The Temporary Program, is no longer accepting applications. *Visit Permanent Dining Out website for information: https://www.diningoutnyc.info/ The New York City Open Restaurant is an effort to implement a citywide multi-phase program to expand outdoor seating options for food establishments to promote open space, enhance social distancing, and help them rebound in these difficult economic times. For real time updates on restaurants registered in the program, please visit NYC Open Restaurants dashboard: https://bit.ly/2Z00kn8 ** Please note this Open Restaurant Applications dataset may contain multiple entries (e.g. restaurants submitting 2 or more applications). The Open Restaurants dashboard website containing real time update, noted above, will have fewer total records due to the removal of multiple applications and only list the newest entry.

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance. Data were collected to determine when restaurants in states and territories were subject to closing and reopening requirements through executive orders, administrative orders, resolutions, and proclamations for COVID-19. Data can be used to determine when restaurants in states and territories were subject to closing and reopening requirements through executive orders, administrative orders, resolutions, and proclamations for COVID-19. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level. These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly close or reopen restaurants found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, and the CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 11, 2020 through May 31, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the date provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

About this Dataset

Contact information for over 20,000 restaurants across the US. All restaurants from the NAICS code 72251: Restaurants and Other Eating Places. This includes all set down, fast casual, fast food, and ethnic restaurants. List includes name, address, phone number, website, contact email address, and a brief description. Data was collected from a combination of web scrapping and manual data entry. Similar lists cost over $1500 from lead generation and business data companies.

Category

Lead Generation

Keywords

restaurants,contact,mailing

Row Count

21210

Price

$499.00

MealMe offers in-depth restaurant menu data, including prices, from the top 100,000 restaurants across the USA and Canada. Our proprietary technology collects accurate, real-time menu and pricing information, enabling businesses to make data-driven decisions in competitive intelligence, pricing optimization, and market research. With comprehensive coverage that spans major restaurant platforms and chains, MealMe ensures your business has access to the most reliable data to excel in a rapidly evolving industry.

Platforms and Restaurants Covered: MealMe's database includes data from leading restaurant platforms such as UberEats, Postmates, ToastTakeout, SkipTheDishes, Square, Appfront, Olo, TouchBistro, and Clover, as well as direct menu data from major restaurant chains including Raising Cane’s, Panda Express, Popeyes, Burger King, and Subway. This extensive coverage ensures a detailed view of the market, helping businesses monitor trends, pricing, and availability across a broad spectrum of restaurant types and sizes.

Key Features: Comprehensive Menu Data: Access detailed menu information, including item descriptions, categories, sizes, and customizations. Real-Time Pricing: Monitor up-to-date menu prices for accurate competitive analysis. Restaurant-Specific Insights: Analyze individual restaurant chains such as Raising Cane’s and Panda Express, or platforms like UberEats, for market trends and pricing strategies. Cross-Platform Analysis: Compare menu items and pricing across platforms like ToastTakeout, Olo, and SkipTheDishes for a holistic industry view. Regional Data: Understand geographic variations in menu offerings and pricing across the USA and Canada.

Use Cases: Competitive Intelligence: Track menu offerings, pricing strategies, and seasonal trends across platforms like UberEats and Postmates or chains like Popeyes and Subway. Market Research: Identify gaps in the market by analyzing menus and pricing from top restaurants. Pricing Optimization: Use real-time pricing data to inform dynamic pricing strategies and promotions. Trend Monitoring: Stay ahead by tracking popular menu items, regional preferences, and emerging food trends. Platform Analysis: Assess how restaurants perform across delivery platforms such as SkipTheDishes, Olo, and Square. Industries Benefiting from Our Data Restaurant Chains: Optimize menu offerings and pricing strategies with detailed competitor data. Food Delivery Platforms: Benchmark menu pricing and availability across competitive platforms. Market Research Firms: Conduct detailed analyses to identify opportunities and market trends. AI & Analytics Companies: Power recommendation engines and predictive models with robust menu data. Consumer Apps: Enhance app experiences with accurate menu and pricing data. Data Delivery and Integration

MealMe offers flexible integration options to ensure seamless access to our comprehensive menu data. Whether you need bulk exports for in-depth research or real-time updates via API, our solutions are designed to scale with your business needs.

Why Choose MealMe? Extensive Coverage: Menu data from 100,000+ restaurants, including major chains like Burger King and Raising Cane’s. Real-Time Accuracy: Up-to-date pricing and menu details for actionable insights. Customizable Solutions: Tailored datasets to meet your specific business objectives. Proven Expertise: Trusted by top companies for delivering reliable, actionable data. MealMe empowers businesses with the data needed to thrive in a competitive restaurant and food delivery market. For more information or to request a demo, contact us today!

The Delta Food Outlets Study was an observational study designed to assess the nutritional environments of 5 towns located in the Lower Mississippi Delta region of Mississippi. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns in which Delta Healthy Sprouts participants resided and that contained at least one convenience (corner) store, grocery store, or gas station. Data were collected via electronic surveys between March 2016 and September 2018 using the Nutrition Environment Measures Survey (NEMS) tools. Survey scores for the NEMS Corner Store, NEMS Grocery Store, and NEMS Restaurant were computed using modified scoring algorithms provided for these tools via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one (NEMS-C) contains data collected with the NEMS Corner (convenience) Store tool. Dataset two (NEMS-G) contains data collected with the NEMS Grocery Store tool. Dataset three (NEMS-R) contains data collected with the NEMS Restaurant tool. Resources in this dataset:Resource Title: Delta Food Outlets Data Dictionary. File Name: DFO_DataDictionary_Public.csvResource Description: This file contains the data dictionary for all 3 datasets that are part of the Delta Food Outlets Study.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One NEMS-C. File Name: NEMS-C Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for convenience stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two NEMS-G. File Name: NEMS-G Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for grocery stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three NEMS-R. File Name: NEMS-R Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for restaurants.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

This dataset provides restaurant inspections, violations, grades and adjudication information

Bitext - Restaurants Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [restaurants] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-restaurants-llm-chatbot-training-dataset.

Eateries in in New York City Department of Parks & Recreation properties including snack bars, food carts, mobile food trucks, and restaurants.

LA Restaurants Dataset

  Overview

This dataset is generated by llama3 based on Instagram posts of popular LA based food bloggers. The dataset consists of restaurant information extracted from Instagram captions of popular LA food pages. It includes details such as restaurant names, addresses, Instagram handles, famous dishes, location tags, and types of cuisine.

  Dataset Description

The dataset contains structured information extracted from Instagram captions.… See the full description on the dataset page: https://huggingface.co/datasets/shanto268/la_restaurants.

Synthetic Dataset for Product Descriptions and Ads

The basic process was as follows:

Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items. Split the output into desired format `{"product" : "", "description" : ""} Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.

This data was not cleaned or verified manually.

This dataset provides restaurant inspections, violations, grades and adjudication information

About this dataset

Do you love the convenience of being able to drive through and pick up your food without having to wait? Well, you're not alone. According to a new study by Datafiniti, there are over 10,000 fast food restaurants across the United States.

That's a lot of restaurants! But what does that mean for the average person living in America? Well, it means that there are more than enough options for those who want to grab a quick bite on the go. And it also means that there are plenty of opportunities for those who want to open their own fast food restaurant.

So, if you're thinking about starting your own fast food business, or if you're just curious about where the most (and least) fast food options are in America, then this dataset is for you!

How to use the dataset

To find out where the most fast food restaurants are in the United States, you can use this dataset. The dataset includes the restaurant's name, address, city, state, and website. You can use this information to rank cities with the most and least fast food options

Research Ideas

1. Fast food delivery service that delivers from multiple restaurants

2. An app that allows users to find the healthiest fast food options near them

3. A website that ranks cities by their number of fast food restaurants per capita

Acknowledgements

The original source of the data is Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more

Columns

File: Datafiniti_Fast_Food_Restaurants.csv | Column name | Description | |:----------------|:---------------------------------------------------------------------| | dateAdded | The date the restaurant was added to the database. (Date) | | dateAdded | The date the restaurant was added to the database. (Date) | | dateUpdated | The date the restaurant was last updated in the database. (Date) | | dateUpdated | The date the restaurant was last updated in the database. (Date) | | address | The street address of the restaurant. (String) | | address | The street address of the restaurant. (String) | | categories | The category or categories the restaurant is classified as. (String) | | categories | The category or categories the restaurant is classified as. (String) | | city | The city the restaurant is located in. (String) | | city | The city the restaurant is located in. (String) | | country | The country the restaurant is located in. (String) | | country | The country the restaurant is located in. (String) | | keys | The unique identifier for the restaurant. (String) | | keys | The unique identifier for the restaurant. (String) | | latitude | The latitude coordinate of the restaurant. (Float) | | latitude | The latitude coordinate of the restaurant. (Float) | | longitude | The longitude coordinate of the restaurant. (Float) | | longitude | The longitude coordinate of the restaurant. (Float) | | name | The name of the restaurant. (String) | | name | The name of the restaurant. (String) | | postalCode | The postal code of the restaurant. (String) | | postalCode | The postal code of the restaurant. (String) | | province | The province or state the restaurant is located in. (String) | | province | The province or state the restaurant is located in. (String) | | sourceURLs | The source URL of the restaurant. (String) | | sourceURLs | The source URL of the restaurant. (String) | | websites | The website of the restaurant. (String) | | websites | The website of the restaurant. (String) |

File: FastFoodRestaurants.csv | Column name | Description | |:---------------|:-------------------------------------------------------------| | address | The street address of the restaurant. (String) | | address | The street address of the restaurant. (String) | | city | The city the restaurant is located in. (String) | | city | The city the restaurant is located in. (String) | | country | The country the restaurant i...

Content

This is a list of over 10,000 fast food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do with This Data

You can use this data to rank cities with the most and least fast food restaurants across the U.S.. E.g.:

• Cities with the most and least McDonald's per capita
• Fast food restaurants per capita for all states
• Fast food restaurants with the most locations nationally
• Major cities with the most and least fast food restaurants per capita
• Small cities with the most fast food restaurants per capita
• States with the most and least fast food restaurants per capita
• The number of fast food restaurants per capita

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

You can access the full dataset by running the following query with Datafiniti’s Business API.

{ "query": "dateUpdated:[2018-04-01 TO *] AND categories:\"Fast Food\" AND country:US* AND name:*", "format": "csv", "download": true }

**The total number of results may vary.*

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset provides restaurant inspections, violations, grades and adjudication information

About This Data

This is a list of over 18,000 restaurants in the US that serve vegetarian or vegan food provided by Datafiniti's Business Database. The dataset includes address, city, state, business name, business categories, menu data, phone numbers, and more.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to determine the most vegetarian and vegan-friendly cities in the US. E.g.:

• How many restaurants in each metro area offers vegetarian options?
• Which metros among the 25 most popular metro areas have the most and least vegetarian restaurants per 100,000 residents?
• Which metros with at least 10 vegetarian restaurants have the most vegetarian restaurants per 100,000 residents?
• How many restaurants in each metro area offers vegan options?
• Which metros among the 25 most popular metro areas have the most and least vegan restaurants per 100,000 residents?
• Which metros with at least 10 vegan restaurants have the most vegan restaurants per 100,000 residents?
• Which cuisines are served the most at vegetarian restaurants?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

You can access the full dataset by running the following query with Datafiniti’s Business API.

{ "query": "dateUpdated:[2018-01-01 TO *] AND categories:Restaurant* AND cuisines:(Vegan OR Vegetarian)* AND country:US* AND menus:* AND name:*", "format": "csv", "download": true }

**The total number of results may vary.*

Get this data and more by creating a free Datafiniti account or requesting a demo.

About this Data

This is a list of 19,439 restaurants and similar businesses with menu items containing "burrito" or "taco" in their names provided by Datafiniti's Business Database.

The dataset includes the category, cuisine, restaurant information, and more for a menu item. Each row corresponds to a single menu item from the restaurant, and the entirety of each restaurant's menu is not listed. Only burrito or taco items are listed.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do with this Data

You can use this data to discover which parts of the country offer the most for Mexican food aficionados. E.g.:

What is the ratio of burritos and tacos on restaurant menus from each city? What is the ratio of burritos and tacos on restaurant menus from cities with the most restaurants per capita (10,000 residents)? What is the ratio of cities with the most authentic Mexican restaurants per capita (10,000 residents)? Which cities have the most authentic Mexican restaurants? Which cities have the most Mexican restaurants? Which Mexican restaurants have the most locations nationally?

Data Schema

A full schema for the data is available in support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset was created by Datafiniti and contains around 77,000 samples along with City, Categories Restaureant, Menus_amount Max, technical information, and other features such as:

• Cuisines
• Menu_description
• priceRangeMin and priceRangeMax
• and more.

How to use this dataset

• Exploratory Data Analysis
• Study the influence of price on menus_name
• Study the words that are most often used to describe burritos and tacos dishes

Acknowledgements

If you use this dataset in your research, please credit Datafiniti

Start A New Notebook!

The Ubereats Restaurant Dataset: Over 100,000 US Restaurants

Delicious And Affordable Eats

By Jeff [source]

About this dataset

The Ubereats Restaurant Dataset contains information on over 100,000 restaurants in the United States, including location, contact information, price range, review rating, and more. This dataset is a great resource for anyone looking for information on the best and most delicious restaurants in the US

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The Ubereats Restaurant Dataset contains information on over 100,000 restaurants in the United States, including location, contact information, price range, review rating, and more. This information can be used to find the best and most delicious restaurants in the US.

To find the best restaurants in the US, filter the dataset by is_open = 1 and review_rating >= 4.5. This will give you a list of open restaurants that have a high review rating. You can then use the other columns in the dataset to find more information about each restaurant, such as location, price range, and delivery time

Research Ideas

• Location-based restaurant recommendations - using the latitude and longitude of a user's current location, restaurants in the vicinity can be recommended
• Price range-based restaurant recommendations - using the price_bucket column, restaurants within a certain price range can be recommended
• Review-based restaurant recommendations - using the review_rating and review_count columns, restaurants with high ratings and/or a large number of reviews can be recommended

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

See the dataset description for more information.

Columns

File: Ubereat_US_Merchant.csv | Column name | Description | |:-------------------|:------------------------------------------------------------------------| | city | The city in which the restaurant is located. (String) | | state | The state in which the restaurant is located. (String) | | zipcode | The zip code in which the restaurant is located. (String) | | address | The address of the restaurant. (String) | | loc_name | The name of the location at which the restaurant is located. (String) | | loc_number | The number of the location at which the restaurant is located. (String) | | url | The URL of the restaurant. (String) | | promotion | Any promotions that the restaurant is currently running. (String) | | latitude | The latitude of the restaurant. (Float) | | longitude | The longitude of the restaurant. (Float) | | is_open | Whether or not the restaurant is currently open. (Integer) | | closed_message | The message displayed when the restaurant is closed. (String) | | delivery_fee | The delivery fee charged by the restaurant. (Float) | | delivery_time | The estimated delivery time for the restaurant. (Float) | | review_count | The number of reviews for the restaurant. (Integer) | | review_rating | The average review rating for the restaurant. (Float) | | price_bucket | The price bucket in which the restaurant falls. (String) | | img1 | The URL of the first image for the restaurant. (String) | | img2 | The URL of the second image for the restaurant. (String) | | img3 | The URL of the third image for the restaurant. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jeff.

The dataset is provided by one of the largest fast-food restaurant chains in the US. It includes (1) transaction information such as menu items that were purchased and quantities of each item; (2) ingredient lists for individual menu items; (3) metadata on restaurants, including location, and store type. The data observation window is from early March, 2015 to 06/15/2015 and includes transactional data from 2 stores in Berkeley, CA and 2 stores in New York, NY.

In 2018, food waste in the United States was a significant issue with substantial environmental and economic consequences. Here are some key statistics:

Overall Waste Volume and Percentage:

Approximately 103 million tons (206 billion pounds) of food waste were generated in the US in 2018, according to the EPA.

This amounted to between 30-40% of the entire US food supply going uneaten.

On a per-person basis, it was roughly one pound of food wasted per person per day.

Economic Impact:

The annual food waste in America had an approximate value of $161 billion to $218 billion.

The average American family of four reportedly threw out $1,500 in wasted food per year (based on 2010 price data, which would be higher in 2018).

The restaurant industry alone incurred an estimated $162 billion in costs related to wasted food.

Environmental Impact:

Food waste was the number one material in American landfills, accounting for 24.1% of all municipal solid waste (MSW).

When food rots in landfills, it produces methane, a potent greenhouse gas that is 28 times more powerful than CO2 at trapping heat. Food waste was responsible for an estimated 58% of landfill methane emissions to the atmosphere.

The production of wasted food in the US was equivalent to the greenhouse gas emissions of 37 million cars.

Wasted food also means wasted resources like land, water, and energy. Annually, food loss and waste took up an area of agricultural land the size of California and New York combined, and wasted enough energy to power 50 million US homes for a year.

Approximately 21% of agricultural water resources and 19% of US croplands were wasted for food that was ultimately thrown away.

Sources of Food Waste:

Food waste occurs across the entire supply chain, with significant contributions from:

Households: An estimated 43% of food waste came from homes.

Grocery stores, restaurants, and food service companies: Accounted for about 40% of food waste.

Farms: Responsible for around 16% of food loss.

Manufacturers: Contributed about 2% of food waste.

Breakdown by Material (within MSW):

Food waste comprised the fourth largest material category in total MSW generation, estimated at 63.1 million tons or 21.6% in 2018.

These statistics highlight the significant scale of food waste in the US in 2018 and its wide-ranging negative impacts on the economy and the environment

Dataset's Link: https://catalog.data.gov/dataset/u-s-food-waste-flows-between-sectors-2018-v1-3-2

Food waste flows between waste-generating sectors and waste management routes are captured by these Flow-By-Sector (FBS) databases. Typically, the sectors use codes from the 2012 North American Industry Classification System (NAICS). Method 1 (m1 dataset file), the first dataset, assigns sectors to food waste creation and disposal statistics from the USEPA Wasted Food Report. The National Commercial Non-Hazardous Waste (CNHW) FBS dataset's discarded food data is attributed to sectors using the second approach, method 2 (m2 dataset file).

Column's Information

The CSV file "Food_Waste_national_2018_m2_v1.3.2_9b1bb41.csv" contains the following columns with their likely meanings:

Flowable: The type of material being tracked, in this case, "Food Waste".

Class: A classification for the "Flowable" material, here "Other".

SectorProducedBy: A numerical code indicating the sector that produced the food waste.

SectorConsumedBy: A numerical code indicating the sector that consumed or received the food waste.

SectorSourceName: The source of the sector classification, which is "NAICS_2012_Code" (North American Industry Classification System 2012 Code).

Context: This column appears to be empty in the provided data.

Location: This column seems to contain a location code, e.g., "=""00000""".

LocationSystem: The system used for location identification, which is "FIPS" (Federal Information Processing Standards).

FlowAmount: The quantity of food waste.

Unit: The unit of measurement for "FlowAmount", which is "kg" (kilograms).

FlowType: The type of flow, which is "WASTE_FLOW".

Year: The year the data pertains to, in this case, "2018".

MeasureofSpread: This column appears to be empty in the provided data.

Spread: A value related to the spread of the data, here "0.0".

DistributionType: This column appears to be empty in the provided data.

Min: Minimum value, here "0.0".

Max: Maximum value, here "0.0".

DataReliability: Data reliability value, here "0.0".

TemporalCorrelation: Temporal correlation value, here "0.0".

GeographicalCorrelation: Geographical correlation value, here "0.0".

TechnologicalCorrelation: Technological correlation value, here "0.0".

DataCollection: Data collection method or source, here "CalRecycle_WasteCharacterization".

**MetaSources...

Column description:

1) business_id: Unique alphanumeric business identifier.

2) name: Name of business (not unique)

3) address: Address of business

4) city: City where business is located

5) state: State where business is located

6) postal_code: Mailing code of the business

7) latitude: The angular distance of the business north or south of the earth's equator.

8) longitude: The angular distance of the business east or west of the meridian at Greenwich.

9) stars: The average star rating of the business.

10) review_count: The sum of reviews received by the business.

11) is_open : 1 for open businesses and 0 for closed businesses (no longer in business).

12) attributes: This variable tells us many different things about the business. Some attributes include price range, whether or not it's good for kids, if it accepts credit cards or bitcoin. This variable will require extensive cleaning.

13) categories: Tells us about the type of business.

14) hours: Tells us about the days and hours of service provided by the business.