Omkar2118/datasets huggingface dataset:
Multi-focus thermal database

The database was acquired using a thermographic camera TESTO 882-3 equipped with an uncooled detector and a spectral sensitivity range from 8 to 14 μm. It has a removable German optic lens with these main features:

image resolution: 320 × 240 px,
spectral sensitivity: 8 to 14 μm,
thermal sensitivity (NETD)<0.06 °C at 30 °C,
geometric resolution (IFOV): 1.7 mrad,
detector type: silicon microbolometer uncooled, temperature stabilized,
FOV: 32° × 23°; focal distance: 15 mm; fixed aperture: f/0.95.

The database consists of six image sets. In each set, the camera acquires one image of the scene at each lens position. In our case we have manually moved the lens in 1 mm steps, which provides a total of 96 positions. Thus, each set consists of 96 different images of the one scene. For this purpose, we have attached a millimeter tape to the objective. We also used a stable tripod in order to acquire the same scene for each scene position and a dimmer to fix the bulb current.

We have acquired six image sets:

Image set 1: scene is made up of mobile phone and RS-232 interface in different distances and homogenous heat absorbing background. Distance between camera and the first object is 35 cm and its temperature is 41.2 °C. The distance between objects is 40 cm for all images sets. The maximum temperature of the second object is 32.9 °C.
Image set 2: scene is made up of mobile phone and RS-232 interface in different distances and homogenous heat absorbing background. Distance between camera and the first object is only 15 cm and its temperature is 39.4 °C. The maximum temperature of the second object is 55.9 °C.
Image set 3: scene is made up of two bulbs in different distances and non-homogenous background (partially black and partially white). The bulbs are acquired with a view to the holders. Distance between camera and the first object is 30 cm as in all bulb image sets. The temperature of 1st bulb is 51.7 °C and 2nd is 50.4 °C.
Image set 4: scene is made up of two bulbs in different distances and homogenous white background. The bulbs are acquired with a view to the holders. The temperature of the first bulb is 43.3 °C and 2nd is 41.3 °C.
Image set 5: scene is made up of two bulbs in different distances and homogenous white background. The bulbs are acquired without a view to the holders. The temperature of the first bulb is 57.0 °C and 2nd is 53.6 °C.
Image set 6: scene is made up of two bulbs in different distances and homogenous heat absorbing black background. The bulbs are acquired with a view to the holders. The temperature of the first bulb is 57.9 °C and 2nd is 54.7 °C.
The reference images, where both objects are sharp, were created very easily using this command in MATLAB: img = [img1(:,1:thr) img2(:,thr+1:end)]; where img1 and img2 is the image with a perfectly sharp object 1 and 2 respectively. Variable thr determines the border between these two objects.

Introducing paper: Multi-focus thermal image fusion
i3DMM Test Dataset

A new dataset consisting of 64 people with different expressions and hairstyles.

Introducing paper: i3DMM: Deep Implicit 3D Morphable Model of Human Heads
NOAA Atmospheric Temperature Dataset

This dataset contains meteorological observations (temperature) at the
land-based weather stations located in the United States, collected from the Online Climate
Data Directory of the National Oceanic and Atmospheric Administration (NOAA). The weather
stations are sampled from the Western and Southeastern states that have actively measured
meteorological observations during 2015. The 1-year sequential data of hourly temperature
records are divided into small sequences of 24 hours. For training, validation, and test a sequential
8-2-2 (months) split is used.

Introducing paper: Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics
NEMO Sea Surface Temperature Dataset

This dataset contains spatiotemporal sequences of SST generated by the
NEMO ocean engine.  The observations correspond to 250 randomly selected data sites
within a [0, 550] × [100, 650] square cropped from the area between 50 deg N − 65 deg N and 75W deg −
10W deg starting from 01-01-2016 to 12-31-2017. The data is divided into 24 sequences, each
lasting 30 days (extra days in each month are truncated). Data corresponding to 2016 are used for
training and the rest is used for validation and testing, in the equal sequential split.

Introducing paper: Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics