Context

Veganism is one emergent topic which many people are not aware of. So, by having a big dataset of these news, it can be developed something in order to rise awareness of this topic

Acknowledgements

These news come from: Plant Based News: https://plantbasednews.org/ VegNews: https://vegnews.com/ Vegconomist: https://vegconomist.com/

BackgroundVegetarians have been shown to have better metabolic profiles than non-vegetarians, and vegetarianism has potential beneficial effects on cardiovascular disease. However, there is a lack of studies on vegetarians that examine both metabolic profiles and lifestyle habits, such as physical activity, smoking habits, and dietary patterns, which are equally important in the context of cardiovascular disease. We explored whether a vegetarian diet is associated with both metabolic traits and lifestyle habits by assessing cardiovascular health (CVH) metrics.MethodsThis was a cross-sectional study conducted in a Taiwanese population. Data collected between 2000 and 2016 were extracted from the MJ Health database. Participants aged 40 years and older without cardiovascular disease were included. CVH metrics included smoking habits, blood pressure, total cholesterol, serum glucose, body mass index, physical activity, and healthy diet score. Vegetarian participants were full-time vegetarians who did not consume meat or fish. All the data were assessed from self-report questionnaires, physical examinations, and blood analyses following standard protocol. Multiple logistic regression analysis was used to evaluate the association between vegetarianism and CVH metrics.ResultsOf 46,287 eligible participants, 1,896 (4.1%) were vegetarian. Overall, vegetarians had better CVH metrics (OR = 2.09, 95% CI = 1.84–2.37) but lower healthy diet scores (OR = 0.41, 95% CI = 0.33–0.51) after adjustment. No difference in physical activity (OR = 0.86, 95% CI = 0.73–1.02) was identified between vegetarians and non-vegetarians. Additionally, vegetarians had higher whole grain intake (OR = 2.76, 95% CI = 2.28–3.35) and lower sugar-sweetened beverage consumption (OR = 1.36, 95% CI = 1.18–1.58).ConclusionsOur results suggested that vegetarians had better overall ideal CVH metrics but lower ideal healthy diet scores than non-vegetarians, which was likely due to the lack of fish consumption in this population group. When assessing CVH metrics and healthy diet scores for vegetarians, metrics and scores chosen should be suitable for use with vegetarian populations.

Food is an essential part of our lives. Food provides us with the nutrients and energy we need to grow, learn, develop and do all the activities in our day-to-day lives. Foods are directly related to our bodies and their functions since they contain nutrition like proteins, carbohydrates, minerals, vitamins and fats. These are all important for our physical and mental health. The main sources of nutrition and energy for our bodies are food and water, but many of the foods we eat may not contain the essential nutrition we need. Some of these foods can actually lead to health problems, such as high blood pressure and heart disease. So, you should choose more balanced foods with enough nutrition for your body. Foods provide us with nutrients. There are many different nutrients. We divide them into: Macronutrients that we need in large amounts. These are:

carbohydrates fats proteins Micronutrients that we need in small amounts. There are many different micronutrients, but the ones listed below are most likely to be lacking in our diets:

minerals vitamins The importance of food in our lives is like the importance of oxygen for breathing. If you stop breathing oxygen, it can kill you in a matter of minutes, and if you stop eating food, it will kill you in a few days or weeks. Both of them are necessary to continue life. The food we eat fulfils the nutritious needs of our body, and there is a variety of food for us to use. Everyone has their preferences when it comes to food; some people are vegetarian (that means they only eat a plant-based diet), and most are omnivorous (Meaning that they eat both plants and meat). No matter what diet you are on, it should be giving you the nutrition your body needs to be healthy. Every cell in your body depends on the nutrients and calories that are present in the food that you eat. However, the need for food is not only limited to continuing life. There are different food sources. The primary sources are plants and animals. Foods such as oil, meat, fish, fruits, vegetables, tea, chocolate, coffee and dairy are obtained from these primary sources. Not all the food that we eat is made from plants and animals. For example, mushrooms are obtained from edible fungi. Food has become a major part of our social lives, economy, and comfort.

Background and Objectives In October 1993, British Market Research Bureau International (BMRB) was commissioned to conduct a study for MAFF amongst the vegetarian population of Great Britain. This research was required to recruit a representative sample of 400 vegetarians and investigate their overall diet and intake of certain dietary components. The key objectives were as follows: To obtain accurate and detailed consumption data by coding in such a way as to allow extraction of mean and extreme consumption of individual foods. To identify vegetarians who are consumers of pulses, nuts, legumes, vegetables and fruit. To provide information on the age, gender, social class and regional profile of vegetarians. Participants in the survey were asked to answer a questionnaire about their dietary habits, including reasons for becoming vegetarian, dietary changes since becoming vegetarian and consumption and purchasing habits for different fruits and vegetables. They were then asked to keep a seven day weighed record of their food consumption. This involved weighing and recording in detail everything they ate and drank for seven days in a specially designed diary. On collection of the diary, a further interview was conducted which recorded details on the usage of mineral waters, dietary supplements, alternative protein sources and herbal teas. The dataset contains : (i) the seven day detailed record of the food and drink consumption in electronic format.
The foods consumed during the survey have each been assigned an individual food code; names/descriptions are included. (ii) table (in an MS Access database and an alternative text form) of the questionnaire data together with coding frame information on most fields.

Standard Measures The Social Class system of classifying households according to information on education history, occupational type and employment responsibilities of the chief income earner or head of the household - in this case the chief income earner was used, defined as the household member with the largest income, whether from employment, pensions, state benefits, investments or any other source. Social Class was used in order to provide discrimination between the type of respondents/ households which took part in the survey. Social Class is a system which produces one of six outcomes depending on the respondent interviewed - these are the well known A, B, C1, C2, D and E gradings.
To summarise each group: A = Professional people, very senior management in business or commerce, or top-level civil servants. B = Middle management executives in large organisations, principal officers in local government and the civil service, top management or owners of small business concerns, educational and service establishments. C1 = Junior management, owners of small establishments, and all others in non-manual positions. C2 = All skilled manual workers, and those manual workers with responsibility for other people. D = All semi-skilled and unskilled manual workers, and apprentices and trainees to skilled workers. E = All those entirely dependent on the state long-term, through sickness, unemployment, old age, or other reasons, and all unemployed for over six months. N.B. Where an individual is retired, and/or the chief income earner is no longer alive, the occupation prior to retirement is used for social grading.

Background Fibromyalgia engulfs patients in a downward, reinforcing cycle of unrestorative sleep, chronic pain, fatigue, inactivity, and depression. In this study we tested whether a mostly raw vegetarian diet would significantly improve fibromyalgia symptoms. Methods Thirty people participated in a dietary intervention using a mostly raw, pure vegetarian diet. The diet consisted of raw fruits, salads, carrot juice, tubers, grain products, nuts, seeds, and a dehydrated barley grass juice product. Outcomes measured were dietary intake, the fibromyalgia impact questionnaire (FIQ), SF-36 health survey, a quality of life survey (QOLS), and physical performance measurements. Results Twenty-six subjects returned dietary surveys at 2 months; 20 subjects returned surveys at the beginning, end, and at either 2 or 4 months of intervention; 3 subjects were lost to follow-up. The mean FIQ score (n = 20) was reduced 46% from 51 to 28. Seven of the 8 SF-36 subscales, bodily pain being the exception, showed significant improvement (n = 20, all P for trend < 0.01). The QOLS, scaled from 0 to 7, rose from 3.9 initially to 4.9 at 7 months (n = 20, P for trend 0.000001). Significant improvements (n = 18, P < 0.03, paired t-test) were seen in shoulder pain at rest and after motion, abduction range of motion of shoulder, flexibility, chair test, and 6-minute walk. 19 of 30 subjects were classified as responders, with significant improvement on all measured outcomes, compared to no improvement among non-responders. At 7 months responders' SF-36 scores for all scales except bodily pain were no longer statistically different from norms for women ages 45–54. Conclusion This dietary intervention shows that many fibromyalgia subjects can be helped by a mostly raw vegetarian diet.

The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying the factors such as • Location of the restaurant • Approx Price of food • Theme based restaurant or not • Which locality of that city serves that cuisines with maximum number of restaurants • The needs of people who are striving to get the best cuisine of the neighborhood • Is a particular neighborhood famous for its own kind of food.

“Just so that you have a good meal the next time you step out”

The data is accurate to that available on the zomato website until 15 March 2019. The data was scraped from Zomato in two phase. After going through the structure of the website I found that for each neighborhood there are 6-7 category of restaurants viz. Buffet, Cafes, Delivery, Desserts, Dine-out, Drinks & nightlife, Pubs and bars.

Phase I,

In Phase I of extraction only the URL, name and address of the restaurant were extracted which were visible on the front page. The URl's for each of the restaurants on the zomato were recorded in the csv file so that later the data can be extracted individually for each restaurant. This made the extraction process easier and reduced the extra load on my machine. The data for each neighborhood and each category can be found here

Phase II,

In Phase II the recorded data for each restaurant and each category was read and data for each restaurant was scraped individually. 15 variables were scraped in this phase. For each of the neighborhood and for each category their onlineorder, booktable, rate, votes, phone, location, resttype, dishliked, cuisines, approxcost(for two people), reviewslist, menu_item was extracted. See section 5 for more details about the variables.

Acknowledgements The data scraped was entirely for educational purposes only. Note that I don’t claim any copyright for the data. All copyrights for the data is owned by Zomato Media Pvt. Ltd..

Inspiration I was always astonished by how each of the restaurants are able to keep up the pace inspite of that cutting edge competition. And what factors should be kept in mind if someone wants to open new restaurant. Does the demography of an area matters? Does location of a particular type of restaurant also depends on the people living in that area? Does the theme of the restaurant matters? Is a food chain category restaurant likely to have more customers than its counter part? Are any neighborhood similar ? If two neighborhood are similar does that mean these are related or particular group of people live in the neighborhood or these are the places to it? What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. There are infacts dozens of question in my mind. lets try to find out the answer with this dataset.

For detailed discussion of the business problem, please visit this link

Please visit this link to find codebook cum documentation for the data

GITHUB LINk : https://github.com/mohitbhadauria02/Zomato-Dataset-using-Exploratory-Data-Analysis.git

Yeast derived-dairy (YDD) produced using cellular agriculture technologies is already available for purchase in the United States, though there has been little study of public understanding of these products. Our pilot study explored consumer perception and acceptance of YDD and yeast-derived agriculture (YDA). The study employed a questionnaire consisting of Likert scale, multiple-choice and open-ended questions, which was disseminated to vegans and the food-interested public in the province of British Columbia, Canada. Quantitative data was analyzed using SPSS 27.0, and qualitative data was collected and analyzed (in English) using thematic analysis. A binary logistic regression model indicated that among our participants, being vegan or 35 years of age or older negatively predicted having positive feelings towards YDA [chi-square (10) = 29.086, p = 0.001]. Vegans were less likely to try or purchase YDD than non-vegans. Consumers in our study shared concerns regarding the health and safety of YDD with many viewing it as non-vegan and a highly processed product. Although vegans receive a disproportionate amount of media attention with regards to cellular agriculture, our pilot study suggests this group may be unlikely to accept or consume YDA or YDD. Rather, our preliminary work indicates non-vegans and individuals under the age of 35 may be a more receptive market. Across groups, confusion about YDA processes may be a barrier to adoption.

Context

Cheese is nutritious food made mostly from the milk of cows but also other mammals, including sheep, goats, buffalo, reindeer, camels and yaks. Around 4000 years ago people have started to breed animals and process their milk. That's when the cheese was born.

Content

Geography: Global

Time period: 2024

Unit of analysis: Global Cheese Dataset

Explore this site to find out about different kinds of cheese from all around the world.

248 cheeses have listed fat content. Is there a relationship between fat content and cheese type? What about texture, flavor, or aroma?

Variables

Acknowledgements

Datasource: Cheese.com

Inspiration: Cheese Blog

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7974466%2Feb5cfed908d25e9ff2f8605dd6f98116%2FAlbedoBase_XL_A_stylized_representation_of_a_world_map_where_d_1.jpg?generation=1745662571597520&alt=media" alt="">

This dataset offers a fascinating glimpse into the culinary landscape of the world, featuring a wide array of recipes sourced from different cultures and traditions. It provides a structured collection of information, including recipe names, their originating cuisines, a detailed list of ingredients, preparation and cooking times, serving sizes, estimated calories per serving, and associated dietary restrictions.

• recipe_name: The name of the recipe. (Text/String)
• cuisine: The geographical or cultural origin of the recipe. (Categorical/String)
• ingredients: A list of ingredients required for the recipe. (List/String - often needs parsing)
• cooking_time_minutes: The estimated time required for cooking the dish in minutes. (Numerical/Integer)
• prep_time_minutes: The estimated time required for preparing the ingredients in minutes. (Numerical/Integer)
• servings: The number of people the recipe is intended to serve. (Numerical/Float)
• calories_per_serving: The estimated number of calories in one serving of the dish. (Numerical/Float)
• dietary_restrictions: Any specific dietary categories the recipe falls under (e.g., vegetarian, vegan, gluten-free, dairy-free, nut-free). A single recipe can have multiple restrictions, often listed as a comma-separated string. (Categorical/String)

🥝🍓🫐🥭🍌🍏🥕🫚 "Healthy Smoothie Recipes for Dietary Restrictions" 🍅🥑🍊🍋🥥🍍🍒🥒

This dataset is a curated collection of healthy smoothie recipes designed to cater to a variety of dietary needs, including vegan, nut-free, and non-dairy options. Compiled with both taste and health in mind, each recipe includes a detailed list of ingredients, step-by-step preparation instructions, and suggested substitutions to accommodate common dietary restrictions.

My aim is to make healthy eating more accessible and enjoyable by providing delicious smoothie recipes that can be easily adapted to fit individual dietary preferences. Whether you're looking for a quick breakfast option, a nutritious post-workout snack, or simply a refreshing beverage, my dataset offers a range of choices to suit your needs.

Contents

Recipe Name: The name of the smoothie. Ingredients: A list of ingredients used in the recipe, with quantities. Preparation Steps: Detailed instructions on how to prepare the smoothie. Vegan Friendly (Yes/No): Indicates whether the recipe is suitable for vegans. Nut-Free (Yes/No): Indicates whether the recipe is suitable for individuals with nut allergies. Non-Dairy Friendly (Yes/No): Indicates whether the recipe is suitable for individuals who avoid dairy. Vegan Substitute: Suggested substitutions to make the recipe vegan, if applicable. Dairy Substitute: Suggested substitutions to make the recipe dairy-free, if applicable. Nut Substitute: Suggested substitutions to make the recipe nut-free, if applicable. Dietary Tags: Additional tags indicating the recipe's suitability for various dietary needs. Source URL: The original source of the recipe.

Usage Examples

1. Recipe Recommendation System: Developers and data scientists can use this dataset to build a simple AI-powered recipe recommendation system. By inputting available ingredients and specifying dietary restrictions, users can receive tailored smoothie recipe suggestions. This application can be particularly useful for mobile apps focused on healthy living and dietary planning.

2. Dietary Adaptation Analysis: Nutritionists and dietitians might find this dataset valuable for analyzing how common smoothie recipes can be adapted to meet various dietary needs without compromising on nutritional value. This analysis can support dietary counseling and the development of inclusive meal plans.

3. Culinary Education: Culinary students and enthusiasts can use the dataset to explore the diversity of smoothie recipes and learn about ingredient substitutions that cater to different dietary restrictions. This can enhance their understanding of dietary needs and expand their repertoire of healthy recipes.

Contribution to Kaggle Community:

By sharing this dataset on Kaggle, I hope to contribute to the broader conversation around healthy eating and dietary inclusiveness. I encourage the community to explore the dataset, develop innovative applications, and share insights on how to further adapt these recipes to cater to an even wider range of dietary preferences.

Personal Information and Life Status Dataset

This is a synthetic dataset containing various personal and life status details of individuals, structured in a table with 100 different rows. The primary purpose of this dataset is to serve as a beginner-friendly resource for data science, machine learning, and data visualization projects. The data has been generated with a focus on consistency and realism, but it intentionally includes missing (None) and mistyped (typo) values in some features to highlight the importance of data preprocessing.

Dataset Content

The dataset consists of 14 columns, with each row representing an individual:

FirstName: The individual's first name. (String)

LastName: The individual's last name. (String)

Age: The individual's age. Some values are missing. (Integer)

Country: The individual's country of residence. Primarily includes developed countries and Türkiye. Some values may contain typos. (String)

Marital: Marital status. (Married, Single, Divorced) (String)

Education: Education level. Some values are missing. (High School, Bachelor's Degree, Master's Degree, PhD) (String)

Wages: Annual gross wages. Some values are missing. (Integer)

WorkHours: Weekly working hours. Some values are missing. (Integer)

SmokeStatus: Smoking status. (Smoker, Non-smoker) (String)

CarLicense: Possession of a driver's license. (Yes, No) (String)

VeganStatus: Vegan status. Some values are missing. (Yes, No) (String)

HolidayStatus: Holiday status. Some values are missing. (Yes, No) (String)

SportStatus: Sports activity level. (Active, Inactive) (String)

Score: A general life score for the individual. This is a synthetic value randomly assigned based on other features. Some values are missing. (Integer)

Potential Use Cases

This dataset is an ideal resource for various types of analysis, including but not limited to:

Data Science and Machine Learning: Applying data preprocessing techniques such as imputation for missing values, outlier detection, and categorical encoding. Subsequently, you can build regression models to predict values like wages or score, or classification models to categorize individuals.

Data Visualization: Creating interactive charts to show the relationship between education level and wages, the distribution of working hours by age, or the correlation between smoking status and overall life score.

Exploratory Data Analysis (EDA): Exploring average wage differences across countries, sports habits based on marital status, or the link between education level and having a car license.

Acknowledgement

We encourage you to share your work and findings after using this dataset. Your feedback is always welcome and will help us improve the quality of our datasets.

The Swiggy Chennai dataset is a comprehensive collection of restaurant and food-related information for the city of Chennai, India. This dataset is designed to provide valuable insights into the dining options available in various sub-cities of Chennai, along with essential details like restaurant ratings, rating counts, menu items, prices, and cuisine types. The dataset aims to assist researchers, analysts, and data enthusiasts in understanding the food landscape of Chennai and exploring trends in restaurant preferences and consumer choices.

Columns:

1. City: The city to which the dataset pertains, which, in this case, is "Chennai." All the data entries in this dataset are specific to restaurants and food establishments within Chennai.

2. Sub-City: This column contains the names of various sub-cities or neighborhoods within Chennai. Chennai is a vast metropolitan area with several distinct regions, and this column helps segment the data based on these sub-cities.

3. Rating: The average rating of a restaurant in the Swiggy app, as provided by users who have ordered from or dined at the restaurant. Ratings are typically represented on a scale of 1 to 5, with higher values indicating better customer satisfaction.

4. Rating Counts: The number of individual ratings or reviews that have contributed to the average rating. A higher number of rating counts indicates a more substantial sample size and, thus, higher confidence in the displayed average rating.

5. Restaurant: The name or identifier of the restaurant listed on the Swiggy platform.

6. Cost: The cost or price range associated with dining at the restaurant. This can range from budget-friendly to high-end and is usually represented using dollar signs ($), with more signs indicating higher prices.

7. Cuisine: The type of cuisine offered by the restaurant. Chennai is known for its diverse culinary scene, and this column may include various cuisines such as Indian, Chinese, Italian, South Indian, North Indian, etc.

8. Menu: The menu refers to the list of food items available at the restaurant. It can include main dishes, appetizers, desserts, beverages, and more.

9. Item: This column contains the specific name of the food item listed on the restaurant's menu.

10. Price: The price of the corresponding food item mentioned in the "Item" column.

11. Veg or Non-Veg: A categorical indicator specifying whether the food item is vegetarian (Veg) or non-vegetarian (Non-Veg). This information is crucial for individuals with dietary preferences or restrictions.

Researchers and analysts can use this dataset to perform various analyses, such as exploring the distribution of restaurant ratings across sub-cities, identifying popular cuisines, comparing average costs in different areas, and investigating correlations between restaurant ratings and the presence of vegetarian or non-vegetarian options in the menu. It serves as a valuable resource for understanding the food preferences and choices of people in Chennai and can be leveraged to make data-driven decisions in the food industry and related domains.

The dataset is designed to assist in predicting recommended medications for patients based on their fever condition, symptoms, medical history, and other relevant factors. It incorporates a mix of patient health data, environmental variables, and lifestyle choices to improve model accuracy and better simulate real-world scenarios.

Dataset Characteristics: Total Samples: 1000 (modifiable based on user needs). Number of Features: 19 features + 1 target column. File Format: CSV (enhanced_fever_medicine_recommendation.csv). Features Description: Column Name Description Data Type Temperature Body temperature of the patient in Celsius (e.g., 36.5 - 40.0). Float Fever_Severity Categorized fever severity: Normal, Mild Fever, High Fever. Categorical Age Age of the patient (1-100 years). Integer Gender Gender of the patient: Male or Female. Categorical BMI Body Mass Index of the patient (e.g., 18.0 - 35.0). Float Headache Whether the patient has a headache: Yes or No. Categorical Body_Ache Whether the patient has body aches: Yes or No. Categorical Fatigue Whether the patient feels fatigued: Yes or No. Categorical Chronic_Conditions If the patient has any chronic conditions (e.g., diabetes, asthma): Yes or No. Categorical Allergies If the patient has any allergies to medications: Yes or No. Categorical Smoking_History If the patient has a history of smoking: Yes or No. Categorical Alcohol_Consumption If the patient consumes alcohol: Yes or No. Categorical Humidity Current humidity level in the patient’s area (e.g., 30-90%). Float AQI Current Air Quality Index in the patient’s area (e.g., 0-500). Integer Physical_Activity Daily physical activity level: Sedentary, Moderate, Active. Categorical Diet_Type Diet preference: Vegetarian, Non-Vegetarian, or Vegan. Categorical Heart_Rate Resting heart rate of the patient in beats per minute (e.g., 60-100). Integer Blood_Pressure Blood pressure category: Normal, High, or Low. Categorical Previous_Medication Medication previously taken by the patient: Paracetamol, Ibuprofen, Aspirin, or None. Categorical Recommended_Medication Target variable indicating the recommended medicine: Paracetamol or Ibuprofen. Categorical

1. Diet alteration may lead to nutrient limitations even in the absence of food limitation, and this may affect physiological functions, including immunity. Nutrient limitations may also affect the maintenance of body mass and key life history events that may affect immune function. Yet, variation in immune function is largely attributed to energetic trade-offs rather than specific nutrient constraints. 2. To test the effect of diet on life history traits, we tested how diet composition affects innate immune function, body mass and moult separately and in combination with each other, and then used path analyses to generate hypotheses about the mechanistic connections between immunity and body mass under different diet composition. 3. We performed a balanced parallel and crossover design experiment with omnivorous Common Bulbuls Pycnonotus barbatus in out-door aviaries in Nigeria. We fed 40 wild-caught bulbuls ad libitum on fruits or invertebrates for 24 weeks, switching half of each group between treatments after 12 weeks. We assessed innate immune indices (haptoglobin, nitric oxide and ovotransferrin concentrations, and haemagglutination and haemolysis titres), body mass and primary moult, fortnightly. We simplified immune indices into three principal components (PCs), but we explored mechanistic connections between diet, body mass and each immune index separately. 4. Fruit fed bulbuls had higher body mass, earlier moult and showed higher values for two of the three immune PCs compared to invertebrate fed bulbuls. These effects were reversed when we switched bulbuls between treatments after 12 weeks. Exploring the correlations between immune function, body mass and moult, showed that an increase in immune function was associated with a decrease in body mass and delayed moult in invertebrate fed bulbuls, while fruit fed bulbuls maintained body mass despite variation in immune function. Path analyses indicated that diet composition was most likely to affect body mass and immune indices directly and independently from each other. Only haptoglobin concentration was indirectly linked to diet composition via body mass. 5. We demonstrated a causal effect of diet composition on innate immune function, body mass and moult: bulbuls were in better condition when fed on fruits than invertebrates, confirming that innate immunity is nutrient specific. Our results are unique because they show a reversible effect of diet composition on wild adult birds whose immune systems are presumably fully developed and adapted to wild conditions – demonstrating a short-term consequence of diet alteration on life history traits.

Methods This dataset includes information from a diet manipulation experiment on Common Bulbuls in out-door aviaries in Nigeria. We caught 40 adult Common Bulbuls using mist nets around the A. P. Leventis Ornithological Research Institute (APLORI) in Nigeria (09°52’N, 08°58’E) between 28 October to 7 November 2016 and housed them in groups of 10 birds in four adjacent out-door aviaries at APLORI. Birds were fed fruits and invertebrates in captivity until the experiment started on the 2 December 2016. Birds were supplied water and food ad libitum before and throughout the experiment. All birds were sampled for blood, assessed for moult and weighed to determine baseline body mass and innate immune function on 1 or 2 December, before diet restriction commenced on 2 December. During the experiment, birds in two aviaries were fed fruits , and the other two were fed invertebrates and sampled fortnightly. After 12 weeks of diet treatment, five birds from each aviary were switched between treatments, and the other five birds of each aviary remained on the same treatment. Switched birds replaced each other in aviaries with the alternative diet treatment, so we maintained four aviaries with the same diet treatment throughout the experiment. In one of the fruit treatment aviaries, we moved only four birds to the invertebrate treatment because we had nine birds left in this aviary. The experiment continued for another 12 weeks. Thus, we grouped individuals as: invertebrate throughout, invertebrate to fruit, fruit to invertebrate and fruit throughout.

There were six females and 14 males on fruit diet and nine females and 11 males on invertebrate diet at the start of the experiment, but we were blind to the sex of individuals during the experiment, because sexes were only determined molecularly after the experiment. All birds were sexed using gel electrophoresis.

Birds were sampled between 6:00 and 10:00 hours daily in two consecutive days per sampling session. Two aviaries of alternate diet treatments were sampled per day, with sampling order rotating between sampling sessions.Plasma and blood cells were stored at -20° C for one week and then moved to -80° C until transported for immune assays in Groningen, the Netherlands. Haptoglobin, nitric oxide and ovotransferrin concentration were carried by colorimetric assays, absorbance were measured using a Versamax plate reader (Molecular Devices Sunnyvale, California, US). Natural antibody-mediated haemagglutination and complement-mediated haemolysis titres of plasma samples against 1% rabbit red blood cells (Envigo RMS (UK) Ltd.) in phosphate buffered saline were measured as described by Matson et al. (2005).

Additonal morphometric measurements, including wing length, tarsus length and body mass are included in the dataset. Moult status and scores of feathers for each individual are also included in the dataset.

Haematocrit measurements (PCV) and occurrence of ectoparasite, and microfilaria are also recorded in the data where available, although these were not analysed for the current manuscript.

Animal-free products consumption frequency in Great Britain 2019, by eating habits Published by Nils-Gerrit Wunsch, Jun 16, 2020 As of 2019, frequent consumption of meat-free and animal-free products was most likely to occur among surveyed individuals who identified themselves as vegans, vegetarians, and pescatarians. In contrast, over 50 percent of polled meat-eaters stated that they had never consumed meat alternatives or dairy substitutes. How frequently, if at all, do you consume specifically meat-free or animal-free products such as meat alternatives or dairy substitutes?

As of 2019, frequent consumption of meat-free and animal-free products was most likely to occur among surveyed individuals who identified themselves as vegans, vegetarians, and pescatarians. In contrast, over 50 percent of polled meat-eaters stated that they had never consumed meat alternatives or dairy substitutes.

This data set is provided by Statista. Big cheers to them. You can find more about them in the link below: link= https://www.statista.com/statistics/1065843/animal-free-products-consumption-frequency-in-great-britain-by-eating-habits/

Characteristics of vegetarian and control populations.

Cheese

This data comes from cheese.com.

Cheese is nutritious food made mostly from the milk of cows but also other mammals, including sheep, goats, buffalo, reindeer, camels and yaks. Around 4000 years ago people have started to breed animals and process their milk. That's when the cheese was born.

Explore this site to find out about different kinds of cheese from all around the world.

248 cheeses have listed fat content. Is there a relationship between fat content and cheese type? What about texture, flavor, or aroma?

Data Dictionary

cheeses.csv

Proportion of the 2020 US human population who could be fed with food energy savings associated with vegan diets.