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.

Dataset

This dataset was created by SeshuRaju 🧘‍♂️

Released under CC0: Public Domain

Contents

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

License Plate Recognition Dataset

Dataset comprises 118,798 images of vehicle license plates, meticulously annotated for OCR tasks, object detection, and real-time traffic analysis. Designed to advance research in autonomous vehicles, traffic management, and urban mobility. By leveraging this dataset, researchers and developers can enhance license plate recognition (LPR) systems, improve traffic monitoring, and develop intelligent transportation solutions - Get the data The… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/united-kingdom-license-plate-dataset.

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).

Fruit And Vegetable Finder

## Overview

Fruit And Vegetable Finder is a dataset for object detection tasks - it contains Fruits Vegetables annotations for 230 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).

Dataset

This dataset was created by Singh Prince Rinku

Released under Other (specified in description)

Contents

sweetCaro/original-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The "Plant Pathogen Dataset" is a comprehensive collection of labeled images depicting various types of pathogens affecting plant species. This dataset is curated to facilitate research and development in the field of plant pathology, enabling the development of machine learning models for automated disease diagnosis and monitoring.

Image Categories: The dataset contains images representing different types of plant diseases, including bacterial infections, fungal diseases, pest infestations, and viral infections.

Data Sources

The images in this dataset were sourced from various sources, including research institutions, agricultural organizations, and open-access repositories. Care was taken to ensure high-quality images with accurate disease annotations.

Potential Applications

Disease Diagnosis: The dataset can be used to train machine learning models for automated diagnosis of plant diseases based on image analysis. Disease Monitoring: By continuously monitoring plant health using machine learning models trained on this dataset, farmers and agricultural professionals can detect diseases early and implement timely interventions.

Acknowledgments

We would like to acknowledge the contributions of the research community, agricultural experts, and dataset contributors who have made this dataset possible. Their efforts in collecting, labeling, and sharing plant disease images are invaluable to advancing research in plant pathology and agricultural technology.

techneum/dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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

First National Bank of America 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.

City State Bank location dataset — United States subset. Verified addresses, coordinates, phones, and operating hours. Licensed via CREHQ Data Store.

The Mobile Brazilian Retinal Dataset (mBRSET) presents a pioneering collection of retinal images captured via portable cameras, encompassing a diverse range of ethnic backgrounds from Itabuna, Bahia, Brazil. Comprising 5164 images from 1291 patients diagnosed with diabetes, this dataset is augmented with clinical and demographic metadata. Its significance lies in its provision as a resource for the development and validation of algorithms tailored for portable retinal cameras, which are increasingly being deployed, particularly in low and middle-income countries. By providing a dataset of retinal fundus photos captured via portable cameras, mBRSET offers an opportunity to develop and validate computer vision models for diabetic retinopathy. This resource has the potential to contribute to advancements in medical imaging and diagnostic technologies.

Dataset

This dataset was created by ponpandi

Released under MIT

Contents

Hydrometeorological time series and catchment attributes from the CABra dataset. The manuscript of "CABra: a novel large-sample dataset for Brazilian catchments" is under review in Hydrology and Earth System Sciences (HESS) journal.

Here we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land-use and land-cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model and the coordinates of the streamflow stations provided by the Brazilian Water Agency (ANA), where only the stations with 30 years (1980-2010) of data and less than 10% of missing records were included. Catchment areas range from 9 to 4,800,000 km² and the mean daily streamflow varies from 0.02 to 9 mm day^-1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage areas for each catchment, aside from more than 100 attributes within the attribute classes.

Data can also be accessed at: thecabradataset.shinyapps.io/CABra

* This version includes water demand in CABra catchments for 2020 and 2040 (projection).