Omkar2118/datasets huggingface dataset:
UAV Trajectory
Full name: Simulated Dataset of UAV Food Delivery using Air Traffic Simulator

The UAV Delievery dataset created to advance the research in drone delivery, contains trajectory details of UAV in different speed, altitude, and wind conditions.  The dataset is created  from the Truck based "Online Food Delivery Platform" using Open Air Traffic Simulator (ATS) with a UAVTrajectory.py plugin.  A pre-processing step is used to select the deliveries under distance 5km, due to battery constraints of UAVs. After the pre-processing, the dataset consists of a total number of 6911 deliveries that are simulated and collected in log files.
Eurovision 2018 votes

Eurovision 2018 official votes dataset.
more details can be found here: https://towardsdatascience.com/social-network-analysis-from-theory-to-applications-with-python-d12e9a34c2c7

Introducing paper: Social Network Analysis: From Graph Theory to Applications with Python
PhotoTour

The dataset consists of 1024 x 1024 bitmap (.bmp) images, each containing a 16 x 16 array of image patches. Each patch is sampled as 64x64 grayscale, with a canonical scale and orientation. For details of how the scale and orientation is established, please see the paper.

Two associated metadata files are included. The first file "info.txt" contains the match information. Each row of info.txt corresponds corresponds to a separate patch, with the patches ordered from left to right and top to bottom in each bitmap image. The first number on each row of info.txt is the 3D point ID from which that patch was sampled -- patches with the same 3D point ID are projected from the same 3D point (into different images). The second number in is not used at present.

The file "interest.txt" has information about the original interest points. Each row of interest.txt also corresponds to a separate patch, so it has the same number of rows as info.txt. The first number is the ID of the reference image in which the interest point was found. IMPORTANT: in order to establish matches and non-matches, you must use patches with the same reference image ID. Correspondences were found by projecting between images using this reference image only, so it is possible that patches with different 3D point ID's that have different reference image ID's could actually correspond to the same 3D point. The other information in interest.txt is: x, y, orientation, scale (log2 units). In order to make sure that non-matches were sufficiently different, we checked that these values were sufficiently far apart when establishing non-matches.

To allow researchers to replicate our learning results (if desired), we have include the match files that we used to generate the results in the paper. These are name "m50_n1_n2.txt" where n1 and n2 are the number of matches and non-matches present in the file. The format of the file is as follows:

patchID1   3DpointID1   unused1   patchID2   3DpointID2   unused2
...
"matches" have the same 3DpointID, and correspond to interest points that were detected with 5 pixels in position, and agreeing to 0.25 octaves of scale and pi/8 radians in angle. "non-matches" have different 3DpointID's, and correspond to interest points lying outside a range of 10 pixels in position, 0.5 octaves of scale and pi/4 radians in angle.
HD1k

An autnonomous driving dataset and benchmark for optical flow. This dataset was created by the Heidelberg Collaboratory for Image Processing in close cooperation with Robert Bosch GmbH.

For the public training dataset, we provide:

1) > 1000 frames at 2560x1080 with diverse lighting and weather scenarios

2) reference data with error bars for optical flow

3) evaluation masks for dynamic objects

4) specific robustness evaluation on challenging scenes

The data was captured in a controlled environment with systematic variation of traffic scenarios, weather, and lighting conditions. The data was acquired at a frame rate of 200Hz with a resolution of 2560x1080.
QM7

QM7 dataset is a subset of the GDB-13 database. GDB-13 contains nearly 1 billion stable and synthetically accessible organic molecules. In the QM7 subset, only molecules with up to 23 atoms are included. These atoms consist of carbon ©, nitrogen (N), oxygen (O), and sulfur (S). The total number of molecules in the QM7 dataset is 7165. Each molecule is represented using the Coulomb matrix, which captures the interactions between atoms.

Introducing paper: QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
QM8

QM8 dataset is a collection of molecular data used for studying quantum mechanical calculations of electronic spectra and excited state energy of small molecules. The QM8 dataset consists of approximately 7,165 molecules. These molecules are a subset of the GDB-13 database, which contains nearly 1 billion stable and synthetically accessible organic molecules. The subset includes all molecules with up to 23 atoms, including 7 heavy atoms (C, N, O, and S).