-ECUH): A deidentified collection of electronic health records from a rural academic health system for antimicrobial resistance research
Open the record for dataset details and reuse information.
Software Product Quality Evaluation Guide for Electronic Health Record Systems
<p>Apresentação do artigo "Software Product Quality Evaluation Guide for Electronic Health Record Systems".</p> <p>SBES 2020 - Trilha de Ideias Inovadoras e Resultados Emergentes.</p>
Data for publication: A food system transformation can enhance global health, environmental conditions and social inclusion
<p>Data related to the publication "A food system transformation can enhance global health, environmental conditions and social inclusion"</p>
Tuberculosis and HIV/AIDS-attributed mortalities and associated sociodemographic factors in Papua New Guinea: Evidence from the comprehensive health and epidemiological surveillance system
<p>Tuberculosis (TB) and HIV/AIDS are public health concerns in Papua New Guinea (PNG). This study examines TB and HIV/AIDS mortalities and associated sociodemographic factors in PNG. Method: As part of a longitudinal study, verbal autopsy (VA) interviews were conducted using the WHO 2016 VA Instrument to collect data of 926 deaths occurred in the communities within the catchment areas of the Comprehensive Health and Epidemiological Surveillance System from 2018-2020. InterVA-5 cause of deaths analytic tool was used to assign specific causes of death (COD). Multinomial logistic regression analyses were conducted to identify associated sociodemographic factors, estimate odds ratios (OR), 95% confidential intervals and p-values. Result: TB and HIV/AIDS were the leading CODs from infectious diseases, attributed to 9% and 8% of the total deaths, respectively. Young adults (25-34 years) had the highest proportion of deaths from TB (20%) and the risk of dying from TB among this age group was five times more likely than those aged 75+ years (OR: 5.5 [1.4-21.7]). Urban population were 46% less likely to die from this disease compared rural ones (OR: 0.54 [0.3-1.0]). People from middle household wealth quintile were three times more likely to die from TB than those in the richest quintile (OR: 3.0 [1.3-7.4]). Young adults also had the highest proportion of deaths to HIV/AIDS (18%) and were nearly seven times more likely to die from this disease compared with those aged 75+ years (OR: 6.7 [1.7-25.4]). Males were 48% less likely to die from HIV/AIDS than females (OR: 0.52 [0.3-0.9]). The risk of dying from HIV/AIDS in urban population was 54% less likely than their rural counterparts (OR: 0.46 [0.2-0.9]). Conclusion: TB and HIV/AIDS interventions are needed to target high-risk and vulnerable populations to reduce premature mortality from these diseases in PNG.</p>
Critical barriers to sustainable capacity strengthening in global health: A systems perspective on development assistance
<p>These qualitative datasets are associated with work done under the Inter-agency Working Group for Capacity Strengthening initiative. The data was captured via Miro - a digital whiteboarding tool - using an innovative co-creation process called Systems Acupuncture and details the nine systemic barriers impeding a better, more sustainable country-driven model for capacity strengthening. The data was captured over a series of stakeholder workshops and discussion sessions, and synthesized into nine "system syndrome" maps. The maps illustrate the multi-dimensional causal relationships between individual system drivers and display feedback loops (i.e., sets of drivers that reinforce each other) within and between the system syndrome maps. The two pending manuscripts, both submitted to Gates Open Research, this data is associated with are:</p> <ol> <li>Critical barriers to sustainable capacity strengthening in global health: A systems perspective on development assistance</li> <li>Exploring system drivers of gender inequity in development assistance for health and opportunities for action</li> </ol> <p> </p>
Development of a Self-Powered Structural Health Monitoring System for Transportation Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17PTAM03. Abstract of the final report is stated below for reference:</p> <p>"Roadways and bridges play an important role in the economic and social health of society by connecting commerce and people. Economic growth and population expansion pose considerable burden on the aging infrastructure (i.e., pavements and bridges). There is a pressing need to develop structural health monitoring (SHM) technologies capable of collecting infrastructure utilization data. Doing so inexpensively with self-powered systems will revolutionize infrastructure monitoring technology, and will improve decision making enabling roadway and bridge preservation. In this study, a self-powered battery-less structural health monitoring (SHM) system was developed. It is powered by a thermal energy harvester equipped with thermoelectric generators (TEGs) driven by temperature differentials between the top of asphalt pavements and their lower layers. An innovative 2-tier TEG harvester was designed to limit the downtime of the SHM system when the temperature differentials are insufficient to power a single unit. The 2-tier system requires a minimum of 2.1⁰C in temperature differential to generate the minimum of 40 mV needed to power the SHM system. The SHM system consists of a DC-DC booster to increase the voltage generated by the harvester, a buck controller to bring this voltage down to the 3.3 Volts required for powering the microcontroller, a microcontroller and a wireless transceiver for transmitting the data. Another transceiver carried on-board a pilot vehicle is needed to retrieve the data. Software was developed in this project to allow data communication between the two transceivers. The SHM system developed accepts analogue voltage input from any sensor that generates analogue voltage, (e.g., piezoelectric axle load sensors, strain gauges, temperature gauges and so on). A prototype of this SHM system was constructed and tested in the lab and it is ready for field implementation."</p>
A low-carbon health diet to change the Chinese food system
<p><strong>These are data about all the figures in the manuscript, mainly including: Food GHG emissions from field to table, food consumption and GHG emissions in 19 provinces, food consumption under different scenes, GHG emissions under different scenes in 2050 and Low carbon Health Dictionary Guidelines of China.</strong></p>
Synergism between production and soil health through crop diversification, organic amendments and crop protection in wheat-based systems
<ol> <li class="MsoNormal"><span>One of the critical challenges in agriculture is enhancing yield without compromising its foundation, a healthy environment, and, particularly, soils. Hence, there is an urgent need to identify management practices that simultaneously support soil health and production and help achieve environmentally sound production systems.</span></li> <li class="MsoNormal"><span>To investigate how management influences production and soil health under realistic agronomic conditions, we conducted an on-farm study involving 60 wheat fields managed conventionally, under no-till, or organically. We assessed 68 variables defining management, production, and soil health properties. We examined how management systems and individual practices describing crop diversification, fertiliser inputs, agrochemical use, and soil disturbance influenced production – quantity and quality – and soil health focusing on aspects ranging from soil organic matter over soil structure to microbial abundance and diversity.</span></li> <li class="MsoNormal"><span>Our on-farm comparison showed marked differences between soil health and production in the current system: organic management resulted in the best overall soil health (+ 47%) but the most significant yield gap (- 34%) compared to conventional management. No-till systems were generally intermediate, exhibiting a smaller yield gap (- 17%) and only a marginally improved level of soil health (+ 5%) compared to conventional management. Yet, the overlap between management systems in production and soil health properties was considerably large.</span></li> <li class="MsoNormal"><span>Our results further highlight the importance of soil health for productivity by revealing positive associations between crop yield and soil health properties, particularly under conventional management, whereas factors such as weed pressure were more dominant in organic systems.</span></li> <li class="MsoNormal"><span>None of the three systems showed advantages in supporting production-soil health-based multifunctionality. In contrast, a cross-system analysis suggests that multifunctional agroecosystems could be achieved through a combination of crop diversification and organic amendments with effective crop protection.</span></li> <li class="MsoNormal"><span><em>Synthesis and applications</em>: Our on-farm study implies that current trade-offs in managing production and soil health could be overcome through more balanced systems incorporating conventional and alternative approaches. Such multifunctionality supporting systems could unlock synergies between vital ecosystem services and help achieve productive yet environmentally sound agriculture supported by healthy soils.</span></li> </ol>
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.