Sugar rose to 16.56 USd/Lbs on July 11, 2025, up 1.83% from the previous day. Over the past month, Sugar's price has risen 1.84%, but it is still 13.76% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Sugar - values, historical data, forecasts and news - updated on July of 2025.

The construction of diabetes dataset was explained. The data were collected from the Iraqi society, as they data were acquired from the laboratory of Medical City Hospital and (the Specializes Center for Endocrinology and Diabetes-Al-Kindy Teaching Hospital). Patients' files were taken and data extracted from them and entered in to the database to construct the diabetes dataset. The data consist of medical information, laboratory analysis. The data attribute are: The data consist of medical information, laboratory analysis… etc. The data that have been entered initially into the system are: No. of Patient, Sugar Level Blood, Age, Gender, Creatinine ratio(Cr), Body Mass Index (BMI), Urea, Cholesterol (Chol), Fasting lipid profile, including total, LDL, VLDL, Triglycerides(TG) and HDL Cholesterol , HBA1C, Class (the patient's diabetes disease class may be Diabetic, Non-Diabetic, or Predict-Diabetic).

heart rate

This dataset contains data from 1067 participants that was collected between from July 19, 2023 and July, 31 2024. Data from multiple modalities are included. The data in this dataset contain no protected health information (PHI). Information related to the sex and race/ethnicity of the participants as well as medication used has also been removed. A detailed description of the dataset is available in the AI-READI documentation for v2.0.0 of the dataset at https://docs.aireadi.org

GLUCOSE is a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions. Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement.

This Dataset contains state-wise daily wholesale and retail price of essential commodity Sugar

Sugar Price Index in World decreased to 103.70 Index Points in June from 109.40 Index Points in May of 2025. This dataset includes a chart with historical data for World Sugar Price Index.

Selling prices of sugar beet (unit value)

This dataset provides a collection of Continuous Glucose Monitoring (CGM) data, insulin dose administration, meal ingestion counted in carbohydrate grams, steps, calories burned, heart rate, and sleep quality and quantity assessment acquired from 25 people with type 1 diabetes mellitus (T1DM). CGM data was acquired by FreeStyle Libre 2 CGMs, and Fitbit Ionic smartwatches were used to obtain steps, calories, heart rate, and sleep data for at least 14 days. This dataset could be utilized to obtain glucose prediction models, hypoglycemia and hyperglycemia prediction models, and research on the relationships among sleep, CGM values, and the rest of the mentioned variables. This dataset could be used directly from the preprocessed version or customized from raw data.

This dataset provides values for SUGAR reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

1) Data Introduction • The Diabetes Prediction Dataset is a dataset built for the purpose of predicting diabetes and analyzing related risk factors. It contains various characteristics such as demographics, lifestyle, and clinical measurements, so it can be used to predict a patient's risk of developing diabetes.

2) Data Utilization (1) Diabetes Prediction Dataset has characteristics that: • Key columns (characteristics) include a variety of clinical and lifestyle indicators related to diabetes, including age, gender, body mass index (BMI), blood pressure, blood sugar levels (Glucose), insulin, family history, and physical activity. (2) Diabetes Prediction Dataset can be used to: • Machine Learning/Deep Learning Model Development: It can be used to develop classification models (logistic regression, decision tree, random forest, neural network, etc.) that predict the risk of developing diabetes based on patient characteristics. • Data Analysis and Visualization: It is suitable for correlation analysis, risk factor derivation, Exploratory Data Analysis (EDA) and many other variables such as demographics, clinical figures, lifestyle, and more.

Diabetes Analytics Dashboard – Power BI 🩺📊 This practice dashboard is built for Data Analytics, Data Visualization, and Data Science learning. It provides meaningful insights into diabetes risk factors using interactive visuals and advanced analytics.

🔹 Key Metrics – Total patients, BMI, glucose, blood pressure, and insulin levels. 🔹 Diabetes Risk Segmentation – Categorized into High, Medium, and Low risk groups. 🔹 Trends & Distribution – Glucose vs. Age, BMI categories, and Blood Pressure analysis. 🔹 Correlation Analysis – Exploring the relationships between glucose, BMI, and diabetes risk. 🔹 Gauge & Pie Charts – Visualizing risk percentage, BMI distribution, and glucose levels. 🔹 Interactive Filters & Drilldowns – Allowing deeper exploration of specific patient groups. 🔹 Predictive Insights – Identifying potential risk patterns through visual analytics.

This project helps in understanding data-driven healthcare insights using Power BI. Thanks to Kaggle for the dataset!

Dataset

This dataset was created by PeerChristensen

Contents

This dataset contains Food Prices data for India, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Sugar City. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Sugar City. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Sugar City, householders within the 45 to 64 years age group have the highest median household income at $65,288, followed by those in the under 25 years age group with an income of $53,750. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $43,333. Notably, householders within the 25 to 44 years age group, had the lowest median household income at $20,500.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Sugar City median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Sugar City by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Sugar City. The dataset can be utilized to understand the population distribution of Sugar City by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Sugar City. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Sugar City.

Key observations

Largest age group (population): Male # 45-49 years (76) | Female # 40-44 years (61). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Sugar City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sugar City is shown in the following column.
• Population (Female): The female population in the Sugar City is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Sugar City for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Sugar City Population by Gender. You can refer the same here

📈 Daily Historical Stock Price Data for Dhampur Sugar Mills Limited (2002–2025)

A clean, ready-to-use dataset containing daily stock prices for Dhampur Sugar Mills Limited from 2002-07-01 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Dhampur Sugar Mills Limited Ticker Symbol: DHAMPURSUG.NS Date Range: 2002-07-01 to 2025-05-28 Frequency: Daily Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-dhampur-sugar-mills-limited-20022025.

Imports of Cane & Beet Sugar in the United States increased to 302.15 USD Million in February from 291.45 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Cane & Beet Sugar.

The mean servings/times sugar-sweetened beverages consumed daily by California residents. These data are from the 2013 California Dietary Practices Surveys (CDPS), 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and 2013 California Children’s Healthy Eating and Exercise Practices Survey (CalCHEEPS). These surveys are now discontinued. Adults, adolescents, and children (with parental assistance) were asked about the sugar-sweetened beverages they drank over the previous 24 hour period. Child/Adolescent: Fruit and vegetable, beverage, and junk food consumption, along with physical activity, sedentary time, active transport, sport participation, school environment, home neighborhood environment, fruit and vegetable access and availability, household/family rules, weight status, school breakfast/lunch participation, attitudes, and beliefs. Adult: Fruit and vegetable, beverage, and junk food consumption, along with physical activity, sedentary time, worksite environment, school environment, home neighborhood environment, fruit and vegetable access and availability, household/family rules, weight status and weight loss practices, and food security. According to the Dietary Guidelines for Americans, 2010, sugar-sweetened beverages provide excess calories and few essential nutrients to the diet and should only be consumed when nutrient needs have been met and without exceeding daily calorie limits.

This dataset contains Food Prices data for Mongolia, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.