This word file contains the template for presentation of results in tabular format for mammalian toxicology studies, replacing the Appendix F of the EFSA administrative guidance on submission of dossiers and assessment reports for the peer‐review of pesticide active substances (EFSA, 2019; doi:10.2903/sp.efsa.2019.EN-1612). The filled-in template shall be used when compiling HTML tables or alternatively be uploaded in Attached (sanitised) documents for publication in the relevant endpoint study record.

If the information is added as a HTML table it can be included in the report generated, but it can not if it is attached.

Background: Patient representatives are increasingly engaged in quality in health care, and even though quality data are publicly available, correct interpretation may be challenging. We designed a randomized study with the primary aim to examine the association between preferred data presentation format and the interpretation of quality data among cancer patients and relatives. Material and methods: Surveys were distributed to the Danish Cancer Society Citizens’ Panel between 31 March and 14 April 2019 and 55% completed the survey (N = 464) including six storyboards that presented authentic quality data in table format, league table and point estimates. The storyboards were randomized to expose participants to the data in the three different formats and in varying presentation order. Logistic regression models were used to calculate Odds Ratios (ORs) and 95% confidence intervals (CIs) for the association between preferred presentation format, health literacy, education and cohabitation status as exposures and interpretation of quality data as outcome. Results: The majority of participants (97%) had high literacy and 57% had a medium or long higher education. A total of 60% found the questions difficult or very difficult and 33% were not able to correctly interpret at least one format. Correct interpretation was associated with preferred league table (OR = 1.62; 95% CI = 1.04–5.52) and if the data was presented in the preferred format. Medium and long education were associated with correct interpretation of at least one format (OR = 1.93; 95% CI = 1.16–3.21 and OR = 3.89; 95% CI = 1.90–7.95, respectively) while health literacy and cohabitation status were not. Conclusions: More than one third of the participants were not able to correctly interpret the data and the understanding of quality data improved with longer education and if the data was presented in the preferred format. Decision-makers should carefully consider displaying quality data according to preferred presentation format and to guide interpretation for individuals with short education.

Tables and charts have long been seen as effective ways to convey data. Much attention has been focused on improving charts, following ideas of human perception and brain function. Tables can also be viewed as two-dimensional representations of data, yet it is only fairly recently that we have begun to apply principles of design that aid the communication of information between the author and reader. In this study, we collated guidelines for the design of data and statistical tables. These guidelines fall under three principles: aiding comparisons, reducing visual clutter, and increasing readability. We surveyed tables published in recent issues of 43 journals in the fields of ecology and evolutionary biology for their adherence to these three principles, as well as author guidelines on journal publisher websites. We found that most of the over 1,000 tables we sampled had no heavy grid lines and little visual clutter. They were also easy to read, with clear headers and horizontal orientation. However, most tables did not aid the vertical comparison of numeric data. We suggest that authors could improve their tables by the right-flush alignment of numeric columns typeset with a tabular font, clearly identify statistical significance, and use clear titles and captions. Journal publishers could easily implement these formatting guidelines when typesetting manuscripts. Methods Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles. Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these tables failed to meet the outlined guidelines. We initially both reviewed the same 10 tables to ensure that we agreed in our assessment of whether these tables followed each of the principles. Having ensured agreement on how to classify tables, we proceeded to review all subsequent journals individually, while resolving any uncertainties collaboratively. These preliminary table evaluations also showed that assessing whether tables used long format or a tabular font was hard to evaluate objectively without knowing the data or the font used. Therefore, we did not systematically review the extent to which these two guidelines were adhered to.

This is a dataset of high resolution upper air soundings collected by European Centre for Medium-Range Weather Forecasts (ECMWF) in the World Meteorological Organization's (WMO) Binary Universal Form for the Representation of meteorological data (BUFR) format. The parameters and metadata captured at each level contain time displacement, latitude displacement, longitude displacement, geopotential height, pressure, temperature, dew point temperature, wind speed, wind direction, level significance (flags). Many reports are at 2-second resolution ~3500 levels for a full ascent, some are at 1-second resolution: ~7000 levels (but we have seen up to 14500 levels). Few observations in this data set are at low resolution (standard+significant levels). That data are from Oct 2, 2014 - present, updated monthly. In 2003, the WMO members approved a migration from traditional alphanumeric codes (TAC)to table driven code forms (TDCF) BUFR for data distribution on the Global Telecommunications System (GTS). The TDCF BUFR, also known as just BUFR, is a binary data format maintained by the World Meteorological Organization (WMO). Compared with the traditional alphanumeric codes (TAC), the BUFR offers great advantages of flexibility and expandability, allowing for the dissemination of much higher vertical resolution with the reporting of the time and position at each level and extra metadata. The Commission agreed on the deadline of November 2014 to stop the parallel distribution of TAC and BUFR data. The European Centre for Medium-Range Weather Forecasts (ECMWF) has maintained an archive of global radiosonde BUFR data since October of 2014, which will complement NCEI's real-time archiving of National Weather Service (NWS) BUFR stream commencing in May of 2017.

EmokineDataset

Companion resources

Paper

Christensen, Julia F. and Fernandez, Andres and Smith, Rebecca and Michalareas, Georgios and Yazdi, Sina H. N. and Farahi, Fahima and Schmidt, Eva-Madeleine and Bahmanian, Nasimeh and Roig, Gemma (2024): "EMOKINE: A Software Package and Computational Framework for Scaling Up the Creation of Highly Controlled Emotional Full-Body Movement Datasets".

Code https://github.com/andres-fr/emokine

EmokineDataset is a pilot dataset showcasing the usefulness of the emokine software library. It featuers a single dancer performing 63 short sequences, which have been recorded and analyzed in different ways. This pilot dataset is organized in 3 folders:

Stimuli: The sequences are presented in 4 visual presentations that can be used as stimulus in observer experiments:

Silhouette: Videos with a white silhouette of the dancer on black background.

FLD (Full-Light Display): video recordings with the performer's face blurred out.

PLD (Point-Light Display): videos featuring a black background with white circles corresponding to the selected body landmarks.

Avatar: Videos produced by the XSENS motion capture propietary software, featuring a robot-like avatar performing the captured movements on a light blue background.

Data: In order to facilitate computation and analysis of the stimuli, this pilot dataset also includes several data formats:

MVNX: Raw motion capture data directly recorded from the XSENS motion capture system.

CSV: Translation of a subset of the MVNX sequences into CSV, included for easier integration with mainstream analysis software tools). The subset includes the following features: acceleration, angularAcceleration, angularVelocity, centerOfMass, footContacts, orientation, position and velocity.

CamPos: While the MVNX provides 3D positions with respect to a global frame of reference, the CamPos JSON files represent the position from the perspective of the camera used to render the PLD videos. Specifically, their 3D positions are given with respect to the camera as (x, y, z), where (x, y) go from (0, 0) (left, bottom) to (1, 1) (right, top), and z is the distance between the camera and the point in meters. It can be useful to get a 2-dimensional projection of the dancer position (simply ignore z).

Kinematic: Analysis of a selection of relevant kinematic features, using information from MVNX, Silhouette and CamPos, provided in tabular form.

Validation: Data and experiments reported in our paper as part of the data validation, to support its meaningfulness and usefulness for downstream tasks.

TechVal: A collection of plots presenting relevant statistics of the pilot dataset.

ObserverExperiment: Results in tabular form of an online study conducted with human participants, tasked to recognize emotions of the stimuli and rate their beauty.

More specifically, the 63 unique sequences are divided into 9 unique choreographies, each one being performed once as an explanation, and then 6 times with different intended emotions (angry, content, fearful, joy, neutral and sad). Once downloaded, the pilot dataset should have the following structure:

EmokineDataset├── Stimuli│ ├── Avatar│ ├── FLD│ ├── PLD│ └── Silhouette├── Data│ ├── CamPos│ ├── CSV│ ├── Kinematic│ ├── MVNX│ └── TechVal└── Validation ├── TechVal └── ObserverExperiment

Where each of the stimuli, MVNX, CamPos and Kinematic have this structure:

├── explanation│ ├── _seq1_explanation.│ ├── ...│ └── _seq9_explanation.├── _seq1_angry.├── _seq1_content.├── _seq1_fearful.├── _seq1_joy.├── _seq1_neutral.├── _seq1_sad....└── _seq9_sad.

The CSV directory is slightly different, because instead of a single file for each seq and emotion, it features a folder containing a .csv file for each one of the 8 features being extracted (acceleration, velocity...).

This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2021 (Census 2022 for Scotland) and Land Cover Map 2021 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2021/2022 UK Census population data on Output Area level with Land Cover Map 2021 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum). Full details about this dataset can be found at https://doi.org/10.5285/7beefde9-c520-4ddf-897a-0167e8918595

This statistic shows the results of a survey conducted in the United States in 2018 on online shopping. Some ** percent of the respondents stated that a detailed description and information on the product is an attractive presentation in an online shop. The Survey Data Table for the Statista survey Online-Shopping in the U.S. 2018 contains the complete tables for the survey including various column headings.

Overview This project focuses on web scraping and data extraction from the Bodybuilding.com website's training category. The goal is to extract relevant information from each article, including the article title, link, description, author, publish date, time to read, and gender category. The extracted data is displayed as a table and saved to a CSV file for further analysis and exploration.

Project Components

1. Web Scraping The project utilizes the Python programming language and libraries such as Requests and BeautifulSoup for web scraping.

The Requests library is used to send HTTP GET requests to the Bodybuilding.com website and retrieve the HTML content of the training category page. BeautifulSoup is used for parsing the HTML content and extracting specific elements and data. 2. Data Extraction

The BeautifulSoup library is used to find and extract the desired information from the HTML content. The project extracts the following information for each article:

Article title: Extracted from the h3 element with the class "title". Link to the article: Extracted from the a element. Description: Extracted from the p element with the class "BBCMS_content--article-description". Author: Extracted from the a element with the class "BBCMS_content--author-name". Publish Date: Extracted from the div element with the class "BBCMS_content--author-date". Time to Read: Extracted from the span element with the class "bb-read-time_time". Gender Category: Determined by searching the article description or entire article using regular expressions to identify if it is targeted for men or women.

1. Data Presentation The extracted information is displayed in a tabular format using the Tabulate library. The table includes columns for the article title, link to the article, description, author, publish date, time to read, and gender category. The tabulated data is printed to the console, providing a clear and organized view of the extracted information.

2. Data Storage The extracted information is saved to a CSV (Comma-Separated Values) file using the CSV module in Python. The CSV file includes the same columns as the displayed table: article title, link to the article, description, author, publish date, time to read, and gender category. The CSV file serves as a persistent storage for the extracted data, allowing for further analysis and exploration. Usage Instructions.

You can view the Code and Other information here: https://www.kaggle.com/code/sohaibraoufy/web-scraping-and-data-extraction-for-bodybuilding

This statistic shows the results of a survey conducted in the United States in 2018 on online shopping. Some ** percent of the respondents stated that a detailed description and information on the product is an appealing presentation in an online shop. The Survey Data Table for the Statista survey Online-Shopping in the U.S. 2018 contains the complete tables for the survey including various column headings.

About this dataset

Context

The dataset tabulates the Table Rock population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Table Rock. The dataset can be utilized to understand the population distribution of Table Rock by age. For example, using this dataset, we can identify the largest age group in Table Rock.

Key observations

The largest age group in Table Rock, NE was for the group of age 35-39 years with a population of 49 (13.32%), according to the 2021 American Community Survey. At the same time, the smallest age group in Table Rock, NE was the 80-84 years with a population of 1 (0.27%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Table Rock is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Table Rock total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Table Rock Population by Age. You can refer the same here

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fe06b8e2244e50f055658217c601e6854%2Fgraphs12221.png?generation=1708806134708808&alt=media" alt="">

Table of Contents 1. Overview 2. World and Regional Tables 3. Country Tables 4. Annex I. Analytic Presentation of the Balance of Payments 5. Annex II. Standard Presentation of the Balance of Payments 6. Annex III. Standard Components of the International Investment Position 7. Annex IV. Reporting Currency 8. Annex V. Conceptual Framework of the Balance of Payments and International Investment Position 9. Annex VI. Classification and Standard Components of the Balance of Payments and International Investment Position Overview The electronic release of the Balance of Payments Statistics Yearbook (BOPSY), produced by the International Monetary Fund (IMF), contains balance of payments and international investment position (IIP) data in accordance with the sixth edition of the Balance of Payments and International Investment Position Manual (BPM6) published in 2009. Individual country data for all available periods along with world and regional aggregates for the period 2005-2021 are included in this release. The IMF is grateful for countries’ cooperation in providing comprehensive, timely, and regular 1 Volume 1 of the Yearbook, published in 1949, was based on the first edition of the IMF’s Balance of Payments Manual, issued in 1948; Volumes 2–12 were compiled pursuant to the second edition of the Manual, issued in 1950; Volumes 13–23 were based on the third edition of the Manual, issued in 1961; and Volumes 24– 29 were associated with that edition as well as the Balance of Payments Manual: Supplement to Third Edition, issued in 1973. Volumes 30–45 followed the guidance of the fourth edition of the data to the Fund for re-dissemination. These data support the IMF’s Statistics Department (STA) in its efforts to respond to the analytical and policy needs of the IMF, member countries, and the international community. The electronic release, available through the online database accessible at http://data.imf.org, contains a section on the World and Regional Tables, which presents 21 World and Regional Tables for major components of the balance of payments and IIP accounts. Individual country tables covering annual balance of payments and IIP data of individual countries, jurisdictions, and other reporting entities, as well as Balance of Payments and IIP metadata are also published through the online database.. The release of the Yearbook based on BPM61 was endorsed by the IMF’s Committee on Balance of Payments Statistics. The BPM6 provides updated international standards covering the methodologies for compiling, and the presentation of, balance of payments and IIP statistics. It incorporates clarifications and improvements reflecting significant developments and expansion in globalized international trade arrangements and financial markets that had been identified since the release of the fifth edition of the Balance of Payments Manual (BPM5) in 1993. Moreover, the linkages to and consistency with other macroeconomic statistics are maintained and enhanced through the parallel update of the OECD Benchmark Definition of Foreign Direct Investment and the System of National Accounts. Manual, published in 1977. Volumes 46–62 were presented in accordance with the standard components of the BPM5. However, the standard components changed with the publication of Financial Derivatives, a Supplement to the Fifth Edition (1993) of the Balance of Payments Manual, published in 2000 and amended in 2002. As noted, Volume 63 and subsequent volumes were presented based on BPM6. Beginning 2019, BOPSY is released in electronic format only. International Monetary Fund: Balance of Payments Statistics: Introductory Notes, as of November 2023 3 For many decades, the IMF has published data on a basis that is consistent across countries and across time periods. Such data consistency is required to perform cross-country data comparisons, track growth rates across time, and produce regional or global data aggregates. Data conversion work undertaken by IMF staff, in close consultation with IMF member countries, has made possible the presentation in the BPM6 format of data for the few economies that have not yet implemented BPM6. To assist users in understanding the impact of conversion to BPM6, as well as in understanding major methodological changes from BPM5 to BPM6, see FAQs on Conversion from BPM5 to BPM6 The methodologies, compiling practices, and data sources available through data.imf.org are based on information provided to the IMF by reporting countries. The descriptions are intended to enhance user understanding of the coverage, as well as the limitations, of individual country data. At the same time, they are useful in informing compilers of data sources and practices used by their counterparts in other countries.

About this dataset

Context

The dataset tabulates the Table Grove population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Table Grove. The dataset can be utilized to understand the population distribution of Table Grove by age. For example, using this dataset, we can identify the largest age group in Table Grove.

Key observations

The largest age group in Table Grove, IL was for the group of age 20-24 years with a population of 29 (9.83%), according to the 2021 American Community Survey. At the same time, the smallest age group in Table Grove, IL was the 45-49 years with a population of 8 (2.71%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Table Grove is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Table Grove total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Table Grove Population by Age. You can refer the same here

Subjects

MEG was recorded in 7 healthy subjects (5 young right-handed and 1 left-handed and 1 elderly) in the waking state with their eyes open or closed, who were sitting in a comfortable chair. The experimental technique was approved by the ethical commission of the Institute of Higher Nervous Activity and Neurophysiology of RAS (protocol No. 5 dated 02.12.2020).

Equipment

MEG was recorded on a VectorView device (Elekta Neuromag Oy, Finland), which was placed inside a magnetically protected chamber made of multilayer permalloy (AK3b, Vacuumschmelze GmbH, Germany). Before MEG recording, the coordinates of anatomical reference points (left and right preauricular points and nasion) were determined, as well as indicator coils attached to the surface of the scalp of the subject in the upper part of the forehead and behind the auricles. These points were determined using a FASTRAK 3D digitizer (Polhemus, USA). Each subject had a virtual model of the brain and head obtained from an anatomical 3D MRI taken the day before (file: MRI_V1_7.zip).

Registration and pre-processing

The subject's head was covered by a helmet, which is part of a fiberglass Dewar vessel with an array of sensors immersed in liquid helium. The subject sat down in such a way that the surface of the head was as close as possible to the sensors. The magnetic signal was recorded from 102 triplets, each of which consisted of 1 magnetometer and 2 gradiometers at rest with eyes closed and upon presentation of visual and speech stimuli. Recording was performed with a sampling frequency of 1000 Hz in a bandwidth of 0.1–330 Hz and was processed by the MaxFilter program (Elekta Neuromag Oy, Finland), which eliminates artifacts (the tSSS method—spatio-temporal separation of signals). The signal levels were corrected in accordance with the data on the position of the subject's head in relation to the MEG sensors. The position of the head during the experiment was controlled using special inductors.

Visual and verbal stimuli

After recording the background MEG for 3 minutes with closed eyes, the subject opened his eyes on command and observed the fixation point on the projection screen. After 15 seconds, stimulation was started and the subject's responses were received in the form of pressing a button. In response to the 0 degrees and 90 degrees stimuli, the subject had to press the button with the index finger, and to the 45 degrees and 135 degrees inclined stimuli, the adjacent button with the middle finger. Stimuli lasting 100 ms were presented randomly every 3100±100 ms (intervals between stimuli varied randomly). In two series, 42 stimuli of each orientation were presented. Between the series, the subject rested for 2-3 minutes. Visual stimuli in the form of Gabor contrast gratings (1.9 cycles per angular degree) with dimensions of 5.25 angular degrees and an average brightness of 4 lux were projected onto a screen located at a distance of 95 cm from the subject's eyes using a Panasonic PT-stimulating projector D7700E-K, which is part of the MEG facility. Visual stimulus patterns were generated at http://www.cogsci.nl/pages/gabor-generator with edge parameters: Circular (sharp edge). The samples are contained in the GaborStim.zip file (the names of the sample files correspond to their name in the script file, but do not match their geometric meaning, see table below). The stimulator was programmed using the Presentation software (USA, Neurobehavioral Systems, Inc). Stimulation scripts are contained in the sce.zip file.

Table

Stimulus or response code Type of stimulus or response

STI101_1 Fixation point
STI101_2 90 degrees
STI101_4 135 degrees

STI101_8 0 degrees
STI101_16 45 degrees
STI101_32 First button (index finger)

STI101_64 Second button (middle finger)

After 2 series of visual stimuli, the subject closed his eyes and was presented with 3 series of speech stimuli for 2 minutes with a break of 1 minute. In each series, recordings of audio files of 8 separate adjectives of the Russian language were presented, which were repeated 5 times in a pseudo-random order. The series began with 3 words, which were not taken into account in further analysis. The subject listened to the words and had to press the button after he understood the meaning of the presented word. After pressing or no response, the next word followed in 2±1 s. The audio files are contained in the words101_343.zip file (the names correspond to the script file).

Data received

The records are contained in files with the name of the type V1m24r, where V1 is the number of the subject, m is the sex (m/f), 24 is the age, and r is the right-handed subject. This dataset can be easily loaded into the Brainstorm program. Spontaneous and evoked MEG can be used for source localization and reconstruction of traveling waves.

Acknowledgments

The reported study was funded by RFBR, project number 20-015-00475.

17 tables and two figures of this paper. Table 1 is a subset of explicit entries identified in NHANES demographics data. Table 2 is a subset of implicit entries identified in NHANES demographics data. Table 3 is a subset of NHANES demographic Codebook entries. Table 4 presents a subset of explicit entries identified in SEER. Table 5 is a subset of Dictionary Mapping for the MIMIC-III Admission table. Table 6 shows high-level comparison of semantic data dictionaries, traditional data dictionaries, approaches involving mapping languages, and general data integration tools. Table A1 shows namespace prefixes and IRIs for relevant ontologies. Table B1 shows infosheet specification. Table B2 shows infosheet metadata supplement. Table B3 shows dictionary mapping specification. Table B4 is a codebook specification. Table B5 is a timeline specification. Table B6 is properties specification. Table C1 shows NHANES demographics infosheet. Table C2 shows NHANES demographic implicit entries. Table C3 shows NHANES demographic explicit entries. Table C4 presents expanded NHANES demographic Codebook entries. Figure 1 is a conceptual diagram of the Dictionary Mapping that allows for a representation model that aligns with existing scientific ontologies. The Dictionary Mapping is used to create a semantic representation of data columns. Each box, along with the “Relation” label, corresponds to a column in the Dictionary Mapping table. Blue rounded boxes correspond to columns that contain resource URIs, while white boxes refer to entities that are generated on a per-row/column basis. The actual cell value in concrete columns is, if there is no Codebook for the column, mapped to the “has value” object of the column object, which is generally either an attribute or an entity. Figure 2 presents (a) A conceptual diagram of the Codebook, which can be used to assign ontology classes to categorical concepts. Unlike other mapping approaches, the use of the Codebook allows for the annotation of cell values, rather than just columns. (b) A conceptual diagram of the Timeline, which can be used to represent complex time associated concepts, such as time intervals.

About this dataset

Context

The dataset tabulates the population of Table Rock by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Table Rock across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 54.74% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Table Rock is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Table Rock total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Table Rock Population by Race & Ethnicity. You can refer the same here