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2,518 results for “smoking”
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>
Inter-Chemical Correlation results for the study: HHEARx2017-1863 (Investigating Overweight/Obesity and Poor Diet as Susceptibility Factors for Secondhand Smoke in Childhood Asthma)
Title: Investigating Overweight/Obesity and Poor Diet as Susceptibility Factors for Secondhand Smoke in Childhood Asthma <br>Species: Homo sapiens <br>Number of samples: 960 <br>Number of named analytes: 37 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=29 <br>
Dataset for: Smoking does not accelerate leukocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts
<p>Summary dataset (.csv file) and R script (.R file) for the manuscript entitled:</p> <p>Smoking does not accelerate leukocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts.</p> <p>The column names are explained at the beginning of the R script.</p> <p> </p>
MetFrag Local CSV: CompTox (7 March 2019 release) Smoking MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p><a>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</a></p> <p>The Smoking MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>- PubMedNeuro: the <a href="https://comptox.epa.gov/dashboard/chemical_lists/LITMINEDNEURO">LITMINEDNEURO</a> list with total PubMed reference counts in the column</p> <p>- <a href="https://comptox.epa.gov/dashboard/chemical_lists/CIGARETTES">CIGARETTES</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/INDOORCT16">INDOORCT16</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SRM2585DUST">SRM2585DUST</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SLTCHEMDB">SLTCHEMDB</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/THSMOKE">THSMOKE</a> as suspect lists.</p> <p>First release (July 2019, not archived) contained a smaller subset of the SRM2585DUST list.</p>
Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis
<p>Underlying dataset and analysis tests of the results described in the article "Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis".</p>
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
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>
S52 | THSMOKE | Thirdhand Smoke (THS) Compounds
<p>This is the collection associated with list S52 THSMOKE on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S52 </p> <p>THSMOKE</p> <p><strong>Thirdhand Smoke (THS) Compounds</strong></p> <p>THSMOKE XLSX , CSV (06/05/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/thsmoke">THSMOKE List</a></p> <p>THSMOKE InChIKeys (06/05/2019)</p> <p>Thirdhand Smoke (THS, the tobacco-related gases and particles that become embedded in materials), suspect list compiled by Sonia Torres and Noelia Ramirez (IISPV-URV) and Emma Schymanski (LCSB)</p>
S81 | THSTPS | Thirdhand Smoke Specific Metabolites
<p>This is the collection associated with list S81 THSTPS Thirdhand Smoke Specific Metabolites on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Data compiled by Carla Merino and colleagues, URV, Spain.</p> <p> </p> <p>v0.1.1: fixed formatting issues in CSV files (spacing around CIDs, name errors, char errors in refs)</p>
Study of wildfire smoke effects on ecosystem metabolism in 10 California lakes (2018, 2020, 2021)
This dataset was collected as part of a large-scale study to assess impacts of smoke cover on gross primary production (GPP) and ecosystem respiration (R) in California lakes. The 10 study lakes span large gradients in elevation, size, nutrient concentrations, and water clarity. They include 5 ponds and lakes in Sequoia National Park, Lake Tahoe, Dulzura Lake, Clear Lake, Castle Lake, and a site in the Sacramento-San Joaquin River Delta. Metabolic rates in the upper mixed layer of each lake was estimated from hourly in-situ sensor data during the three smokiest years in California since 2006 (2018, 2020, 2021). The dataset includes daily estimates of GPP and R, mean daily values of variables used in metabolism models (water temperature, dissolved oxygen, mixed layer depth, photosynthetically active radiation, wind speed), and mean daily values of metrics related to smoke cover (shortwave radiation, PM2.5, smoke density derived from remote-sensing).
FIRESENSE database of videos for flame and smoke detection
<p>The FIRESENSE database, developed within the FP7-ENV-244088<br> ''FIRESENSE - Fire Detection and Management through a Multi-Sensor Network for<br> the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme<br> Weather" project contains videos for testing flame and smoke detection algorithms.</p> <p>Specifically:</p> <p>a) for flame detection 11 positive and 16 negative videos are provided, while</p> <p>b) for smoke detection, 13 positive and 9 negative videos are provided.</p> <p>Results using this database are presented in many papers, including:</p> <ol> <li><strong>K. Dimitropoulos, P. Barmpoutis, N. Grammalidis, </strong>"<em>Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection</em>", IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Vol. 25, No. 2, February 2015, pp. 339-351.</li> </ol> <p> </p> <p> </p>
Smoke designations for eastern Kansas monitoring sites during March-May 2022
<p class="Abstract">Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM<sub>2.5</sub>). PM<sub>2.5</sub> can penetrate deep into the lungs and harm cardiovascular and respiratory systems. The Flint Hills region of Kansas experiences extensive prescribed burning each spring (March - May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM<sub>2.5</sub> concentrations to the prescribed burning in the Flint Hills. To determine PM<sub>2.5</sub> increases from local burning, we used low-cost PM<sub>2.5</sub> sensors (PurpleAir) and satellite observations. The Flint Hills were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hour median PM<sub>2.5</sub> increased by 5.2 µg m<sup>-3</sup> on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. We found the Flint Hills to be the most smoke PM<sub>2.5</sub> impacted region compared to the surrounding area across satellite products and in-situ measurements. </p>
Climate and habitat type interact to influence contemporary dispersal potential in Prairie Smoke (Geum triflorum)
<p>Understanding dispersal potential, or the probability a species will move a given distance, under different environmental conditions is essential to predicting species' ability to move across the landscape and track shifting ecological niches. Two important drivers of dispersal ability are climatic differences and variation in local habitat type. Despite the likelihood these global drivers act simultaneously on plant populations, and thus dispersal potential is likely to change as a result, their combined effects on dispersal are rarely examined. To understand the effect of climate and varying habitat types on dispersal potential, we studied <em>Geum triflorum</em> - a perennial grassland species that spans a wide range of environments, including both prairie and alvar habitats. We explored how the climate of the growing season and habitat type (prairie vs alvar) interact to alter dispersal potential. We found a consistent interactive effect of climate and habitat type on dispersal potential. Across prairie populations, an increased number of growing degree days favored traits that increase dispersal potential, or the probability of dispersing farther distances. However, for alvar populations, dispersal potential tended to decrease as the number of growing degree days increase. Our findings suggest that under continued warming, populations in prairie habitats will benefit from increased gene flow, while alvar populations will become increasingly segregated, with reduced potential to track shifting fitness optima.</p>
Physical features and other characteristics of smoke from cigarettes sold in Australia
Open the record for dataset details and reuse information.
Fire, smoke and gas leakage detection Dataset
<p>This is an open free dataset associated with fire, smoke and gas leakage detection captured from the Smart Building sensor system, namely SB112, with edge computing and Next Generation (NG) 112 emergency call capabilities. In case you use or process this dataset please refer to your work/study the following paper in which the SB112 is described in detail. </p> <p>Maltezos, E.; Petousakis, K.; Dadoukis, A.; Karagiannidis, L.; Ouzounoglou, E.; Krommyda, M.; Hadjipavlis, G.; Amditis, A. A Smart Building Fire and Gas Leakage Alert System with Edge Computing and NG112 Emergency Call Capabilities. <em>Information</em> 2022, <em>13</em>, 164. <a href="https://doi.org/10.3390/info13040164">https://doi.org/10.3390/info13040164</a></p> <p>The excel files contain data from 08/03/2022 to 11/03/2022. From 08/03 to 10/03/2022 the data is gathered from continuous operation without physical triggering. On 11/03/2022 a physical trigger of the sensors was performed by:</p> <ul> <li>Burning a piece of paper in a distance of 15 cm - 20cm to the Smoke and CO sensors.</li> <li>By exposing the LPG/CNG sensors to lighter gas in a distance of 5 cm - 10cm.</li> <li>By exposing the Flame/IR sensor to a moving flame from a lighter in a distance of 15 cm - 100cm.</li> </ul> <p>In each sheet of the excel files there are four columns: i) the datetime that a measurement was captured, ii) the type of the sensor, iii) the unit of the measurement, and iv) the data value.</p> <p><strong>Acknowledgments: </strong>This work is a part of the S4AllCities project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883522. Content reflects only the authors’ views; the Research Executive Agency (REA)/European Commission is not responsible for any use that may be made of the information it contains.</p>
Supplementary Tables for "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort"
<p>Supplementary Tables from "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort".</p>
Figure 6. The mental spaces set up by the sentence Mary thinks that John smokes.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>The proper nouns Mary and John setup a base space (B). By the help of background<br> knowledge and activated frames we know that they are names of female and male humans. Not<br> having access to the previous discourse, we also consider their existence presupposed. The SVSB<br> Mary thinks that sets up a belief space (L) relative to space B (Figure 6). The identity connector<br> maintains the referential link between elements a and a ׳ both referring to the same person.</p>
Data from: Wildfire smoke impacts the body condition and capture rates of birds in California
<p>Despite the increased frequency with which wildfire smoke now blankets portions of world, the effects of smoke on wildlife, and birds in particular, are largely unknown. We used two decades of banding data from the San Francisco Bay Bird Observatory to investigate how fine particulate matter (PM<sub>2.5</sub>) – a major component and indicator of wildfire smoke – influenced capture rates and body condition of 21 passerine or near-passerine bird species. Across all study species, we found a negative effect of acute PM<sub>2.5</sub> exposure and a positive effect of chronic PM<sub>2.5</sub> exposure on avian capture rates. Together, these findings are indicative of decreased bird activity or local site removal during acute periods of wildfire smoke, but increased activity or site colonization under chronic smoke conditions. Importantly, we also observed a negative relationship between chronic PM<sub>2.5</sub> exposure and body mass change in individuals with multiple captures per season. Our results indicate that wildfire smoke likely influences the health and behavior of birds, ultimately contributing to a shift in activity and body condition, with differential short-term versus long-term impacts. Although more research is needed on the mechanisms driving these observed changes in bird health and behavior, as well as validation of these relationships in more areas, our results suggest that wildfire smoke is a potentially frequent large-scale environmental stressor to birds that deserves increasing attention and recognition.</p>
Fig. 2 in Assessment Of The Influence Of Teratogenic Activity Of Cigarette Smoke On Drosophila Melanogaster
Fig. 2. Frequency of abdominal tergite Fig. 4. Growth of imaginal disks of fruit fly abnormality morphoses in wild type fruit flies (Drosophila melanogaster) in embryonic and (Drosophila melanogaster) depending on postembryonic stages of development the"Elita" cigarette smoke dose and the age of the larvae (hours since the eggs were laid)
Fig. 1 in Assessment Of The Influence Of Teratogenic Activity Of Cigarette Smoke On Drosophila Melanogaster
Fig. 1. Frequency of abdominal tergite Fig. 3. Frequency of abdominal tergite abnormality (morphoses) in w+/w and w+/Y fruit abnormality morphoses in wild type fruit flies flies (Drosophila melanogaster) depending on the (Drosophila melanogaster) depending on "Prima HEBO" cigarette smoke dose and the age the"Elita" cigarette smoke dose and the age of of the larvae (hours since the eggs were laid). the pupae (hours since the eggs were laid)
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.