BackgroundPeritonsillar abscess (PTA) is a prevalent infection for specialists in otorhinolaryngology and pediatric primary care providers, that has the potential to cause severe complications. The aim of this study is to investigate the surgical treatment of pediatric peritonsillar abscesses and to compare the risk profiles of bilateral surgery versus surgery on the affected side alone. In addition, the evaluation of the microbiological smears obtained intraoperatively should provide information on whether the calculated antibiotic therapy adequately covers the microbial spectrum.MethodsWe conducted a retrospective analysis of pediatric patients (n = 150), who were treated for PTA between 2009 and 2024 by unilateral tonsillectomy (UTE) or bilateral tonsillectomy (BTE). Patient charts were analyzed regarding risk of bleeding, occurrence of other complications, recurrence rates in case of UTE as well as microbiological flora and antibiotic treatment.ResultsPostoperative bleeding did not differ significantly between both groups. In 4.4% of the patients treated by UTE a recurrent PTA was found. No other severe complications after surgical treatment were found. Antibiotic treatment mainly relied on Cefuroxime and Ampicillin-Sulbactam, which is in accordance with the detected microbiological flora.ConclusionsNo relevant differences were found with regard to the complication rate between UTE und BTE in pediatric patients. Broad-spectrum antibiotics were used in accordance with the detected microbiological flora. Since 2019, calculated antibiotic therapy with Ampicillin-Sulbactam has been the treatment of choice for pediatric PTA.

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.

The Tables file contains all tabular data included in the manuscript. Tables are an essential part of presenting quantitative and categorical data clearly and concisely.Content: The tables in this file display key data that support the findings of the research. This might include:Descriptive statistics (e.g., means, standard deviations, medians) for different treatment groups.Raw data or aggregated data used for statistical analysis.Results of statistical tests (e.g., p-values, confidence intervals) showing group comparisons.Experimental details (e.g., participant or animal characteristics, treatment groups).Format: Tables are typically provided in a Word document, Excel file, or PDF, depending on the journal's submission guidelines. Each table should be clearly labeled with a Table number (e.g., Table 1, Table 2, etc.) and a table legend that explains what the table represents.Organization: Each table is numbered and described within the file. The order of the tables should correspond to the order in which they are referenced in the manuscript text.Table Legends: Each table should have an accompanying legend explaining its contents in detail. This helps readers interpret the data presented in the table.

Original excel files of tabular data that have been used to generate the visual presentation using graphs and charts of the techniques for the current research trends within 6 years (from years 2013 to 2018).

The New York City Police Department records reported crime and offense data based upon the New York State Penal Law and other New York State laws. For statistical presentation purposes the numerous law categories and subsections are summarized by law class, Felony, Misdemeanor and Violation. The tabular data compiles reported crime and offense data recorded by the New York City Police Department. Separate tables are presented for the Seven Major Felonies, Non-Seven Major Felony Crimes, Misdemeanors and Violations.

The datatablesview extension for CKAN enhances the display of tabular datasets within CKAN by integrating the DataTables JavaScript library. As a fork of a previous DataTables CKAN plugin, this extension aims to provide improved functionality and maintainability for presenting data in a user-friendly and interactive tabular format. This tool focuses on making data more accessible and easier to explore directly within the CKAN interface. Key Features: Enhanced Data Visualization: Transforms standard CKAN dataset views into interactive tables using the DataTables library, providing a more engaging user experience compared to plain HTML tables. Interactive Table Functionality: Includes features such as sorting, filtering, and pagination within the data table, allowing users to easily navigate and analyze large datasets directly in the browser. Improved Data Accessibility: Makes tabular data more accessible to a wider range of users by providing intuitive tools to explore and understand the information. Presumed Customizable Appearance: Given that it is based on DataTables, users will likely be able to customize the look and feel of the tables through DataTables configuration options (note: this is an assumption based on standard DataTables usage and may require coding). Use Cases (based on typical DataTables applications): Government Data Portals: Display complex government datasets in a format that is easy for citizens to search, filter, and understand, enhancing transparency and promoting data-driven decision-making. For example, presenting financial data, population statistics, or environmental monitoring results. Research Data Repositories: Allow researchers to quickly explore and analyze large scientific datasets directly within the CKAN interface, facilitating data discovery and collaboration. Corporate Data Catalogs: Enable business users to easily access and manipulate tabular data relevant to their roles, improving data literacy and enabling data-informed business strategies. Technical Integration (inferred from CKAN extension structure): The extension likely operates by leveraging CKAN's plugin architecture to override the default dataset view for tabular data. Its implementation likely uses CKAN's templating system to render datasets using DataTables' JavaScript and CSS, enhancing data-viewing experience. Benefits & Impact: By implementing the datatablesview extension, organizations can improve the user experience when accessing and exploring tabular datasets within their CKAN instances. The enhanced interactivity and data exploration features can lead to increased data utilization, improved data literacy, and more effective data-driven decision-making within organizations and communities.

This presentation provides an overview of the Table to Data (T2D) research project.

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.

Vegetation cover types used to develop a forest inventory conducted by the State of Alaska Division of Forestry. Inventory with supporting ground plots on State, Federal and Native Corporation land in the Cordova Area.

Table A2.5 - A tabular presentation summarizing British Columbia revenue by source, actual values, 1999-2000 through 2010-2011, including percent of average annual change; Table A2.6 A tabular presentation of British Columbia revenue by source supplementary information, actual values, 2000-2001 through 2010-2011, including percent of average annual change.

Vegetation classified in the Copper River area by the State of Alaska, Department of Natural Resources, Division of Forestry, Fairbanks Area, from 2009 through 2010.

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.

Example tabular presentation of data for the scoping review.

17 Global import shipment records of Table Display with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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

The metadata set does not comprise any description or summary. The information has not been provided.

Vegetation classification in the Lower Kuskokwim area utilizes Spot 2015 and ESRI basemap imagery and has been interpreted by the State of Alaska, Department of Natural Resources, Division of Forestry, Northern Region. Vegetation layer includes attributes for volume calculations of timbered polygons. Sample plot layer includes individual sample tree attributes. Sample stand layer includes volume calculations for sample stands. Selected stands were sampled for volume in 2004.

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

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

102 Active Global Display Table suppliers, manufacturers list and Global Display Table exporters directory compiled from actual Global export shipments of Display Table.