Omkar2118/datasets huggingface dataset:
4D-Lung

This data collection consists of images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients. The images include four-dimensional (4D) fan beam (4D-FBCT) and 4D cone beam CT (4D-CBCT). All patients underwent concurrent radiochemotherapy to a total dose of 64.8-70 Gy using daily 1.8 or 2 Gy fractions. 

4D-FBCT images were acquired on a 16-slice helical CT scanner (Brilliance Big Bore, Philips Medical Systems, Andover, MA) as respiration-correlated CTs with 10 breathing phases (0 to 90%, phase-based binning) and 3 mm slice thickness. 4D-FBCT images were acquired during simulation, prior to therapy, and used for therapy planning. In 14 of the 20 subjects, 4D-FBCTs were also acquired on the same scanner weekly during therapy. 4D-CBCT images were acquired on a commercial CBCT scanner (On-Board Imager™, Varian Medical Systems, Inc.). An external surrogate (Real-time Position Management, Varian Medical Systems, Inc.) was integrated into the CBCT acquisition system to stamp each CBCT projection with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with half bow-tie filter. The technique was 125 kVp, 20 mA, and 20 ms in a single 360° slow gantry arc. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases (0 to 90%, phase-based binning) and reconstructed with an in-house FDK reconstruction algorithm.

Audio-visual biofeedback was performed for all 4D-FBCT and 4D-CBCT acquisitions in all subjects. A single Radiation Oncologist delineated targets and organs at risk in all 4D-FBCT and a limited number of 4D-CBCT images, on all 10 phases per scan. Seven of the subjects had gold coils implanted as fiducial markers in or near the tumor. 

The dataset is most fully described in detail in Balik et al.1  Seven of the subjects had gold coils implanted as fiducial markers in or near the tumor. The implantation procedure and details of marker location are described in detail in Roman et al.2

References

1 S. Balik et al., “Evaluation of 4-Dimensional Computed Tomography to 4-Dimensional Cone-Beam Computed Tomography Deformable Image Registration for Lung Cancer Adaptive Radiation Therapy.” Int. J. Radiat. Oncol. Biol. Phys. 86, 372–9 (2013) PMCID: PMC3647023.

2 N.O. Roman, W. Shepherd, N. Mukhopadhyay, G.D. Hugo, and E. Weiss, “Interfractional Positional Variability of Fiducial Markers and Primary Tumors in Locally Advanced Non-Small-Cell Lung Cancer during Audiovisual Biofeedback Radiotherapy.” Int. J. Radiat. Oncol. Biol. Phys. 83, 1566–72 (2012).
RealWorldQA

**RealWorldQA** is a benchmark designed to evaluate the **real-world spatial understanding capabilities** of multimodal AI models. It assesses how well these models comprehend physical environments. The benchmark consists of over **700 images**, each accompanied by a question and a verifiable answer. These images are drawn from various real-world scenarios, including those captured from vehicles. The goal is to advance AI models' understanding of our physical world.
GMDCSA

The GMDCSA dataset contains 16 ADL (not fall) activities and 16 Fall activities. The GMDCSA dataset has been created by performing the fall and the ADL activities by a single subject wearing a different set of clothes. The web camera of a laptop (HP 348 G5 Laptop: Core i5 8th Gen/8
GB/512 GB SSD/Windows 10) was used to capture the activities.

Introducing paper: Real-Time Human Fall Detection using a Lightweight Pose Estimation Technique
CIC-AndMal2017
Full name: Android malware dataset (CIC-AndMal2017)

Collected more than 10,854 samples (4,354 malware and 6,500 benign) from several sources.
UCI SMS spam dataset

The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research.
ExAIS
Full name: ExAIS_SMS Spam dataset

This ExAIS_SMS Spam dataset was a project conducted at the Federal University of Agriculture, Abeokuta, Nigeria with the aim of building an indigenous SMS Spam corpus with African-English context.
SQuAD-sr

Synthetic Question Answering dataset in Serbian, acquired by automatic translation of SQuAD.

Introducing paper: Synthetic Dataset Creation and Fine-Tuning of Transformer Models for Question Answering in Serbian
360+x: A Panoptic Multi-modal Scene Understanding Dataset

The $\textbf{360+x}$ dataset is a large-scale database that emphasizes a comprehensive multifaceted understanding of daily scenes. It provides diverse viewpoints and data modalities to emulate how humans obtain daily information in real-world scenarios. It includes 232 scene examples, each with an average duration of 6.2 minutes, spanning across 28 scene categories (comprising 15 indoor scenes and 13 outdoor scenes). 

Every example in this dataset comprises the following components:

  *  A third-person 360-degree panoptic video (5760 × 2880, 25 FPS) along with its frontal views version.
  *   Multiple first-person binocular videos (2432 × 1216, 60 FPS) and their monocular counterparts.
  *   Four-channel audio accompanied by directional binaural delay information.
  *  Annotation: Scene class label and Temporal segments label.
  *  Metadata: including textual scene descriptions, weather conditions, capture time, and GPS information.

Introducing paper: 360+x: A Panoptic Multi-modal Scene Understanding Dataset
NeurIT Dataset

NeurIT Dataset is open-sourced for public research usage. It is collected using the customized robotic platform across three buildings. We collect the training, validation, and test-seen sets in Building A, and build the test-seen and test-unseen set in Building B and C. During data collection, the robot moves at varying speeds up to the maximum value (1.5m/s). The dataset contains 110 sequences, totaling around 15 hours of tracking data that corresponds to a travel distance of about 33.7 km. Each sequence of data lasts 6~10 minutes, containing both IMU data (acceleration, gyroscope, magnetometer) and the ground truth trajectory. The ratio of the training set, validation set, test-seen set, and test-unseen set is 15:3:3:4.

Introducing paper: NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoT
Vript
Full name: 🎬 Vript: A Video Is Worth Thousands of Words

We construct a fine-grained video-text dataset with 12K annotated high-resolution videos (~400k clips). The annotation of this dataset is inspired by the video script. If we want to make a video, we have to first write a script to organize how to shoot the scenes in the videos. To shoot a scene, we need to decide the content, shot type (medium shot, close-up, etc), and how the camera moves (panning, tilting, etc). Therefore, we extend video captioning to video scripting by annotating the videos in the format of video scripts. Different from the previous video-text datasets, we densely annotate the entire videos without discarding any scenes and each scene has a caption with ~145 words. Besides the vision modality, we transcribe the voice-over into text and put it along with the video title to give more background information for annotating the videos.

In addition, we propose Vript-Bench, a new benchmark consisting of three challenging video understanding tasks (which are carefully double-checked by humans):

- Vript-CAP (Caption): A benchmark with detailed captions rather than short captions.

- Vript-RR (Retrieve then Reason): A video reasoning benchmark by first giving a detailed description of the scene as a hint and then asking questions about details in the scene.

- Vript-ERO (Event Re-ordering): A benchmark that tests the temporal understanding by offering the descriptions of scenes located in two/four different timelines of the same video, and asks the model to give the right temporal order of the scenes.
Interweaved ADL Activities

The dataset concerns ADL activities performed in a smart home environment in an Interwoven manner.