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Satlas is a remote sensing dataset and benchmark that is large in both breadth, featuring all of the aforementioned applications and more, as well as scale, comprising 290M labels under 137 categories and 7 label modalities. Source: Satlas: A Large-Scale, Multi-Task Dataset for Remote Sensing Image Understanding Image Source: https://arxiv.org/pdf/2211.15660v1.pdf
FLIP includes several benchmark datasets that contain a variety of protein sequences, each with a real-valued label indicating its "fitness" (how well the protein performs some particular function). The goal is to predict the fitness of a given protein sequence using the sequence. Different representations of protein sequences (e.g. learned embeddings from large language models) may prove helpful here. Some of the benchmark datasets (thermostability) contain a highly diverse set of sequences from many different protein families. Others (AAV, GB1) contain all sequences that are mutants of a single parent sequence. Each benchmark dataset features multiple "splits" -- different ways of train-test splitting the data to assess how well a model might generalize given limited information. The AAV benchmark, for example, features the "mutant vs designed" split in which a model is trained on randomly generated mutants and asked to predict the fitness of designed sequences, and the "seven vs many" split in which a model is trained on sequences with seven mutations and asked to make predictions for sequences with a different number of mutations.
SciRepEval is a comprehensive benchmark for training and evaluating scientific document representations. It includes 25 challenging and realistic tasks, 11 of which are new, across four formats: classification, regression, ranking and search. Source: SciRepEval: A Multi-Format Benchmark for Scientific Document Representations Image Source: https://arxiv.org/pdf/2211.13308v1.pdf
Median house prices for California districts derived from the 1990 census. About Dataset Context This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome. The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning. Content The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows, their names are pretty self-explanatory: - longitude - latitude - housing_median_age - total_rooms - total_bedrooms - population - households - median_income - median_house_value - ocean_proximity Acknowledgements This data was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297. and I encountered it in 'Hands-On Machine learning with Scikit-Learn and TensorFlow' by Aurélien Géron. Aurélien Géron wrote: This dataset is a modified version of the California Housing dataset available from: Luís Torgo's page (University of Porto) Inspiration See my kernel on machine learning basics in R using this dataset, or venture over to the following link for a python based introductory tutorial: https://github.com/ageron/handson-ml/tree/master/datasets/housing
Data variables and description. Parameters/Features Abbreviation Type Measurement Date Date year-month-day – Rented Bike count Count Continuous 0, 1, 2, .. ., 3556 Hour Hour Continuous 0, 1, 2, .. ., 23 Temperature Temp Continuous ◦C Humidity Hum Continuous % Windspeed Wind Continuous m/s Visibility Visb Continuous 10 m Dew point temperature Dew Continuous ◦C Solar radiation Solar Continuous MJ/m2 Rainfall Rain Continuous Mm Snowfall Snow Continuous cm Seasons Seasons Categorical Autumn, Spring, Summer, Winter Holiday Holiday Categorical Holiday, Workday Functional Day Fday Categorical NoFunc, Func Week status Wstatus Categorical Weekday (Wday), Weekend (Wend) Day of the week Dweek Categorical Sunday, Monday, .. ., Saturday
ADORE is a benchmark dataset for machine learning for ecotixicology, covering acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches. The dataset contains acute toxicity data (lethal concentration 50; LC50 or effective concentration 50; EC50) on 2,408 chemicals in 203 different species of algae, crustaceans, and fish. This encompasses a total of 33K data points, 26K of which are on fish (140 species). The task is to predict the ecotoxicological outcome based on historic ecotoxicity data. ADORE was originally published in Nature ScientificData: Schür, Christoph, Lilian Gasser, Fernando Perez-Cruz, Kristin Schirmer, and Marco Baity-Jesi. 2023. “A Benchmark Dataset for Machine Learning in Ecotoxicology.” Scientific Data 10 (1): 718. https://doi.org/10.1038/s41597-023-02612-2.
This data set comes from breast cancer tissue samples deposited to The Cancer Genome Atlas (TCGA) project. TCGA contains data on tumour samples were assayed on several platforms; this data set compiles results obtained using Agilent mRNA expression microarrays. BRCA1 is the first gene identified that increases the risk of early onset breast cancer. Because BRCA1 is likely to interact with many other genes, including tumor suppressors and regulators of the cell division cycle, it is of interest to find genes with expression levels related to that of BRCA1, which we treat as the outcome of this analysis. These genes may be functionally related to BRCA1 and are useful candidates for further studies. Expression measurements of 17,814 genes from 536 patients; all expression measurements are recorded on the log scale. There are are 491 genes with missing data, which we have excluded.
Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.
From the official description: > The corpus contains 10-K reports from many US companies during years > 1996-2006, as well as measured volatility of stock returns for the > twelve-month periods preceding and following each report. The data > are organized by the year of the report.
The dataset includes two parts corresponding to the cities of Abakan (65524 nodes, 340012 edges) and Omsk (231688 nodes, 1149492 edges). Along with the road network graph, it includes trip records represented as sequences of visited nodes (making the dataset suitable both for path-blind and path-aware settings). There are two types of target values for a regression task: real travel time and real length of a trip.
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems---known as ``dark vessels''---is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require a domain-specific treatment and are not widely accessible to the ML community. Maritime objects (vessels and offshore infrastructure) are relatively small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in SAR imagery. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each. The images are annotated using a combination of automated and manual analysis. Co-located bathymetry and wind state rasters accompany every SAR image. We also provide an overview of the xView3 Computer Vision Challenge, an international competition using xView3-SAR for ship detection and characterization at large scale. We release the data (https://iuu.xview.us/) and code (https://github.com/DIUx-xView) to support ongoing development and evaluation of ML approaches for this important application.
In this dataset we added [Company Name, Car Model, Car Type, Fuel Type, Transmission, Engine (cc), Mileage, Kms_driven, Buyers, Horsepower (kw), Year Price (Lakhs)]
Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. Number of instances: 1030 Number of Attributes: 9 Attribute breakdown: 8 quantitative input variables, and 1 quantitative output variable
IOPS and Latency measurements of a real data storage system
The DRIFT dataset includes 25k image patches collected in five European countries sourced from aerial and nanosatellite image archives. Each image patch is associated with three target variables to predict: 1. Canopy height: average height value for pixels containing woody vegetation. 2. Tree count: number of overstory (visible from an overhead perspective) trees in the images. 3. Tree cover fraction: percentage of the image being covered by overstory tree crowns. The DRIFT dataset includes significant shifts between label and visual distributions due to sensor and area differences. Furthermore, vegetation tends to grow to fit the local climate, therefore introducing concept drift in the data: same tree species may appear differently in different subsets. The label distribution also varies among different subsets (countries). The dataset is a good choice for: * image-level regression * domain adaption for regression * remote sensing for forest applications
FLIP includes several benchmark datasets that contain a variety of protein sequences, each with a real-valued label indicating its "fitness" (how well the protein performs some particular function). The goal is to predict the fitness of a given protein sequence using the sequence. Different representations of protein sequences (e.g. learned embeddings from large language models) may prove helpful here. This sub-dataset (AAV) is a set of 201,426 training sequences and 82,583 test sequences in which the goal is to predict the fitness of mutants of the capsid protein from the adeno-associated virus (AAV). The training set proteins were designed, while the test set proteins are random mutants. The absolute value of the fitness is not important, but its ranking / relative value is -- protein designers would like to be able to pick a sequence with high fitness relative to those in the training set. Performance is therefore usually assessed using Spearman's r correlation coefficient.
Here the dataset described in Hitchhiking Rides Dataset: Two decades of crowd-sourced records on stochastic traveling(https://arxiv.org/abs/2506.21946) is published. This is the largest dataset of crowd-sourced real-world hitchhiking rides. Data has been collected on online platforms such das liftershalte.info, hitchwiki.org and hitchmap.com starting in 2005 until today. The structure that this dataset follows is described in the Hitchhiking Data Standard(https://github.com/Hitchwiki/hitchhiking_data_standard). # Uses To inform hitchhikers to make more data-based decisions on the road. General research towards hitchhiking as a cultural phenomenon.
We present two multi-modal datasets, one for Main Board IPOs, and the other for Small and Medium Enterprises (SME) IPOs. It consists of various features relating to the company going for IPOs, and other macroeconomic factors. The objective is to estimate the direction and under pricing with respect to opening, high and closing prices of stocks on the IPOlisting day.
A representative event-based eye-tracking dataset, collected with two event cameras mounted on a glass frame. It features variable recording lengths and event counts from 30 volunteers, providing an ideal benchmark for modeling the heterogeneity of event-based eye tracking in real-world scenarios.
Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are "ready-to-use" for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of LimeSoDa is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in Zenodo data repository or using the R or Python package implementations. However, for a more in-depth analysis, we refer to the published paper "LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping" by Schmidinger et al. (2025).
The dataset for masked autoencoder for X-ray fluorescence (XRF) is a following development after the dataset (Chao et al., 2022). Besides the published XRF spectra-target measurements (CaCO3 and TOC) pairs of data, we further upload the XRF spectra in that project but without alignments of the target measurements here. As the first XRF large dataset compiled in a ML friendly format, we expect to kickoff more ML studies in the field of XRF and geology, especially DL studies. The investigated cores, which form the datast, are mostly retrieved across the high- to mid-latitude Northwest Pacific (37°N-52°N) and the Pacific sector of the Southern Ocean (53°S-63°S), with a water depth coverage from 1211 to 4853 m: <br> 1. Cruise SO264 in the subarctic Northwest Pacific with R/V SONNE in 2018<br> 2. Cruise PS97 in the central Drake Passage with RV Polarstern in 2016<br> 3. Cruise PS75 in the Pacific sector of the Southern Ocean in 2009/2010.<br> 4. Cruise KOMEX I and KOMEX II with R/V Akademik Lavrentyev in 1998 and cruise SO178 in 2004 in the Okhotsk Sea.
This dataset contains demographic and personal health information for individuals, along with the corresponding medical insurance charges billed to them. It is commonly used to build predictive models for insurance costs and to explore relationships between factors such as age, BMI, smoking status, and region on medical expenses. Features: - age: Age of the primary beneficiary (integer) - sex: Gender of the individual (male, female) - bmi: Body mass index, providing a measure of body fat based on height and weight (float) - children: Number of children/dependents covered by the insurance (integer) - smoker: Smoking status of the individual (yes, no) - region: Residential area in the US (northeast, northwest, southeast, southwest) - charges: Individual medical costs billed by health insurance (float, in USD) Applications: This dataset is frequently used in regression modeling, cost prediction, and data visualization tasks. It is ideal for learning how lifestyle and demographic factors impact healthcare expenses and serves as a foundational dataset for applied machine learning in health economics.
Meta Omnium is a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression. Meta Omnium enables meta-learning researchers to evaluate model generalization to a much wider array of tasks than previously possible, and provides a single framework for evaluating meta-learners across a wide suite of vision applications in a consistent manner. Source: Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn Image Source: Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Predicting stock market prices following corporate earnings calls remains a significant challenge for investors and researchers alike, requiring innovative approaches that can process diverse information sources. This study investigates the impact of corporate earnings calls on stock prices by introducing a multi-modal predictive model. We leverage textual data from earnings call transcripts, along with images and tables from accompanying presentations, to forecast stock price movements on the trading day immediately following these calls. To facilitate this research, we developed the MiMIC (Multi-Modal Indian Earnings Calls) dataset, encompassing companies representing the Nifty 50, Nifty MidCap 50, and Nifty Small 50 indices. The dataset includes earnings call transcripts, presentations, fundamentals, technical indicators, and subsequent stock prices. We present a multimodal analytical framework that integrates quantitative variables with predictive signals derived from textual and visual modalities, thereby enabling a holistic approach to feature representation and analysis.
We present a comprehensive dataset comprising a vast collection of raw mineral samples for the purpose of mineral recognition. The dataset encompasses more than 5,000 distinct mineral species and incorporates subsets for zero-shot and few-shot learning. In addition to the samples themselves, some entries in the dataset are accompanied by supplementary natural language descriptions, size measurements, and segmentation masks. For detailed information on each sample, please refer to the minerals_full.csv file.
Dataset composed of two main parts 1. Material characterization of a metal (from lab test) Single file: metal-tensile.txt Material characteristics: - isotropic elastoplastic material with isotropic hardening - poisson ratio = 0.3 Geometrical characteristics: - specimen geometry: bar - initial length = 1000 mm - initial cross section = 35.28 mm^2 Loading characteristics: - uniaxial tension 2. Composite open-hole substructure characterization (from FE simulations) Multiple files: EaX_EbY_EsZ.txt (see README.txt for nomenclature) Layup: - [45/90/-45/0]s Damage criterion: - Hashin with cohesive damage propagation - No delaminations considered Loading characteristics: - Homogenized strain loading over the plate edges - In plane loading: different combinations of (eps_1, eps_2, eps_12) - In plane response: (sig_1, sig_2, sig_12), i.e. homogenized stresses
Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset
The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005°. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.
Enriched Voxceleb speakers' data of 1,715 celebrities with height gathered from Wikidata Other columns are taken from voxceleb_enrichment_age_gender
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