The Human Activity Recognition Dataset has been collected from 30 subjects performing six different activities (Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). It consists of inertial sensor data that was collected using a smartphone carried by the subjects.

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The M4 dataset is a collection of 100,000 time series used for the fourth edition of the Makridakis forecasting Competition. The M4 dataset consists of time series of yearly, quarterly, monthly and other (weekly, daily and hourly) data, which are divided into training and test sets. The minimum numbers of observations in the training test are 13 for yearly, 16 for quarterly, 42 for monthly, 80 for weekly, 93 for daily and 700 for hourly series. The participants were asked to produce the following numbers of forecasts beyond the available data that they had been given: six for yearly, eight for quarterly, 18 for monthly series, 13 for weekly series and 14 and 48 forecasts respectively for the daily and hourly ones.

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The First Temporal Benchmark Designed to Evaluate Real-time Anomaly Detectors Benchmark

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UK-DALE is an open-access dataset from the UK recording Domestic Appliance-Level Electricity to conduct research on disaggregation algorithms, with data describing not just the aggregate demand per building but also the `ground truth' demand of individual appliances. It was built at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual appliances. This is the first open access UK dataset at this temporal resolution. It wAS recorded from five houses, one of which was recorded for 655 days.

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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.

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The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline (1-nearest neighbor classification), a large fraction may be misattributing the reasons for their improvement. Moreover, they may have been able to achieve the same improvement with a

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This dataset includes time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate). It is collected with an iPhone 6s kept in the participant's front pocket using SensingKit which collects information from Core Motion framework on iOS devices. All data is collected in 50Hz sample rate. A total of 24 participants in a range of gender, age, weight, and height performed 6 activities in 15 trials in the same environment and conditions: downstairs, upstairs, walking, jogging, sitting, and standing.

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The Shifts Dataset is a dataset for evaluation of uncertainty estimates and robustness to distributional shift. The dataset, which has been collected from industrial sources and services, is composed of three tasks, with each corresponding to a particular data modality: tabular weather prediction, machine translation, and self-driving car (SDC) vehicle motion prediction. All of these data modalities and tasks are affected by real, `in-the-wild' distributional shifts and pose interesting challenges with respect to uncertainty estimation.

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Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

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This meta-dataset is composed of previously known datasets.

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VISUELLE is a repository build upon the data of a real fast fashion company, Nunalie, and is composed of 5577 new products and about 45M sales related to fashion seasons from 2016-2019. Each product in VISUELLE is equipped with multimodal information: its image, textual metadata, sales after the first release date, and three related Google Trends describing category, color and fabric popularity.

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ChangeSim is a dataset aimed at online scene change detection (SCD) and more. The data is collected in photo-realistic simulation environments with the presence of environmental non-targeted variations, such as air turbidity and light condition changes, as well as targeted object changes in industrial indoor environments. By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and 3D reconstructions. While the previous online SCD datasets evaluate models given well-aligned image pairs, ChangeSim also provides raw unpaired sequences that present an opportunity to develop an online SCD model in an end-to-end manner, considering both pairing and detection. Experiments show that even the latest pair-based SCD models suffer from the bottleneck of the pairing process, and it gets worse when the environment contains the non-targeted variations.

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A new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation.

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The dataset contains a collection of physiological signals (EEG, GSR, PPG) obtained from an experiment of the auditory attention on natural speech. Ethical Approval was acquired for the experiment. Details of the experiment can be found here https://phyaat.github.io/experiment

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This database includes 25 long-term ECG recordings of human subjects with atrial fibrillation (mostly paroxysmal).

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Engine degradation simulation was carried out using C-MAPSS. Four different were sets simulated under different combinations of operational conditions and fault modes. Records several sensor channels to characterize fault evolution. The data set was provided by the Prognostics CoE at NASA Ames.

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The dataset is approved for public release, distribution unlimited.

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The largest and most realistic dataset available for TCC. It consists of 600 real-world videos recorded with a high-resolution mobile phone camera shooting 1824 x 1368 sized pictures. The length of these videos ranges from 3 to 17 frames (7.3 on average, the median is 7.0 and mode is 8.5). Ground truth information is present only for the last frame in each video (i.e., the shot frame), and was collected using a gray surface calibration target.

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The time series segmentation benchmark (TSSB) currently contains 66 annotated time series (TS) with 2-7 segments. Each TS is constructed from one of the UEA & UCR time series classification datasets. We group TS by label and concatenate them to create segments with distinctive temporal patterns and statistical properties. We annotate the offsets at which we concatenated the segments as change points (CPs). Addtionally, we apply resampling to control the dataset resolution and add approximate, hand-selected window sizes that are able to capture temporal patterns.

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UI-PRMD is a data set of movements related to common exercises performed by patients in physical therapy and rehabilitation programs. The data set consists of 10 rehabilitation exercises. A sample of 10 healthy individuals repeated each exercise 10 times in front of two sensory systems for motion capturing: a Vicon optical tracker, and a Kinect camera. The data is presented as positions and angles of the body joints in the skeletal models provided by the Vicon and Kinect mocap systems.

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We introduce a new dataset, Watch and Learn Time-lapse (WALT), consisting of multiple (4K and 1080p) cameras capturing urban environments over a year.

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This experiment was performed in order to empirically measure the energy use of small, electric Unmanned Aerial Vehicles (UAVs). We autonomously direct a DJI ® Matrice 100 (M100) drone to take off, carry a range of payload weights on a triangular flight pattern, and land. Between flights, we varied specified parameters through a set of discrete options, payload of 0 , 250 g and 500 g; altitude during cruise of 25 m, 50 m, 75 m and 100 m; and speed during cruise of 4 m/s, 6 m/s, 8 m/s, 10 m/s and 12 m/s.

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Three-dimensional position of external markers placed on the chest and abdomen of healthy individuals breathing during intervals from 73s to 222s. The markers move because of the respiratory motion, and their position is sampled at approximately 10Hz. Markers are metallic objects used during external beam radiotherapy to track and predict the motion of tumors due to breathing for accurate dose delivery.

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IowaRain is a dataset of rainfall events for the state of Iowa (2016-2019) acquired from the National Weather Service Next Generation Weather Radar (NEXRAD) system and processed by a quantitative precipitation estimation system. The dataset presented in this study could be used for better disaster monitoring, response and recovery by paving the way for both predictive and prescriptive modeling

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The Lorenz dataset contains 100000 time-series with length 24. The data has 5 modes and it is obtained using the Lorenz equation with 5 different seed values.

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The original dataset from Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting contains traffic readings collected from 207 loop detectors on highways in Los Angeles County, aggregated in 5 minutes intervals over four months between March 2012 and June 2012.

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The original dataset from Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting contains 6 months of traffic readings from 01/01/2017 to 05/31/2017 collected every 5 minutes by 325 traffic sensors in San Francisco Bay Area. The measurements are provided by California Transportation Agencies (CalTrans) Performance Measurement System (PeMS).

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The PRONOSTIA (also called FEMTO) bearing dataset consists of 17 accelerated run-to-failures on a small bearing test rig. Both acceleration and temperature data was collected for each experiment.

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Overview This database of simulated arterial pulse waves is designed to be representative of a sample of pulse waves measured from healthy adults. It contains pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database contains a baseline set of pulse waves for each of the six age groups, created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It also contains 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. This allows for extensive in silico analyses of haemodynamics and the performance of pulse wave analysis algorithms.

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The Rainforest Automation Energy (RAE) dataset was create to help smart grid researchers test their algorithms which make use of smart meter data. This initial release of RAE contains 1Hz data (mains and sub-meters) from two residential houses. In addition to power data, environmental and sensor data from the house's thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures which is of interest to power utilities.

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RSDD-Time is a dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Additionally, the dataset includes exact temporal spans that relate to the date of diagnosis.

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The softwarised network data zoo (SNDZoo) is an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. Most of the published data sets focus on, but are not limited to, the performance of virtualised network functions (VNFs). The data is collected using fully automated NFV benchmarking frameworks, such as tng-bench, developed by us or third party solutions like Gym. The collection of the presented data sets follows the general VNF benchmarking methodology described in.

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Solar Power Data for Integration Studies NREL's Solar Power Data for Integration Studies are synthetic solar photovoltaic (PV) power plant data points for the United States representing the year 2006.

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The original paper presented a model of the industrial chemical process named Tennessee Eastman Process and a model-based TEP simulator for data generation. The most widely used benchmark consists of 22 datasets, 21 of which (Fault 1–21) contain faults and 1 (Fault 0) is fault-free. It is available in repository. All datasets have training (500 samples) and testing (960 samples) parts: training part has healthy state observations, testing part begins right after training, and contains faults which appear after 8 h since the training part. Each dataset has 52 features or observation variables with a 3 min sampling rate for most of all.

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Visuelle 2.0 is a dataset containing real data for 5355 clothing products of the retail fast-fashion Italian company, Nuna Lie. Specifically, Visuelle 2.0 provides data from 6 fashion seasons (partitioned in Autumn-Winter and Spring-Summer) from 2017-2019, right before the Covid-19 pandemic. Each product is accompanied by an HD image, textual tags and more. The time series data are disaggregated at the shop level, and include the sales, inventory stock, max-normalized prices (for the sake of confidentiality} and discounts. Exogenous time series data is also provided, in the form of Google Trends based on the textual tags and multivariate weather conditions of the stores’ locations. Finally, we also provide purchase data for 667K customers whose identity has been anonymized, to capture personal preferences. With these data, Visuelle 2.0 allows to cope with several problems which characterize the activity of a fast fashion company: new product demand forecasting, short-observation new pr

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Ward2ICU is a vital signs dataset of inpatients from the general ward. It contains vital signs with class labels indicating patient transitions from the ward to intensive care units

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The eSports Sensors dataset contains sensor data collected from 10 players in 22 matches in League of Legends. The sensor data collected includes:

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4D-OR includes a total of 6734 scenes, recorded by six calibrated RGB-D Kinect sensors 1 mounted to the ceiling of the OR, with one frame-per-second, providing synchronized RGB and depth images. We provide fused point cloud sequences of entire scenes, automatically annotated human 6D poses and 3D bounding boxes for OR objects. Furthermore, we provide SSG annotations for each step of the surgery together with the clinical roles of all the humans in the scenes, e.g., nurse, head surgeon, anesthesiologist.

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Multimodal object recognition is still an emerging field. Thus, publicly available datasets are still rare and of small size. This dataset was developed to help fill this void and presents multimodal data for 63 objects with some visual and haptic ambiguity. The dataset contains visual, kinesthetic and tactile (audio/vibrations) data. To completely solve sensory ambiguity, sensory integration/fusion would be required. This report describes the creation and structure of the dataset. The first section explains the underlying approach used to capture the visual and haptic properties of the objects. The second section describes the technical aspects (experimental setup) needed for the collection of the data. The third section introduces the objects, while the final section describes the structure and content of the dataset.

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AnoShift is a large-scale anomaly detection benchmark, which focuses on splitting the test data based on its temporal distance to the training set, introducing three testing splits: IID, NEAR, and FAR. This testing scenario proves to capture the in-time performance degradation of anomaly detection methods for classical to masked language models.

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The BRUSH dataset (BRown University Stylus Handwriting) contains 27,649 online handwriting samples from a total of 170 writers. Every sequence is labeled with intended characters such that dataset users can identify to which character a point in a sequence corresponds. The dataset was introduced in the paper "Generating Handwriting via Decoupled Style Descriptors" by Atsunobu Kotani, Stefanie Tellex, James Tompkin from Brown University, presented at European Conference on Computer Vision (ECCV) 2020.

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Forty prismatic lithium-ion pouch cells were built at the University of Michigan Battery Laboratory. The cells have a nominal capacity of 2.36Ah and comprise a NCM111 cathode and graphite anode. Cells were formed using two different formation protocols: "fast formation" and "baseline formation". After formation, cells were put under cycle life testing at room temperature and 45degC. Cells were cycled until the discharge capacities dropped below 50% of the initial capacities. Data was collected by the cycler equipment (Maccor) during both the formation process as well as during the cycling test. Data was processed in the Voltaiq software and subsequently exported as .csv files.

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Boombox is a multi-modal dataset for visual reconstruction from acoustic vibrations. Involves dropping objects into a box and capturing resulting images and vibrations. Used for training ML systems that predict images from vibration.

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The dataset provided is a collection of real-world industrial vibration data collected from a brownfield CNC milling machine. The acceleration has been measured using a tri-axial accelerometer (Bosch CISS Sensor) mounted inside the machine. The X- Y- and Z-axes of the accelerometer have been recorded using a sampling rate equal to 2 kHz. Thereby normal as well as anomalous data have been collected for 4 different timeframes, each lasting 5 months from February 2019 until August 2021 and labelled accordingly. It can be used to investigate the scalability of models and research process variations as the anomaly impact differs. In total there is data from three different CNC milling machines each executing 15 processes. For a detailed description of the data and experimental set-up, please refer to the paper: https://doi.org/10.1016/j.procir.2022.04.022

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The original paper contains a high-level explanation of the dataset characteristics, and potential use cases of the dataset. ArchABM can help to quantify the impact of some of these building- and company policy-related measures.

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The CANDOR corpus is a large, novel, multimodal corpus of 1,656 recorded conversations in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, along with an extensive survey of speaker post conversation reflections.

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The CIP dataset is composed of 2 subsets, containing low-cost (MPU9250) and high-end (MTwAwinda) Magnetic, Angular Rate, and Gravity (MARG) sensor data respectively. It provides data for the analysis of the complete inertial pose pipeline, from raw measurements, to sensor-to-segment calibration, multi-sensor fusion, skeleton kinematics, to the complete human pose. Multiple trials were collected with 21 and 10 subjects respectively, performing 6 types of movements (ranging from calibration, to daily-activities, range-of-motion and random). It presents a high degree of variability and complex dynamics while containing common sources of error found on real conditions. This amounts to 3.5M samples, synchronized with a ground-truth inertial motion capture system (Xsens) at 60hz. This dataset may contribute to assess, benchmark and develop novel algorithms for each of the pipelines' processing steps, with applications in classic or data-driven inertial pose estimation algorithms, human movem

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This is a benchmark dataset for mid-price forecasting of limit order book data. It is a dataset of high-frequency limit order markets for mid-price prediction. The authors extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ~4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies.

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