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HAM is a dataset for molecular graph partitioning. This dataset contains coarse-grained (CG) mappings of 1206 organic molecules with less than 25 heavy atoms. Each molecule was downloaded from the PubChem database as SMILES. One molecule was assigned to two annotators to compare the human agreement between CG mappings. Downloaded SMILES were hand-mapped. The completed annotations were reviewed by a third person, to identify and remove unreasonable mappings (eg: one bead mappings) which did not agree with the given guidelines. Hence, there are 1.68 annotations per molecule in the current database (16% removed). Source: https://github.com/rochesterxugroup/HAM_dataset

HAM10000 is a dataset of 10000 training images for detecting pigmented skin lesions. The authors collected dermatoscopic images from different populations, acquired and stored by different modalities. Source: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000 Image Source: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000

The WSJ0 Hipster Ambient Mixtures (WHAM!) dataset pairs each two-speaker mixture in the wsj0-2mix dataset with a unique noise background scene. It has an extension called WHAMR! that adds artificial reverberation to the speech signals in addition to the background noise. The noise audio was collected at various urban locations throughout the San Francisco Bay Area in late 2018. The environments primarily consist of restaurants, cafes, bars, and parks. Audio was recorded using an Apogee Sennheiser binaural microphone on a tripod between 1.0 and 1.5 meters off the ground.

WHAMR! is a dataset for noisy and reverberant speech separation. It extends WHAM! by introducing synthetic reverberation to the speech sources in addition to the existing noise. Room impulse responses were generated and convolved using pyroomacoustics. Reverberation times were chosen to approximate domestic and classroom environments (expected to be similar to the restaurants and coffee shops where the WHAM! noise was collected), and further classified as high, medium, and low reverberation based on a qualitative assessment of the mixture’s noise recording.

Node classification on Chameleon with the fixed 48%/32%/20% splits provided by Geom-GCN.

Node classification on Chameleon with 60%/20%/20% random splits for training/validation/test.

The EARS-WHAM dataset mixes speech from the EARS dataset with real noise recordings from the WHAM! dataset. Speech and noise files are mixed at signal-to-noise ratios (SNRs) randomly sampled in a range of [−2.5, 17.5] dB, where the SNR is computed using loudness K- weighted relative to full scale (LKFS) standardized in ITU-R BS.1770 to obtain a more perceptually meaningful scaling and also to remove silent regions from the SNR computation.

Bentham manuscripts refers to a large set of documents that were written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). Volunteers of the Transcribe Bentham initiative transcribed this collection. Currently, >6 000 documents or > 25 000 pages have been transcribed using this public web platform. For our experiments, we used the BenthamR0 dataset a part of the Bentham manuscripts.

The Nottingham Dataset is a collection of 1200 American and British folk songs. Source: Rethinking Recurrent Latent Variable Model for Music Composition Image Source: https://highnoongmt.wordpress.com/2018/10/02/going-to-use-the-nottingham-music-database/

We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers.

HAMMER dataset contains 13 Scenes. Each scene has two setups, with/without objects (with : scene includes several objects with various surface material, without : scene with only backgrounds - naked) and each scene has two camera trajectories. Each trajectories composed with roughly 300 frames, which adds up to 16k frames in total (13 x 2 x 2 x 300). Each trajectory contains corresponding images from each cameras : d435 – stereo, l515 – Lidar (D-ToF), polarization – RGBP (RGB with polarization), tof – (I-ToF). Each camera folder contains its intrinsic file and its own recorded images together with rendered depth GT / instance GT and camera pose. All the cameras are fully synchronized via robotic arm’s data acquisition setup. Source: On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks Image Source: On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks

A data set introduced for training on the protein design task.

The Concept and Hint-Annotated Math Problems (CHAMP) consists of high school math competition problems, annotated with concepts, or general math facts, and hints, or problem-specific tricks. These annotations allow us to explore the effects of additional information, such as relevant hints, misleading concepts, or related problems.

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

Monitoring and evaluating of driving behavior is the main goal of this paper that encourage us to develop a new system based on Inertial Measurement Unit (IMU) sensors of smartphones. In this system, a hybrid of Discrete Wavelet Transformation (DWT) and Adaptive Neuro Fuzzy Inference System (ANFIS) is used to recognize overall driving behaviors. The behaviors are classified into the safe, the semi-aggressive, and the aggressive classes that are adopted with Driver Anger Scale (DAS) self-reported questionnaire results. The proposed system extracts four features from IMU sensors in the forms of time series. They are decomposed by DWT in two levels and their energies are sent to six ANFISs. Each ANFIS models the different perception about driving behavior under uncertain knowledge and returns the similarity or dissimilarity between driving behaviors. The results of these six ANFISs are combined by three different decision fusion approaches. Results show that Coiflet-2 is the most suitable mother wavelet for driving behavior analysis. In addition, the proposed system recognizes the overall driving behavior patterns with 92% accuracy without necessity to evaluate the maneuvers one by one. We show that without longitude acceleration data, the driver behavior cannot be recognized successfully while the results do not disturb substantially when the gyroscope is not available.

WHAMR_ext is an extension to the WHAMR corpus with larger RT60 values (between 1s and 3s)

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We introduce an open-source physical-layer dataset of Bluetooth Low Energy (BLE) IoT sensor devices recorded in an anechoic chamber using USRP x310. With a 100Msps sampling rate, it covers the entire BLE spectrum, featuring on-body and off-body scenarios with 13 BLE devices (ESP32s) from the same manufacturer. the goal is to study the physical layer characteristics of both on-body and off-body signals. The dataset is also available through MongoDB with a Python tool for analysis; for more details, please visit our GitHub page. https://github.com/mkashani-phd/BLEWBAN_Dataset