Filter Results
96458 results
First draft, necessary for zenodo integration
Data Types:
  • Software/Code
Added jmax (maximum rate of RuBP regeneration) to the list of returned variables by the rpmodel() function. Made calc_viscosity_h2o() and calc_density_h2o() to public functions, exported as part of the rpmodel package.
Data Types:
  • Software/Code
User control of output directory in scSplit count
Data Types:
  • Software/Code
No description provided.
Data Types:
  • Software/Code
No description provided.
Data Types:
  • Software/Code
MRIqual is a Matlab toolbox that allows users to examine the quality of structural (SNR) and functional (tSNR, SFNR) magnetic resonance imaging (MRI) data. It also enables conversion of DICOM to NIfTI or Analyze (.img/.hdr) format and visualization of 3D images (slice mosaic, 3D rendering, statistical overlays). MRI quality assurance measures include multiple types of signal to noise ratio for structural and functional images, as well as temporal signal-to-noise ratio (tSNR) and Signal-to-Fluctuation-Noise Ratio (SFNR) for functional images.
Data Types:
  • Software/Code
Docker-based Geospatial toolkit for R, built on versioned Rocker images
Data Types:
  • Software/Code
The Libre Multilingual Analyzer, a Natural Language Processing (NLP) C++ toolkit.
Data Types:
  • Software/Code
qeeg application
Data Types:
  • Software/Code
CJS-pop This document explains the R scripts used in Şen and Akçakaya (in review), which describes CJS-pop, applies it to simulated and MAPS data, and uses the estimated parameters for population projections. The code in general are presented as "notebooks" which are R Markdown files. You can knit the whole file or you can run the code chunks seperately. Each notebook includes detailed notes about the code for data wrangling and analysis. We can't share MAPS data directly. Brown Creeper analysis code will not work unless you have access to full MAPS data. However, we share an example analysis with simulated data under sim_example_CJSpop_run.R. Clone the repository and run sim_models_notebook.rmd to generate the .jags files. Then run sim_example_CJSpop_run.R to obtain example results. We'll update this reposttiory if we get permisson to share Brown Creeper data. Meanwhile the results of the Brown Creeper models can be accessed at BRCR_results.rds. If you have access to data, the main analysis has three parts: MAPS, Simulations, and Projections and Plotting. 1. MAPS Run the brcr_data_wrangling_notebook.rmd to prepare Brown Creeper MAPS data for CJS-pop in JAGS. Run brcr_models_notebook.rmd to generate the .jags files used in applying CJS-pop to BRCR capture history data. Easiest way is to knit the whole notebook. Run brcr_models.R to apply 4 different mark-recapture models (3 different IRMs and one CJS) to Brown Creeper capture history data. These results are also available in the file BRCR_results.rds. 2. Simulations Run data_generation_notebook.rmd to simulate capture history data as explained in Şen and Akçakaya (in review). Run sim_models_notebook.rmd to generate the .jags files used in applying CJS-pop to simulated data. Easiest way is to knit the whole notebook. We analysied the simulated data sets using the high performance comuputing cluster of Stony Brook University, which is named SeaWulf. We provide a sample code, sim_example_CJSpop_run.R, that can be run locally and analyses a single simulation set. This sample code can easily be extended to analyse all simulated sets in a single script in another cluster. 3. Projections and Plotting The plots and population projections in Şen and Akçakaya (in review) can be reproduced with Plots.R.
Data Types:
  • Software/Code
4