The majority of consumers living in single households in Japan cooked homemade meals, according to a survey conducted in December 2024. Only around ** percent of respondents living alone stated that they did not cook meals for themselves.

Most U.S. consumers who prepared meals at home used some combination of cooking from scratch and full-prepared meal items. Only ***** percent of consumers used mostly semi or fully-prepared items for at-home cooking in 2019.

More than 37 percent of the people living in the Unites Stated (U.S.) declared that they cooked between three and five times per week, according to a survey released by Kitchen Stories during 2019. It also emerged that more than eight percent of the respondents asserted that they cooked less than once per week.

Better Recipes for a Better Life

Delicious, Nutritious, and Easy-to-Prepare Recipes for Everyone!

By [source]

About this dataset

Do you love spending time in the kitchen whipping up delicious, homemade meals? If so, then you'll love this dataset! The Ultimate Recipe Recommendation Dataset contains a large variety of recipes that are delicious, nutritious, and easy to prepare. This dataset is perfect for researchers who are interested in exploring recipe recommendations, nutrition data, and cooking times. With this dataset, you can answer important questions such as: What are the most popular recipes? What are the most nutritional recipes? What are the quickest recipes to prepare? So whether you're a seasoned chef or a beginner cook, this dataset is sure to have something for everyone!

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How to use the dataset

If you're interested in exploring recipe recommendations, nutrition data, and cooking times, this is the dataset for you! With over 100,000 recipes, there's something for everyone. Whether you're looking for a quick and easy meal or something more substantial, you'll find it here. You can use this dataset to answer important questions such as: What are the most popular recipes? What are the most nutritional recipes? What are the quickest recipes to prepare?

Research Ideas

• Recipe recommendations: With this dataset, you can recommend recipes to users based on their preferences. For example, if a user likes quick and easy recipes, you can recommend recipes that have a short prep time and cook time.
• Nutrition data: This dataset contains nutrition information for each recipe. This data can be used to recommend recipes that are high in protein, low in fat, etc.
• Cooking times: With this dataset, you can find recipes that are quick and easy to prepare. This is perfect for busy home cooks who don't have a lot of time to spend in the kitchen!

Acknowledgements

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

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: recipes.csv | Column name | Description | |:-----------------|:----------------------------------------------------------------------------| | recipe_name | The name of the recipe. (String) | | prep_time | The amount of time required to prepare the recipe. (Integer) | | cook_time | The amount of time required to cook the recipe. (Integer) | | total_time | The total amount of time required to prepare and cook the recipe. (Integer) | | servings | The number of servings the recipe yields. (Integer) | | ingredients | A list of ingredients required to make the recipe. (List) | | directions | A list of directions for preparing and cooking the recipe. (List) | | rating | The recipe rating. (Float) | | url | The recipe URL. (String) | | cuisine_path | The recipe cuisine path. (String) | | nutrition | The recipe nutrition information. (Dictionary) | | timing | The recipe timing information. (Dictionary) |

File: test_recipes.csv | Column name | Description | |:----------------|:---------------------------------| | url | The recipe URL. (String) | | Name | The recipe name. (String) | | Prep Time | The recipe prep time. (String) | | Cook Time | The recipe cook time. (String) | | Total Time | The recipe total time. (String) | | Servings | The recipe servings. (String) | | Yield | The recipe yield. (String) | | Ingredients | The recipe ingredients. (String) | | Directions | The recipe directions. (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 .

According to a survey from 2020, a preference to consume more home-cooked food was prevalent in the Asia-Pacific region. The strongest preference was in China and Indonesia, where nearly ** percent of the respondents expressed their interest in consuming more home-cooked meals in 2020.

Comprehensive YouTube channel statistics for Village Home Cooking Channel, featuring 2,450,000 subscribers and 1,284,793,454 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in IN. Track 253 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

ABSTRACT OBJECTIVE To develop and validate an instrument for measuring the home cooking skills of health professionals involved with guidelines for promoting adequate and healthy food in primary health care. METHODS This is a methodological study with a psychometric approach, carried out in the city of São Paulo between January and November 2020, to develop and validate a self-applied online instrument. The data of the 472 participants were presented by descriptive statistics. Content validation was performed by expert judgment using the two round Delphi technique and empirical statistics for consensus evidence. Exploratory factor analysis was used for construct validation and reliability analysis, and the model adjustment rates and composite reliability were analyzed. RESULTS The instrument presented satisfactory content validity for CVRc indices and �� in the two rounds of the Delphi technique. After the factor analysis, the final model of the Primary Health Care Home Cooking Skills Scale presented 29 items with adequate factorial loads (> 0.3). Bartlett’s and Kaiser-Meyer-Olkin’s (KMO) tests of sphericity performed in exploratory factorial analysis suggested interpretability in the correlation matrix, the parallel analysis indicated four domains and explained variance of 64.1%. The composite reliability of the factors was adequate (> 0.70) and the H-index suggested replicable factors in future studies. All adjustment rates proved to be adequate. CONCLUSIONS The Primary Health Care Home Cooking Skills Scale presented evidence of validity and reliability. It is short and easy to apply and will make it possible to reliably ascertain the need for qualification of the workforce, favoring the planning of actions and public policies of promotion of adequate and healthy food in primary health care.

In 2021, approximately ** percent of respondents in the United States (U.S.) stated that they have cooked more often, since the start of the COVID-19 pandemic. Furthermore, approximately ** percent stated that they have cooked more with family members. Food preparation & cooking since the start of the pandemic In April 2020, another survey comparing changes in eating and food preparation since the beginning of the pandemic found that nearly ** percent of participants in the U.S. stated that they cook at home more often than before. When the survey was repeated in May 2021, this figure shrank to ** percent. According to the survey results, nearly ** percent stated that they are snacking more. Other behavioral trends since the start of the pandemic The pandemic has had many effects on our behavior. One survey in May 2020 asked respondents in the U.S. about changes in their activities while staying at home due to the pandemic. Nearly ** percent of participants in the U.S. stated that they are watching more TV than before. This figure was significantly higher than the share of participants in the United Kingdom (U.K.) and Germany, where approximately ** and ** percent stated the same, respectively. In the United States, nearly ** percent stated that they are watching online streaming services (e.g. Netflix) more often, while this figure was ** and ** percent in the U.K. and Germany, respectively. Another trend is that the share of digital spending has increased as well, particularly among (18-23 years olds) and Millennials (24-39 years olds).

Comprehensive YouTube channel statistics for 집밥요리 Home Cooking, featuring 1,390,000 subscribers and 198,158,287 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Education category and is based in KR. Track 296 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Comprehensive YouTube channel statistics for Home Cooking with Somjit, featuring 126,000 subscribers and 24,834,504 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Food category and is based in MY. Track 1,507 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Explore the Recipes and Reviews Dataset from Taste of Home, a comprehensive collection of web-scraped data featuring popular recipes and authentic user reviews. This dataset showcases a diverse range of cuisines, cooking techniques, ingredient combinations, and meal categories, making it an invaluable resource for culinary research.

Gain insights into trending dishes, highly-rated recipes, and customer feedback to inform your culinary research, food product development, and menu planning. Whether you're a food enthusiast, chef, or industry analyst, this dataset provides a detailed understanding of evolving food trends and consumer preferences.

For those seeking access to rich and actionable food and beverage datasets, check out the Food and Beverage Data available on Crawl Feeds. It’s an excellent resource for enhancing your culinary projects and data-driven strategies.

Replication Data for: "Home Cooking in the digital age: When observing food influencers on social media triggers the imitation of their practices". Journal of Psychology and Marketing.

Comprehensive YouTube channel statistics for My Home Style Cooking, featuring 562,000 subscribers and 88,320,355 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Food category and is based in IN. Track 1,006 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Comprehensive YouTube channel statistics for Cooking House By MeghaSachdev, featuring 624,000 subscribers and 79,787,174 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Food category and is based in IN. Track 2,673 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Comprehensive YouTube channel statistics for 가루씨의 집밥Garussi home cooking, featuring 467,000 subscribers and 219,737,680 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in KR. Track 2,102 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

The dataset includes variables used to construct the Household Sustainable Home Cooking (HSHC) Index, which measures sustainability across eight behavioural dimensions: meal planning, purchasing, cooking ability, storage, eating patterns, handling leftovers, waste management, and use of eco-friendly products. Socio-demographic variables such as education, occupation, family composition, etc. are also provided.This dataset enables replication of the composite-index development, robustness checks (equal- vs PCA-weighted indices), and cluster analysis identifying four household profiles: Food Management Leaders, Sustainable Diet Households, Planning and Leftover Strugglers, and Least Sustainable Households.Researchers and policymakers can use the data to study sustainable food consumption behaviours, evaluate policy interventions, or conduct comparative analyses of household sustainability in other urban contexts.

While a crucial part of our daily routine, food does not solely play a nutritional role in it. Indeed, food has a way of bringing people together and is often described as a good approach to a new culture.
France, often associated with food, is the country where people spend the most time eating and drinking per day. And while French people seem to enjoy eating, a majority also takes part in the step that comes before the meal: ** percent of French people cook at home every day or almost every day.

Comprehensive dataset containing 53 verified Brother Home Cooking locations in China with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 183 verified Northeast Home Cooking locations in China with complete contact information, ratings, reviews, and location data.

Comprehensive YouTube channel statistics for Village Home Cooking Channel, featuring 2,450,000 subscribers and 1,287,906,138 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in IN. Track 253 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.