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203 results for “human health”
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>
Review of the evidence for Oceans and Human Health relationships in Europe: A systematic map.
<p>This database details the results of a systematic mapping exercise linking marine exposures to measured human health outcomes for the Seas, Oceans and Public Health in Europe Project.</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>
The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland – in preparation.</p> <p> </p> <p><strong>Funding acknowledgments: </strong>The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract:<br></strong></p> <p><span>In the context of the European Union's intensified efforts to curb greenhouse gas emissions and meet climate targets, wood pellets have emerged as a pivotal element in the renewable energy strategy. Yet, biomass pellet combustion has been linked to a range of pollutants impacting air quality and public health. As biomass utilization gains popularity as a fuel for residential heating, it is important to determine this impact and enhance sustainable practices throughout the entire biomass energy production cycle. </span></p> <p><span>This study investigates the intricate dynamics of biomass pellet properties on their combustion emissions, with a specific focus on the differences observed between pellets of woody and non-woody origins. The data reveal a variation in pellet characteristics, especially regarding their ash and fines contents, mechanical durability, and impurity levels, and significant differences in the type and amount of utilization emissions. The results highlight potential health risks posed by the combustion of biomass fuels, particularly non-woody (agro) pellets, due to elevated concentrations of emitted particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), ammonia (NH<sub>3</sub>), chlorine (Cl<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and formaldehyde (HCHO), all surpassing recommended limits.</span></p> <p><span>Moreover, the study reveals that emissions from pellet combustion could be partially predicted by analyzing pellet characteristics. Statistical analysis identified several key variables—including bark content, fines content, mechanical durability, bulk density, heating value, net calorific value, sulfur, and nitrogen content—that impact emissions of CO, NO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>, HCHO, and respiratory tract irritants. These findings underscore the need for proactive measures, including the implementation of stricter standards for fuel quality and emissions, alongside public education initiatives promoting the cleanest and safest fuels possible. </span></p> <p><strong> </strong></p>
Supplementary data and summary statistics - Genetic influences on circulating retinol and its relationship to human health
<p><strong>Summary statistics from the circulating retinol GWAS</strong></p> <p>See -<em><strong> GWAS_summary_stats_README.txt </strong></em>for details of these files and the header names. METSIM+INTERVAL meta-analyses have a sample size of 17268. The full meta-analysis that includes ATBC+PLCO has a sample size of 22274.</p> <p><strong>Please cite the following if you use any of these data </strong>- Reay, W.R. et al. Genetic influences on circulating retinol and its relationship to human health. Nature Communications (2024).</p> <p>By downloading these summary statistics, investigators agree to the following:</p> <ol> <li>Investigators acknowledge that these data are provided on an “as-is” basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose.</li> <li>Investigators will not cross-post these data or make them available elsewhere – this website is the definitive source for these data without express written permission from the study corresponding authors.</li> <li>Investigators will never attempt to identify any participant who contributed to these data.</li> <li>Any commercial or for-profit use of these data is forbidden unless express permission is sought from the study corresponding authors.</li> <li>Investigators will cite the associated manuscript when using these data.</li> </ol> <p><strong>Supplementary data from the circulating retinol GWAS phenome-wide Mendelian randomisation study</strong></p> <p>1. MR_retinol_as_exp - full output from the MR-pheWAS using circulating retinol as the exposure</p>
Fig. 1. Investigated 8 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 1. Investigated 8 urban territories of Lithuania (Alytus, Kaunas, Klaipeda, Palanga, Panevezys, Siauliai, Vilnius, Visaginas urban municipalities).
Fig. 1 in Epidemiology of Trichinella infection in wild boar from Spain and its impact on human health during the period 2006-2019
Fig. 1. Prevalence of Trichinella infection in wild boar from several Spanish autonomous communities.
Fig. 4 in Epidemiology of Trichinella infection in wild boar from Spain and its impact on human health during the period 2006-2019
Fig. 4. Box-plot and post hoc pairwise comparison via Dunn's test of the incidence. Box-plot with different letters indicate a statistically significant difference.
Fig. 3 in Epidemiology of Trichinella infection in wild boar from Spain and its impact on human health during the period 2006-2019
Fig. 3. Three-dimensional plots for the interaction effects of the total number of wild boars hunted and the prevalence of the Trichinella infection in wild boar on incidence of trichinellosis in humans.
Extended dataset and Coreq Checklist for 'Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources"
<p>Individual user test interview guides and group interview guides for multiple stakeholders</p> <p>Coreq checklist</p> <p>Design reporting checklist</p>
Evaluating Human Health Risks with Microbial Source Tracking and Quantitative Microbial Risk Assessment in Little Bay, Texas
<p>These results are the median human health risks associated with a range of recreational activities (i.e., swimming, kayaking, fishing, boating, jet skiing) in Little Bay, Texas. The health risks were estimated based on a quantiative microbial risk assessment (QMRA) that was performed using enterococci concentrations as well as host-associated molecular marker (i.e., human, canine, gull) concentrations measured previously in Little Bay. These human health risks represent the probability of a gastrointestinal illness for recreators.</p>
Linked collectors and determiners for: BCCM/IHEM - Fungi Collection: Human & Animal Health.
Natural history specimen data linked to collectors and determiners held within, "BCCM/IHEM - Fungi Collection: Human & Animal Health". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f3a5b919-9eec-48fe-b96e-ef7fb9c7e822">https://bionomia.net/dataset/f3a5b919-9eec-48fe-b96e-ef7fb9c7e822</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f3a5b919-9eec-48fe-b96e-ef7fb9c7e822">https://gbif.org/dataset/f3a5b919-9eec-48fe-b96e-ef7fb9c7e822</a>. Formatted as a Frictionless Data package.
Low molecular weight seaweed–derived polysaccharides lead to increased faecal bulk but do not alter human gut health markers
<p>Differential analysis of taxa before and after the consumption of either agar, alginate or maltodextrin showed no significant change at phylum or family level (<strong>Supplementary tables 1-2</strong>). </p> <p><em>Supplementary Table 1: Differential abundance analysis with ALDEX2 on family level</em></p> <p><em>Supplementary Table 2: Differential abundance analysis with ALDEX2 on phylum level</em></p>
Dataset for "Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources"
<p>A qualitative dataset for the article: Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources</p> <p>We collected this data in Phase 2 of the work described in the article, to inform development of educational resources (<em>Be Smart About Your Health</em>) to support teaching critical thinking about health claims and making informed health choices for use in secondary schools, based on a set of Informed Health Choices Key Concepts. </p> <p>Data collection methods: individual and group interviews, observation of classroom pilots, in Kenya, Rwanda, and Uganda, and via email from an international advisory group. Timeframe for data collection and analysis: 2020-2022</p> <p>This dataset is a part of the research project: <em>Enabling sustainable public engagement in improving health and health equity, </em>2019-2024. Funded by GLOBVAC programme, Research Council of Norway. </p> <p> </p>
Quantification of the global and regional impacts of gas flaring on human health via spatial differentiation
<p>Supplementary material for the associated publication.</p>
Supplementary Dataset S8 for publication "Universality in human cortical folding in health and disease"
<p>The data sources and method of extracting the data are described in the publication "Universality in human cortical folding in health and disease". PNAS 2016</p> <p>2019 update: The matlab code for extraction of relevant variables from Freesurfer subjects is now published on <a href="https://doi.org/10.5281/zenodo.3608675">Zenodo</a> and <a href="https://github.com/cnnp-lab/CorticalFoldingAnalysisTools">Github</a>.</p>
Figure 2. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 2. - Time line of the tasks in UNICORN-project
Figure 1. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 1. - Links and interactions between the work packages
HUMANE:Summary of e-Health focus groups and interviews
<p>We run a series of dedicated stakeholder consultations (interviews, focus groups, and meetings) where the topics of the e-Health roadmap has been addressed. The purpose was to obtain more detailed feedback, by going deeper into the subjects of the roadmap. The participants in these consultation have been policy-makers representatives, health professionals, IT experts and researchers.</p> <p>Here we provide a summary of this consultation for eHealth roadmap, and a summary of the results. </p>
Genomic epidemiology of Escherichia coli: antimicrobial resistance through a One Health lens in sympatric humans, livestock and peri-domestic wildlife in Nairobi, Kenya
<p><strong><span>Background</span></strong></p> <p><span>Livestock systems have been proposed as a reservoir for antimicrobial-resistant (AMR) bacteria and AMR genetic determinants that may infect or colonise humans, yet quantitative evidence regarding their epidemiological role remains lacking. Here we used a combination of genomics, epidemiology and ecology to investigate patterns of AMR gene carriage in <em>Escherichia</em> <em>coli</em>, regarded as a sentinel organism.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We conducted a structured epidemiological survey of 99 households across Nairobi, Kenya, and whole genome sequenced <em>E</em>. <em>coli</em> isolates from 311 human, 606 livestock, and 399 wildlife faecal samples. We used statistical models to investigate the prevalence of AMR carriage and characterise AMR gene diversity and structure of AMR genes in different host populations across the city. We also investigated house-hold level risk factors for exchange of AMR genes between sympatric humans and livestock.</span></p> <p><strong><span>Findings</span></strong></p> <p><span>We detected 56 unique acquired genes along with 13 point mutations present in variable proportions in human and animal isolates, known to confer resistance to nine antibiotic classes. We find that AMR gene community composition is not associated with host species, but AMR genes were frequently co-located, potentially enabling the acquisition and dispersal of multi-drug resistance in a single step. We find that whilst keeping livestock had no influence on human AMR gene carriage, the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of and in larger households.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Findings of widespread carriage of AMR bacteria in human and animal populations, including in long-distance wildlife species, in community settings, highlight the value of evidence-based surveillance to address antimicrobial resistance on a global scale. Our genomic analysis provided in-depth understanding of AMR determinants at the interfaces of One-Health sectors that will inform AMR prevention and control.</span></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.