Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
152
datasets available to search
ShareScore release 0.9.0
Dataset results
152 results for “Smoke exposure”
Inter-Chemical Correlation results for the study: HHEARx2018-2120 (The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children)
Title: The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children <br>Species: Homo sapiens <br>Number of samples: 1090 <br>Number of named analytes: 12 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=42 <br>
Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure (Data)
<p>Origin projects, figures and LabVIEW software used for the article "Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure", published in <em>Sensors </em>on 14 Jun 2021.</p>
Datasets and R code associated with: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires
<p>The attached code and csv files accompany the manuscript titled: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires by Duncan et al. accepted for publication in the journal GeoHealth in September 2023. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Project%20wildfire%20data.csv">Project wildfire data.csv</a> contains a subset wildfire data obtained from:</p> <p>Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6</p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Modeled%20PM2.5%20data_clean.csv">Modeled PM2.5 data_clean.csv</a> includes modeled PM<sub>2.5</sub> concentrations used to make Figure 2. Model results can be obtained from <a href="https://www.epa.gov/hesc/rsig-related-downloadable-data-files">EPA's Fused Air Quality Surfaces Using Downscaling Tool</a>. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/plot_Modeled.R">plot_Modeled.R</a> contains the R code to generate this figure. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/ORs%20all%20Counties.csv">ORs all Counties.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figures 3, 4, and 5.<a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/PM2.5%2035ug-m3%20ORs.csv">PM2.5 35ug-m3 ORs.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figure S1. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Wildfire_Forest.R">Wildfire_Forest.R</a> contains the R code to generate these figures. </p>
Lake exposure to wildfire smoke in North America, 2019-2021
Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing ash within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and underrecognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019-2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes >=10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.
Impact of exposure to second-hand smoke during childhood on blood DNA methylation
<p>Meta-analysis results for CpGs associated with household postnatal exposure to second hand smoke (SHS). Cohorts order are GenR, PIAMA, HELIX, BAMSE_Medall, BAMSE_EpiGene, and ALSPAC. Effect size represents the change in DNAm by number of household smokers (from 0 smokers to 2 smokers or more). Gene and CpG Island relative position are indicated (TSS200 is the region from Transcription start site (TSS) to − 200 nt upstream of TSS; TSS1500 covers − 200 to − 1500 nt upstream of TSS; S_Shore corresponds to the 2 kb regions immediately downstream of the CpG island; N_Shore corresponds to the 2 kb regions immediately upstream of the CpG island). Chr stands for Chromosome, SE stands for standard error, I2 stands for heterogeneity index, and FDR stands for False Discovery Rate. (DNA data obtained through Iluumina 450K array profiling)</p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
GeoScan and Remote Geo Smoking Study: Neural and Behavioral Correlates of Smokers' Exposure to Retail Environments
ClinicalTrials.gov study NCT04279483. IPD Sharing: YES. Countries: 1. Publications: 99.
An Intervention to Reduce Second Hand Smoke Exposure Among Pediatric Emergency Patients
ClinicalTrials.gov study NCT02531594. IPD Sharing: NO. Countries: 1. Publications: 8.
Isradipine Enhancement of Virtual Reality Cue Exposure for Smoking Cessation
ClinicalTrials.gov study NCT03083353. IPD Sharing: YES. Countries: 1. Publications: 1.
Smoking Topography and Harm Exposure in Menthol Cigarettes
ClinicalTrials.gov study NCT01329263. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reduction of Exposure, Inflammation, and Oxidative Stress Following At Least 2 Years of Switching to THS Use Compared to Cigarette Smoking
ClinicalTrials.gov study NCT05385055. IPD Sharing: NO. Countries: 6. Publications: 1.
New Mexico PM2.5 concentrations by ZIP code for 4 smoke exposure estimates
Open the record for dataset details and reuse information.
Cigarette smoke exposure as a potential risk factor for sleep problems in pregnant women
<p><strong><span>Abstract</span></strong></p> <p><span>Cigarette smoking and exposure to cigarette smoke during pregnancy have detrimental effects on the health of expectant mothers, increasing the likelihood of respiratory diseases or infections. Due to the stimulant effect of smoking, the negative effect on diurnal rhythm and sleep is also observed. Sleep quantity and quality are directly related to health and well-being, especially during times of excess stress, such as the perinatal period. This prospective study aimed to examine the relationship between cigarette smoking and exposure to cigarette smoke during pregnancy and sleep patterns in pregnant women. An online survey was conducted among Polish women from May 2020 to September 2021, during the COVID-19 pandemic. Participants reported smoking during pregnancy and rated sleep in three categories (difficulty falling asleep, waking up too early, and difficulty staying asleep). Data from 3365 mothers aged 18-43 (mean 30.7; SD 3.87) were analyzed using multivariate logistic regression. Passive smoking was associated with one of the three variables used to assess sleep problems (waking up too early) while controlling for a range of individual variables (age, education, place of residence, satisfaction with the woman's life, and economic situation, pregnancy complications, levels of state anxiety and depressive symptoms, trimester of pregnancy, nausea or vomiting during pregnancy, and COVID-19 infections). Interestingly, results showed no evidence of an association between active smoking and sleep difficulties. Our findings suggest that passive smoking is a potential risk factor for sleeping problems in pregnant women, especially in the aspect of waking up too early. These results are worth considering when formulating pro-health measures for pregnant women and their close ones.</span></p>
Supplementary tables for the paper: "Comprehensive computational analysis via Adverse Outcome Pathways and Aggregate Exposure Pathways in exploring synergistic effects from radon and tobacco smoke on lung cancer."
<p><strong>Authors</strong>:<br>Thomas Jaylet, Vinita Chauhan, Laura Mezquita, <em>Nadia Boroumand</em><em>, </em>Olivier Laurent, <em>Karine Elihn</em><em>, Lovisa Lundholm</em><em>, </em>Olivier Armant, Karine Audouze</p>
Urinary metabolic dysregulations associated to thirdhand smoke exposure in mice: Implications for human urine biomonitoring
<p>This dataset contains all raw LC-MS and GC-MS data of urine extracts from mice exposed to thirdhand smoke (THS) used in the paper<em> Urinary metabolic dysregulations associated to thirdhand smoke exposure in mice: Implications for human urine biomonitoring</em>.</p> <p><strong>Objective:</strong> The goal of this study is to characterize the THS-induced molecular alterations in urine using an LC-MS and GC-MS based untargeted metabolomic approaches. We have selected urine as a target matrix because it is a non-invasive valuable diagnostic biofluid, rich in metabolites that reflect dysregulations of all biochemical pathways, which also has been one of the most widely used biofluid to assess human exposure to tobacco smoke.</p> <p><strong>Experimental groups:</strong> Urine extracts were from two mice groups: control group (CTRL, n=5), which was never exposed to THS; and THS exposed group (THS, n=5), which was exposed to THS from weaning (three weeks of age) to 24 weeks weeks without exposure to SHS during the study.</p> <p><strong>Sample extraction:</strong> Metabolites were extracted using a mixture of methanol and Milli-Q water. In the case of GC-MS, samples were pre-treated with urease prior metabolite extraction and dried under nitrogen, lyophilized, and derivatized after extraction. Quality control samples (QCs) were prepared by pooling equal volumes of each extract and analysed every five samples to ensure data quality and reproducibility.</p> <p><strong>LC-MS analysis: </strong>LC-MS urine extracts were analysed using a 1290 UHPLC system coupled to a 6550 quadrupole time of flight (QTOF) mass spectrometer from Agilent Technologies operated in ESI+ mode. Chromatographic separation was conducted on a ACQUITY UPLC HSS T3 C18 column (1.8 µm, 150 × 2.1 mm, Waters). MS/MS was performed in targeted mode, and the instrument was set to acquire over the m/z range 50-1000, with a default iso width of 4 m/z. The collision energy was fixed at 20 V.</p> <p><strong>GC-MS analysis:</strong> GC-MS urine extracts were analysed using a 7890A gas chromatograph coupled to a 7200 QTOF mass spectrometer from Agilent Technologies. The MS was operated in the electron impact ionization mode at 70 eV, with a fixed emission current of 20 µA.</p> <p><strong>Files are named following this code:</strong></p> <ol> <li><strong>Folders: </strong>mzDATA: contains GC-MS and LC-MS raw data in mzXML format; LC-MSMS: contains LC-MS/MS data used for identification of metabolites in mzXML format.</li> <li><strong>Analytical approaches:</strong> GCMS: data from GC-MS urine extracts; LCMS: data from LC-MS urine extracts.</li> <li><strong>Samples: </strong>CTRL: control group; THS: THS exposed group.</li> </ol> <p><strong>Technical information</strong></p> <ol> <li>Sample extraction and data acquisition (single date, range, approximate date): 2016-2017</li> <li>Data analysis (single date, range, approximate date): 2017-2021</li> <li>Geographic location of data collection: <ul> <li>Mice exposure: Department of Molecular, Cell and Systems Biology, University of California, Riverside CA 92521, U.S.A.</li> <li>Sample extraction and data acquisition: Universitat Rovira i Virgili, Departament d’Enginyeria Electrònica, Elèctrica i Automàtica, Tarragona, Spain.</li> </ul> </li> <li>Information about funding sources that supported the collection of the data: This research was funded by the European Union’s Horizon 2020 research and innovation pro-gramme under the Marie Sklodowska-Curie grant agreement No. 660034; the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya through C.M.’s predoctoral grant number 2020 FI_B2 00118; the Spanish Ministry of Science & Innovation through N.R.’s Juan de la Cierva Incorporación grant No. (IJCI- 2015-23158); N.R.’s Miguel Servet contract (CP19/00060) from Instituto de Salud Carlos III, co-financed by Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, “Una manera de hacer Europa”; and the Tobacco-Related Disease Research Program (TRDRP) of the University of California under projects 22RT- 0121 and 23DT-0103.</li> </ol>
Benefits of a Cognitive Behavioral Therapy Intervention for Smoking Cessation Supported by Virtual Reality Smoking Cue Exposure
ClinicalTrials.gov study NCT03707106. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cognition in Older Breast Cancer Survivors: Treatment Exposure, APOE and Smoking History
ClinicalTrials.gov study NCT02122107. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Characterization of Nicotine Exposure and Urge-to-Smoke Following Use of Electronic and Conventional Cigarettes
ClinicalTrials.gov study NCT02210754. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reducing Second-hand Smoke Exposure Among Young Children in Rural China
ClinicalTrials.gov study NCT03567512. IPD Sharing: NO. Countries: 1. Publications: 4.
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.