In February 2016, LIGO announced the first observation of gravitational waves from a binary black hole merger, known as GW150914. To establish the confidence of this detection, large-scale scientific workflows were used to measure the event's statistical significance. These workflows used code written by the LIGO Scientific Collaboration and were executed on the LIGO Data Grid.


This data set contains a copy of the PyCBC v1.3.2 PyInstaller bundled executables used by the analysis in "Observation of Gravitational Waves from a Binary Black Hole  Merger" B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration) Phys. Rev. Lett. 116, 061102 (2016). The executables are used by the script to create and execute the PyCBC search workflow. 

The data set also contains the HDF5 file that was produced by running the workflow on a mixure of Open Science Grid and USC/ISI compute resources to reproduce the LIGO result. This HDF5 file is used as input to the script to create the right-hand histogram shown in Figure 4 of Abbott et al.

For more information on the files, see the file


The dataset contains two sets of planetary models used in the Reproducibility Challenge Student Cluster Competition at the SC19 conference. During this challenge the competitors reproduced parts of the SC18 paper: "Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver." by Shi, Jia, et al. (



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Cosmological simulation dataset based on LBNL compressible cosmological hydrodynamics simulation code Nyx ( The Nyx simulation data are post-analysis data composed of 3D arrays in space (such as dark matter density, baryon density, temperature, and velocity).

  • Dimension: 3D
  • Dimensional size: 512x512x512
  • Data nature (Interger or Decimal): Decimal
  • Endian format (Big or Little): Little
  • Precision (Double or Single): Single
  • More description: some fields such as the dark matter density in the dataset have very large value ranges while a large majority of data are fairly small (close to 1 or 0). 



This is the last timestep of a run of the dark matter simulation created using the HACC simulation.


The file can be read using the LANL Foresight ( project on github.


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:





It is possible to construct "aerosol cytometers" based on different types of Zhulanov's laser  aerosol counters | diffusion aerosol spectrometers (DAS) [1-8] and "hydrosol cytometers" based on hydrosol particle counters (adopted for ocean marine, ocean and hydrothermal conditions [9,10]).


On February 11th 2016 LIGO-Virgo collaboration gave the announce of the discovery of Gravitational Waves, just 100 years after the Einstein’s paper on their prediction. The LIGO Scientific Collaboration (LSC) and the Virgo Collaboration prepared a web page to inform the broader community about a confirmed astrophysical event observed by the gravitational-wave detectors, and to make the data around that time available for others to analyze