The Electricity Transformer Temperature (ETT) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months. Source: https://arxiv.org/pdf/2012.07436.pdf Image Source: https://github.com/zhouhaoyi/ETDataset

Letter Recognition Data Set is a handwritten digit dataset. The task is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. Source: UCL Machine Learning Repository Letter Recognition Image Source: http://www.cs.uu.nl/docs/vakken/mpr/Frey-Slate.pdf

The VOICES corpus is a dataset to promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions. For this corpus, audio was recorded in furnished rooms with background noise played in conjunction with foreground speech selected from the LibriSpeech corpus. Multiple sessions were recorded in each room to accommodate for all foreground speech-background noise combinations. Audio was recorded using twelve microphones placed throughout the room, resulting in 120 hours of audio per microphone.

Imagenette is a subset of 10 easily classified classes from Imagenet (bench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute). Source: https://github.com/fastai/imagenette Image Source: https://docs.fast.ai/tutorial.imagenette.html

The Caltech 101 Silhouettes dataset consists of 4,100 training samples, 2,264 validation samples and 2,307 test samples. The datast is based on CalTech 101 image annotations. Each image in the CalTech 101 data set includes a high-quality polygon outline of the primary object in the scene. To create the CalTech 101 Silhouettes data set, the authors center and scale each outline and render it on a DxD pixel image-plane. The outline is rendered as a filled, black polygon on a white background. Many object classes exhibit silhouettes that have distinctive class-specific features. A relatively small number of classes like soccer ball, pizza, stop sign, and yin-yang are indistinguishable based on shape, but have been left-in in the data. Source: 1 Introduction Image Source: https://people.cs.umass.edu/~marlin/data.shtml

The Electricity Transformer Temperature (ETT) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months. Source: https://arxiv.org/pdf/2012.07436.pdf Image Source: https://github.com/zhouhaoyi/ETDataset

The Electricity Transformer Temperature (ETT) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months. Source: https://arxiv.org/pdf/2012.07436.pdf Image Source: https://github.com/zhouhaoyi/ETDataset

The datasets introduced in Chapter 6 of my PhD thesis are below. See the thesis for more details. If you use any of these datasets for research purposes you should use the following citation in any resulting publications: @phdthesis{MnihThesis, author = {Volodymyr Mnih}, title = {Machine Learning for Aerial Image Labeling}, school = {University of Toronto}, year = {2013} }

The LastLetterConcat dataset is a collection of word concatenations formed by taking the last letters of individual words and joining them together. Each entry in the dataset consists of a question and an answer, where the answer is the result of concatenating the last letters of specific words. Here are some examples: ylyo: Take the last letters of each word in “Kelly Cheryl Nancy Jojo” and concatenate them. adey: Take the last letters of each word in “Gisela Gerald Mackenzie Ashley” and concatenate them. The dataset contains a total of 500 such examples.

The official dataset contains a training set (137 images), a validation set (4 images), and a testing set (10 images)

The dataset contains the main components of the news articles published online by the newspaper named <a href="https://gazzettadimodena.gelocal.it/modena">Gazzetta di Modena</a>: url of the web page, title, sub-title, text, date of publication, crime category assigned to each news article by the author. The news articles are written in Italian and describe 11 types of crime events occurred in the province of Modena between the end of 2011 and 2021. Moreover, the dataset includes data derived from the abovementioned components thanks to the application of Natural Language Processing techniques. Some examples are the place of the crime event occurrence (municipality, area, address and GPS coordinates), the date of the occurrence, and the type of the crime events described in the news article obtained by an automatic categorization of the text. In the end, news articles describing the same crime events (duplciates) are detected by calculating the document similarity. Now, we are working on the application of question answering to extract the 5W+1H and we plan to extend the current dataset with the obtained data. Other researchers can employ the dataset to apply other algorithms of text categorization and duplicate detection and compare their results with the benchmark. The dataset can be useful for several scopes, e.g., geo-localization of the events, text summarization, crime analysis, crime prediction, community detection, topic modeling.

This dataset contains a set of face images taken between April 1992 and April 1994 at AT&T Laboratories Cambridge.

This resource is designed to allow for research into Natural Language Generation. In particular, with neural data-to-text approaches although it is not limited to these.

StreetTryOn, the new in-the-wild Virtual Try-On dataset, consists of 12,364 and 2,089 street person images for training and validation, respectively. It is derived from the large fashion retrieval dataset DeepFashion2, from which we filter out over 90% of DeepFashion2 images that are infeasible for try-on tasks (e.g., non-frontal view, large occlusion, dark environment, etc.). Combining with the garment and person images in VITON-HD, we obtain a comprehensive suite of in-domain and cross-domain try-on tasks that have garment and person inputs from various sources, including Shop2Model, Model2Model, Shop2Street, and Street2Street. - 12,364 training images and 2,089 test images - Tests included: - Shop2Model - Model2Model - Shop2Street - Model2Street - Street2Street

A movie ticketing dialog dataset with 23,789 annotated conversations. The movie ticketing conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns. In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to "make sense" 86.5 percent of the time, almost the same as human responses in the same contexts. Source: TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems

This dataset was collected during a LoRaWAN measurement campaign in a multi-room indoor office environment in the University of Siegen, Germany. It contains over 1.7 million time-stamped records from 6 LoRaWAN nodes transmitting once per minute to a single gateway. Each record includes environmental parameters: temperature, relative humidity, barometric pressure, particulate matter (PM2.5), and carbon dioxide (CO₂); as well as device metadata such as RSSI, SNR, spreading factor (SF), etc. The dataset also includes the effective signal power (ESP) and the noise (NP) for LoRaWAN propagation analysis purposes. The dataset is designed to support research on indoor wireless propagation, distance estimation, environment-aware modeling, among other IoT use cases and applications in line with the 6G flagship demands. A detailed methodology and initial analysis are presented in our accompanying IEEE Access paper: Nahshon Mokua Obiri and Kristof Van Laerhoven, "A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors," in IEEE Access, vol. 13, pp. 83148-83170, 2025, doi: 10.1109/ACCESS.2025.3569164.

This large collection of over 161,000 video-label pairs of video clips, shows humans drawing letters and digits in the air, and is used to evaluate a model’s ability to classify articulated motions correctly. Unlike existing video datasets, AirLetters’ accurate classification predictions rely on discerning motion patterns and integrating information presented by the video over time (i.e., over many frames of video). That study revealed that while trivial for humans, accurate representations of complex articulated motions remain an open problem for end-to-end learning for video understanding models.

BDD100K-weather is a dataset which is inherited from BDD100K using image attribute labels for Out-of-Distribution object detection. All images in BDD100K are categorized into six domains, including clear, overcast, foggy, partly cloudy, rainy and snowy. Clear and overcast are used for training while the rest is used for testing, moreover, per training domain is sampled 1.5k images at most while per testing domain is sampled 0.5k images at most. Thus, we have BDD100K-weather (paper is under review).

Hand-disambiguation of a sample of U.S. patents inventor mentions from PatentsView.org. Inventors we selected indirectly by sampling inventor mentions uniformly at random. This results in inventor sampled with probability proportional to their number of granted patents. The time period considered is from 1976 to December 31, 2021, corresponding to the disambiguation labeled "disamb_inventor_id_20211230" in PatentsView's bulk data downloads "g_persistent_inventor.tsv" file (https://patentsview.org/download/data-download-tables). That is, the benchmark disambiguation intends to contain all inventor mentions for the sampled inventors from that time period. Note that the benchmark disambiguation contains a few extraneous mentions to patents granted outside of that time period. These should be ignored for evaluation purposes. The methodology used for the hand-disambiguation is described in Binette et al. (2022) (https://arxiv.org/abs/2210.01230). We used one disambiguation of 200 inventors from Binette et al. (2022), as well as an additional disambiguation of 200 inventors provided by an additional staff member. The two disambiguations were reviewed and validated. However, they should be expected to contain errors due to the ambiguous nature of inventor disambiguation. Furthermore, given the use as the December 30, 2021, disambiguation from PatentsView as a starting point of the hand-labeling, a bias towards this disambiguation should be expected.

In order to avoid the training process of the model being influenced by a portion of HOI classes with a very small number of instances, we remove some of the HOI classes containing a very small number of instances and HOI classes with no interaction from the training \textbf{S}et for the \textbf{D}ouble \textbf{C}hallenge. Finally, there are total 321 HOI classes, 74 object classes and 93 action classes. The training and testing set contain 37,155 and 9,666 images, respectively.

This dataset was collected during a LoRaWAN measurement campaign in a multi-room indoor office environment at the University of Siegen, Germany. It contains over 1.7 million time-stamped records from 6 LoRaWAN nodes transmitting once per minute to a single gateway. Each record includes environmental parameters: temperature, relative humidity, barometric pressure, particulate matter (PM2.5), and carbon dioxide (CO₂); as well as device metadata such as RSSI, SNR, spreading factor (SF), etc. The dataset also includes the effective signal power (ESP) and the noise (NP) for LoRaWAN propagation analysis purposes. The dataset is designed to support research on indoor wireless propagation, distance estimation, environment-aware modeling, among other IoT use cases and applications in line with the 6G flagship demands. 🔗 Additional Notes The original dataset pulled directly from InfluxDB is provided as 1.unsorted_combined_measurements_data.csv. The preprocessed version: filtered, sorted, and with variable renaming, aligned to the structure described in our journal paper, is included as 2.aggregated_measurements_data.csv. The fully cleaned dataset, where anomalies have been removed and SF11/SF12 data isolated, is available as 3.cleaned_dataset_per_device.csv. Intermediate processing steps and transformations are fully reproducible and documented in the accompanying GitHub repository, where each stage of the pipeline is implemented and version-controlled. Users can generate intermediate versions as needed by following the processing scripts or reviewing the commit history.

Dataset Description: NBA Team Statistics, Historical Performance & Betting Odds (2015-2019) ## Overview This dataset contains team-level box score statistics, historical win percentages, and closing betting odds for NBA games from 2015 to 2019. It supports research in sports analytics, predictive modeling, and betting market efficiency. ## Contents Box Score Statistics (2015-2019): Points, rebounds, assists, turnovers, shooting percentages, etc. Historical Team Performance (2014-2018): Each team’s winning percentage over five seasons. Betting Odds (2018-2019): Closing moneyline odds and more ## Use Cases Predictive Modeling: Forecasting NBA game outcomes using historical data. Betting Market Analysis: Evaluating odds efficiency in capturing true probabilities. Sports Analytics: Analyzing trends in team performance and statistical patterns.

Pentachromatic Cultural Palette Dataset is characterized by unique cultural semantics and values. It is constructed through a carefully - designed multi - step data synthesis process based on PRISM dataset, focusing on the cultural perspectives of different continents (Africa, Asia, Europe, America, and Oceania).

This is a C++ vulnerability detection dataset following realistic settings. For details, please check our study Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets (Partha et al., 2024) The column names are self-describing. The most important two columns are, Target: int: vulnerable to not. Code: str: the code segment. For details, please check our huggingface repository.

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