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Fig. 3 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 3. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polysterene) smoke and larvae age (hours after eggs are laid)

Fig. 4 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 4. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polysterene) smoke and pupae age (hours after eggs are laid)

Fig. 9 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 9. Abdominal tergite anomalies in wild type Drosophila melanogaster in the result of treating pupae with the smoke of burning polysterene; 1 – females, 2 – males

Fig. 2 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 2. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polyethyleneterephtalane) smoke and pupae age (hours after eggs are laid)

Fig. 1 in Evaluation Of Teratogenic Activity Of The Smoke Of Burning Combustible Plastic Influencing The Drosophila Melanogaster

Fig. 1. Amount of abdominal tergite anomalies in wild type Drosophila melanogaster depending on the dose of burning plastic (polyethyleneterephtalane) smoke and larvae age (hours after eggs are laid)

opencc-by-4.0Aug 2024View details →

Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry

These are a collection of EWAS summary statistics for the following publication: Deng Wei Q, Cawte Nathan, Campbell Natalie, Azab Sandi M, de Souza Russell J, Lamri Amel, Morrison Katherine M, Atkinson Stephanie A, Subbarao Padmaja, Turvey Stuart E, Moraes Theo J, Teo Koon K, Mandhane Piush, Azad Meghan B, Simons Elinor, Pare Guillaume, Anand Sonia S (2024) Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry eLife 13:RP93260, https://doi.org/10.7554/eLife.93260.3 1. CHILD_450K_R1_March2024_mateversmk_Regression_CpGWide.csv Maternal smoking using ever definition in CHILD (HM450K array). 2. CHILD_450K_R1_March2024_matsmoke_Regression_CpGWide.csv Maternal smoking using current smoking definition in CHILD (HM450K array). 3. CHILD_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv Maternal smoking exposure (hours per week) in CHILD (HM450K array). 4. FAMILY_EPIC_R1_March2024_mateversmk_Regression_CpGWide.csv Maternal smoking using ever definition in FAMILY (customized EPIC array). 5. FAMILY_EPIC_R1_March2024_matsmoke_Regression_CpGWide.csv Maternal smoking using current smoking definition in FAMILY (customized EPIC array). 6. FAMILY_EPIC_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv Maternal smoking exposure (hours per week) in FAMILY (customized EPIC array). 7. START_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv Maternal smoking exposure (hours per week) in START (HM450K array). 8. mateversmk_meta_annot_R1.csv Meta-analyzed european EWAS of maternal smoking using ever definition. 9. matsmoke_meta_annot_R1.csv Meta-analyzed european EWAS of maternal smoking using current smoking definition. 10. mblsmkexp_meta_annot_R1.csv Meta-analyzed european EWAS of maternal smoking exposure (hours per week).

opencc-by-4.0Aug 2024View details →

Data from: Cross-sectional personal network analysis of adult smoking in rural areas

This data package, titled Data from: Cross-sectional personal network analysis of adult smoking in rural areas, includes several files. First, there are annonymized raw data files in .rds file format (ego_data.rds & alter_data.rds). Second, there is the R code that allow the replication of various statistical analyses. Interested parts may consult the R code as .pdf file format (Supplementary_Material_R_Code.pdf), .Rmd file format (that can be run to create the .pdf file format) and the .R file format (that can be accesed with R and RStudio). Moreover, the labels files are useful for recreating the Supplementary Material pdf file.  Readers should know that this dataset corresponds to the study (paper) Cross-sectional personal network analysis of adult smoking in rural areas.  The ego_data.rds file includes 20 variables by 76 observations (respondents) while the alter_data.rds file includes 46 variables by 1681 observations (social contacts). We collected this information by deploying a personal network analysis research design. Initially, we interviewed 83 respondents (dubbed egos). Due to missing data, we kept in the analysis 76 egos and dropped seven respondents. We recruited the respondents using a link-tracining sampling framework. We started from a number of six seeds. We interviewed the seeds then we asked them to recommend other people in the study. We continued in a referee-referral fashion until 83 interviews were completed. The study was performed in a small rural Romanian community (4124 residents): Lerești (Argeș county).  Our study was carried out in accordance with the recommendations, relevant guidelines, and regulations (specifically, those provided by the Romanian Sociologists Society, i.e., the professional association of Romanian sociologists). The research was performed in accordance with the Declaration of Helsinki. The research protocol was approved by a named institutional/licensing committee. Specifically, the Ethics Committee of the Center for Innovation in Medicine (InoMed) reviewed and approved all these study procedures (EC-INOMED Decision No. D001/09-06-2023 and No. D001/19-01-2024). All participants gave written informed consent. The privacy rights of the study participants were observed. The authors did not have access to information that could identify participants. Face-to-face interviews were collected between September 13 – 23, 2023, in Lerești, Romania. After each interview, information that could identity the participants were anonymized. Before conducting the interview, we provided each participant with a dossier containing informative materials about the project's objectives, how the data would be analyzed and reported, and their participation rights (e.g., the right to withdraw from the project at any time, even after the interview was completed). All study participants gave their written informed consent prior to enrolment in the study. The variables in the ego_data.rds file are as follows: (1) "networkCanvasEgoUUID" (unique alpha numeric code for each observation);  (2) "ego_age" (the age of each study participant);  (3) "ego_age.cen" (the age of each study participant, centered);  (4) "ego_educ_b" (the education of each ego, binary);  (5) "ego_educ_f" (the education of each ego, educational achievement);  (6) "ego_marital.s_f" (the marital status of each ego); (7) "ego_occupation.cat2_f" (the occupation of each ego);  (8) "ego_occupation_b" (the occupation of each ego, unemployed vs employed);  (9) "ego_relstatus_b" (whether the ego is in a relationship or not);  (10) "ego_sex_f" (the sex of the ego assigned at birth; male & female);  (11) "ego_sex_n" (the sex of the ego assigned at birth; 0 = male & 1 = female);   (12) "ego_smk_status_b1" (smoking status: 1 smoking, 0 others); (13) "ego_smk_status_b2" (smoking status: 1 former smoker, 0 others);   (14) "ego_smk_status_b3" (smoking status: 1 not a smoker, 0 others);  (15) "ego_smkstatus_f"  (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);  (16) "ego_smoking_3cat"  (smoking status: non-smoker, former smoker, smoker); (17) "net.size" (number of social contacts, alters, that were elicited by an ego); (18) "net.components" (number of strong components in the personal network); (19) "net.deg.centralization" (personal network degree centralization); (20) "net.density" (personal network density).  The variables in the alter_data.rds file are as follows: (1) "alter_age" (the age of the alter);  (2) "alter_age.cen" (the age of the alter - centered);  (3) "alter_btw" (alter's betweenness score);  (4) "alter_btw.cen" (alter's betweenness score - centered);  (5) "alter_deg" (alter's degree score);  (6)  "alter_deg.cen" (alter's degree score - centered);   (7) "alter_educ_b" (alter's education);  (8) "alter_educ_f"  (alter's education);  (9) "alter_marital.s_f" (alter's marital status);  (10) "alter_relstatus_b" (alter's marital status - binary variable); (11) "alter_sex_f" (alter's sex assigned at birth); (12) "alter_sex_n" (alter's sex assigned at birth; 1 - female; 0 - male);  (13) "alter_smk_status_b1" (alter's smoking status; 1 smoker, 0 others); (14) "alter_smk_status_b2" (alter's smoking status; 1 former smoker, 0 others); (15) "alter_smk_status_b3" (alter's smoking status; 1 non-smoker, 0 others); (16) "alter_smoking_3cat" (alter's smoking status: three categories - smoker, non-smoker, former smoker); (17) "assortativity_score_fsmoker" (assortativity score for alter, former smoker); (18) "assortativity_score_nsmoker" (assortativity score for alter, non-smoker); (19) "assortativity_score_smoker" (assortativity score for alter, smoker); (20) "ego.alter_meet_f" (ego's meeting frequency with alter);  (21) "ego_alter_meet_b" (ego's meeting frequency with alter, binary variable); (22) "ego.alter_meet_n" (ego's meeting frequency with alter, numerical codes); (23) "alter_rel.w.ego_f" (type of alters in an ego's network); (24) "networkCanvasUUID" (alpha numeric code for alter); (25) "networkCanvasEgoUUID" (alpha numeric code for ego);  (26) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);  (27) "ego_smoking_3cat" (three categories, smoking status: former smoker, non-smoker, smoker); (28) "ego_type_fsmk" (former smoking egos by type of ego-alter relationship); (29) "ego_type_nsmk" (non smoking egos by type of ego-alter relationship); (30) "ego_type_smk" (smoking egos by type of ego-alter relationship); (31) "ego_sex_f" (ego's sex, binary); (32) "ego_sex_n" (ego's sex, numerical code, 1 female, 0 male);  (33) "ego_educ_b" (ego's education, binary variable) (34) "ego_age" (ego's age) (35) "ego_age.cen" (ego's age, centered) (36) "ego_relstatus_b" (ego's marital status, binary variable) (37) "ego_occupation_b" (ego's employment status, binary variable) (38) "net.components" (number of strong components in the personal network) (39) "net.deg.centralization" (degree centralization score in the personal networ) (40) "net.density" (density score in the personal network) (41) "prop_fsmokers" (proportion of former smokers in the personal network - alters) (42) "prop_fsmokers.cen" (proportion of former smokers in the personal network, centered- alters) (43) "prop_nsmokers" (proportion of non-smokers in the personal network- alters) (44) "prop_nsmokers.cen" (proportion of non-smokers in the personal network, centered- alters) (45) "prop_smokers" (proportion of smokers in the personal network- alters) (46) "prop_smokers.cen" (proportion of smokers in the personal network, centered- alters)

opencc-by-4.0Oct 2021View details →

Data for paper publication 'Important role of stratospheric injection height for the distribution and radiative forcing of smoke aerosol from the 2019/2020 Australian wildfires'

This repository contains version 2.0 data from the aerosol-climate simulations performed with the ECHAM6.3-HAM2.3 model and aerosol lidar profiles, as presented in the paper publication by Heinold et al.: Important role of stratospheric injection height for the distribution and radiative forcing of smoke aerosol from the 2019/2020 Australian wildfires, submitted to Atmos. Chem. Phys. For details, please refer to the enclosed data description (README) file.

opencc-by-4.0Jan 2023View details →

Projected Smoke Impacts from Increased Prescribed Fire Activity (PHIRE) Smoke Modeling Datasets

This dataset support the publication, Kramer et al., 2023, submitted to JGR: Atmospheres on 01-13-2023 This project was supported by a grant from the CAL FIRE Forest Health Research Program (Agreement #8GG19803), as part of California Climate Investments. California Climate Investments is a statewide program that puts billions of Cap-and-Trade dollars to work reducing greenhouse gas emissions, strengthening the economy, and improving public health and the environment—particularly in disadvantaged communities. This study has not been reviewed by the funding agency and may not represent their opinion and interpretation of the findings. We also would like to acknowledge all participating organizations in the PHIRE team: The California Department of Public Health- Sumi Hoshiko; The Sequoia Foundation- Jeff Sanchez; U.S. EPA- Ana G. Rappold; Michigan Technological University- Nancy French; the U.S. Forest Service- Leland Tarnay; and Sonoma Technology- Fred Lurmann, ShihMing Huang, Samantha J. Kramer, Crystal McClure, and Melissa Chaveste. Beyond the authors of this publication, we would also like to acknowledge Sonoma Technology team members Kenneth J. Craig and Anondo Mukherjee for their contributions.

opencc-by-4.0Jul 2022View details →

Smoking-induced subgingival dysbiosis precedes clinical signs of periodontal disease

Using 16 rRNA sequencing, a total of 233 subgingival sites from 8 smokers and 9 non-smokers over 6-12 months(804 subgingival samples total) were analyzed to study subgingival microbiome dysbiosis in smokers over time.

opencc-by-4.0May 2023View details →

The imbalance of wanting and liking contribute to a bias of internal attention towards positive consequences of tobacco smoking

Data set used for the publication entitled "The imbalance of wanting and liking contribute to a bias of internal attention towards positive consequences of tobacco smoking". Variable names: sex: gender of respondent; classes: smoker profile; attempt: quit attempt in the past 12 months; imoportance: importance attributed to quit smoking; plan: whether they plan to quit smoking; prob1-prob3: how likely they will not smoke (1) within a year, (2) within 5 years, (3) within 10 years; time: when they plan to quit; nicotine_dependence: Fagerstöm score; smoking_freq: frequency of smoking; smokng_quantity: number of cigarettes on a day they smoke; wb: wanting-before; wd: wanting-during; wa: wanting-after; lb:liking-before; ld: liking-during; la: liking-after; ist-before: wd minus ld; ist_during: wd-ld; ist_after: wa-la; i1-14: importance scores of smoking consequences; regret: regret about start smoking; ease: assumed difficulity of quitting; environment: amont of smokers around; people: how much smoking bothers others around. For more info please email domonkos.file@gmail.com

opencc-by-4.0Jun 2023View details →

Evaluating the implementation of adult smoking cessation programs in community settings. Protocol for a scoping review

Tables and figures from Evaluating the implementation of adult smoking cessation programs in community settings. Protocol for a scoping review

opencc-by-4.0Oct 2023View details →

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

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.  <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: 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 <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 PM2.5 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.  <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.

ClinicalTrials.gov40/100

The Influence of Smoking Status on Prasugrel and Clopidogrel Treated Subjects Taking Aspirin and Having Stable Coronary Artery Disease

ClinicalTrials.gov study NCT01260584. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov40/100

Connecting Alaska Native People to Quit Smoking

ClinicalTrials.gov study NCT03645941. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov40/100

Cessation of Smoking Trial in the Emergency Department

ClinicalTrials.gov study NCT04854616. IPD Sharing: NO. Countries: 1. Publications: 3.

closedIPD-NOFeb 2026View details →

publicApr 2024View details →

Climate and habitat type interact to influence contemporary dispersal potential in Prairie Smoke (Geum triflorum)

Open the record for dataset details and reuse information.

publicNov 2023View details →

Smoke designations for eastern Kansas monitoring sites during March-May 2022

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publicJul 2025View details →

Model output tracking smoke from agricultural fires in south Florida from October 2022 - May 2023

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