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Dataset results
203 results for “human health”
Human health impact based on adult European consumers' dietary exposure to chemical contaminants and consumption of unprocessed red meat, processed meat, and legumes
<p>This dataset was generated within the PRISMA project (Marie Skłodowska-Curie Grant Agreement No. 101110615) for assessing the health impact for adult consumers from 25 European countries due to dietary exposure to chemical contaminants and consumption of food based on different dietary patterns.</p><p>The risk coefficients used to generate data are presented in Table S1.</p><p>File S2 presents the health impact based on the chronic dietary exposure to chemical contaminants for each country.</p><p>File S3 presents figures regarding the health impact for each country for adult consumers due to dietary exposure to chemical contaminants and consumption of food based on different dietary patterns.</p><p>File S4 presents the health impact based on the consumption of unprocessed red meat, processed meat, and legumes for each country.</p><p> </p>
Data and Scripts for "Temperature Extremes and Human Health in Cyprus Investigating the Impact of Heat and Cold Waves"_Version 2
<p>This folder contains the R data and scripts required for reproducing the results of the manuscript "Temperature Extremes and Human Health in Cyprus: Investigating the Impact of Heat and Cold Waves". (Version2)</p>
Microbiome of domestic water from rural communities in the southern caribbean: water quality and human health implications
<p><strong>LIST OF SUPPLEMENTARY DATA FILES</strong></p> <p>Chapter 4 - The microbiome of domestic water in the rainwater harvesting-dependent island community of Carriacou, Grenada in the Southern Caribbean</p> <p>Chapter 5 - Microbial composition of drinking water in Speightstown, Barbados</p> <p>Chapter 6 - Bacterial diversity of drinking water in Nariva, Trinidad</p> <p>Chapter 7 - A preliminary assessment of protozoans in various water sources in Nariva, Trinidad</p>
GEOS-Chem 2015 Speciated PM2.5 Outputs for "Impact of circular- and single-sector waste-heat reuse pathways on PM2.5-air quality, CO2 emissions, and human health in India; material exchanges more viable to achieve sustainability targets"
<p>Daily PM2.5 and species outputs from GEOS-Chem Modeling over India (0.5° x 0.625°) </p>
Umbrella review data of colour-associated bioactive pigments found in fruit and vegetables, compared to placebo or low intakes, on human health outcomes relevant to public health
<p>This dataset comprises data extracted from 86 publications included in an umbrella review which compared the effect of colour-associated bioactive pigments found in fruit and vegetables (carotenoids, flavonoids, betalains and chlorophyll) on human health outcomes relevant to public health. Meta-analysed data from 83 systematic literature reviews were available for 17 different bioactive pigments spanning all colours of fruit and vegetables except green, with additional data from two randomised controlled trials and one cohort study for chlorophyll. This dataset represents 2,847 original research studies and data from over 37 million participants. There were 449 meta-analysed health outcomes extracted from the 83 systematic literature reviews. Extracted health outcomes were categorised according to pigment, comparator type, broad health outcome, study design, age group, source of pigment, risk of bias, and confidence in the estimated effect. Data extracted were dose, intervention duration, sample size, number of original studies, effect estimate, 95% confidence intervals, I2 statistics, publication bias and p-value. Rigorous analysis, including estimations of a common effect size, stratification of the evidence, study level sensitivity analyses and reporting on heterogeneity and potential biases, may be carried out using these data, to further evaluate the effect of colour-associated bioactive pigments in fruit and vegetables on human health.</p>
MicrobiomeHD: the human gut microbiome in health and disease
<p><strong>Overview</strong></p> <p>MicrobiomeHD is a standardized database of human gut microbiome studies in health and disease. This database includes publicly available 16S data from published case-control studies and their associated patient metadata. Raw sequencing data for each study was downloaded and processed through a standardized pipeline.</p> <p>To be included in MicrobiomeHD, datasets have:</p> <ul> <li>publicly available raw sequencing data (fastq or fasta)</li> <li>publicly available metadata with at least case and control labels for each patient</li> </ul> <p>Currently, MicrobiomeHD is focused on stool samples. Additional samples may be included in certain datasets, as indicated in the metadata.</p> <p><strong>Files</strong></p> <p>Additional information about the datasets included in this MicrobiomeHD release are in the MicrobiomeHD github repo <a href="https://github.com/cduvallet/microbiomeHD">https://github.com/cduvallet/microbiomeHD</a>, in the file <em>db/dataset_info.yaml</em>. Top-level identifiers correspond to dataset IDs labeled by disease_first-author. For the most part, sample sizes in the yaml file are those that were described in the papers, and may not exactly reflect the actual data (due to missing/extra data, samples which didn't pass quality control, etc).</p> <p>Each dataset was downloaded and processed through a standardized pipeline. The raw processing results are available in the *.tar.gz files here. Each file has the same directory structure and files, as described in the pipeline documentation: <a href="http://amplicon-sequencing-pipeline.readthedocs.io/en/latest/output.html">http://amplicon-sequencing-pipeline.readthedocs.io/en/latest/output.html</a>.</p> <p>Specific files of interest in each *.tar.gz folder include:</p> <ul> <li><strong>summary_file.txt</strong>: this file contains a summary of all parameters used to process the data</li> <li><strong>datasetID.metadata.txt</strong>: the metadata associated with the samples. Note that some samples in the metadata may not have sequencing data, and vice versa.</li> <li><strong>RDP/datasetID.otu_table.100.denovo.rdp_assigned</strong>: the 100% OTU tables with Latin taxonomic names assigned using the RDP classifier (c = 0.5).</li> <li><strong>datasetID.otu_seqs.100.fasta</strong>: representative sequences for each OTU in the 100% OTU table. OTU labels in the OTU table end with <code>d__denovoID</code> - these denovoIDs correspond to the sequences in this file.</li> <li><strong>README.txt</strong>: additional information about steps taken to download and process each dataset, as needed.</li> </ul> <p>The raw data was acquired as described in the supplementary materials of Duvallet et al.'s "Meta analysis of microbiome studies identifies shared and disease-specific patterns" and, when available, the respective dataset README files.</p> <p>Raw sequencing data was processed with the Alm lab's in-house 16S processing pipeline: <a href="https://github.com/thomasgurry/amplicon_sequencing_pipeline">https://github.com/thomasgurry/amplicon_sequencing_pipeline</a></p> <p>Pipeline documentation is available at: <a href="http://amplicon-sequencing-pipeline.readthedocs.io/">http://amplicon-sequencing-pipeline.readthedocs.io/</a></p> <p>Metadata was extracted from the original papers and/or data sources, and formatted manually. When possible, these steps are documented in each dataset's associated README.txt file.</p> <p><strong>Contributing</strong></p> <p>MicrobiomeHD is a resource that can be used to extract disease-specific microbiome signals in individual case-control studies. Many microbes respond non-specifically to health and disease, and the majority of bacterial associations within individual studies overlap with this non-specific response. Researchers should cross-check their results with the data presented here to ensure that their identified microbial associations are specific to their disease under study.</p> <p>We provide an updated list of non-specific microbes here, as well as the raw OTU tables for anyone who wishes to reproduce and adapt this analysis to their study question.</p> <p>If you would like to include your case-control dataset in MicrobiomeHD, please email ejalm[at]mit.edu and duvallet[at]mit.edu.</p> <p>For us to process your data through our standard pipeline, you will need to provide the following files and information about your data:</p> <ul> <li>raw sequencing data in fastq or fasta format (preferably fastq)</li> <li>information about which processing steps will be required (e.g. removing primers or barcodes, merging paired-end reads, etc)</li> <li>sample IDs associated with the sequencing data (either mapped to barcodes still in the sequences, or to each de-multiplexed sequencing file)</li> <li>case/control metadata of each sample</li> <li>other relevant metadata (e.g. sampling site, if not all samples are stool; sampling time point, if multiple samples per patient were taken; etc)</li> </ul> <p>By using MicrobiomeHD in your own analyses, you agree to contribute your dataset to this database and to make your raw sequencing data (i.e. fastq files) publicly available.</p> <p><strong>Citing MicrobiomeHD</strong></p> <p>The MicrobiomeHD database and original publications for each of these datasets are described in Duvallet et al. (2017): <a href="http://dx.doi.org/10.1038/s41467-017-01973-8">http://dx.doi.org/10.1038/s41467-017-01973-8</a></p> <p>Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A., & Alm, E. J. (2017). Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. <em>Nature communications</em>, 8(1), 1784.</p> <p>If you use any of these datasets in your analysis, please cite both MicrobiomeHD (Duvallet et al. (2017)) and the original publication for each dataset that you use.</p> <p>The code used to process and analyze this data in the paper is available on github: <a href="https://github.com/cduvallet/microbiomeHD">https://github.com/cduvallet/microbiomeHD</a></p> <p><strong>Files</strong></p> <p><em>Data files</em></p> <p><strong>file-S3.nonspecific_genera.txt</strong>: Supplemental Table 3 from Duvallet et al. (2017), listing the non-specific health- and disease-associated microbes.<br> <strong>dataset_info.yaml</strong>: yaml file with additional dataset metadata.</p> <p><em>Datasets</em></p> <p>Note that MicrobiomeHD contains all 28 datasets from Duvallet et al. (2017), as well as additional datasets which did not meet the inclusion criteria for the meta-analysis presented in the paper. Additional information about the datasets included in this MicrobiomeHD release are in the original publications and the MicrobiomeHD github repo https://github.com/cduvallet/microbiomeHD, and in the file <em>dataset_info.yaml</em>.</p> <p>The sample sizes listed here reflect what was reported in the original publications. Some may have discrepancies between what is reported and what is in the actual data due to missing data, quality issues, barcode mismatches, etc.</p> <ul> <li><strong>asd_son_results.tar.gz</strong> (<em>asd_son</em>): NT: 44, ASD: 59 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0137725</li> </ul> </li> <li><strong>autism_kb_results.tar.gz</strong> (<em>asd_kang</em>): H: 20, ASD: 20 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0068322</li> </ul> </li> <li><strong>cdi_schubert_results.tar.gz</strong> (<em>cdi_schubert</em>): H: 155, nonCDI: 89, CDI: 94 <ul> <li>http://dx.doi.org/10.1128/mBio.01021-14</li> </ul> </li> <li><strong>cdi_vincent_v3v5_results.tar.gz</strong> (<em>cdi_vincent</em>): H: 25, CDI: 25 <ul> <li>http://dx.doi.org/10.1186/2049-2618-1-18</li> </ul> </li> <li><strong>cdi_youngster_results.tar.gz</strong> (<em>cdi_youngster</em>): H: 4, CDI: 19 <ul> <li>http://dx.doi.org/10.1093/cid/ciu135</li> </ul> </li> <li><strong>crc_baxter_results.tar.gz</strong> (<em>crc_baxter</em>): adenoma: 198, H: 172, CRC: 120 <ul> <li>http://dx.doi.org/10.1186/s13073-016-0290-3</li> </ul> </li> <li><strong>crc_xiang_results.tar.gz</strong> (<em>crc_chen</em>): H: 22, CRC: 21 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0039743</li> </ul> </li> <li><strong>crc_zackular_results.tar.gz</strong> (<em>crc_zackular</em>): adenoma: 30, H: 30, CRC: 30 <ul> <li>http://dx.doi.org/10.1158/1940-6207.CAPR-14-0129</li> </ul> </li> <li><strong>crc_zeller_results.tar.gz</strong> (<em>crc_zeller</em>): H: 75, CRC: 41 <ul> <li>http://dx.doi.org/10.15252/msb.20145645</li> </ul> </li> <li><strong>crc_zhao_results.tar.gz</strong> (<em>crc_wang</em>): H: 56, CRC: 46 <ul> <li>http://dx.doi.org/10.1038/ismej.2011.109}</li> </ul> </li> <li><strong>edd_singh_results.tar.gz</strong> (<em>edd_singh</em>): STEC: 28, CAMP: 71, SALM: 66, SHIG: 34, H: 75 <ul> <li>http://dx.doi.org/10.1186/s40168-015-0109-2</li> </ul> </li> <li><strong>hiv_dinh_results.tar.gz</strong> (<em>hiv_dinh</em>): H: 16, HIV: 21 <ul> <li>http://dx.doi.org/10.1093/infdis/jiu409</li> </ul> </li> <li><strong>hiv_lozupone_results.tar.gz</strong> (<em>hiv_lozupone</em>): H: 13, HIV: 25 <ul> <li>http://dx.doi.org/10.1016/j.chom.2013.08.006</li> </ul> </li> <li><strong>hiv_noguerajulian_results.tar.gz</strong> (<em>hiv_noguerajulian</em>): H: 34, HIV: 206 <ul> <li>https://doi.org/10.1016%2Fj.ebiom.2016.01.032</li> </ul> </li> <li><strong>ibd_alm_results.tar.gz</strong> (<em>ibd_papa</em>): IBDundef: 1, nonIBD: 24, UC: 43, CD: 23 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0039242</li> </ul> </li> <li><strong>ibd_engstrand_maxee_results.tar.gz</strong> (<em>ibd_willing</em>): CCD: 12, H: 35, ICD: 15, UC: 16, ICCD: 2 <ul> <li>http://dx.doi.org/10.1053/j.gastro.2010.08.049</li> </ul> </li> <li><strong>ibd_gevers_2014_results.tar.gz</strong> (<em>ibd_gevers</em>): H: 31, CD: 224 <ul> <li>http://dx.doi.org/10.1016/j.chom.2014.02.005</li> </ul> </li> <li><strong>ibd_huttenhower_results.tar.gz</strong> (<em>ibd_morgan</em>): H: 18, UC: 48, CD: 62 <ul> <li>http://dx.doi.org/10.1186/gb-2012-13-9-r79</li> </ul> </li> <li><strong>mhe_zhang_results.tar.gz</strong> (<em>liv_zhang</em>): CIRR: 25, H: 26, MHE: 26 <ul> <li>http://dx.doi.org/10.1038/ajg.2013.221</li> </ul> </li> <li><strong>nash_chan_results.tar.gz</strong> (<em>nash_wong</em>): H: 22, NASH: 16 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0062885</li> </ul> </li> <li><strong>nash_ob_baker_results.tar.gz</strong> (<em>nash_ob_zhu</em>): H: 16, NASH: 22, OB: 25 <ul> <li>http://dx.doi.org/10.1002/hep.26093</li> </ul> </li> <li><strong>ob_escobar_results.tar.gz</strong> (<em>ob_escobar</em>): OW: 10, H: 10, OB: 10 <ul> <li>https://doi.org/10.1186/s12866-014-0311-6</li> </ul> </li> <li><strong>ob_goodrich_results.tar.gz</strong> (<em>ob_goodrich</em>): OW: 322, H: 433, OB: 183 <ul> <li>http://dx.doi.org/10.1016/j.cell.2014.09.053</li> </ul> </li> <li><strong>ob_gordon_2008_v2_results.tar.gz</strong> (<em>ob_turnbaugh</em>): H: 61, OB: 219 <ul> <li>http://dx.doi.org/10.1038/nature07540</li> </ul> </li> <li><strong>ob_jumpertz_results.tar.gz</strong> (<em>ob_jumpertz</em>): H: 12, OB: 9 <ul> <li>http://ajcn.nutrition.org/content/early/2011/05/03/ajcn.110.010132</li> </ul> </li> <li><strong>ob_ross_results.tar.gz</strong> (<em>ob_ross</em>): H: 26, OB: 37 <ul> <li>http://dx.doi.org/10.1186/s40168-015-0072-y</li> </ul> </li> <li><strong>ob_wu_results.tar.gz</strong> (<em>ob_wu</em>): bmi_data: 101 <ul> <li>http://dx.doi.org/10.1126/science.1208344</li> </ul> </li> <li><strong>ob_zeevi_results.tar.gz</strong> (<em>ob_zeevi</em>): bmi_data: 870 <ul> <li>http://dx.doi.org/10.1016/j.cell.2015.11.001</li> </ul> </li> <li><strong>ob_zupancic_results.tar.gz</strong> (<em>ob_zupancic</em>): H: 167, OB: 117 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0043052</li> </ul> </li> <li><strong>par_scheperjans_results.tar.gz</strong> (<em>par_scheperjans</em>): H: 72, PAR: 72 <ul> <li>http://dx.doi.org/10.1002/mds.26069</li> </ul> </li> <li><strong>ra_littman_results.tar.gz</strong> (<em>art_scher</em>): H: 28, NORA: 44, CRA: 26, PSA: 16 <ul> <li>http://dx.doi.org/10.7554/eLife.01202</li> </ul> </li> <li><strong>t1d_alkanani_results.tar.gz</strong> (<em>t1d_alkanani</em>): T1D: 21, H: 55, T1D_new-onset: 35 <ul> <li>http://dx.doi.org/10.2337/db14-1847</li> </ul> </li> <li><strong>t1d_mejialeon_results.tar.gz</strong> (<em>t1d_mejialeon</em>): T1D: 21, H: 8 <ul> <li>http://dx.doi.org/10.1038/srep03814</li> </ul> </li> </ul> <p><strong>Version changes</strong></p> <p>Version 3</p> <ul> <li>added missing ob_escobar metadata</li> <li>added ob_jumpertz, ob_zeevi, and ob_wu</li> <li>added README.txt files to all folders, with info about data downloading and processing steps</li> <li>removed deprecated quality_control folders from all dataset results</li> <li>changed Supplemental File S3 to the most updated version of non-specific genera (as published in Duvallet et al 2017)</li> </ul> <p>Version 2</p> <ul> <li>added crc_zhu and ob_escobar datasets</li> <li>added list of core genera and dataset_info.yaml</li> </ul>
Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease.
<p>Contains all intermediate data files for the article <strong>Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease, </strong>Quinn-Bohmann et al. 2024. </p> <p>NP and OP refers to nasopharyngeal or oropharyngeal samples, respectively. As some study identifiers are duplicated across these groups, qiime_NP and qiime_OP should be unzipped in distinct directories. The same applies for metadata data files. </p> <p>Analyses of these data can be found at https://github.com/Gibbons-Lab/2023_URTmetaanalysis/tree/main. </p>
Fig. 3 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 3. The correlation between the forest coverage and air pollution indicators.
Fig. 2 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 2. The correlation between the forest and human health indicators.
Fig. 1 in Detection of rodent-borne parasitic pathogens of wild rats in Serdang, Selangor, Malaysia: A potential threat to human health
Fig. 1. The study area, Universiti Putra Malaysia showing the study sites circled in black.
Fig. 2 in Epidemiology of Trichinella infection in wild boar from Spain and its impact on human health during the period 2006-2019
Fig. 2. Occurrence of Trichinella infection in wild boar in Spain from 2006 to 2019.
Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility testing and human health risks - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Hiller E., Faragó T., Kolesár M., Filová L., Mihaljevič M., Jurovič L., Demko R., Mchlica A., Štefánek J., Vítková M. (2024): Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility and human health risks. <em>Chemosphere</em> <strong>362</strong>, 142677. DOI: 10.1016/j.chemosphere.2024.142677</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Data from: Heterogeneous zonal impacts of climate change on a wide hyperendemic area of human and animal fascioliasis assessed within a One Health action for prevention and control
Open the record for dataset details and reuse information.
Geo-gender-based analysis of human health data
<p>Data supporting analysis in "Geo-gender-based analysis of human health: the presence of cut flower farms can attenuate pesticide exposure in communities, yet women remain most vulnerable"</p>
Imptox Public Workshop Micro- Nanoplastics & Human Health: Insights from Science and Research (pt2)
<p>Experts gather at the Imptox workshop to discuss the emerging understanding of micro- and nanoplastics' impact on human health. On Friday, March 24th, 2023, the successful Imptox public workshop attracted a large audience discussing the potential health impacts of micro- and nanoplastics with experts from all over Europe. The Imptox project has received funding from the EU’s H2020 framework program for research and innovation under grant agreement n. 965173</p>
Imptox Public Workshop Micro- Nanoplastics & Human Health: Insights from Science and Research (pt3)
<p>Experts gather at the Imptox workshop to discuss the emerging understanding of micro- and nanoplastics' impact on human health. On Friday, March 24th, 2023, the successful Imptox public workshop attracted a large audience discussing the potential health impacts of micro- and nanoplastics with experts from all over Europe. The Imptox project has received funding from the EU's H2020 framework program for research and innovation under grant agreement n. 965173. </p>
Use of a Novel Synbiotic to Change Human Gut Bacteria and Improve Health in Obese Adults
ClinicalTrials.gov study NCT02355210. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Human Pilot Study - HA35 (Hyaluronan Molecular Weight 35) Dietary Supplement for Promoting Intestinal Health
ClinicalTrials.gov study NCT02867605. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Targeting Cellular Senescence With Senolytics to Improve Skeletal Health in Older Humans
ClinicalTrials.gov study NCT04313634. IPD Sharing: NO. Countries: 1. Publications: 2.
Umbrella review data of colour-associated bioactive pigments found in fruit and vegetables, compared to placebo or low intakes, on human health outcomes relevant to public health
Open the record for dataset details and reuse information.
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.