Overview

This dataset is built upon SQL Create Context, which in turn was constructed using data from WikiSQL and Spider. There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-SQL LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-SQL datasets. The CREATE TABLE statement can often be… See the full description on the dataset page: https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction.

Fork of b-mc2/sql-create-context

  Overview

This dataset builds from WikiSQL and Spider. There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/sql-create-context-copy.

Context

BigQuery provides a limited number of sample tables that you can run queries against. These tables are suited for testing queries and learning BigQuery.

Content

• gsod: Contains weather information collected by NOAA, such as precipitation amounts and wind speeds from late 1929 to early 2010.

• github_nested: Contains a timeline of actions such as pull requests and comments on GitHub repositories with a nested schema. Created in September 2012.

• github_timeline: Contains a timeline of actions such as pull requests and comments on GitHub repositories with a flat schema. Created in May 2012.

• natality: Describes all United States births registered in the 50 States, the District of Columbia, and New York City from 1969 to 2008.

• shakespeare: Contains a word index of the works of Shakespeare, giving the number of times each word appears in each corpus.

• trigrams: Contains English language trigrams from a sample of works published between 1520 and 2008.

• wikipedia: Contains the complete revision history for all Wikipedia articles up to April 2010.

Fork this kernel to get started.

Acknowledgements

Data Source: https://cloud.google.com/bigquery/sample-tables

Banner Photo by Mervyn Chan from Unplash.

Inspiration

How many babies were born in New York City on Christmas Day?

How many words are in the play Hamlet?

Here are a few use cases for this project:

1. Document Analysis: The model could be used in parsing and analyzing different kinds of documents or reports where extraction and interpretation of data from tables is essential - like research papers, corporate reports, financial statements etc.

2. Form Processing: In bureaucratic and administrative settings where the information from application forms or surveys often comes in table form, the model could be used to automate the processing and parsing of these forms.

3. E-Book Reader Applications: The model could be used to enhance user interaction in e-book reader apps. For instance, it could identify tables to allow for zoom-in functionality or assist in text-to-speech functionality specifically for tables.

4. Scanned Document Processing: The model could be used in solutions dealing with scanned documents especially in instances where optical character recognition (OCR) alone is not sufficient.

5. Education Resource Development: The model could aid in developing digital educational resources, helping to differentiate descriptive text from tabulated data in textbooks and learning materials. It could also extract table data to create interactive study aids or quizzes.

If you have geographic information stored as a table, ArcGIS Pro can display it on a map and convert it to spatial data. In this tutorial, you'll create spatial data from a table containing the latitude-longitude coordinates of huts in a New Zealand national park. Huts in New Zealand are equivalent to cabins in the United States—they may or may not have sleeping bunks, kitchen facilities, electricity, and running water. The table of hut locations is stored as a comma-separated values (CSV) file. CSV files are a common, nonproprietary file type for tabular data.Estimated time: 45 minutesSoftware requirements: ArcGIS Pro

Here are a few use cases for this project:

1. Digital Archiving: This model can be used to digitize tables or tabular data from old books, journals, articles, or physical documents. This would be highly beneficial for libraries, research institutions, and museums.

2. Data Extraction: Companies or researchers focusing on big data might find this model useful for extracting and structuring data from numerous documents in a very short period, eliminating manual labor and saving time.

3. Accessibility: The model can help create more accessible digital formats for people with visual impairments by identifying and describing tables within documents or webpages.

4. Invoice Processing: Companies dealing with numerous invoices, receipts or order forms can use this model to automate the extraction of tabular information.

5. Educational Resource Building: Educators can leverage this model to create interactive and engaging digital resources from traditional textbooks or study materials containing tables. This can transform how students learn complex subjects.

Extracted from dblp (https://dblp.org), and augmented with data from openalex (https://openalex.org).

Stored as an SQLITE database with the given schema:

CREATE TABLE IF NOT EXISTS authors (
      id INTEGER PRIMARY KEY,
      name TEXT NOT NULL,
      orcid TEXT,
      dblp_id TEXT,
      openalex_id TEXT
    );

CREATE TABLE IF NOT EXISTS institutions (
      id INTEGER PRIMARY KEY,
      openalex_id TEXT NOT NULL UNIQUE,
      name TEXT NOT NULL,
      country_code TEXT
    );

CREATE TABLE IF NOT EXISTS venues (
      id INTEGER PRIMARY KEY,
      name TEXT NOT NULL,
      dblp_key TEXT NOT NULL,
      short_name TEXT NOT NULL,
      venue_type TEXT NOT NULL
    );

CREATE TABLE IF NOT EXISTS articles (
      id INTEGER PRIMARY KEY,
      title TEXT NOT NULL,
      doi TEXT,
      year INTEGER NOT NULL,
      venue_id INTEGER NOT NULL,
      dblp_key TEXT NOT NULL UNIQUE,
      FOREIGN KEY (venue_id) REFERENCES venues (id)
    );

CREATE TABLE IF NOT EXISTS article_authors (
      article_id INTEGER NOT NULL,
      author_id INTEGER NOT NULL,
      PRIMARY KEY (article_id, author_id),
      FOREIGN KEY (article_id) REFERENCES articles (id),
      FOREIGN KEY (author_id) REFERENCES authors (id)
    );

CREATE TABLE IF NOT EXISTS author_institutions (
      author_id INTEGER NOT NULL,
      institution_id INTEGER NOT NULL,
      PRIMARY KEY (author_id, institution_id),
      FOREIGN KEY (author_id) REFERENCES authors (id),
      FOREIGN KEY (institution_id) REFERENCES institutions (id)
    );

CREATE INDEX IF NOT EXISTS idx_authors_name ON authors (name);
CREATE INDEX IF NOT EXISTS idx_authors_dblp_id ON authors (dblp_id);
CREATE INDEX IF NOT EXISTS idx_articles_year ON articles (year);
CREATE INDEX IF NOT EXISTS idx_articles_venue ON articles (venue_id);
CREATE INDEX IF NOT EXISTS idx_venues_dblp_key ON venues (dblp_key);

This dataset contains data from the Council’s Annual Budget. The budget is comprised of Tables A to F and Appendix 1. Each table is represented by a separate data file.Table C is the Calculation of the Annual Rate on Valuation for the Financial Year for Balbriggan Town Council. It contains –Estimate of ‘Money Demanded’Adopted ‘Money Demanded’Estimated ‘Irrecoverable rates and cost of collection’Adopted ‘Irrecoverable rates and cost of collection’Total Sum to be Raised is the sum of ‘Money Demanded’ and ‘Irrecoverable rates and cost of collection’‘Annual Rate on Valuation to meet Total Sum to be Raised’This dataset is used to create Table C in the published Annual Budget document, which can be found at www.fingal.ie The data is best understood by comparing it to Table C.Data fields for Table C are as follows –Doc : Table ReferenceHeading : Indicates sections in the Table - Table C is comprised of one section, therefore Heading value for all records = 1Ref : Town ReferenceDesc : Town DescriptionMD_Est : Money Demanded EstimatedMD_Adopt : Money Demanded AdoptedIR_Est : Irrecoverable rates and cost of collection EstimatedIR_Adopt : Irrecoverable rates and cost of collection AdoptedNEV : Annual Rate on Valuation to meet Total Sum to be Raised

Table

## Overview

Table is a dataset for object detection tasks - it contains Empty Text annotations for 302 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

YouCat is a query service for catalogues hosted by CANFAR. This service provides Table Access Protocol (TAP) API access to catalogues created by project teams. YouCat also implements a prototype VOSI-tables extension that allows users to create, update table metadata add table content (rows), create indices, and delete tables in the database. Users can also control access to their own tables (public to allow anonymous querying, protected so only members of specified groups can query, and private where only the owner can query). Users who want to create tables must request an allocation (schema) by email to support@canfar.net. Currently available catalogues include: VLASS, CFHTLS, KiDS, PAndAS, RCSLens.

This dataset contains data from the Council’s Annual Budget. The budget is comprised of Tables A to F and Appendix 1. Each table is represented by a separate data file.Table C is the Calculation of the Annual Rate on Valuation for the Financial Year for Balbriggan Town Council. It contains –Estimate of ‘Money Demanded’Adopted ‘Money Demanded’Estimated ‘Irrecoverable rates and cost of collection’Adopted ‘Irrecoverable rates and cost of collection’Total Sum to be Raised is the sum of ‘Money Demanded’ and ‘Irrecoverable rates and cost of collection’‘Annual Rate on Valuation to meet Total Sum to be Raised’This dataset is used to create Table C in the published Annual Budget document, which can be found at www.fingal.ieThe data is best understood by comparing it to Table C.Data fields for Table C are as follows –Doc : Table ReferenceHeading : Indicates sections in the Table - Table C is comprised of one section, therefore Heading value for all records = 1Ref : Town ReferenceDesc : Town DescriptionMD_Est : Money Demanded EstimatedMD_Adopt : Money Demanded AdoptedIR_Est : Irrecoverable rates and cost of collection EstimatedIR_Adopt : Irrecoverable rates and cost of collection AdoptedNEV : Annual Rate on Valuation to meet Total Sum to be Raised

Glass Table Market Outlook

The global glass table market size was valued at approximately USD 2.5 billion in 2023 and is anticipated to reach around USD 4.1 billion by 2032, growing at a CAGR of 6% over the forecast period. The growth of the glass table market is significantly driven by the rising demand for aesthetically appealing and modern furniture, coupled with the increasing trend of home renovations and interior design enhancements. The surge in disposable income and changing consumer preferences toward high-end and visually attractive furniture pieces have further propelled the market growth.

One of the primary growth factors for the glass table market is the increasing urbanization and the consequent rise in residential complexes. As more people move to urban areas, there is a heightened demand for stylish and functional furniture, including glass tables, to furnish new homes and apartments. Additionally, the growing middle-class population across various regions, especially in developing nations, is increasingly investing in home decor, contributing significantly to the market expansion. The fusion of traditional and contemporary designs in glass tables offers a versatile appeal that caters to a broad consumer base.

Technological advancements in glass manufacturing have also played a crucial role in the growth of the glass table market. Innovations such as tempered and laminated glass have provided consumers with durable, scratch-resistant, and safe options, making glass tables a preferred choice for both residential and commercial settings. Moreover, the rising trend of sustainable and eco-friendly furniture has led to an increased adoption of recyclable glass tables, further supporting market growth. Manufacturers are continually investing in R&D to develop innovative designs and enhance the functional attributes of glass tables, thereby attracting a larger customer base.

The expanding commercial sector, particularly the hospitality and corporate industries, has also been a significant driver for the glass table market. Hotels, restaurants, and offices are increasingly adopting stylish and elegant glass tables to enhance their interiors and create a sophisticated ambiance. The growing trend of open office layouts and collaborative workspaces has further boosted the demand for glass tables in office settings, as they offer a sleek and modern appeal. Additionally, the rise of the e-commerce sector and online furniture stores has made it easier for consumers to access a wide variety of glass table designs, fueling market growth.

Regionally, North America is expected to dominate the glass table market, followed by Europe and Asia Pacific. The high disposable income, well-established real estate sector, and strong inclination towards interior design and home decor in these regions are key factors contributing to market growth. Moreover, the presence of leading furniture manufacturers and designers in these regions offers a diverse range of glass table options to consumers. The Asia Pacific region, with its rapidly growing urban population and increasing adoption of modern furniture trends, is anticipated to witness significant growth, driven by countries like China and India.

In the realm of modern furniture, Original Design Coffee Tables have carved a niche for themselves by offering unique aesthetics and functionality. These tables are not just pieces of furniture but are considered art forms that enhance the overall decor of a space. With a focus on originality and creativity, these coffee tables often feature innovative designs that blend seamlessly with contemporary interiors. The demand for such tables is driven by consumers who seek to express their personal style through distinctive furniture pieces. As the trend towards bespoke and personalized home decor continues to grow, Original Design Coffee Tables are becoming increasingly popular among homeowners and interior designers alike.

Product Type Analysis

Tempered glass tables have emerged as the most popular product type within the glass table market. The superior strength and safety features of tempered glass make it a preferred choice for both residential and commercial applications. Tempered glass is approximately four to five times stronger than standard glass and is designed to shatter into small, blunt pieces rather than sharp shards, significantly reducing the risk of injury. This safety aspect has made tempered glass tables particularly popular in households w

Overview

Este dataset é uma versão traduzida para o português do dataset b-mc2/sql-create-context, que foi construído a partir dos datasets WikiSQL e Spider. Ele contém exemplos de perguntas em português, instruções SQL CREATE TABLE e consultas SQL que respondem às perguntas utilizando a instrução CREATE TABLE como contexto. O principal objetivo deste dataset é ajudar modelos de linguagem natural em português a gerar consultas SQL precisas e contextualizadas, prevenindo a… See the full description on the dataset page: https://huggingface.co/datasets/emdemor/sql-create-context-pt.

Overview

This dataset is a fork from sql-create-context This dataset builds from WikiSQL and Spider. There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from… See the full description on the dataset page: https://huggingface.co/datasets/detakarang/sql-create-context-id.

Kids Table and Chair Market Outlook

The global kids table and chair market size was valued at approximately USD 2.1 billion in 2023 and is expected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.5% during the forecast period. The growth factor driving this market includes increased consumer spending on kids' furniture, coupled with the growing awareness about the importance of ergonomically designed furniture for children.

One of the primary growth factors for the kids table and chair market is the rising emphasis on early childhood education and development. Parents and educational institutions are increasingly investing in furniture that supports the physical and cognitive development of children. Ergonomically designed tables and chairs can significantly contribute to better posture and concentration, which are crucial during the formative years. This focus on developmental furniture is pushing manufacturers to innovate and create products that not only appeal to children but also meet various safety and health standards.

Another factor contributing to the market growth is the increasing urbanization and the growth of nuclear families. As more families move to urban areas, there is a growing need for space-efficient, versatile furniture that can fit into smaller living spaces. KidsÂ’ tables and chairs that are foldable or multi-functional are particularly in demand. Additionally, the rise of dual-income households means that parents have more disposable income to spend on quality furniture for their children, further driving market growth.

The growing trend of online shopping also plays a significant role in the expansion of the kids table and chair market. E-commerce platforms offer a wide range of products, often at competitive prices, and provide the convenience of home delivery. These platforms also enable consumers to compare different products and read reviews, which helps them make informed purchasing decisions. The ease of online shopping, coupled with attractive discounts and offers, has led to an increase in online sales of kids' furniture.

In the context of modern living spaces, Play Room Furniture has become an essential component of children's interior design. As families increasingly prioritize creating dedicated play areas within their homes, the demand for versatile and safe playroom furniture has surged. This trend is not only about aesthetics but also about functionality and safety, as parents seek furniture that can withstand the rigors of active play while ensuring the safety of their children. Manufacturers are responding by offering a variety of options, from modular play sets to themed furniture that can spark creativity and imagination in young minds. The integration of playroom furniture into the broader category of kids' furniture highlights the evolving needs of modern families and the importance of creating engaging environments for children.

Regionally, the Asia Pacific region is expected to witness significant growth in the kids table and chair market. Factors such as increasing disposable income, rising awareness about child development, and the growing middle-class population are driving the demand for kids' furniture in this region. North America and Europe are also expected to show substantial growth due to high consumer spending on children's products and the presence of major market players.

Product Type Analysis

The kids table and chair market is segmented by product type into plastic, wood, metal, and others. Plastic furniture is quite popular due to its lightweight, durability, and cost-effectiveness. It is also easy to clean and maintain, which makes it a preferred choice for many parents. Additionally, plastic furniture is available in a wide range of colors and designs, which can be appealing to children. However, concerns about environmental impact and the use of non-toxic materials are pushing manufacturers to innovate and produce eco-friendly plastic furniture.

Wooden kids' tables and chairs are often favored for their durability and aesthetic appeal. Wood is a sturdy material that can withstand rough usage, making it ideal for young children. Moreover, wooden furniture is often perceived as more environmentally friendly compared to plastic. The timeless appeal of wood, combined with its ability to blend seamlessly with different types of home decor, has kept it popular among

🇮🇪 아일랜드 English This dataset contains data from the Council’s Annual Budget. The budget is comprised of Tables A to F and Appendix 1. Each table is represented by a separate data file.Table A is the Calculation of the Annual Rate on Valuation for the Financial Year. It is comprised of a number of sections and a series of calculations to determine the Annual Rate on Valuation.This dataset is used to create Table A in the published Annual Budget document, which can be found at www.fingal.ieThe data is best understood by comparing it to Table A.Section 1 of Table A contains the Budgeted ‘Expenditure’ and ‘Income’ per Council Division for the Financial Year and the ‘Estimated Net Outturn’ per Council Division for the previous Financial Year. The ‘Estimated Net Expenditure’ for the Financial Year is the sum of ‘Expenditure’ and ‘Income’.Section 2 of Table A contains ‘Minus County Charge’ and ‘Provision for Debit Balance’The ‘Adjusted Gross Expenditure and Income’ is the total of Section 1 and Section 2Section 3 of Table A contains ‘Provision for Credit Balance’, ‘Local Government Fund/General Purpose Grant’ and ‘Pension Levy Deduction’The ‘Amount of Rates to be Levied’ is the ‘Adjusted Gross Expenditure and Income’ minus the total of Section 3Section 4 of Table A contains ‘Net Effective Valuation’The ‘General Annual Rate on Valuation’ is the ‘Amount of Rates to be Levied’ divided by the ‘Net Effective Valuation’Data fields for Table A are as follows –Doc : Table ReferenceHeading : Indicates sections in the Table - Table A is comprised of four sections; each section is represented by a sequential number in the heading field i.e. Heading = 1 for all records in the first section; etc.Ref : Item Reference (In section 1 = Division Reference; In other sections, CAL = Minus County Charge; DB = Provision for Debit Balance; CB = Provision for Credit Balance; LGF = Local Government Fund/General Purpose Grant; PL = Pension Levy Deduction; NEV = Net Effective Valuation)Description : Item DescriptionExpenditure : Expenditure for this ItemIncome : Income for this ItemPY : Estimated Outturn for this Item for previous Financial Year

First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatio-temporally correlated. Consequently, the non-independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second-order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 years of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long-term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.

Methods

These files were written by: G. V. DiRenzo

If you have any questions, please email: grace.direnzo@gmail.com

This repository provides the code, data, and simulations to recreate all of the analysis, tables, and figures presented in the manuscript.

In this file, we direct the user to the location of files.

All methods can be found in the manuscript and associated supplements.

All file paths direct the user in navigating the files in this repo.

######## Objective & Table of contents

File objectives & Table of contents:

# 1. To navigate to files explaining how to simulate and analyze data using the main text parameterization
# 2. To navigate to files explaining how to simulate and analyze data using the alternative parameterization (hidden Markov model)
# 3. To navigate to files that created the parameter combinations for the simulation studies
# 4. To navigate to files used to run scenarios in the manuscript
  # 4a. Scenario 1: data generated without site memory & without site heterogenity
  # 4b. Scenario 2: data generated with site memory & without site heterogenity
  # 4c. Scenario 3: data generated with site memory & with site heterogenity
# 5. To navigate to files for general sample design guidelines
# 6. Parameter accuracy, precision, and bias under different parameter combinations
# 7. Model comparison under different scenarios
# 8. To specifically navigate to code that recreates manuscript:
  # 8a. Figures
  # 8b. Tables
# 9. To navigate to files for empirical analysis

### 1. Main text parameterization

To see model parameterization as written in the main text, please navigate to: /MemModel/OtherCode/MemoryMod_main.R

### 2. Alternative parameterization

To see alternative parameterization using a Hidden Markov Model, please navigate to: /MemModel/OtherCode/MemoryMod_HMM.R

### 3. Parameter Combinations

To see how parameter combinations were generated, please navigate to: /MemModel/ParameterCombinations/LHS_parameter_combos.R

To see stored parameter combinations for simulations, please navigate to: /MemModel/ParameterCombinations/parameter_combos_MemModel4.csv

### 4a. Scenario #1

To simulate data WITHOUT memory and analyze using: - memory model & - first-order dynamic occupancy model

Please navigate to: /MemModel/Simulations/withoutMem/Code/ MemoryMod_JobArray_withoutMem.R = code to simulate & analyze data MemoryMod_JA1.sh = file to run simulations 1-5000 on HPC MemoryMod_JA2.sh = file to run simulations 5001-10000 on HPC

All model output is stored in: /MemModel/Simulations/withoutMem/ModelOutput

### 4b. Scenario #2

To simulate data WITH memory and analyze using: - memory model & - first-order dynamic occupancy model

Please navigate to: /MemModel/Simulations/withMem/Code/ MemoryMod_JobArray_withMem.R = code to simulate & analyze data MemoryMod_JA1.sh = file to run simulations 1-5000 on HPC MemoryMod_JA2.sh = file to run simulations 5001-10000 on HPC

All model output is stored in: /MemModel/Simulations/withMem/ModelOutput

### 4c. Scenario #3

To simulate data WITH memory and WITH site heterogenity- analyze using: - memory model & - first-order dynamic occupancy model

Please navigate to: /MemModel/Simulations/Hetero/Code/ MemoryMod_JobArray_Hetero.R = code to simulate & analyze data MemoryMod_JA1.sh = file to run simulations 1-5000 on HPC MemoryMod_JA2.sh = file to run simulations 5001-10000 on HPC

All model output is stored in: /MemModel/Simulations/Hetero/ModelOutput

### 5. General sample design guidelines

To see methods for the general sample design guidelines, please navigate to: /MemModel/PostProcessingCode/Sampling_design_guidelines.R

### 6. Parameter accuracy, precision, and bias under different parameter combinations

To see methods for model performance under different parameter combinations, please navigate to: /MemModel/PostProcessingCode/Parameter_precison_accuracy_bias.R

### 7. Comparison of model performance

To see methods for model comparison, please navigate to: /MemModel/PostProcessingCode/ModelComparison.R

### 8a. Manuscript Figures

To create parts of Figure 1 of main text (case study): - Fig 1D & 1E: /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

To create Figure 2 of main text (Comparison across simulation scenarios): - /MemModel/PostProcessingCode/ModelComparison.R

To create Figure S1, S2, & S3 use file: - /MemModel/PostProcessingCode/Parameter_precison_accuracy_bias.R

To create Figure S4 & S5 use file: - /MemModel/PostProcessingCode/ModelComparison.R

### 8b. Manuscript Tables

To create Table 1 of main text (General sampling recommendations): - /MemModel/PostProcessingCode/Sampling_design_guidelines.R

To create Table S1: - /MemModel/PostProcessingCode/Parameter_precison_accuracy_bias.R

To create Table S2: - /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

To create Table S3: - /MemModel/PostProcessingCode/ModelComparison.R

To create Table S4 & S5: - /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

### 9. Empirical analysis

To recreate the empirical analysis of the case study, please navigate to: - /MemModel/EmpiricalAnalysis/Code/Analysis/AZ_CaseStudy.R

Aircraft Seat Table Market Outlook

The global aircraft seat table market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 2.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.3% over the forecast period. A major growth factor contributing to this market is the increasing demand for enhanced passenger comfort and convenience in both commercial and business aviation sectors.

One of the primary growth factors in the aircraft seat table market is the rising number of air travelers globally. With an increase in disposable income and a growing middle-class population, more individuals are opting for air travel. This surge in passenger numbers has put pressure on airlines to improve their service offerings, including the provision of more advanced, ergonomic, and user-friendly seat tables. Enhanced passenger experience has become a critical differentiator for airlines, contributing significantly to the demand for high-quality aircraft seat tables.

Another key driver is the advancement in material technology. Innovations in materials such as lightweight composites and high-strength plastics have allowed manufacturers to create seat tables that are not only durable but also lighter, thereby contributing to overall fuel efficiency. As airlines continually seek ways to reduce operational costs and carbon footprints, the adoption of these advanced materials in aircraft seat tables is becoming increasingly widespread. Moreover, regulatory bodies are also imposing stringent guidelines for in-flight safety and comfort, further driving the need for superior seat tables.

Customization and modular designs are other significant growth factors. Passengers today expect more personalized experiences, and this trend is extending to aviation. Customizable and modular seat tables that can easily adjust to various passenger needs, such as meal dining, working on laptops, or resting, are gaining popularity. These innovations allow airlines to better utilize cabin space and enhance passenger experience, thereby boosting the market for aircraft seat tables.

Regionally, North America holds a substantial share of the aircraft seat table market due to the presence of major aircraft manufacturers and a high rate of air travel. Europe and Asia Pacific are also expected to witness significant growth, driven by increasing aircraft deliveries and the expansion of low-cost carriers in these regions. The Middle East, with its growing focus on luxury air travel, also presents lucrative opportunities for the aircraft seat table market.

Product Type Analysis

The aircraft seat table market can be segmented by product type into fixed, foldable, and sliding tables. Fixed seat tables are often found in economy class and are permanently attached to the seat structure. These tables are generally more cost-effective and require less maintenance, making them a popular choice for budget airlines. However, their lack of flexibility can be a drawback for passengers seeking more comfort and utility during their flights. Despite this, the fixed seat table segment continues to hold a significant market share due to its cost-efficiency and ease of installation.

Foldable seat tables are designed with a hinge mechanism, allowing them to be folded away when not in use. These tables are commonly found in business and first-class cabins, where space efficiency and passenger convenience are prioritized. The ability to fold the table away provides passengers with more legroom and flexibility, making it a preferred choice for premium airline services. This segment is expected to witness substantial growth, driven by increasing passenger expectations and the airline industry's focus on enhancing in-flight experiences.

Sliding seat tables offer a blend of flexibility and usability. These tables can be adjusted horizontally, allowing passengers to position them according to their comfort and needs. Sliding tables are particularly popular in business jets and premium economy sections, where passengers may require more workspace for laptops or dining. The segment is gaining traction due to its versatility and the growing trend of multi-functional cabin interiors. Airlines are increasingly adopting sliding seat tables to cater to the diverse needs of modern travelers.

The competition among these product types also prompts manufacturers to innovate continuously. Companies are investing in research and development to create more durable, lightweight, and user-friendly seat tables. The intro

Multiple Tables, Table Tennis

## Overview

Multiple Tables, Table Tennis is a dataset for object detection tasks - it contains Table annotations for 4,457 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).