Dataset of fluorescent mice brain vessels Confocal 3D volumes aligned to Light-Field images.

Confocal:

  • Single volume dimension: 1287x1287x64.
  • Number of samples: 362
  • Voxel size: 0.086x0.086x0.9 um.
  • Objective: 40x/1.3 Oil.
  • Stain: tomato lectin (DyLight594 conjugated, DL-1177, Vector Laboratories).

 

LightField:

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The files here support the analysis presented in the paper in IEEE Transactions on Geoscience and Remote Sensing, "Snow Property Inversion from Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach with Examples from MODIS and Landsat 8 OLI" Spectral mixture analysis has a history in mapping snow, especially where mixed pixels prevail. Using multiple spectral bands rather than band ratios or band indices, retrievals of snow properties that affect its albedo lead to more accurate estimates than widely used age-based models of albedo evolution.

Instructions: 

These HDF5 files contain snow cover over the Sierra Nevada USA from water year 2001-2019 using the Snow Property Inversion from Remote Sensing (SPIRES) approach. Each file covers one water year (October through September). They are stored with block compression so individual days can be read without reading the whole file. The method is described by E.H. Bair, T. Stillinger, and J. Dozier, "Snow Property Inversion from Remote Sensing (SPIReS): A generalized multispectral unmixing approach with examples from MODIS and Landsat 8 OLI," IEEE Trans. Geosci. Remote Sens., 2020 (manuscript number TGRS-2020-02003). Source code is at https://github.com/edwardbair/SPIRES The projection is the Albers equaconic (also called the California Teale projection) with WGS84 datum and 500 m square pixels. The standard meridian for the projection is 120 W; the standard parallels are 34 N and 40.5 N; False Northing is -40,000,000. The h5 files can be read with several software packages. We use MATLAB. They contain: MATLAB date numbers, ISO dates in format YYYYDDD, geographic information, spacetime cubes of snow fraction, raw (unadjusted) snow fraction, grain size (um), and dust (ppmw). The spacetime cubes have a slice for each day, begin on October 1 and end on September 30.

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CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.

Instructions: 

Download and extract the ZIP file containing all files. There is python code available (under 'scripts') to easily load the data set. Other programming languages should also handle .jpg, .hdf and .csv files for easy access. For easy access with python, a pickle dump file has been added. This has no extra information compared to the .csv file.

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CHALLENGE ON ULTRASOUND BEAMFORMING WITH DEEP LEARNING (CUBDL)

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Cosmological simulation dataset based on LBNL compressible cosmological hydrodynamics simulation code Nyx (https://ccse.lbl.gov/Research/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).

Instructions: 
  • 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). 

 

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Cosmological simulation dataset based on compressible cosmological hydrodynamics simulation code Nyx (https://ccse.lbl.gov/Research/NYX/). The Nyx simulation data are post-analysis data composed of 3D arrays in space (such as dark matter density and temperature).

Instructions: 

ARRAY DIMENSION: 3D

DIMENSIONAL SIZE: 512x512x512

DATA NATURE (INTEGER or DECIMAL): DECIMAL

ENDIAN FORMAT: LITTLE

PRECISION (DOUBLE, SINGLE): SINGLE

 

More description:

Note that some fields such as the dark matter density in Nyx dataset have very large value ranges while a large majority of data are fairly small (close to 1 or 0). 

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Our Signing in the Wild dataset consists of various videos harvested from YouTube containing people signing in various sign languages and doing so in diverse settings, environments, under complex signer and camera motion, and even group signing. This dataset is intended to be used for sign language detection.

 

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346 Views

This dataset contains heavy-machinery data from the Brazilian industrial sector. The data was collected in a poultry feed factory located  in the state of Minas Gerais, Brazil. Its process can be summarized to creating pellets of ration for poultry from corn or soybeans and added nutrients. The factory produces at fullscale over the entire year, thus it has well-behaved usage patterns at any time. It operates from Mondays through Fridays (and occasionally on Saturdays, in case production is below the monthly target) on a daily three-turn shift from 10:00 PM to 05:00 PM.

Instructions: 

Based on NILM METADATA.

Each CSV file is named according to the appliance or circuit it represents.

 

IMDELD.hdf5 is the complete dataset that uses NILM METADATA and is fully compatible with NILMTK.

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