Input Data for Box TS.5, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Box TS.5, Figure1 shows the carbon cycle sensitivity to forcings, future CO2 emissions pathways and the associated sinks and sink fractions.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.

Figure subpanels

The figure has 7 panels with data provided for all of them. A single python script plots the entire figure.

List of data provided

This dataset contains: - Carbon cycle feedback sensitivities from the CMIP6 models - CO2 concentration data used as input to the concentration driven historical and ssp simulations - CO2 concentrations from CMIP6 models from the emissions-driven SSP5-8.5 simulation - CO2 concentrations from MAGICC 7.5.1 model for the other scenarios - Carbon fluxes and derived emissions from the CMIP6 models up to 2100 - Carbon fluxes from the CMIP6 models up to 2300

Data provided in relation to figure

Data provided in relation to Box TS.5, Figure 1:

carbon cycle feedback sensitivities from the CMIP6 models (courtesy Charlie Koven): - carbon_feedback_parameters.nc

Sub-directory CMIP_data:

CO2 concentration data used as input to the concentration driven historical and ssp simulations: - CMIP6_HIST_CO2.dat - CMIP6_SSP2300_CO2.dat - CMIP6_SSP_CO2.dat

CO2 concentrations from CMIP6 models from the emissions-driven SSP5-8.5 simulation: -CMIP6_e-CO2.dat

Carbon fluxes from the CMIP6 models up to 2300: - CESM2-WACCM_fgco2.dat - CESM2-WACCM_nbp.dat - CanESM5_fgco2.dat - CanESM5_nbp.dat - IPSL-CM6A-LR_fgco2.dat - IPSL-CM6A-LR_nbp.dat - UKESM1-0-LL_fgco2.dat - UKESM1-0-LL_nbp.dat

Sub-directory MAGIC_data: CO2 concentrations from MAGICC 7.5.1 model for the other scenarios (courtesy Zeb Nicholls and MAGICC team): - MAGICCv7.5.1_atmospheric-co2_esm-ssp119.nc - MAGICCv7.5.1_atmospheric-co2_esm-ssp126.nc - MAGICCv7.5.1_atmospheric-co2_esm-ssp245.nc - MAGICCv7.5.1_atmospheric-co2_esm-ssp370.nc - MAGICCv7.5.1_atmospheric-co2_esm-ssp534-over.nc - MAGICCv7.5.1_atmospheric-co2_esm-ssp585.nc MAGICC is maintained and developed by Malte Meinshausen, Jared Lewis and Zebedee Nicholls. If you have any questions about MAGICC's output or would like to use it in a publication, please contact Malte Meinshausen, Jared Lewis and Zebedee Nicholls. The setup used to generate this data is described extensively in Cross-chapter Box 7.1 and is based on Meinshausen et al. 2009, [2011](doi:... For full abstract see: https://catalogue.ceda.ac.uk/uuid/d6a301f3429b44e7924296f840f68fe6.

We provide instructions, codes and datasets for replicating the article by Kim, Lee and McCulloch (2024), "A Topic-based Segmentation Model for Identifying Segment-Level Drivers of Star Ratings from Unstructured Text Reviews." This repository provides a user-friendly R package for any researchers or practitioners to apply A Topic-based Segmentation Model with Unstructured Texts (latent class regression with group variable selection) to their datasets. First, we provide a R code to replicate the illustrative simulation study: see file 1. Second, we provide the user-friendly R package with a very simple example code to help apply the model to real-world datasets: see file 2, Package_MixtureRegression_GroupVariableSelection.R and Dendrogram.R. Third, we provide a set of codes and instructions to replicate the empirical studies of customer-level segmentation and restaurant-level segmentation with Yelp reviews data: see files 3-a, 3-b, 4-a, 4-b. Note, due to the dataset terms of use by Yelp and the restriction of data size, we provide the link to download the same Yelp datasets (https://www.kaggle.com/datasets/yelp-dataset/yelp-dataset/versions/6). Fourth, we provided a set of codes and datasets to replicate the empirical study with professor ratings reviews data: see file 5. Please see more details in the description text and comments of each file. [A guide on how to use the code to reproduce each study in the paper] 1. Full codes for replicating Illustrative simulation study.txt -- [see Table 2 and Figure 2 in main text]: This is R source code to replicate the illustrative simulation study. Please run from the beginning to the end in R. In addition to estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships, you will get dendrograms of selected groups of variables in Figure 2. Computing time is approximately 20 to 30 minutes 3-a. Preprocessing raw Yelp Reviews for Customer-level Segmentation.txt: Code for preprocessing the downloaded unstructured Yelp review data and preparing DV and IVs matrix for customer-level segmentation study. 3-b. Instruction for replicating Customer-level Segmentation analysis.txt -- [see Table 10 in main text; Tables F-1, F-2, and F-3 and Figure F-1 in Web Appendix]: Code for replicating customer-level segmentation study with Yelp data. You will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 3 to 4 hours. 4-a. Preprocessing raw Yelp reviews_Restaruant Segmentation (1).txt: R code for preprocessing the downloaded unstructured Yelp data and preparing DV and IVs matrix for restaurant-level segmentation study. 4-b. Instructions for replicating restaurant-level segmentation analysis.txt -- [see Tables 5, 6 and 7 in main text; Tables E-4 and E-5 and Figure H-1 in Web Appendix]: Code for replicating restaurant-level segmentation study with Yelp. you will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 10 to 12 hours. [Guidelines for running Benchmark models in Table 6] Unsupervised Topic model: 'topicmodels' package in R -- after determining the number of topics(e.g., with 'ldatuning' R package), run 'LDA' function in the 'topicmodels'package. Then, compute topic probabilities per restaurant (with 'posterior' function in the package) which can be used as predictors. Then, conduct prediction with regression Hierarchical topic model (HDP): 'gensimr' R package -- 'model_hdp' function for identifying topics in the package (see https://radimrehurek.com/gensim/models/hdpmodel.html or https://gensimr.news-r.org/). Supervised topic model: 'lda' R package -- 'slda.em' function for training and 'slda.predict' for prediction. Aggregate regression: 'lm' default function in R. Latent class regression without variable selection: 'flexmix' function in 'flexmix' R package. Run flexmix with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, conduct prediction of dependent variable per each segment. Latent class regression with variable selection: 'Unconstraind_Bayes_Mixture' function in Kim, Fong and DeSarbo(2012)'s package. Run the Kim et al's model (2012) with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, we can do prediction of dependent variables per each segment. The same R package ('KimFongDeSarbo2012.zip') can be downloaded at: https://sites.google.com/scarletmail.rutgers.edu/r-code-packages/home 5. Instructions for replicating Professor ratings review study.txt -- [see Tables G-1, G-2, G-4 and G-5, and Figures G-1 and H-2 in Web Appendix]: Code to replicate the Professor ratings reviews study. Computing time is approximately 10 hours. [A list of the versions of R, packages, and computer...

Data for Figure SPM.1 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.1 shows global temperature history and causes of recent warming.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.

Figure subpanels

The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.

List of data provided

Panel a

The dataset contains:

• Estimated temperature during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago (4500 BCE), multi-centennial average, from AR6 WGI Chapter 2
• Global surface temperature change time series relative to 1850-1900 for 1-2020 from: • 1-2000 CE reconstruction from paleoclimate archives, decadal smoothed, from PAGES2k Consortium (2019, DOI: 10.1038/s41561-019-0400-0) • 1850-2020 CE, observations, decadal smoothed, from AR6 WGI Chapter 2 assessed mean

Panel b:

The dataset contains global surface temperature change time series relative to 1850-1900 for 1850-2020 from simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and observations:

• CMIP6 historical+ssp245 simulations (simulations with human and natural forcing, 1850-2019)
• CMIP6 hist-nat simulations (simulations with natural forcing, 1850-2019)
• Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020)

Data provided in relation to figure

Panel a:

• panel_a/SPM1_1-2000_recon.csv, 1-2000 time series, decadal smoothed, for years centred on 5-1996 CE [column 1 grey line, columns 2 and 3 grey shading]
• panel_a/SPM1_1850-2020_obs.csv, 1850-2020 time series, decadal smoothed, for years centered on 1855-2016 CE [black line]
• panel_a/SPM1_6500_recon.csv, bar for the warmest multi-century period in more than 100,000 years (around 6500 years ago: 4500 BCE) [grey bar]

Panel b:

• panel_b/gmst_changes_model_and_obs.csv. Global surface temperature change time series relative to 1850-1900 for 1850-2020 from: • CMIP6 historical+ssp245 simulations (1850-2019) [mean, brown line] • CMIP6 historical+ssp245 simulations (1850-2019) [5% range, brown shading, bottom] • CMIP6 historical+ssp245 simulations (1850-2019) [95% range, brown shading, top] • CMIP6 hist-nat simulations (1850-2019) [mean, green line] • CMIP6 hist-nat simulations (1850-2019) [5% range, green shading, bottom] • CMIP6 hist-nat simulations (1850-2019) [95% range, green shading, top] • Global Surface Temperature Anomalies (GSTA) relative to 1850-1900 from observations assessed in IPCC AR6 WG1 Chapter 2 (1850-2020) [black line]

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers), the Technical Summary (Cross-Section Box TS.1, Figure 1a) and the Supplementary Material for Chapters 2 and 3, which contains details on the input data used in Tables 2.SM.1 (Figure 2.11a) and 3.SM.1 (Figure 3.2c; FAQ 3.1, Figure 1). - Link to related publication for input data - Link to the webpage of the WGI report

Update July 2024

From 1 July 2024, the dataset will no longer publicly distinguish between relevant qualifications or training courses or approved qualifications or training courses.

Update March 2024

From 1 March 2024, the dataset will be updated to include 5 new fields and 1 existing field will also be updated (see help file for details).

Update August 2023

From 24 August 2023, the dataset will be updated to include 1 new field, ABLE_TO_PROVIDE_TFAS, (see help file for details).

Update April 2022

We have replaced the .xlsx file resources for all our datasets. This was required due to the API and web page search functionality no longer being supported for .xlsx files on the Data.Gov platform.

Update January 2022

From 10 January 2022, the field ADV_FASEA _APPROVED_QUAL will be renamed to ADV_APPROVED_QUAL.

Update November 2019 - additional fields

From 21 November 2019, the dataset will be updated to include 7 new fields (see help file for details)

These fields are included in conjunction with the professional standards reforms for financial advisers. More information can be found on the ASIC website https://asic.gov.au/regulatory-resources/financial-services/professional-standards-for-financial-advisers-reforms/.

Note: For most advisers the new fields will be unpopulated on 21 November 2019. As advisers provide this data to ASIC it will appear in the dataset.

Dataset summary

ASIC is Australia’s corporate, markets and financial services regulator. ASIC contributes to Australia’s economic reputation and wellbeing by ensuring that Australia’s financial markets are fair and transparent, supported by confident and informed investors and consumers.

Australian Financial Services Licensees are required to keep the details of their financial advisers up to date on ASIC's Financial Advisers Register. Information contained in the register is made available to the public to search via ASIC's Moneysmart website.

Select data from the Financial Advisers Register will be uploaded each week to www.data.gov.au. The data made available will be a snapshot of the register at a point in time. Legislation prescribes the type of information ASIC is allowed to disclose to the public.

The information included in the downloadable dataset is:

• Adviser name
• Adviser number
• Adviser role
• Adviser sub type
• Adviser role status
• Adviser ABN
• Year first provided advice
• Licence name
• Licence number
• Licence ABN
• Licence controlled by
• Adviser start date
• Adviser end date
• Overall registration status
• Registration status under financial licence
• Registration start date under financial licence
• Registration end date under financial licence
• Adviser CPD failure year
• Adviser principal business address suburb
• Adviser principal business address State/Territory
• Adviser principal business address postcode
• Adviser principal business address Country
• Appointing representative name
• Appointing representative number
• Appointing representative ABN
• Disciplinary action start date
• Disciplinary action end date
• Disciplinary action type
• Disciplinary action description (Financial Services and Credit Panel (FSCP) decision)
• Product authorisations (for a full list see the Financial Adviser Register – Help File)
• Ability to provide tax financial advice
• Qualifications and Training
• Memberships
• Further restrictions

Additional information about financial advisers can be found via ASIC's website. Accessing some information may attract a fee.

More information about searching ASIC's registers.

Data for Figure SPM.8 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.8 shows selected indicators of global climate change under the five core scenarios used in this report.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

Figure subpanels

The figure has five panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d and panel_e.

List of data provided

This dataset contains:

• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global Surface Air Temperature (GSAT) anomalies relative to 1850-1900 (20 year means)
• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 September sea-ice area
• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global ocean surface pH
• Historical sea level relative to 1900 from gauges (to 1992) and altimeters (1993 on) (offset 0.158 m vs. 1995-2014)
• AR6 sea level projections relative to 1900 (offset 0.158 m vs. 1995-2014)
• AR6 assessed global mean sea level at 2300 relative to 1900 (offset 0.158 m vs. 1995-2014)

The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

Data provided in relation to figure

Panel a: Near-Surface Air Temperature

• Data file: panel_a/tas_global_Historical.csv (black line and grey shading)
• Data file: panel_a/tas_global_SSP1_1_9.csv (cyan line)
• Data file: panel_a/tas_global_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_a/tas_global_SSP2_4_5.csv (orange line)
• Data file: panel_a/tas_global_SSP3_7_0.csv (red line and red shading)
• Data file: panel_a/tas_global_SSP5_8_5.csv (brown line)

Panel b: Sea-Ice Area

• Data file: panel_b/sia_arctic_september_Historical.csv (black line and grey shading)
• Data file: panel_b/sia_arctic_september_SSP1_1_9.csv (cyan line)
• Data file: panel_b/sia_arctic_september_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_b/sia_arctic_september_SSP2_4_5.csv (orange line)
• Data file: panel_b/sia_arctic_september_SSP3_7_0.csv (red line and red shading)
• Data file: panel_b/sia_arctic_september_SSP5_8_5.csv (brown line)

Panel c: Ocean Surface pH

• Data file: panel_c/phos_global_Historical.csv (black line and grey shading
• Data file: panel_c/phos_global_SSP1_1_9.csv (cyan line
• Data file: panel_b/phos_global_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_c/phos_global_SSP2_4_5.csv (orange line)
• Data file: panel_c/phos_global_SSP3_7_0.csv (red line and red shading)
• Data file: panel_c/phos_global_SSP5_8_5.csv (brown line)

Panel d: Sea Level

• Data file: panel_d/global_sea_level_observed.csv (black line)
• Data file: panel_d/global_sea_level_projected.csv (cyan, blue, orange, red and brown lines, red and blue shading)

Panel e: Sea Level

• Data file: panel_e: global_sea_level_2300_assessed.csv (columns 2 and 3, SSP1-2.6 scenario; columns 4 to 6 SSP5-8.5 scenario)

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record:

• Link to the report component containing the figure (Summary for Policymakers)

Summary:The datasets described here were gathered while investigating the molecular processes by which some human ductal carcinoma in situ (DCIS) lesions advance to the more aggressive form while others remain indolent.Data access:All RNA sequencing data have been deposited in the Gene Expression Omnibus with accession https://identifiers.org/geo:GSE143790 All Chip-Exo data have been deposited in the Gene Expression Omnibus with accession https://identifiers.org/geo:GSE143313 RPPA data is included together with this data record, in the file Supplementary Figure 3-RPPA.xlsx. The Raw RPPA and ANOVA tabs are the results, while the other tabs are IPA analysis performed by the authors. Permission to use figures and data generated using QIAGEN Ingenuity Pathway Analysis (IPA) is given in the file QIAGEN Ingenuity Product Support Permission letter for Dr. Behbod.pdf.The specific data underlying each figure and supplementary figure in the manuscript are provided as part of this data record, and are as follows:Figure 1-BCL9-STAT3 interaction.xlsxFigure 2-ChIP Exo.xlsxFigure 3-ChIP.xlsxFigure 4-MMP16 and avb3 MIND xenografts.xlsxFigure 5-MMP16 avb3 TMA analysis.xlsxFigure 6-Carnosic data.xlsxSupplementary Figure 3-RPPA.xlsxSupplementary Figure 4-STAT3 Reporter.xlsxSupplementary Figure 5-ChIP Exo Motifs.xlsxSupplementary Figure 6-integrin data.xlsxSupplementary Figure 7-MMP data.xlsxStudy approval and patient consent: Patients gave written informed consent for participation in the University of Kansas Medical Center Institutional Review Board–approved study allowing collection of additional biopsies and or surgical tissue for research. Animal experiments were conducted following protocols approved by the University of Kansas School of Medicine Animal Care and Use and Human Subjects Committee. Study aims and methodology: The aim of the related study was to determine the molecular processes underlying progression to invasion in DCIS using PDX DCIS MIND animal models. Using a novel intraductal model they identify downregulation of specific STAT3 targets to promote progression and use a purified component from rosemary extract to show that treatment in vivo decreases DCIS progression in patient derived DCIS and cell line models.

**Citation Request: ** Koklu, N., & Sulak, S.A. (2024). Using artificial intelligence techniques for the analysis of obesity status according to the individuals' social and physical activities. Sinop Üniversitesi Fen Bilimleri Dergisi, 9(1), 217-239. https://doi.org/10.33484/sinopfbd.1445215

Obesity Dataset

Obesity is a serious and chronic disease with genetic and environmental interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. Currently, more than 2 billion people worldwide are obese or overweight. Research has shown that obesity can be prevented. In this study, artificial intelligence methods were used to identify individuals at risk of obesity. An online survey was conducted on 1610 individuals to create the obesity dataset. To analyze the survey data, four commonly used artificial intelligence methods in literature, namely Artificial Neural Network, K Nearest Neighbors, Random Forest and Support Vector Machine, were employed after pre-processing. As a result of this analysis, obesity classes were predicted correctly with success rates of 74.96%, 74.03%, 74.03% and 87.82%, respectively. Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%.

Attributes/Values Sex Male (712) Female (898)

Age Values in integers

Height Values in integers (cm)

Overweight/Obese Families Yes (266) No (1344)

Consumption of Fast Food Yes (436) No (1174)

Frequency of Consuming Vegetables Rarely (400) Sometimes (708) Always (502)

Number of Main Meals Daily 1. 1-2 (444) 3 (928) 3+ (238)

Food Intake Between Meals Rarely (346) Sometimes (564) Usually (417) Always (283)

Smoking Yes (492) No (1118)

Liquid Intake Daily amount smaller than one liter (456) Within the range of 1 to 2 liters (523) In excess of 2 liters (631)

Calculation Of Calorie Intake Yes (286) No (1324)

Physical Exercise No physical activity (206) In the range of 1-2 days (290) In the range of 3-4 days (370) In the range of 5-6 days (358) 6+ days (386)

Schedule Dedicated to Technology Between 0 and 2 hours (382) Between 3 and 5 hours (826) Exceeding five hours (402)

Type of Transportation Used Automobile (660) Motorbike (94) Bike (116) Public transportation (602) Walking (138)

Class Underweight (73) Normal (658) Overweight (592) Obesity (287)