The open dataset, software, and other files accompanying the manuscript "An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models," submitted for publication to Integrated Materials and Manufacturing Innovations. Machine learning and autonomy are increasingly prevalent in materials science, but existing models are often trained or tuned using idealized data as absolute ground truths. In actual materials science, "ground truth" is often a matter of interpretation and is more readily determined by consensus. Here we present the data, software, and other files for a study using as-obtained diffraction data as a test case for evaluating the performance of machine learning models in the presence of differing expert opinions. We demonstrate that experts with similar backgrounds can disagree greatly even for something as intuitive as using diffraction to identify the start and end of a phase transformation. We then use a logarithmic likelihood method to evaluate the performance of machine learning models in relation to the consensus expert labels and their variance. We further illustrate this method's efficacy in ranking a number of state-of-the-art phase mapping algorithms. We propose a materials data challenge centered around the problem of evaluating models based on consensus with uncertainty. The data, labels, and code used in this study are all available online at data.gov, and the interested reader is encouraged to replicate and improve the existing models or to propose alternative methods for evaluating algorithmic performance.

Modeling data and analysis scripts generated during the current study are available in the github repository: https://github.com/USEPA/CompTox-MIEML. RefChemDB is available for download as supplemental material from its original publication (PMID: 30570668). LINCS gene expression data are publicly available and accessible through the gene expression omnibus (GSE92742 and GSE70138) at https://www.ncbi.nlm.nih.gov/geo/ .

This dataset is associated with the following publication: Bundy, J., R. Judson, A. Williams, C. Grulke, I. Shah, and L. Everett. Predicting Molecular Initiating Events Using Chemical Target Annotations and Gene Expression. BioData Mining. BioMed Central Ltd, London, UK, issue}: 7, (2022).

Context: Exception handling (EH) bugs stem from incorrect usage of exception handling mechanisms (EHMs) and often incur severe consequences (e.g., system downtime, data loss, and security risk). Tracking EH bugs is particularly relevant for contemporary systems (e.g., cloud- and AI-based systems), in which the software's sophisticated logic is an additional threat to the correct use of the EHM. On top of that, bug reporters seldom can tag EH bugs --- since it may require an encompassing knowledge of the software's EH strategy. Surprisingly, to the best of our knowledge, there is no automated procedure to identify EH bugs from report descriptions.Objective: First, we aim to evaluate the extent to which Natural Language Processing (NLP) and Machine Learning (ML) can be used to reliably label EH bugs using the text fields from bug reports (e.g., summary, description, and comments). Second, we aim to provide a reliably labeled dataset that the community can use in future endeavors. Overall, we expect our work to raise the community's awareness regarding the importance of EH bugs.Method: We manually analyzed 4,516 bug reports from the four main components of Apache’s Hadoop project, out of which we labeled ~20% (943) as EH bugs. We also labeled 2,584 non-EH bugs analyzing their bug-fixing code and creating a dataset composed of 7,100 bug reports. Then, we used word embedding techniques (Bag-of-Words and TF-IDF) to summarize the textual fields of bug reports. Subsequently, we used these embeddings to fit five classes of ML methods and evaluate them on unseen data. We also evaluated a pre-trained transformer-based model using the complete textual fields. We have also evaluated whether considering only EH keywords is enough to achieve high predictive performance.Results: Our results show that using a pre-trained DistilBERT with a linear layer trained with our proposed dataset can reasonably label EH bugs, achieving ROC-AUC scores of up to 0.88. The combination of NLP and ML traditional techniques achieved ROC-AUC scores of up to 0.74 and recall up to 0.56. As a sanity check, we also evaluate methods using embeddings extracted solely from keywords. Considering ROC-AUC as the primary concern, for the majority of ML methods tested, the analysis suggests that keywords alone are not sufficient to characterize reports of EH bugs, although this can change based on other metrics (such as recall and precision) or ML methods (e.g., Random Forest).Conclusions: To the best of our knowledge, this is the first study addressing the problem of automatic labeling of EH bugs. Based on our results, we can conclude that the use of ML techniques, specially transformer-base models, sounds promising to automate the task of labeling EH bugs. Overall, we hope (i) that our work will contribute towards raising awareness around EH bugs; and (ii) that our (publicly available) dataset will serve as a benchmarking dataset, paving the way for follow-up works. Additionally, our findings can be used to build tools that help maintainers flesh out EH bugs during the triage process.

The Data Labeling Solutions and Services market is experiencing robust growth, driven by the escalating demand for high-quality training data in the artificial intelligence (AI) and machine learning (ML) sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $75 billion by 2033. This expansion is fueled by several key factors. Firstly, the increasing adoption of AI across diverse industries, including automotive, healthcare, and finance, necessitates vast amounts of accurately labeled data for model training and improvement. Secondly, advancements in deep learning algorithms and the emergence of sophisticated data annotation tools are streamlining the labeling process, boosting efficiency and reducing costs. Finally, the growing availability of diverse data sources, coupled with the rise of specialized data labeling companies, is further contributing to market growth. Despite these positive trends, the market faces some challenges. The high cost associated with data annotation, particularly for complex datasets requiring specialized expertise, can be a barrier for smaller businesses. Ensuring data quality and consistency across large-scale projects remains a critical concern, necessitating robust quality control measures. Furthermore, addressing data privacy and security issues is essential to maintain ethical standards and build trust within the market. The market segmentation by type (text, image/video, audio) and application (automotive, government, healthcare, financial services, etc.) presents significant opportunities for specialized service providers catering to niche needs. Competition is expected to intensify as new players enter the market, focusing on innovative solutions and specialized services.

The MLCommons Dollar Street Dataset is a collection of images of everyday household items from homes around the world that visually captures socioeconomic diversity of traditionally underrepresented populations. It consists of public domain data, licensed for academic, commercial and non-commercial usage, under CC-BY and CC-BY-SA 4.0. The dataset was developed because similar datasets lack socioeconomic metadata and are not representative of global diversity.

This is a subset of the original dataset that can be used for multiclass classification with 10 categories. It is designed to be used in teaching, similar to the widely used, but unlicensed CIFAR-10 dataset.

These are the preprocessing steps that were performed:

Only take examples with one imagenet_synonym label

Use only examples with the 10 most frequently occuring labels

Downscale images to 64 x 64 pixels

Split data in train and test

Store as numpy array

This is the label mapping:

Category label

day bed 0

dishrag 1

plate 2

running shoe 3

soap dispenser 4

street sign 5

table lamp 6

tile roof 7

toilet seat 8

washing machine 9

Checkout this notebook to see how the subset was created.

The original dataset was downloaded from https://www.kaggle.com/datasets/mlcommons/the-dollar-street-dataset. See https://mlcommons.org/datasets/dollar-street/ for more information.

Consisting of six multi-label datasets from the UCI Machine Learning repository.

Each dataset contains missing values which have been artificially added at the following rates: 5, 10, 15, 20, 25, and 30%. The “amputation” was performed using the “Missing Completely at Random” mechanism.

File names are represented as follows:

   amp_DB_MR.arff

where:

   DB = original dataset;


   MR = missing rate.

For more details, please read:

IEEE Access article (in review process)

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to assess status and trends of bat populations while developing innovative and community-driven conservation solutions using its unique data and technology infrastructure. To support scalability and transparency in the NABat acoustic data pipeline, we developed a fully-automated machine-learning algorithm. This dataset includes audio files of bat echolocation calls that were considered to develop V1.0 of the NABat machine-learning algorithm, however the test set (i.e., holdout dataset) has been excluded from this release. These recordings were collected by various bat monitoring partners across North America using ultrasonic acoustic recorders for stationary acoustic and mobile acoustic surveys. For more information on how these surveys may be conducted, see Chapters 4 and 5 of “A Plan for the North American Bat Monitoring Program” (https://doi.org/10.2737/SRS-GTR-208). These data were then post-processed by bat monitoring partners to remove noise files (or those that do not contain recognizable bat calls) and apply a species label to each file. There is undoubtedly variation in the steps that monitoring partners take to apply a species label, but the steps documented in “A Guide to Processing Bat Acoustic Data for the North American Bat Monitoring Program” (https://doi.org/10.3133/ofr20181068) include first processing with an automated classifier and then manually reviewing to confirm or downgrade the suggested species label. Once a manual ID label was applied, audio files of bat acoustic recordings were submitted to the NABat database in Waveform Audio File format. From these available files in the NABat database, we considered files from 35 classes (34 species and a noise class). Files for 4 species were excluded due to low sample size (Corynorhinus rafinesquii, N=3; Eumops floridanus, N =3; Lasiurus xanthinus, N = 4; Nyctinomops femorosaccus, N =11). From this pool, files were randomly selected until files for each species/grid cell combination were exhausted or the number of recordings reach 1250. The dataset was then randomly split into training, validation, and test sets (i.e., holdout dataset). This data release includes all files considered for training and validation, including files that had been excluded from model development and testing due to low sample size for a given species or because the threshold for species/grid cell combinations had been met. The test set (i.e., holdout dataset) is not included. Audio files are grouped by species, as indicated by the four-letter species code in the name of each folder. Definitions for each four-letter code, including Family, Genus, Species, and Common name, are also included as a dataset in this release.

MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.

This dataset contains tile imagery from the OpenStreetMap project alongside label masks for buildings from OpenStreetMap. Besides the original clean label set, additional noisy label sets for random noise, removed and added buildings are provided.

The purpose of this dataset is to provide training data for analysing the impact of noisy labels on the performance of models for semantic segmentation in Earth observation.

The code for downloading and creating the datasets as well as for performing some preliminary analyses is also provided, however it is necessary to have access to a tile server where OpenStreetMap tiles can be downloaded in sufficient amounts.

To reproduce the dataset and perform analysis on it, do the following:

• unzip data.zip and code.zip
• create the folder structure from data
• Build and activate a python environment from environment.yml
• Insert the url of a suitable tile server for OSM tiles in line 76 of utils.py
• Execute download_OSM_dataset.py to download OSM image tiles alongside OSM labels
• Execute create_noisy_labels.py for the OSM dataset to create noisy label sets
• Divide the images and labels into train and test data. split_data.py can be used as a baseline for this, but pathnames have to be adjusted and the corresponding directories have to be created first.
• Call train_model.py to train a model on the data. Specify the data size and the label set by giving command line arguments as shown in train_model.sh

MLRSNet provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.

The Dataset includes: 1. Images folder: 46 categories, 109,161 high-spatial resolution remote sensing images. 2. Labels folders: each category has a .csv file. 3. Categories_names. xlsx: Sheet1 lists the names of 46 categories, and the Sheet2 shows the associated multi-label to each category.

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Introduction

This task focuses on sound event detection in a few-shot learning setting for animal (mammal and bird) vocalisations. Participants will be expected to create a method that can extract information from five exemplar vocalisations (shots) of mammals or birds and detect and classify sounds in field recordings.

For more info please reffer to the official website: https://dcase.community/challenge2023/task-few-shot-bioacoustic-event-detection

Description

Few-shot learning is a highly promising paradigm for sound event detection. It is also an extremely good fit to the needs of users in bioacoustics, in which increasingly large acoustic datasets commonly need to be labelled for events of an identified category (e.g. species or call-type), even though this category might not be known in other datasets or have any yet-known label. While satisfying user needs, this will also benchmark few-shot learning for the wider domain of sound event detection (SED).

Few-shot learning describes tasks in which an algorithm must make predictions given only a few instances of each class, contrary to standard supervised learning paradigm. The main objective is to find reliable algorithms that are capable of dealing with data sparsity, class imbalance and noisy/busy environments. Few-shot learning is usually studied using N-way-K-shot classification, where N denotes the number of classes and K the number of examples for each class.

Some reasons why few-shot learning has been of increasing interest:

Scarcity of supervised data can lead to unreliable generalisations of machine learning models. Explicitly labeling a huge dataset can be costly both in time and resources. Fixed ontologies or class labels used in SED and other DCASE tasks are often a poor fit to a given user’s goal. Development Set The development set is pre-split into training and validation sets. The training set consists of five sub-folders deriving from a different source each. Along with the audio files multi-class annotations are provided for each. The validation set consists of two sub-folders deriving from a different source each, with a single-class (class of interest) annotation file provided for each audio file.

Training Set

The training set contains four different sub-folders (BV, HV, JD, MT,WMW). Statistics are given overall and specific for each sub-folder.

Overall Statistics Values Number of audio recordings 174 Total duration 21 hours Total classes (excl. UNK) 47 Total events (excl. UNK) 14229

BV

The BirdVox-DCASE-10h (BV for short) contains five audio files from four different autonomous recording units, each lasting two hours. These autonomous recording units are all located in Tompkins County, New York, United States. Furthermore, they follow the same hardware specification: the Recording and Observing Bird Identification Node (ROBIN) developed by the Cornell Lab of Ornithology. Andrew Farnsworth, an expert ornithologist, has annotated these recordings for the presence of flight calls from migratory passerines, namely: American sparrows, cardinals, thrushes, and warblers. In total, the annotator found 2,662 from 11 different species. We estimate these flight calls to have a duration of 150 milliseconds and a fundamental frequency between 2 kHz and 10 kHz.

Statistics Values Number of audio recordings 5 Total duration 10 hours Total classes (excl. UNK) 11 Total events (excl. UNK) 9026 Ratio event/duration 0.04 Sampling rate 24,000 Hz

HT

Spotted hyenas are a highly social species that live in "fission-fusion" groups where group members range alone or in smaller subgroups that split and merge over time. Hyenas use a variety of types of vocalizations to coordinate with one another over both short and long distances. Spotted hyena vocalization data were recorded on custom-developed audio tags designed by Mark Johnson and integrated into combined GPS / acoustic collars (Followit Sweden AB) by Frants Jensen and Mark Johnson. Collars were deployed on female hyenas of the Talek West hyena clan at the MSU-Mara Hyena Project (directed by Kay Holekamp) in the Masai Mara, Kenya as part of a multi-species study on communication and collective behavior. Field work was carried out by Kay Holekamp, Andrew Gersick, Frants Jensen, Ariana Strandburg-Peshkin, and Benson Pion; labeling was done by Kenna Lehmann and colleagues.

Statistics Values Number of audio recordings 5 Total duration 5 hours Total classes (excl. UNK) 3 Total events (excl. UNK) 611 Ratio events/duration 0.05 Sampling rate 6000 Hz

JD

Jackdaws are corvid songbirds which usually breed, forage and sleep in large groups, but form a pair bond with the same partner for life. They produce thousands of vocalisations per day, but many aspects of their vocal behaviour remained unexplored due to the difficulty in recording and assigning vocalisations to specific individuals, especia...

These images and associated binary labels were collected from collaborators across multiple universities to serve as a diverse representation of biomedical images of vessel structures, for use in the training and validation of machine learning tools for vessel segmentation. The dataset contains images from a variety of imaging modalities, at different resolutions, using difference sources of contrast and featuring different organs/ pathologies. This data was use to train, test and validated a foundational model for 3D vessel segmentation, tUbeNet, which can be found on github. The paper descripting the training and validation of the model can be found here. Filenames are structured as follows: Data - [Modality][species Organ][resolution].tif Labels - [Modality][species Organ][resolution]labels.tif Sub-volumes of larger dataset - [Modality][species Organ]_subvolume[dimensions in pixels].tif Manual labelling of blood vessels was carried out using Amira (2020.2, Thermo-Fisher, UK). Training data: opticalHREM_murineLiver_2.26x2.26x1.75um.tif: A high resolution episcopic microscopy (HREM) dataset, acquired in house by staining a healthy mouse liver with Eosin B and imaged using a standard HREM protocol. NB: 25% of this image volume was withheld from training, for use as test data. CT_murineTumour_20x20x20um.tif: X-ray microCT images of a microvascular cast, taken from a subcutaneous mouse model of colorectal cancer (acquired in house). NB: 25% of this image volume was withheld from training, for use as test data. RSOM_murineTumour_20x20um.tif: Raster-Scanning Optoacoustic Mesoscopy (RSOM) data from a subcutaneous tumour model (provided by Emma Brown, Bohndiek Group, University of Cambridge). The image data has undergone filtering to reduce the background ​(Brown et al., 2019)​. OCTA_humanRetina_24x24um.tif: retinal angiography data obtained using Optical Coherence Tomography Angiography (OCT-A) (provided by Dr Ranjan Rajendram, Moorfields Eye Hospital). Test data: MRI_porcineLiver_0.9x0.9x5mm.tif: T1-weighted Balanced Turbo Field Echo Magnetic Resonance Imaging (MRI) data from a machine-perfused porcine liver, acquired in-house. Test Data MFHREM_murineTumourLectin_2.76x2.76x2.61um.tif: a subcutaneous colorectal tumour mouse model was imaged in house using Multi-fluorescence HREM in house, with Dylight 647 conjugated lectin staining the vasculature ​(Walsh et al., 2021)​. The image data has been processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. NB: A sub-volume of 480x480x640 voxels was manually labelled (MFHREM_murineTumourLectin_subvolume480x480x640.tif). MFHREM_murineBrainLectin_0.85x0.85x0.86um.tif: an MF-HREM image of the cortex of a mouse brain, stained with Dylight-647 conjugated lectin, was acquired in house ​(Walsh et al., 2021)​. The image data has been downsampled and processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. NB: A sub-volume of 1000x1000x99 voxels was manually labelled. This sub-volume is provided at full resolution and without preprocessing (MFHREM_murineBrainLectin_subvol_0.57x0.57x0.86um.tif). 2Photon_murineOlfactoryBulbLectin_0.2x0.46x5.2um.tif: two-photon data of mouse olfactory bulb blood vessels, labelled with sulforhodamine 101, was kindly provided by Yuxin Zhang at the Sensory Circuits and Neurotechnology Lab, the Francis Crick Institute ​(Bosch et al., 2022)​. NB: A sub-volume of 500x500x79 voxel was manually labelled (2Photon_murineOlfactoryBulbLectin_subvolume500x500x79.tif). References: ​​Bosch, C., Ackels, T., Pacureanu, A., Zhang, Y., Peddie, C. J., Berning, M., Rzepka, N., Zdora, M. C., Whiteley, I., Storm, M., Bonnin, A., Rau, C., Margrie, T., Collinson, L., & Schaefer, A. T. (2022). Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy. Nature Communications 2022 13:1, 13(1), 1–16. https://doi.org/10.1038/s41467-022-30199-6 ​Brown, E., Brunker, J., & Bohndiek, S. E. (2019). Photoacoustic imaging as a tool to probe the tumour microenvironment. DMM Disease Models and Mechanisms, 12(7). https://doi.org/10.1242/DMM.039636 ​Walsh, C., Holroyd, N. A., Finnerty, E., Ryan, S. G., Sweeney, P. W., Shipley, R. J., & Walker-Samuel, S. (2021). Multifluorescence High-Resolution Episcopic Microscopy for 3D Imaging of Adult Murine Organs. Advanced Photonics Research, 2(10), 2100110. https://doi.org/10.1002/ADPR.202100110 ​Walsh, C., Holroyd, N., Shipley, R., & Walker-Samuel, S. (2020). Asymmetric Point Spread Function Estimation and Deconvolution for Serial-Sectioning Block-Face Imaging. Communications in Computer and Information Science, 1248 CCIS, 235–249. https://doi.org/10.1007/978-3-030-52791-4_19 ​

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs.

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review.

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.

MuMu dataset (mapping, metadata, annotations and text reviews)

Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments

These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

NOTE: This version provides simplified files with metadata and splits.

Scientific References

Please cite the following papers if using MuMu dataset or Tartarus library.

Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916

The Food and Food Categories (FFoCat) Dataset

The Food and Food Categories (FFoCat) Dataset contains 58.962 images of food annotated with the food label and the food categories of the Mediterranean Diet. It is one of the most complete datasets regarding the Mediterranean Diet as it is aligned with the standard AGROVOC and HeLiS ontologies and allows to study multitask learning problems in Computer Vision for food recognition and diet recommendation.

The dataset is already divided into the train and test folder. The file label.tsv contains the food labels, the file food_food_category_map.tsv contains the food labels with the corresponding food category labels. The following table compares the FFoCat dataset with previous datasets for food recognition.

This dataset has been published at the International Conference on Image Analysis and Processing (ICIAP - 2019). The source code for reproducing the experiments together with other information about the dataset is available here.

AGROVOC Alignment of Food Categories

The AGROVOC_alignment.tsv file contains the alignment of the food categories in the FFoCat dataset with AGROVOC, the standard ontology of the Food and Agriculture Organization (FAO) of the United Nations. This allows interoperability and linked open data navigation. Such alignment can be derived by querying HeLis, here we propose a shortcut.

Citing FFoCat

If you use FFoCat in your research, please use the following BibTeX entry.

@inproceedings{DonadelloD19Ontology,
 author  = {Ivan Donadello and Mauro Dragoni},
 title   = {Ontology-Driven Food Category Classification in Images},
 booktitle = {{ICIAP} {(2)}},
 series  = {Lecture Notes in Computer Science},
 volume  = {11752},
 pages   = {607--617},
 publisher = {Springer},
 year   = {2019}
}

Context

The MNIST dataset is one of the best known image classification problems out there, and a veritable classic of the field of machine learning. This dataset is more challenging version of the same root problem: classifying letters from images. This is a multiclass classification dataset of glyphs of English letters A - J.

This dataset is used extensively in the Udacity Deep Learning course, and is available in the Tensorflow Github repo (under Examples). I'm not aware of any license governing the use of this data, so I'm posting it here so that the community can use it with Kaggle kernels.

Content

notMNIST _large.zip is a large but dirty version of the dataset with 529,119 images, and notMNIST_small.zip is a small hand-cleaned version of the dataset, with 18726 images. The dataset was assembled by Yaroslav Bulatov, and can be obtained on his blog. According to this blog entry there is about a 6.5% label error rate on the large uncleaned dataset, and a 0.5% label error rate on the small hand-cleaned dataset.

The two files each containing 28x28 grayscale images of letters A - J, organized into directories by letter. notMNIST_large.zip contains 529,119 images and notMNIST_small.zip contains 18726 images.

Acknowledgements

Thanks to Yaroslav Bulatov for putting together the dataset.

TagX data annotation services are a set of tools and processes used to accurately label and classify large amounts of data for use in machine learning and artificial intelligence applications. The services are designed to be highly accurate, efficient, and customizable, allowing for a wide range of data types and use cases.

The process typically begins with a team of trained annotators reviewing and categorizing the data, using a variety of annotation tools and techniques, such as text classification, image annotation, and video annotation. The annotators may also use natural language processing and other advanced techniques to extract relevant information and context from the data.

Once the data has been annotated, it is then validated and checked for accuracy by a team of quality assurance specialists. Any errors or inconsistencies are corrected, and the data is then prepared for use in machine learning and AI models.

TagX annotation services can be applied to a wide range of data types, including text, images, videos, and audio. The services can be customized to meet the specific needs of each client, including the type of data, the level of annotation required, and the desired level of accuracy.

TagX data annotation services provide a powerful and efficient way to prepare large amounts of data for use in machine learning and AI applications, allowing organizations to extract valuable insights and improve their decision-making processes.

Dataset Name: BBC Articles Sentiment Analysis Dataset

Source: BBC News

Description: This dataset consists of articles from the BBC News website, containing a diverse range of topics such as business, politics, entertainment, technology, sports, and more. The dataset includes articles from various time periods and categories, along with labels representing the sentiment of the article. The sentiment labels indicate whether the tone of the article is positive, negative, or neutral, making it suitable for sentiment analysis tasks.

Number of Instances: [Specify the number of articles in the dataset, for example, 2,225 articles]

Number of Features: 1. Article Text: The content of the article (string). 2. Sentiment Label: The sentiment classification of the article. The possible labels are: - Positive - Negative - Neutral

Data Fields: - id: Unique identifier for each article. - category: The category or topic of the article (e.g., business, politics, sports). - title: The title of the article. - content: The full text of the article. - sentiment: The sentiment label (positive, negative, or neutral).

Example: | id | category | title | content | sentiment | |----|-----------|---------------------------|-------------------------------------------------------------------------|-----------| | 1 | Business | "Stock Market Surge" | "The stock market has surged to new highs, driven by strong earnings..." | Positive | | 2 | Politics | "Election Results" | "The election results were a mixed bag, with some surprises along the way." | Neutral | | 3 | Sports | "Team Wins Championship" | "The team won the championship after a thrilling final match." | Positive | | 4 | Technology | "New Smartphone Release" | "The new smartphone release has received mixed reactions from users." | Negative |

Preprocessing Notes: - The text has been preprocessed to remove special characters and any HTML tags that might have been included in the original articles. - Tokenization or further text cleaning (e.g., lowercasing, stopword removal) may be necessary depending on the model and method used for sentiment classification.

Use Case: This dataset is ideal for training and evaluating machine learning models for sentiment classification, where the goal is to predict the sentiment (positive, negative, or neutral) based on the article's text.