The dataset has 150 three-second sampling motor current signals from each synthetically-prepared motors. There are five motors with respective fault condition - bearing axis deviation (F1), stator coil inter-turn short circuit (F2), rotor broken strip (F3), outer bearing ring damage (F4), and healthy (H). The motors are run under five coupling loads - 0, 25, 50, 75, and 100%. The sampling signals are collected and processed into frequency occurrence plots (FOPs). Each image has a label, for example F2_L50_130, where F2 is the fault condition, L50 is the coupling load condition.


The "Dynamic Scenes" Dataset is provided for testing visual loop closure detection algorithms in highly dynamic scenes. It has a strong background in some crucial applications such as autonomous driving systems.


This dataset is a companion to a paper, "Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars" by DeLatte et al. 2019. DOI link:


These are the segmentation target files for the three targets described in the paper: solid filled, thicker edge, and thinner edge. 


These files match with the tiles that can be downloaded from the THEMIS Daytime IR Global Mosaic:

Alternatively, this directory can be used for the download:

Use this file pattern to grab the tiles:

  • 0 to +30N: thm_dir_N00_*.png
  • -30N to 0: thm_dir_N-30_*.png 


Included here are three targets for the 24 tiles ±30º latitude, 0-360º longitude. (Each tile is 30º by 30º, 7680 x 7680 pixels, and has a resolution of 256 pixels per degree). Craters with 2-32km radius are included, as identified by the Robbins & Hynek global Mars dataset ( The original data file for the crater locations and parameters can be found here: 

Any arbitrary range of segmentation crater targets can be created using the file and python OpenCV.


To use for segmentation, download the corresponding THEMIS Daytime IR Global Mosaic tiles and this dataset can be used as the target images for segmentation. The filenames of the target files will match the filenames in the THEMIS Daytime IR Global Mosaic.


The file names for each type match the following patterns:

  • solid filled: thm_dir_N*_2_32_km_segrng.png
  • thicker edge (8): thm_dir_N*_2_32_km_segrng_8_edge.png
  • thinner edge (4): thm_dir_N*_2_32_km_segrng_4_edge.png

(segrng = segmentation range, referring to the 2-32 km radius range of craters in this dataset)

The numbers 4 and 8 above refer to the thickness parameter in python OpenCV. The circle drawing function is described here:





A sample of synthetic noise-free reference image created by combining multiple instances of structurally preserved cilia cross-sections. The author has removed the dataset, the interested users can contact the author via email: 


We collected real crack image data to test and analyze the image-based crack detection results. We used 18 images with 1920×1080 resolution sequentially numbered from 1 to 18. And we performed labeling operation to classify the crack image into crack and background regions. online for free use. For the selection of a crack region, we applied a simple rule of extracting a crack edge and selecting the segment matching the true value. The inner region of the selected crack edge (i.e.


Datasets of confocal microscopy images of cardiomyocytes aimed at development of image recognition systems. Images of live and healthy cardiomyocytes with fluorescently stained sarcolemma were assigned into 5 classes according to their development stages. The least developed cardiomyocytes were considered to be at stage 1 (class 1) while the most developed ones were assigned stage 5 (class 5). All other images belong to a class 0.


We make our dataset publicly avaiable. It consists of 50 H&E stained histopathology annotated images at the nuclei level. This dataset is ideal for those who want an exhaustive annotation of H&E breast cancer patient from a Tripple Negative Breast Cancer cohort.


Machine learning is becoming increasingly important for companies and the scientific community. It allows us to generate solutions for several problems faced by society. In this study, we perform a science mapping analysis on the machine learning research, in order to provide an overview of the scientific work during the last decade in this area and to show trends that could be the basis for future developments in the field of computer science. This study was carried out using the CiteSpace and SciMAT tools based on results from Scopus and Clarivate Web of Science.