Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the competition are provided here, as well as competition details and baseline solutions. The data are derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

Prediction Data of Base Models from AutoGluon on 71 classification datasets from the AutoML Benchmark for Balanced Accuracy and ROC AUC.

The files of this figshare item include data that was collected for the paper: CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure, Lennart Purucker, Joeran Beel, Second International Conference on Automated Machine Learning, 2023.

The data was stored and used with the assembled framework: https://github.com/ISG-Siegen/assembled.

In detail, the data contains the predictions of base models on validation and test as produced by running AutoGluon for 4 hours. Such prediction data is included for each model produced by AutoGluon on each fold of 10-fold cross-validation on the 71 classification datasets from the AutoML Benchmark. The data exists for two metrics (ROC AUC and Balanced Accuracy). More details can be found in the paper.

The data was collected by code created for the paper and is available in its reproducibility repository: https://doi.org/10.6084/m9.figshare.23609226.

Its usage is intended for but not limited to using assembled to evaluate post hoc ensembling methods for AutoML.

Details The link above points to a hosted server that facilitates the download. We opted for a hosted server, as we found no other suitable solution to share these large files (due to file size or storage limits) for a reasonable price. If you want to obtain the data in another way or know of a more suitable alternative, please contact Lennart Purucker.

The link resolves to a directory containing the following:

example_metatasks: contains an example metatask for test purposes before committing to downloading all files.
metatasks_roc_auc.zip: The Metatasks obtained by running AutoGluon for ROC AUC. metatasks_bacc.zip: The Metatasks obtained by running AutoGluon for Balanced Accuracy.

The size after unzipping is:

metatasks_roc_auc.zip: ~85GB metatasks_bacc.zip: ~100GB

The metatask .zip files contain 2 files per metatask. One .json file with metadata information and a .hdf file containing the prediction data. The details on how this should be read and used as a Metatask can be found in the assembled framework and the reproducibility repository. To obtain the data without Metataks, we advise looking at the file content and metadata individually or parsing them by using Metatasks first.

This dataset consists of the synthetic electron backscatter diffraction (EBSD) maps generated for the paper, titled "Hybrid Algorithm for Filling in Missing Data in Electron Backscatter Diffraction Maps" by Emmanuel Atindama, Conor Miller-Lynch, Huston Wilhite, Cody Mattice, Günay Doğan, and Prashant Athavale. The EBSD maps were used to train, test, and validate a neural network algorithm to fill in missing data points in a given EBSD map.The dataset includes 8000 maps for training, 1000 maps for testing, 2000 maps for validation. The dataset also includes noise-added versions of the maps, namely, one more map per each clean map.

The package contains files for two modules designed to improve the accuracy of the indoor positioning system, namely the following:

door detection

videos_test - videos used to demonstrate the application of door detector

videos_res - videos from videos_test directory with detected doors marked

parts detection

frames_train_val - images generated from videos used for training and validation of VGG16 neural network model

frames_test - images generated from videos used for testing of the trained model

videos_test - videos used to demonstrate the application of parts detector

videos_res - videos from videos_test directory with detected parts marked

Context

This dataset stores separate files of training and validation data for Riiid!

These files are made by following notebook. https://www.kaggle.com/its7171/cv-strategy

You can read these files like:

train1 = pd.read_pickle('../input/riiid-cross-validation-files/cv1_train.pickle')
valid1 = pd.read_pickle('../input/riiid-cross-validation-files/cv1_valid.pickle')

Usage example: https://www.kaggle.com/its7171/riiid-cross-validation-files

We provided 1) the training and validation data, and 2) training settings. Particularly, the IEEE 33-bus test system employs 50000 training data and 5000 validation data, and the IEEE 136-bus test system employs 70000 training data and 10000 validation data.

This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 200 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Annotated test and train data sets. Both images and annotations are provided separately.

Validation data set for Hi5, Sf9 and HEK cells.

Confusion matrices for the determination of performance parameters

Dataset Card for Alpaca

I have just performed train, test and validation split on the original dataset. Repository to reproduce this will be shared here soon. I am including the orignal Dataset card as follows.

  Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.… See the full description on the dataset page: https://huggingface.co/datasets/disham993/alpaca-train-validation-test-split.

findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells

Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.

For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here:

Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gtht9d

The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7)

Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. arXiv preprint arXiv:2111.06693.

The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".

Training, test data and model parameters. The last 3 columns show the MinORG, LT and HT parameters used to create the pathogenicity families and build the model for each of the 10 models. Zthr is a threshold value, calculated for each model at the cross validation phase, which is used, given the final prediction score, to decide if the input organisms will be predicted as pathogenic or non-pathogenic. The parameters for each model are chosen after 5-fold cross-validation tests.

These are CARLA Simulation Datasets of the project "Out-Of-Domain Data Detection using Uncertainty Quantification in End-to-End Driving Algorithms". The simulations are generated in CARLA Town 02 for different sun angles (in degrees). You will find image frames, command labels, and steering control values in the respective 'xxxx_files_data' folder. You will find videos of each simulation run in the 'xxxx_files_visualizations' folder.

The 8 simulation runs for Training Data, are with the Sun Angles : 90, 80, 70, 60, 50, 40, 30, 20

The 8 simulation runs for Training Data were seeded at 0000, 1000, 2000, 3000, 4000, 5000, 6000, 7000 respectively

The 4 simulation runs for Validation Data, are with the Sun Angles : 87, 67, 47, 23

The 4 simulation runs for Validation Data were seeded at 0000, 2000, 4000, 7000 respectively

The 29 simulation runs for Testing Data, are with the Sun Angles : 85, 75, 65, 55, 45, 35, 25, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 09, 08, 07, 06, 05, 04, 03, 02, 01, 00, -1, -10

The 29 simulation runs for Testing Data were all seeded at 5000 respectively

Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

Formats1.xlsx contains the descriptions of the columns of the following datasets: Training, validation and test datasets in combination are all the records.sens1.csv and and meansdX.csv are required for testing.

Dataset Description

Context and Methodology

• Research Domain/Project:
  This dataset was created for a machine learning experiment aimed at developing a classification model to predict outcomes based on a set of features. The primary research domain is disease prediction in patients. The dataset was used in the context of training, validating, and testing.

• Purpose of the Dataset:
  The purpose of this dataset is to provide training, validation, and testing data for the development of machine learning models. It includes labeled examples that help train classifiers to recognize patterns in the data and make predictions.

• Dataset Creation:
  Data preprocessing steps involved cleaning, normalization, and splitting the data into training, validation, and test sets. The data was carefully curated to ensure its quality and relevance to the problem at hand. For any missing values or outliers, appropriate handling techniques were applied (e.g., imputation, removal, etc.).

Technical Details

• Structure of the Dataset:
  The dataset consists of several files organized into folders by data type:

  • Training Data: Contains the training dataset used to train the machine learning model.

  • Validation Data: Used for hyperparameter tuning and model selection.

  • Test Data: Reserved for final model evaluation.

  Each folder contains files with consistent naming conventions for easy navigation, such as train_data.csv, validation_data.csv, and test_data.csv. Each file follows a tabular format with columns representing features and rows representing individual data points.

• Software Requirements:
  To open and work with this dataset, you need VS Code or Jupyter, which could include tools like:

  • Python (with libraries such as pandas, numpy, scikit-learn, matplotlib, etc.)

Further Details

• Reusability:
  Users of this dataset should be aware that it is designed for machine learning experiments involving classification tasks. The dataset is already split into training, validation, and test subsets. Any model trained with this dataset should be evaluated using the test set to ensure proper validation.

• Limitations:
  The dataset may not cover all edge cases, and it might have biases depending on the selection of data sources. It's important to consider these limitations when generalizing model results to real-world applications.

Training and validation data used in creating Baltic Sea Region Land Cover Plus (BSRLC+) maps: Dataset link

• landcover_training_data_2006_2018.gpkg: Points data of consistent land cover from 2006 to 2018
• crop_training_data_{year}.gpkg: Points data of crop types derived from EuroCrop dataset in particular year (2019, 2021, 2023)
• landcover_validation_{year}.gpkg: Points data of validation data derived from LUCAS points in particular year (2009, 2012, 2015, 2018)
• Metadata.pdf: Information of land cover code in each dataset

Version notes:

Version 2: Correcting the validation data 2018 and Metadata file

Version 1: Original upload

Abstract: Ziel des Datensatzes ist das Training sowie die Validierung von Modellen zur Prognose von Zeitreihen für Fräsprozesse. Hierfür wurden an einer DMG CMX 600 V durch eine Siemens Industrial Edge Prozesse mit einer Abtastrate von 500 Hz aufgenommen. Es wurde ein Prozess für das Modelltraining und ein Prozess für die Validierung aufgenommen, welche sowohl für die Bearbeitung von Stahl sowie von Aluminium verwendet wurden. Es wurden mehrere Aufnahmen mit und ohne Werkstück (Aircut) aufgenommen, um möglichst viele Fälle abdecken zu können. Abstract: The aim of the data set is the training as well as the validation of models for the prediction of time series for milling processes. For this purpose, processes with a sampling rate of 500 Hz were recorded on a DMG CMX 600 V by a Siemens Industrial Edge. A process for model training and a process for validation were recorded, which were used for both steel and aluminum machining. Several recordings were made with and without the workpiece (aircut) in order to cover as many cases as possible. TechnicalRemarks: Documents: -Design of Experiments: Information on the paths such as the technological values of the experiments -Recording information: Information about the recordings with comments -Data: All recorded datasets. The first level contains the folders for training and validation both with and without the workpiece. In the next level, the individual test executions are located. The individual recordings are stored in the form of a JSON file. This consists of a header with all relevant information such as the signal sources. This is followed by the entries of the recorded time series. -NC-Code: NC programs executed on the machine -Workpiece: Pictures of the raw parts as well as the machined workpieces. The pictures show the unfinished part on the left, the training part in the middle and a part with two validation runs on the right. Experimental data: -Machine: DMG CMX 600 V -Material: S235JR, 2007 T4 -Tools: -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 5mm -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 10mm -VHM-Fräser HPC, TiSi, ⌀ f8 DC: 20mm -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 10mm -Schaftfräser HSS-Co8, TiAlN, ⌀ k10 DC: 5mm -Workpiece blank dimensions: 150x75x50mm License: This work is licensed under a Creative Commons Attribution 4.0 International License. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

This repository consists of two databases- CASE-ONSHORE and CASE-OFFSHORE, generated using OpenFAST v2.4 on NREL's 10-MW reference wind turbine for training data-driven probabilistic load surrogate models. The data is to be used for mapping 10-minute average environmental conditions to the corresponding 10-minute load statistics such as load average, fatigue and range at various locations on the tower and blades.

Utilize our machine learning datasets to develop and validate your models. Our datasets are designed to support a variety of machine learning applications, from image recognition to natural language processing and recommendation systems. You can access a comprehensive dataset or tailor a subset to fit your specific requirements, using data from a combination of various sources and websites, including custom ones. Popular use cases include model training and validation, where the dataset can be used to ensure robust performance across different applications. Additionally, the dataset helps in algorithm benchmarking by providing extensive data to test and compare various machine learning algorithms, identifying the most effective ones for tasks such as fraud detection, sentiment analysis, and predictive maintenance. Furthermore, it supports feature engineering by allowing you to uncover significant data attributes, enhancing the predictive accuracy of your machine learning models for applications like customer segmentation, personalized marketing, and financial forecasting.

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.