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1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using Batch Normalizer (Wang et al. 2012).
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are simulated data sets that include batch effects and data truncation and are not yet normalized.
Data Types:
  • Software/Code
MP endocytosis data
Data Types:
  • Slides
  • Tabular Data
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using EigenMS (Karpievitch et al. 2014).
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using mean centering as described in Reisetter et al.
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using median scaling as described in Reisetter et al.
Data Types:
  • Software/Code
Immersive stereoscopic footage of a Coordinate Response Measure (CRM) recorded from two actors. The audio-visual recorded corpus consists of 8 CALLs and 32 COMMANDs per actor. The CALLs and COMMANDs are to be combined at rendering time into full sentences that always follow the same structure: “ready CALL go to COMMAND now ”. The COMMANDs consists of one in four colors (blue, green, red or white) followed by one in eight numbers (1 to 8). This generates a full combinatorial of 256 individual sentences when combined with one of the 8 CALLS (arrow, baron, charlie, eagle, hopper, laker, ringo, tiger). Additionally the dataset also includes the UV positions to texturize the semi-spheres at the rendering time. These have been calculated from the intrinsic and extrinsic calibration parameters of the cameras to facilitate the correct rendering of the video footage. Our system for recording the actors consists of a custom wide-angle stereo camera system made of two Grasshopper 3 cameras with fisheye Fujinon lenses (2.7mm focal length) reaching 185 degrees of Field of View (FoV). The cameras were mounted parallel to each other and separated by 65 mm distance (average human interpupillary distance39) to provide stereoscopic capturing. The video is encoded in H264 format reaching 28-30 frames per second encoding speed at 1600x1080 resolution per camera/eye. The audio was recorded through a near range microphone at a 44kHz sampling rate and 99kbps and both the audio and video are synchronized within 10ms range and saved in mp4 format. The recording room was equipped for professional recording with monobloc LED lighting and chromakey screen. The actor sat at 1 meter distance from the camera recording setup and read the corpus sentences when presented on the screen behind the cameras. The actors were recorded separately in two sessions, seating each at 30 degrees from the bisection, and their videos can be synthetically attached at the rendering time. In the post processing the audio was equalized for all words, and the video was stitched to combine the actors and generate the full the corpus. Sentences were band passed at 80Hz to 16kHz. The corpus sentences are temporally aligned within the range of 64ms in our case, which is below the described 200ms to be perceived. So two or more CRMs can be played synchronously generating an overlap.
Data Types:
  • Video
  • Text
  • Audio
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using mixnorm as described in Reisetter et al.
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using quantile normalization (Bolstad et al. 2003).
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using quantile + ComBat (Johnson et al. 2007).
Data Types:
  • Software/Code
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