midooy/RDRF-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

ZhaZhaTing-NutriSip Dataset

About Dataset

The ZhaZhaTing-NutriSip Dataset is a comprehensive food and beverage dataset containing 79,653 records and 435 features. It brings together a diverse collection of soups, teas, juices, smoothies, milkshakes, health drinks, detox beverages, and traditional drinks from multiple cuisines around the world.

This dataset was created to support machine learning, data science, nutrition analysis, recommendation systems, business intelligence, and food analytics research. It includes nutritional information, preparation details, cuisine origins, pricing attributes, and many additional engineered features that can be used for predictive modeling and exploratory data analysis.

Key Features

• 79,653 unique food and beverage records
• 435 feature columns
• Global cuisine coverage
• Nutrition and calorie information
• Preparation time and difficulty levels
• Pricing information
• Category and subcategory classifications
• Rich feature set for machine learning applications

Included Categories

• Soups
• Teas
• Juices
• Smoothies
• Milkshakes
• Health Drinks
• Detox Water
• Traditional Beverages

Example Attributes

• Item ID
• Item Name
• Category
• Subcategory
• Cuisine Origin
• Country
• Preparation Time (Minutes)
• Difficulty Level
• Calories Per Serving
• Price (PKR)

Potential Use Cases

Machine Learning

• Food Classification
• Price Prediction
• Calorie Prediction
• Recommendation Systems
• Nutritional Modeling

Data Analysis

• Cuisine Trend Analysis
• Beverage Consumption Studies
• Nutritional Comparisons
• Market and Pricing Analysis

Research & Education

• Food Informatics
• Nutrition Research
• Culinary Analytics
• Data Science Projects
• Academic Research

Data Source

The dataset was compiled, curated, and enriched using food-related information from multiple public references, nutritional resources, culinary data sources, and feature engineering techniques. Some records may include synthetic or generated entries created for educational, analytical, and machine learning purposes.

License

Apache License 2.0

GDPa1: Antibody developability dataset

Contains the assay data for 242 antibodies across 10 assays as described in our latest preprint, PROPHET-Ab: A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training.

  Example usage

Using pandas: import pandas as pd

Login using e.g. huggingface-cli login to access this dataset

df = pd.read_csv("hf://datasets/ginkgo-datapoints/GDPa1/GDPa1_v1.2_20250814.csv")

Using Hugging… See the full description on the dataset page: https://huggingface.co/datasets/ginkgo-datapoints/GDPa1.

MediaEval

## Overview

MediaEval is a dataset for object detection tasks - it contains Swimmers annotations for 509 images.

## Getting Started

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

  ## License

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

About this dataset

Context

The dataset tabulates the population of Woodway by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Woodway across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.36% of total population being female. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Woodway is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Woodway total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Woodway Population by Race & Ethnicity. You can refer the same here

Only Basketball

## Overview

Only Basketball is a dataset for object detection tasks - it contains Basketball Q9SX annotations for 1,334 images.

## Getting Started

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

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset was generated using the RAFT (Retrieval-Augmented Fine-Tuning) methodology to train small language models to answer questions accurately from a retrieved document context — and to refuse gracefully when the relevant passage is absent.

The source document is the ServiceNow Instance Security Best Practices Guide (v4.3, Zurich release, November 2025), a 45-page technical white paper covering authentication, access control, encryption, logging, incident response, and business continuity for ServiceNow platform administrators.

Generation Pipeline

Text was extracted from the PDF using GPT-4o vision (pdf_to_chunks.py), split into 1 024-character overlapping chunks with LangChain's RecursiveCharacterTextSplitter, then processed by raft_datagen.py:

• Question generation — GPT-4o produces 5 factual questions per chunk
• CoT answer generation — GPT-4o generates a chain-of-thought answer that cites evidence with ##begin_quote##...##end_quote## markers and concludes with an

Mountain Heritage location dataset — 4 locations in 1 states. Part of CREHQ's multi-unit intelligence platform covering retail, restaurant, financial services, and healthcare brands. Licensed access via enterprise API or dataset purchase. Training on CREHQ data is not permitted.

Complete dataset containing all 17734 supermarkets in Belgium, including branded and independent locations. Includes geocoded addresses, latitude and longitude coordinates, contact details, opening hours, and administrative areas in CSV format for retail analysis, market research, logistics, and geospatial applications. Last updated: 26 May 2026.

SmartThyro-MV is a large-scale, longitudinal dataset curated to support early diagnosis and progression monitoring of thyroid cancer using structured, multi-visit health records. Collected from anonymized patient data across collaborating clinical centers and telehealth monitoring platforms between 2015 and 2025, this dataset captures temporal health patterns derived from wearable devices, diagnostic scans, and hormonal assays.

It includes detailed records for 80,000 patients, each tracked over multiple clinical visits, making it one of the most comprehensive real-world resources for thyroid disease modeling. Designed to simulate real-world deployments of smart health devices, the dataset covers both static demographics and dynamic physiological indicators across time.

📊 Dataset Highlights: Patient Count: 80,000 unique individuals

Visits per Patient: 2 to 5 temporal records

Time Frame: January 2015 to March 2025

Source: Aggregated from participating healthcare centers and smart health device logs

🧬 Key Feature Categories: Feature Name Description Patient_ID Unique anonymized identifier per patient Visit_Number Sequential clinical visit index Visit_Timestamp Date of each visit Scan_Type Type of scan performed (Ultrasound, FNA, Elastography) Age, Gender, Height, Weight, BMI Basic demographics and body metrics Family_History Binary indicator for thyroid disease in family history Smoking_Status Categorical: Smoker, Non-Smoker, Former Smoker Radiation_Exposure Prior exposure to radiation therapy or sources Nodule_Size_mm, Margin_Sharpness, Shape_Score, Echogenicity_Index Radiomic features from scans Calcification_Presence, Capsular_Invasion_Indicator Structural indicators linked to malignancy Vascularity_Score, Texture_Entropy, Asymmetry_Score Advanced scan-derived imaging biomarkers TSH_Risk_Level, TPOAb_Probability, Thyroglobulin_Level_Predicted, Calcitonin_Level_Predicted, Reverse_T3_Index Hormonal indicators Heart_Rate, Daily_Steps, Sleep_Patterns, Body_Temperature, Pulse_Oximetry Smart device vitals Stress_Level, Symptom_Onset_Duration, Hormonal_Change_Rate, Medication_Adherence Behavioral and progression indicators Diagnosis_Label Binary classification (0: Benign, 1: Malignant) Cancer_Type Categorical subtype (e.g., Papillary, Follicular, Medullary, etc.) Recurrence_Risk Binary risk estimation for recurrence likelihood

The First National Bank of Elmer location dataset — United States subset. Verified addresses, coordinates, phones, and operating hours. Licensed via CREHQ Data Store.

This dataset contains the digitized treatments in Plazi based on the original journal article Borges, Leonardo Maurici, Pirani, José Rubens (2014): When the old guys knew better: The true identity of Mimosa longepedunculata and reestablishment of M. tocantina (Leguminosae, Mimosoideae). Phytotaxa 181 (5): 261-278, DOI: 10.11646/phytotaxa.181.5.2, URL: http://dx.doi.org/10.11646/phytotaxa.181.5.2

Abstract

Megadiverse genera usually have a complex taxonomy. One factor influencing this complexity is concerned to synonyms, which are often numerous in widespread and morphologically variable species. In this article we examined the case of Mimosa longepedunculata and M. tocantina, two sympatric narrowly distributed species from central Brazil, considered to be synonyms in Barneby’s monograph. We show that this was an inaccurate taxonomic decision related to a misinterpretation of the type specimens and, possibly, also to sampling biases in field works. The definition of each species is here clarified and M. tocantina is reestablished and considered a distinct species from M. longepedunculata, having M. pseudosetosa as a new synonym. A regional identification key for the species is provided together with data on distribution and habitat, flowering and fruiting, conservation status, etymology, and notes on morphology. Illustrations, pictures and a full description of M. longepedunculata are also presented.

This dataset contains NMR spectra obtained for the sample -Carapin F (OC3_DongmoAp_0418_CMS34) Nucleus: 1H NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: 1d Temperature: 297.9998 Observed Frequency: 600.13 Magnetic Field Strength: 14.095010308659894 Number of Scans: 16 NMR Pulse Sequence: zg30 Spectral Width: 19.8368493381866 Number of Data Points: 65536 Relaxation Delay: 1 Observed Frequency: 600.133705802 Nucleus: 13C NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: 1d Temperature: 298.0002 Observed Frequency: 150.902808526 Magnetic Field Strength: 14.09286355384592 Number of Scans: 1024 NMR Pulse Sequence: zgpg30 Spectral Width: 236.647117383728 Number of Data Points: 65536 Relaxation Delay: 2 Observed Frequency: 150.917898807 Nucleus: 13C NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: dept Temperature: 298.0009 Observed Frequency: 150.902808526 Magnetic Field Strength: 14.09286355384592 Number of Scans: 256 NMR Pulse Sequence: deptsp135 Spectral Width: 157.767899965848 Number of Data Points: 65536 Relaxation Delay: 2 Observed Frequency: 150.91488075 Nucleus: 1H,1H NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: cosy Temperature: 298.0007 Observed Frequency: 600.13,600.13 Magnetic Field Strength: 14.095010308659894 Number of Scans: 2 NMR Pulse Sequence: cosygpppqf Spectral Width: 11.9021176366777,11.9020999999008 Number of Data Points: 2048,128 Relaxation Delay: 2 Observed Frequency: 600.13330072,600.13330072 Nucleus: 1H,13C NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: hmqc Temperature: 298.0003 Observed Frequency: 600.13,150.902809 Magnetic Field Strength: 14.095010308659894 Number of Scans: 4 NMR Pulse Sequence: hmqcgpqf Spectral Width: 11.9021176366777,164.999999482852 Number of Data Points: 1024,128 Relaxation Delay: 1.5 Observed Frequency: 600.13330072,150.91412671 Nucleus: 1H,13C NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: hmbc Temperature: 297.9987 Observed Frequency: 600.13,150.902809 Magnetic Field Strength: 14.095010308659894 Number of Scans: 8 NMR Pulse Sequence: hmbcgpndqf Spectral Width: 11.9021176366777,239.999999250963 Number of Data Points: 4096,128 Relaxation Delay: 1.5 Observed Frequency: 600.13330072,150.92016282 Nucleus: 1H,1H NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: noesy Temperature: 297.9976 Observed Frequency: 600.13,600.13 Magnetic Field Strength: 14.095010308659894 Number of Scans: 4 NMR Pulse Sequence: noesygpphpp Spectral Width: 9.25721228388213,9.25721228388212 Number of Data Points: 2048,54 Relaxation Delay: 1.985664 Observed Frequency: 600.132673334975,600.132673334975 Nucleus: 1H,1H NMR Solvent: CDCl3 NMR Probe: Z168773_0003 (CPP1.1 BBO 600S3 BB-H&F-D-05 Z XT) NMR Pulse Sequence: noesy Temperature: 297.9976 Observed Frequency: 600.13,600.13 Magnetic Field Strength: 14.095010308659894 Number of Scans: 4 NMR Pulse Sequence: noesygpphpp Spectral Width: 9.25721228388213,9.25721228388212 Number of Data Points: 2048,54 Relaxation Delay: 1.985664 Observed Frequency: 600.132673334975,600.132673334975

This is a simple dataset for binary text classification. The labels here areham andspam. The text data was gotten from the U.C Irvine machine learning repository, with the only changes being conversion from the initial .txt format to a more easily accessible .csv format.

The Allen Brain Observatory – Visual Coding is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. It includes datasets collected with both two-photon imaging and Neuropixels probes, two complementary techniques for measuring the activity of neurons in vivo. The two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. The Neuropixels dataset features spiking activity from distributed cortical and subcortical brain regions, collected under analogous conditions to the two-photon imaging experiments. We hope that experimentalists and modelers will use these comprehensive, open datasets as a testbed for theories of visual information processing.

This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. This subset, for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America, retains all five arrays at the 1-degree resolution but only for the area of interest (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N). The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats.The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate.The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov].

Home Depot location dataset — Mexico subset. Verified addresses, coordinates, phones, and operating hours. Licensed via CREHQ Data Store.

Dataset Card for Synthetic Inline Holographical Images v3 (224px Highly Diverse)

This dataset provides synthetic image triplets representing inline holographical imaging in a simulated environment. This version (v3) uses a native 224x224 resolution optimized for modern Vision Transformers (ViT, Swin) and contains 25,000 samples across 8 noise configurations. Each data sample consists of:

An object-domain field (ground truth), Its corresponding forward-propagated hologram (the… See the full description on the dataset page: https://huggingface.co/datasets/gokhankocmarli/inline-digital-holography-v3.

The table concept_relationship is part of the dataset MedAlign, available at https://stanford.redivis.com/datasets/48nr-frxd97exb. It contains 58831134 rows across 8 variables.