Omkar2118/datasets huggingface dataset:
Battery test data - fast formation study

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.

Introducing paper: Predicting the impact of formation protocols on battery lifetime immediately after manufacturing
TRECVID-AVS16 (IACC.3)

Internet Archive videos (IACC.3) under Creative Commons licenses. The test video collection for TRECVID-AVS2016-TRECVID-AVS2018 contains 335,944 web video clips (600hr).
TRECVID-AVS17 (IACC.3)

Internet Archive videos (IACC.3) under Creative Commons licenses. The test video collection for TRECVID-AVS2016-TRECVID-AVS2018 contains 335,944 web video clips (600hr).
TRECVID-AVS18 (IACC.3)

Internet Archive videos (IACC.3) under Creative Commons licenses. The test video collection for TRECVID-AVS2016-TRECVID-AVS2018 contains 335,944 web video clips (600hr).
TRECVID-AVS19 (V3C1)

The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics,  The test video collection for TRECVID-AVS2019-TRECVID-AVS2021, which contains 1,082,649 web video clips, with even more diverse content, no predominant characteristics and low self-similarity.
TRECVID-AVS20 (V3C1)

The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics,  The test video collection for TRECVID-AVS2019-TRECVID-AVS2021, which contains 1,082,649 web video clips, with even more diverse content, no predominant characteristics and low self-similarity.
TRECVID-AVS21 (V3C1)

The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics,  The test video collection for TRECVID-AVS2019-TRECVID-AVS2021, which contains 1,082,649 web video clips, with even more diverse content, no predominant characteristics and low self-similarity.
RealMCVSR
Full name: Real-world Multi-Camera Video Super-Resolution

Our RealMCVSR dataset provides real-world HD video triplets concurrently recorded by Apple iPhone 12 Pro Max equipped with triple cameras having fixed focal lengths: ultra-wide (30mm), wide-angle (59mm), and telephoto (147mm). To concurrently record video triplets, we built an iOS app that provides full control over exposure parameters (i.e., shutter speed and ISO) of the cameras. For recording each scene, we set the cameras in the auto-exposure mode, where the shutter speeds of the three cameras are synced to avoid varying motion blur across a video triplet. ISOs are adjusted accordingly for each camera to pick up the same exposure. Each video is saved in the MOV format using HEVC/H.265 encoding with the HD resolution (1080 x 1920). The dataset contains triplets of 161 video clips with 23,107 frames in total. The video triplets are split into training, validation, and testing sets, each of which has 137, 8, and 16 triplets with 19,426, 1,141, and 2,540 frames, respectively.

Introducing paper: Reference-based Video Super-Resolution Using Multi-Camera Video Triplets
ActorShift

**ActorShift** is a dataset where the domain shift comes from the change in actor species: we use humans in the source domain and animals in the target domain. This causes large variances in the appearance and motion of activities. For the corresponding dataset we select 1,305 videos of 7 human activity classes from Kinetics-700 as the source domain: sleeping, watching tv, eating, drinking, swimming, running and opening a door. For the target domain we collect 200 videos from YouTube of animals performing the same activities. We divide them into 35 videos for training (5 per class) and 165 for evaluation. The target domain data is scarce, meaning there is the additional challenge of adapting to the target domain with few unlabeled examples.

Introducing paper: Audio-Adaptive Activity Recognition Across Video Domains