Cross-tabulation and associated summary statistics for delivery by caesarian section in Ethiopia.

A validation assessment of Land Cover Monitoring, Assessment, and Projection (LCMAP) Collection 1 annual land cover products (1985–2017) for the Conterminous United States was conducted with an independently collected reference data set. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) to a reference sample of 24,971 Landsat resolution (30m x 30m) pixels. These pixels were randomly selected from a sample frame of all pixels in the Landsat Analysis Ready Data (ARD) grid system which fell within the map area (Dwyer et al., 2018). Interpretation used the TimeSync reference data collection tool which visualizes Landsat images and Landsat data values for all usable images in the time series (1984–2018) (Cohen et al., 2010). Interpreters also referred to air photos and high resolution images available in Google Earth as well as several ancillary data layers. The interpreted land cover attributes were crosswalked to the LCMAP annual land cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Wetland, Water, Ice/Snow and Barren. Validation analysis directly compared reference labels with annual LCMAP land cover map attributes by cross tabulation. The results of that assessment are reported here as confusion matrices for land cover agreement and land cover change agreement. The standard errors have been calculated using post-stratified estimation (Card, 1982). Land cover class proportions were also estimated from the reference data for each year, 1985–2017, using post-stratified estimation. A cluster sampling formulation (Stehman, 1997) was used to calculate standard sampling error for summary tables reporting results for multiple years of data comparison. Overall CONUS land cover agreement across all years was found to be 82.5%. Annual and regional accuracies are also reported.

Table A. Properties of drug-side effect matrix. Table B. Methods of matching SIDER drug names to Pharmaprojects. Table C. Methods of matching SIDER side effect names to MeSH terms. Table D. Reasons SIDER drugs drop out of analysis. Table E. Cross-tabulation of drug-SE properties. Table F. Effects of requiring genetic insight and removing similar indications. Table G. Sensitivity to similarity threshold for inclusion of similar genetic associations. Table H. Sensitivity to similarity threshold for removal of similar indications. Table I. Breakdown by source of genetic evidence. Table J. Breakdown by somatic vs. germline and oncology vs. non-oncology. Table K. Properties of oncology vs. non-oncology SEs Table L. Binned analysis of numerical SE frequency. Table M. Logit model coefficients for numerical SE frequency. Table N. Binned analysis of SE frequency terms. Table O. Logit model coefficients for SE frequency terms, ordinal model. Table P. Logit model coefficients for SE frequency terms, linear term only. Table Q. Binned analysis of placebo status. Table R. Logit model coefficients for placebo status. Table S. Binned analysis of SEs by drug specificity (number of drugs where the SE is observed). Table T. Binned analysis of SEs by severity quartile. Table U. Logit model coefficients for severity analysis. Table V. SE drug specificity versus severity bin. Table W. Linear model coefficients for SE severity vs. drug specificity. Table X. Breakdown by MeSH area. Table Y. Enrichment statistics by GWAS association MeSH term. Table Z. Enrichment statistics by side effect MeSH term. Table AA. Side effects lacking genetic insight, by number of drugs Table AB. Count of drug-indication pairs with and without genetic support. Table AC. Drug-indication pairs with genetic support. Table AD. Count and base rate of drug-SE pairs by genetic support status. Table AE. OR by genetic support status, with and without sim_indic filter. Table AF. Details of drugs whose targets are genetically associated to tachycardia. (XLSX)

The summary table, followed by cross tabulation, represents somatic mutations in a few genes (highlighted by previous studies [44, 45]) presented as percentage of their presence in each of the three integrative clusters. Graphical representation of this table has been provided with S8 Fig.

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

This workshop takes you on a quick tour of Stata, SPSS, and SAS. It examines a data file using each package. Is one more user friendly than the others? Are there significant differences in the codebooks created? This workshop also looks at creating a frequency and cross-tabulation table in each. Which output screen is easiest to read and interpret? The goal of this workshop is to give you an overview of these products and provide you with the information you need to determine whick package fits the requirements of you and your user.

This file contains the recreation scores for all 49 research sites. The legal accessibility, physical accessibility and recreational infrastructures rankings for each sites were combined to form the recreation ecosystem service scores.This file also contains the cross-tabulation analysis of the recreation scores verses four area groups (one - between 1m2 and 2499m2; two - between 2500m2 and 5499m2; three - between 5500m2 and 7999m2; four - larger than or equal to 8000m2) and two type of sites (aquatic, terrestrial).The area groups cross tabulation analysis shows that two sites (33 - Primrose Primary School pond and 38 - Scott Avenue allotment green roof) having a recreation score of two, and they both are less than 2500m2. Site 33 is situated inside the school, and access to the pond is for the staff and students of the school only – public access prohibited. There is a tall fence and locked gate to prevent public access from outside of the school premise – physically restricting access. However, the recreational infrastructures (benches, viewing platform and footpaths) are well maintained. Site 38 is situated inside a council owned allotment. Access into the allotment is only for people who paid to rent out allotment plots for growing food, therefore the general public is prohibited from accessing the site. The allotment green roof requires a ladder for access, and the allotment itself has a tall fence surrounding it and a locked gate. Therefore, access is physically restricted. At the other end of the scale, 22 sites achieved the maximum score of six. Seven of the 22 sites are larger than or equal to 8000m2, and they are sites 14 - Footpath beside David Lewis Sports Ground, 18 - Heaton Park boating pond, 24 - Nutsford Vale, 32 - Platt Field pond, 44 - The Meadows, 45 - Three Sisters and 49 - Woodland walkway within Alexandra Park. These sites are all situated within either public parks or local nature reserves; hence there is no issue with legal accessibility or physical accessibility. They all have well maintained recreational facilities because of their land use purposes. Seven sites within the 21 sites that achieved the maximum score are smaller than 2500m2. They are sites 12 - Chorlton Water park pond, 19 - Heaton Park Dell Garden pond, 21 - Hullard Park pond, 35 - Range Road public garden, 36 - Salford University garden, 43 - Stevenson Square green roof and 47 - Untrimmed vegetation area inside Hulme Park. Area wise, these sites appear to be too small to possess any recreational potential. However, sites 12, 19, 21 and 47 are situated within public parks, site 43 is a public garden, site 36 is situated in the middle of a university campus, and site 43 is in the middle of a public square in the Manchester city centre. Therefore, their maximum scores are justified based on the land use of their surrounding areas.Statistical analysis was performed to find out if there is a relationship between the size of the sites and the recreation scores they can achieve. The 49 sites were further categorised into two categories (1 = sites less than or equals to 5500m2; 2 = sites more than 5500m2) for the analysis. After performing the non-parametric Kruskal-Wallis analysis, it was found that the p-value of 0.943 is larger than 0.05. This implies that there is no significant difference between site sizes compared with the recreation score each site is awarded, out of the 49 sites surveyed.The type of site cross tabulation analysis shows that 22 out of 49 sites (44.9%) achieved the highest recreational score, which is six. The 22 sites are split evenly between sites with only terrestrial characteristics and sites with aquatic characteristics. The two sites that achieved the lowest scores (sites 33 – Primrose Primary School pond, and 38 – Scott Avenue allotment green roof) are also split evenly, with site 33 being aquatic dominated and site 38 being terrestrial dominated. The recreation ecosystem service scores were statistically examined to see if there is a significant difference between aquatic and terrestrial sites. After performing the non-parametric Kruskal-Wallis analysis, it was found that the p-value of 0.181 is larger than 0.05. This implies that there is no significant influence between the type of site compared with the recreation score each site gets, out of 49 sites surveyed.

A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.2 annual land cover products (1985–2018) for the Conterminous United States was conducted with an independently collected reference dataset. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) at to a reference sample of 26,971 Landsat resolution (30m x 30m) pixels. These pixels were selected from a sample frame of all pixels in the ARD grid system which fell within the map area (Dwyer et al., 2018). Interpretation used the TimeSync reference data collection tool which visualizes Landsat images and Landsat data values for all usable images in the time series (1984–2018) (Cohen et al., 2010). Interpreters also referred to air photos and high resolution images available in Google Earth as well as several ancillary data layers. The interpreted land cover attributes were crosswalked to the LCMAP annual land cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Wetland, Water, Snow/Ice and Barren. Validation analysis directly compared reference labels with annual LCMAP land cover map attributes by cross tabulation. The results of that assessment are reported here as confusion matrices for land cover agreement and land cover change agreement. Accuracy and standard errors have been calculated using stratified estimation (Stehman, 2014). Land cover class proportions were also estimated from the reference data for each year, 1985–2018. A cluster sampling formulation was used to calculate standard sampling error for summary tables reporting results for multiple years of data comparison.

This appendix provides the full set of replication materials, coding instruments, and supporting documentation for the study. It details the coding protocol used to classify textual segments, including unit of coding, multi-coding rules, thresholds for inclusion, reliability checks, and confidentiality safeguards. The finalized codebook is presented with hierarchical categories (Constitutional, Collective Choice, Operational Rules, and cross-cutting dimensions) along with operational definitions, typical indicators, inclusion/exclusion criteria, and coding attributes. Supplementary materials include an anonymized quote table illustrating code applications, TF–IDF lexical analysis settings for replication, the NVivo node tree used for structured coding, and document variable templates for cross-tabulation. These resources ensure transparency, enable reproducibility, and support further comparative analysis.

We investigate apparent-time tone variation in the Black Lahu language (Loloish/Ngwi, Tibeto-Burman) of Yunnan, China. These are the supplementary materials for the paper "Generational differences in the low tones of Black Lahu," accepted for publication in Linguistics Vanguard.

Appendices:

• Appendix A: Wordlist (organized by order of appearance in the story)
• Appendix B: Wordless picture book
• Appendix C: Cross-tabulation tables
• Appendix D: F0 trajectory plots by speaker
• Appendix E: LME model results for T45

Script files contained in the analysis:

• F0_estimation.praat is the Praat script used for F0 estimation on the wav + Textgrid pairs
• Combine_Speaker_Files.R is used to combine the Praat script output for individual speakers into a single csv file.
• Data_processing.R is the main processing script, works on an Excel csv file that contains the output of the Combine_Speaker_Files.R script
• Plotting_Fig1_Lahu_tones.R plots the F0 trajectories of all the tones of Lahu, averaged across all the speakers, works on an Excel csv file that contains the output of the F0 estimation.praat script.
• Plotting_Fig2_age_groups.R plots the F0 trajectories of T2 and T7, in 15-age groups, works on an Excel csv file that contains the output of the Data_processing.R script.
• Plotting_Fig3and6_carryover. R plots the F0 trajectories of T2, T7 and T4 when they occur after silence or Tones 1-7 in three age groups; works on an Excel csv file that contains the output of the Data_processing.R script.
• Plotting_Fig4578_scatterplot.R plots the scatterplots of F0 onset versus Age, works on an Excel csv file that contains the output of the Data_processing.R script.

Data files contained in this analysis:

• praat_raw_data.csv is the output of the F0_estimation.praat script. Contains the raw data from all speakers.
• Speaker_info.csv is the demographic data for each speaker
• data_forplotting.csv is the output of the Data_processing.R script. Contains the filtered tokens, normalized for length and converted to speaker-specific semitones. Used to plot Figure 2, Figure 3 and Figure 6
• data_forRbrul.csv is the output of the Data_processing.R script, the F0 onset and F0 offset for Tones 2, 4, and 7. Used to do linear mixed effects modeling in the Rbrul interface (Johnson 2009).

The columns report the names, types, and input of individual preservation statistics (Lbl, module label; Adj, general network adjacency; , numeric data from which a correlation network is constructed). The last 3 columns indicate which of the individual statistics are used in the composite summary statistics , , and , respectively. The definition of cross-tabulation based statistics can be found in Supplementary Text S1.

Libraries Import:

Importing necessary libraries such as pandas, seaborn, matplotlib, scikit-learn's KMeans, and warnings. Data Loading and Exploration:

Reading a dataset named "Mall_Customers.csv" into a pandas DataFrame (df). Displaying the first few rows of the dataset using df.head(). Conducting univariate analysis by calculating descriptive statistics with df.describe(). Univariate Analysis:

Visualizing the distribution of the 'Annual Income (k$)' column using sns.distplot. Looping through selected columns ('Age', 'Annual Income (k$)', 'Spending Score (1-100)') and plotting individual distribution plots. Bivariate Analysis:

Creating a scatter plot for 'Annual Income (k$)' vs 'Spending Score (1-100)' using sns.scatterplot. Generating a pair plot for selected columns with gender differentiation using sns.pairplot. Gender-Based Analysis:

Grouping the data by 'Gender' and calculating the mean for selected columns. Computing the correlation matrix for the grouped data and visualizing it using a heatmap. Univariate Clustering:

Applying KMeans clustering with 3 clusters based on 'Annual Income (k$)' and adding the 'Income Cluster' column to the DataFrame. Plotting the elbow method to determine the optimal number of clusters. Bivariate Clustering:

Applying KMeans clustering with 5 clusters based on 'Annual Income (k$)' and 'Spending Score (1-100)' and adding the 'Spending and Income Cluster' column. Plotting the elbow method for bivariate clustering and visualizing the cluster centers on a scatter plot. Displaying a normalized cross-tabulation between 'Spending and Income Cluster' and 'Gender'. Multivariate Clustering:

Performing multivariate clustering by creating dummy variables, scaling selected columns, and applying KMeans clustering. Plotting the elbow method for multivariate clustering. Result Saving:

Saving the modified DataFrame with cluster information to a CSV file named "Result.csv". Saving the multivariate clustering plot as an image file ("Multivariate_figure.png").

The data present the selection of Census topics and breakdowns of the 2021 EU population and housing census, as defined by Regulation (EC) 763/2008 and its three implementing regulations for the 2021 round of censuses: Regulation (EU) 2017/543; Regulation (EU) 2017/712 and Regulation (EU) 2017/881.

The 2021 data presented in the tables for European Union Member States (27) and EFTA countries (4) are taken from a broader collection of data composed of 119 hypercubes (organized into 41 groups) mandated by Annex I to the Regulation (EU) 2017/712. These hypercubes provide a highly detailed dataset, aligning with the key census features of individual enumeration, simultaneity, universality, availability of small-area data, and defined periodicity. This structure allows detailed cross-tabulation of demographic, socioeconomic, and housing characteristics across various geographic levels (National, NUTS 1, NUTS 2 and NUTS 3).

The census data presented here adhere to the same definitions, technical specifications, and breakdowns as the detailed hypercubes, which can be accessed via the Eurostat Census Hub.

The tables presented here provide key breakdowns and cross-tabulations.

The data tables are structured based on a 2021 Census Hub data topic design, where each table represents a multidimensional breakdown of census data.

The 2021 Census data offer a statistical overview of population, households, families, and dwellings. Datasets are organized around three core areas:

1.Population Characteristics:

Comprehensive demographic details, including sex, age, marital status, and family structures.

Socioeconomic indicators such as education, employment, occupation, and activity status.

Migration-related characteristics, covering citizenship, country of birth, year of arrival, and previous residence. Geographical breakdowns are offered at NUTS 2 in 23 tables and NUTS 3 levels in 12 tables.

2.Families and Households:

Household composition and family structures.

Features family nucleus size, tenure status, and household composition and size.

These tables are primarily provided for NUTS 3 geographical regions in the 3 tables.

3.Dwellings:

Dwelling characteristics, including ownership status, building types, occupancy, and construction periods among others.

Geographical detail, with data split between NUTS 2 in one table and NUTS 3 in 4 tables.

The statistical data are supplemented by national metadata files that facilitate interpretation of the numerical data, including country-specific definitions, information on the data sources and on methodological issues.

A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.0 annual land cover products (2000–2019) for Hawaii was conducted with an independently collected reference dataset. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (2000–2019) to a reference sample of 600 Landsat resolution (30m x 30m) pixels. These pixels were selected from a sample frame of all pixels in the ARD grid system which fell within the map area (Dwyer et al., 2018). Interpretation used the TimeSync reference data collection tool which visualizes Landsat images and Landsat data values for all usable images in the time series (2000–2019) (Cohen et al., 2010). Interpreters also referred to air photos and high resolution images available in Google Earth as well as several ancillary data layers. The interpreted land cover attributes were crosswalked to the LCMAP annual land cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Wetland, Water, Snow/Ice and Barren. Validation analysis directly compared reference labels with annual LCMAP land cover map attributes by cross tabulation. The results of that assessment are reported here as confusion matrices for land cover agreement and land cover change agreement. Accuracy and standard errors have been calculated using stratified estimation (Stehman, 2014). Land cover class proportions were also estimated from the reference data for each year, 2000–2019. A cluster sampling formulation was used to calculate standard sampling error for summary tables reporting results for multiple years of data comparison.

The data present the selection of Census topics and breakdowns of the 2021 EU population and housing census, as defined by Regulation (EC) 763/2008 and its three implementing regulations for the 2021 round of censuses: Regulation (EU) 2017/543; Regulation (EU) 2017/712 and Regulation (EU) 2017/881.

The 2021 data presented in the tables for European Union Member States (27) and EFTA countries (4) are taken from a broader collection of data composed of 119 hypercubes (organized into 41 groups) mandated by Annex I to the Regulation (EU) 2017/712. These hypercubes provide a highly detailed dataset, aligning with the key census features of individual enumeration, simultaneity, universality, availability of small-area data, and defined periodicity. This structure allows detailed cross-tabulation of demographic, socioeconomic, and housing characteristics across various geographic levels (National, NUTS 1, NUTS 2 and NUTS 3).

The census data presented here adhere to the same definitions, technical specifications, and breakdowns as the detailed hypercubes, which can be accessed via the Eurostat Census Hub.

The tables presented here provide key breakdowns and cross-tabulations.

The data tables are structured based on a 2021 Census Hub data topic design, where each table represents a multidimensional breakdown of census data.

The 2021 Census data offer a statistical overview of population, households, families, and dwellings. Datasets are organized around three core areas:

1.Population Characteristics:

Comprehensive demographic details, including sex, age, marital status, and family structures.

Socioeconomic indicators such as education, employment, occupation, and activity status.

Migration-related characteristics, covering citizenship, country of birth, year of arrival, and previous residence. Geographical breakdowns are offered at NUTS 2 in 23 tables and NUTS 3 levels in 12 tables.

2.Families and Households:

Household composition and family structures.

Features family nucleus size, tenure status, and household composition and size.

These tables are primarily provided for NUTS 3 geographical regions in the 3 tables.

3.Dwellings:

Dwelling characteristics, including ownership status, building types, occupancy, and construction periods among others.

Geographical detail, with data split between NUTS 2 in one table and NUTS 3 in 4 tables.

The statistical data are supplemented by national metadata files that facilitate interpretation of the numerical data, including country-specific definitions, information on the data sources and on methodological issues.

The most traditional way to examine land use change is to use a cross-tabulation matrix to identify the most important categorical land use transition from time 1 to time 2. However, such method does not necessarily capture or indicate the real changes on the landscape. For example, assuming that from 1986 to 2015, Utah’s total agricultural land loss (aka, net change) is 200 square miles, but this does not mean that only 200 square miles of agricultural land have experienced land use change in the last 30 years. It is highly possible that a given quantity of agricultural land loss at one location can be accompanied by another quantity of agricultural land gain at another location (aka, swapping). Thus, by purely using net change, we might fail to capture the swapping component of change, and fail to capture the intricate transitions of landscape. This dataset analyzed important categorical land use change while account for persistence and swaps. It provides additional information concerning what happened on the landscape.

This dataset includes a statistical table and a GIS raster file. The table summarizes the persistence and swaps, as well as gross gain and gross loss in the Wasatch Range Metropolitan Area (WRMA). The GIS file is the compiled spatial layer that represents the gain, loss, persistence, and swaps on the landscape. We used Water Related Land Use data of Year 1986 to Year 2015 for this analysis. Land use categories used in this dataset include urban (URB), irrigated agricultural land (IR), and non-irrigated agricultural land (NI), sub-irrigated agricultural land (SubIR), riparian (RIP), water, (WATER), and other (OTHER). We then examined the categorical land use changes with a transition matrix.

A categorical land use gain is determined as the conversion from other sources to this particular categorical land use, and a categorical land use loss is defined as conversion from this particular categorical land use to other uses. For example, the gain of irrigated agricultural (IR) land use will be the sum of areas of urban to IR, non-irrigated agricultural land to IR, sub-irrigated agricultural land to IR, riparian to IR, water to IR, and other to IR. The total change is calculated as the sum of gain and loss. The net change equals to |Gain|-|Loss|. The Swap =2* MIN(Gain,Loss).

Abstract The child population is strongly affected by obesity. Accessible and reliable strategies for the obesity diagnosis are of utmost importance.. The aim of this study was to identify childhood obesity according the WHO (World Health Organization): malnourished, healthy weight, overweight and obese. It was collected measures of height, Body Mass Index (BMI), Waist Circumference (WC) and Triceps Skinfold Thickness (TSF) of 449 children from Municipal School of Araras/SP, from 7 to 10 years old. It was performed a Spearman correlation test between BMI, WC and TSF variables. Also, was realized cross tabulation between the found results by the different methods, constructing a contingency table 2x2, with absolute frequency of boys and girls classified as “without overweight” and “with overweight”. The concordance between methods was analyzed by kappa index. In the results, 28.3% of children presented overweight according to BMI, with higher prevalence in boys. Generally, the found results through TSF showed strong correlation with both BMI and WC (rs=0.7994 e rs=0.7519, respectively). The same was observed when data was analyzed separately by sex. When crossed the TSF data with BMI and WC, the kappa index demonstrated a satisfactory concordance (0.4419 e 0.5161, respectively). The TSF can be suggested a method to body composition assessment and cardiometabolic risk in children.

It is generally accepted that active mobility, mainly walking and cycling, contributes to people’s physical and mental health. One of the current challenges is to improve our understanding of this type of behaviour. This study aims to identify factors from the daily-life environment that may be related to active mobility behaviours, in order to design a new questionnaire for a quantitative study of a large adult population. The new questionnaire obtained through this pilot study combines information from interviews with existing questionnaires materials in order to introduce new factors while retaining the factors already assessed. This approach comprises three stages. The first was a content analysis (Reinert method) of interviews with a sample of participants about daily living activities as well as mobility. This stage led to a typology of factors suggested by interviews. The second was a scoping review of the literature in order to identify the active mobility questionnaires currently used in international literature. The last stage was a cross-tabulation of the factors resulting from the written interviews and the questionnaires. A table of the inter-relationships between the interview-based typology and the questionnaires shows discrepancies between factors considered by the existing questionnaires, and factors coming from individual interviews. Independent factors which were ignored in or absent from the questionnaires are the housing situation within the urban structure, overall consideration of the activity space beyond the limits of the residential neighbourhood, the perception of all the transportation modes, and the time scheduling impacting the modes actually used. Our new questionnaire integrates both the usual factors and the new factors that may be related to active mobility behaviours.