The Experts below are selected from a list of 4077 Experts worldwide ranked by ideXlab platform
Šubic Tine - One of the best experts on this subject based on the ideXlab platform.
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Minimal operating system implementation
2017Co-Authors: Šubic TineAbstract:The following thesis describes an implementation of a minimal operating system which supports user interaction through keyboard and a Terminal Window. The system design is inspired by GNU/Linux operating system and is at its core monolithic kernel with support for memory management, kernel modules and various peripheral drivers, like serial ports, VGA Terminal, timers and interrupts. The second part of the system is the user mode using a REPL Terminal with some inbuilt tools for basic computer tasks and demonstration on system capabilities. The operating system uses C programming language, as well as x86 architecture assembly for some critical parts. Bochs, the virtual machine software was used to emulate hardware while in development, however, the system can be booted on compatible physical hardware
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Minimal operating system implementation
2017Co-Authors: Šubic TineAbstract:V diplomskem delu je predstavljena implementacija minimalnega operacijskega sistema, ki podpira interakcijo z uporabnikom skozi Terminalsko okno. Sam sistem se zgleduje po operacijskem sistemu GNU/Linux in je v osrčju sestavljen iz monolitnega jedra in podpira dinamično upravljanje s pomnilnikom, podporo jedrnim modulom in gonilnike za tipkovnico, VGA Terminal, prekinitve in serijske vhode. Drugi del sistema predstavlja uporabniški del v obliki REPL Terminala z nekaj vgrajenimi osnovnimi orodji za uporabo sistema in prikaz osnovnih funkcionalnosti. Projekt je napisan v programskih jezikih C in zbirniku za arhitekturo x86. Za simulacijo strojne opreme med razvojem je bil uporabljen virtualni stroj Bochs, sistem pa je možno naložiti tudi na zgoščenko in ga zagnati na kompatibilni fizični strojni opremi.The following thesis describes an implementation of a minimal operating system which supports user interaction through keyboard and a Terminal Window. The system design is inspired by GNU/Linux operating system and is at its core monolithic kernel with support for memory management, kernel modules and various peripheral drivers, like serial ports, VGA Terminal, timers and interrupts. The second part of the system is the user mode using a REPL Terminal with some inbuilt tools for basic computer tasks and demonstration on system capabilities. The operating system uses C programming language, as well as x86 architecture assembly for some critical parts. Bochs, the virtual machine software was used to emulate hardware while in development, however, the system can be booted on compatible physical hardware
Philipp Sommer - One of the best experts on this subject based on the ideXlab platform.
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Chilipp/psyplot-conda: v1.0.1: Revised release with additional Windows and OS X installers
2017Co-Authors: Philipp SommerAbstract:v1.0.1 Added added changelog Added Mac OSX .pkg installer Changed Increased psyplot-gui to v1.0.1 Installation on Linux Download the bash script (file ending on '.sh' for linux) and open a Terminal Window. Type bash '' and simply follow the instructions. For more information on the command line options, type bash '' --help It will ask you, whether you want to add a psyplot alias to your .bashrc, such that you can easily start the Terminal and type psyplot to start the GUI. You can avoid this by setting NO_PSYPLOT_ALIAS=1. Hence, to install psyplot-conda without any Terminal interaction, run NO_PSYPLOT_ALIAS=1 bash '' -b -p Uninstallation on Linux Just delete the folder where you installed psyplot-conda. By default, this is $HOME/psyplot-conda, so just type rm -rf $HOME/psyplot-conda If you added a psyplot alias to your .bashrc or chose to add the bin directory to your PATH variable during the installation, open your $HOME/.bashrc in an editor of your choice and delete those parts. Installation on OS X You can either install it from the Terminal using a bash-script (.sh file), or you can install a standalone app using an installer package (.pkg file). The bash script will install a conda installation in your desired location. Both will create a Psyplot.app (see below). Installation using the OS X package This should be straight-forward, however Apple does not provide free Developer IDs for open-source developers. Therefore our installers are not signed and you have to give the permissions to open the files manually. The 4 steps below describe the process. Just download the package installer To open it, you have to Right-click on the file |rarr| Open With |rarr| Installer. In the appearing Window, click the Open button. Follow the instructions. It will create a Psyplot.app in the specified location. To open the app the first time, change to the chosen installation directory for the App (by default $HOME/Applications), right-click the Psyplot app and click on Open. In the appearing Window, again click on Open. Installation using the bash script Download the bash script (file ending on '.sh' for MacOSX) from the releases_ page and open a Terminal Window. Type bash '' and simply follow the instructions. For more informations on the command line options, type bash '' --help By default, the installer asks whether you want to install a Psyplot.app into your Applications directory. You can avoid this be setting NO_PSYPLOT_APP=1. Furthermore it will ask you, whether you want to add a psyplot alias to your .bash_profile, such that you can easily start the Terminal and type psyplot to start the GUI. You can avoid this by setting NO_PSYPLOT_ALIAS=1. Hence, to install psyplot-conda without any Terminal interaction, run NO_PSYPLOT_APP=1 NO_PSYPLOT_ALIAS=1 bash '' -b -p Uninstallation on OSX The uninstallation depends on whether you have used the package installer or the bash script for the installation. Uninstall the App installed through the OS X package Just delete the app from your Applications folder. There have been no changes made to your PATH variable. Uninstall the App installed via bash script As for linux, just delete the folder where you installed psyplot-conda. By default, this is $HOME/psyplot-conda. Open a Terminal and just type rm -rf $HOME/psyplot-conda If you added a psyplot alias to your .bash_profile or chose to add the bin directory to your PATH variable during the installation, open your $HOME/.bash_profile in an editor of your choice and delete those parts. If you chose to add a Psyplot app, just delete the symbolic link in /Applications or $HOME/Applications. Installation on Windows Just double click the downloaded file for 64-bit or 32-bit systems and follow the instructions. The installation will create an item in the Windows menu (Start -> Programs -> Psyplot) which you can use to open the GUI. If you do not want the shortcut, download the installer without shortcut for 64-bit or 32-bit. In any case, if you chose to add the binaries to your PATH variable, you can open a command Window (cmd) and type psyplot. Uninstallation on Windows Just double-click the Uninstall-Anaconda.exe file in the directory where you installed psyplot-conda and follow the instructions
Philipp S. Sommer - One of the best experts on this subject based on the ideXlab platform.
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Chilipp/psyplot-conda: v1.0.1: Conda installers for psyplot - The interactive visualization framework
2017Co-Authors: Philipp S. SommerAbstract:psyplot-conda provides standalone installers for the psyplot interactive visualization framework. psyplot is an open source python project that mainly combines the plotting utilities of matplotlib (Hunter, 2007) and the data management of the xarray package (Hoyer & Hamman, 2017). The main purpose is to have a framework that allows a fast, attractive, flexible, easily applicable, easily reproducible and especially an interactive visualization of your data. The ultimate goal is to help scientists and especially climate model developers in their daily work by providing a flexible visualization tool that can be enhanced by their own visualization scripts. psyplot can be used through the python command line and through the psyplot-gui module which provides a graphical user interface for an easier interactive usage. These installers contains all necessary dependencies for psyplot, psyplot-gui, psy-simple, psy-maps and psy-reg plus the conda package for managing virtual environments. The installers have been created using using the conda constructor package and the packages from the conda-forge channel. Other ways to install psyplot can be found in the psyplot docs: http://psyplot.readthedocs.io Source files These files contain the source python code for the psyplot-conda repository (tag v1.0.1) and it's submodules psyplot (tag v1.0.0), psy-simple (tag v1.0.0), psy-maps (tag v1.0.0), psy-reg (tag v1.0.0) and psyplot-gui (tag v1.0.1). psyplot-conda-1.0.1.zip: The zipped source files including reference figures for psy-simple and psy-maps psyplot-conda-1.0.1-no-refs.zip: The zipped source files without reference figures Documentation files These files contain the documentation for psyplot, psy-maps, psy-reg, psy-simple and psyplot-gui from http://psyplot.readthedocs.io/en/latest/, downloaded on August, 21st, 2017. docs-epub.zip: Documentation files in epub format docs-pdf.zip: Documentation files in pdf format docs-html.zip: Documentation files in html format Standalone installers These installers have been created using the conda constructor package and can be used to install psyplot, psy-simple, psy-maps, psy-reg and psyplot-gui. Installation instructions can be found in the Additional Notes. psyplot-conda-1.0.1-Linux-x86-64.sh: Bash installer for 64-bit Linux systems psyplot-conda-1.0.1-MacOSX-x86-64.sh: Bash installer for 64-bit Mac OS X systems psyplot-conda-1.0.1-MacOSX-x86-64.pkg: Package installer for 64-bit Mac OS X systems psyplot-conda-1.0.1-Windows-x86-64.exe: Windows installer for 64-bit systems psyplot-conda-1.0.1-Windows-x86.exe: Windows installer for 32-bit systems psyplot-conda-1.0.1-Windows-no-shortcut-x86-64.exe: Windows installer for 64-bit systems without menu shortcut psyplot-conda-1.0.1-Windows-no-shortcut-x86.exe: Windows installer for 32-bit systems without menu shortcut Packages These files contain packages for psyplot (1.0.0.post1), psy-simple (1.0.0), psy-maps (1.0.0), psy-reg (1.0.0) and psyplot-gui (1.0.1). python-packages.zip: Python packages from pipy.org conda-packages.zip: Conda packages from https://anaconda.org/conda-forge Installation instructions for standalone installers Installation on Linux Download the bash script and open a Terminal Window. Type: bash '' and simply follow the instructions. For more information on the command line options type: bash '' --help It will ask you, whether you want to add a psyplot alias to your .bashrc, such that you can easily start the Terminal and type psyplot to start the GUI. You can avoid this by setting NO_PSYPLOT_ALIAS=1. Hence, to install psyplot-conda without any Terminal interaction, run: NO_PSYPLOT_ALIAS=1 bash '' -b -p Uninstallation on Linux Just delete the folder where you installed psyplot-conda. By default, this is $HOME/psyplot-conda, so just type: rm -rf $HOME/psyplot-conda If you added a psyplot alias to your .bashrc or chose to add the bin directory to your PATH variable during the installation, open your $HOME/.bashrc in an editor of your choice and delete those parts. Installation on OS X You can either install it from the Terminal using a bash-script (.sh file), or you can install a standalone app using an installer (.pkg file). The bash script will install a conda installation in your desired location. Both will create a Psyplot.app (see below). Installation using the OS X package This should be straight-forward, however Apple does not provide free Developer IDs for open-source developers. Therefore our installers are not signed and you have to give the permissions to open the files manually. The 4 steps below describe the process. Just download the .pkg file To open it, you have to Right-click on the file, then Open With, then Installer. In the appearing Window, click the Open button. Follow the instructions. It will create a Psyplot.app in the specified location. To open the app the first time, change to the chosen installation directory for the App (by default $HOME/Applications), right-click the Psyplot app and click on Open. In the appearing Window, again click on Open. Installation using the bash script Download the bash script (file ending on '.sh' for MacOSX) and open a Terminal Window. Type: bash '' and simply follow the instructions. For more informations on the command line options type: bash '' --help By default, the installer asks whether you want to install a Psyplot.app into your Applications directory. You can avoid this be setting NO_PSYPLOT_APP=1. Furthermore it will ask you, whether you want to add a psyplot alias to your .bash_profile, such that you can easily start the Terminal and type psyplot to start the GUI. You can avoid this by setting NO_PSYPLOT_ALIAS=1. Hence, to install psyplot-conda without any Terminal interaction, run: NO_PSYPLOT_APP=1 NO_PSYPLOT_ALIAS=1 bash '' -b -p Uninstallation on OSX The uninstallation depends on whether you have used the package installer or the bash script for the installation. Uninstall the App installed through the OS X package Just delete the app from your Applications folder. There have been no changes made to your PATH variable. Uninstall the App installed via bash script As for linux, just delete the folder where you installed psyplot-conda. By default, this is $HOME/psyplot-conda. Open a Terminal and just type: rm -rf $HOME/psyplot-conda If you added a psyplot alias to your .bash_profile or chose to add the bin directory to your PATH variable during the installation, open your $HOME/.bash_profile in an editor of your choice and delete those parts. If you chose to add a Psyplot app, just delete the symbolic link in /Applications or $HOME/Applications. Installation on Windows Just download the installer for 64-bit or 32-bit, double click the downloaded file and follow the instructions. The installation will create an item in the Windows menu (Start -> Programs -> Psyplot) which you can use to open the GUI. You can, however, also download installers that create no shortcut from below. In any case, if you chose to modify your PATH variable during the installation, you can open a command Window (cmd) and type psyplot. Uninstallation on Windows Just double-click the Uninstall-Anaconda.exe file in the directory where you installed psyplot-conda and follow the instructions. This will also revert the changes in your PATH variable. Acknowledgements The author thanks the Swiss National Science Foundation (SNF) for their support. Funding for the author came from the ACACIA grant (CR10I2_146314) and the HORNET grant (200021_169598)
Peter Kamerman - One of the best experts on this subject based on the ideXlab platform.
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Analysis scripts and supplementary files: Barriers to implementing clinical trials on non-pharmacological treatments in developing countries – lessons learnt from addressing pain in HIV
2019Co-Authors: Peter Kamerman, Victoria J Madden, Romy Parker, Dershnee Devan, Sarah Cameron, Kirsty Jackson, Cameron Reardon, Antonia WadleyAbstract:DESCRIPTIONThis repository contains analysis scripts (with outputs), figures from the manuscript, and supplementary files the HIV Pain (HIP) Intervention Study. All analysis scripts (and their outputs -- /outputs subdirectory) are found in HIP-study.zip, while PDF copies of the analysis outputs that are cited in the manuscript as supplementary material are found in the relevant supplement-*.pdf file.Note: Participant consent did not provide for the publication of their data, and hence neither the original nor cleaned data have been made available. However, we do not wish to bar access to the data unnecessarily and we will judge requests to access the data on a case-by-case basis. Examples of potential use cases include independent assessments of our analyses, and secondary data analyses. Please contact Peter Kamerman (peter.kamerman@gmail.com), Dr Tory Madden (torymadden@gmail.com, or open an issue on the GitHub repo (https://github.com/kamermanpr/HIP-study/issues).BIBLIOGRAPHIC INFORMATIONRepository citationKamerman PR, Madden VJ, Parker R, Devan D, Cameron S, Jackson K, Reardon C, Wadley A. Analysis scripts and supplementary files: Barriers to implementing clinical trials on non-pharmacological treatments in developing countries – lessons learnt from addressing pain in HIV. DOI: 10.6084/m9.figshare.7654637.Manuscript citationParker R, Madden VJ, Devan D, Cameron S, Jackson K, Kamerman P, Reardon C, Wadley A. Barriers to implementing clinical trials on non-pharmacological treatments in developing countries – lessons learnt from addressing pain in HIV. Pain Reports [submitted 2019-01-31]Manuscript abstractintroduction: Pain affects over half of people living with HIV/AIDS (LWHA) and pharmacological treatment has limited efficacy. Preliminary evidence supports non-pharmacological interventions. We previously piloted a multimodal intervention in amaXhosa women LWHA and chronic pain in South Africa with improvements seen in all outcomes, in both intervention and control groups. Methods: A multicentre, single-blind randomised controlled trial with 160 participants recruited was conducted to determine whether the multimodal peer-led intervention reduced pain in different populations of both male and female South Africans LWHA. Participants were followed up at Weeks 4, 8, 12, 24 and 48 to evaluate effects on the primary outcome of pain, and on depression, self-efficacy and health-related quality of life. Results: We were unable to assess the efficacy of the intervention due to a 58% loss to follow up (LTFU). Secondary analysis of the LTFU found that sociocultural factors were not predictive of LTFU. Depression, however, did associate with LTFU, with greater severity of depressive symptoms predicting LTFU at week 8 (p=0.01). Discussion: We were unable to evaluate the effectiveness of the intervention due to the high LTFU and the risk of retention bias. The different sociocultural context in South Africa may warrant a different approach to interventions for pain in HIV compared to resource-rich countries, including a concurrent strategy to address barriers to health care service delivery. We suggest that assessment of pain and depression need to occur simultaneously in those with pain in HIV. We suggest investigation of the effect of social inclusion on pain and depression. USING DOCKER TO RUN THE HIP-STUDY ANALYSIS SCRIPTSThese instructions are for running the analysis on your local machine.You need to have Docker installed on your computer. To do so, go to docker.com (https://www.docker.com/community-edition#/download) and follow the instructions for downloading and installing Docker for your operating system. Once Docker has been installed, follow the steps below, noting that Docker commands are entered in a Terminal Window (Linux and OSX/macOS) or command prompt Window (Windows). Windows users also may wish to install GNU Make (http://gnuwin32.sourceforge.net/downlinks/make.php) (required for the `make` method of running the scripts) and Git (https://gitforWindows.org/) version control software (not essential).Download the latest imageEnter: docker pull kamermanpr/docker-hip-study:v2.0.0Run the containerEnter: docker run -d -p 8787:8787 -v :/home/rstudio --name threshold -e USER=hip -e PASSWORD=study kamermanpr/docker-hip-study:v2.0.0Where refers to the path to the HIP-study directory on your computer, which you either cloned from GitHub (https://github.com/kamermanpr/HIP-study.git), `git clone https://github.com/kamermanpr/HIP-study`, or downloaded and extracted from figshare (https://doi.org/10.6084/m9.figshare.7654637).Login to RStudio Server- Open a web browser Window and navigate to: `localhost:8787`- Use the following login credentials: - Username: hip - Password: study Prepare the HIP-study directoryThe HIP-study directory comes with the outputs for all the analysis scripts in the _/outputs_ directory (html and md formats). However, should you wish to run the scripts yourself, there are several preparatory steps that are required:1. Acquire the data. The data required to run the scripts have not been included in the repo because participants in the studies did not consent to public release of their data. However, the data are available on request from Peter Kamerman (peter.kamerman@gmail.com). Once the data have been obtained, the files should be copied into a subdirectory named /data-original.2. Clean the /outputs directory by entering `make clean` in the Terminal tab in RStudio.Run the HIP-study analysis scriptsTo run all the scripts (including the data cleaning scripts), enter `make all` in the Terminal tab in RStudio.To run individual RMarkdown scripts (*.Rmd files)1. Generate the cleaned data using one of the following methods: - Enter `make data-cleaned/demographics.rds` in the Terminal tab in RStudio. - Enter `source('clean-data-script.R')` in the Console tab in RStudio. - Open the clean-data-script.R script through the File tab in RStudio, and then click the 'Source' button on the right of the Script console in RStudio for each script. 2. Run the individual script by: - Entering `make outputs/.html` in the Terminal tab in RStudio, OR - Opening the relevant \*.Rmd file through the File tab in RStudio, and then clicking the 'knit' button on the left of the Script console in RStudio. Shutting downOnce done, log out of RStudio Server and enter the following into a Terminal to stop the Docker container: `docker stop hip`. If you then want to remove the container, enter: `docker rm threshold`. If you also want to remove the Docker image you downloaded, enter: `docker rmi kamermanpr/docker-hip-study:v2.0.0`
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Analysis scripts and supplementary files: Was that painful or non-painful? The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context.
2018Co-Authors: Peter Kamerman, Victoria Madden, Valeria Bellan, Mark Catley, Leslie Russek, Danny Camfferman, Lorimer MoseleyAbstract:DESCRIPTIONThis repository contains analysis scripts (with outputs), figures from the manuscript, and supplementary files for two studies on the properties of the Sensation and Pain Rating Scale (SPARS). All analysis scripts (and their outputs -- /outputs subdirectory) are found in SPARS.zip, while PDF copies of the analysis outputs that are cited in the manuscript as supplementary material are found in the relevant supplement_*.pdf. Note: Participant consent did not provide for the publication of their data, and hence neither the original nor cleaned data have been made available. However, we do not wish to bar access to the data unnecessarily and we will judge requests to access the data on a case-by-case basis. Examples of potential use cases include independent assessments of our analyses, and secondary data analyses. Please contact Peter Kamerman (peter.kamerman@gmail.com), Dr Tory Madden (torymadden@gmail.com, or open an issue on the GitHub repo (https://github.com/kamermanpr/SPARS/issues).BIBLIOGRAPHIC INFORMATIONRepository citationKamerman P, Madden V, Bellan V, Catley M, Russek L, Camfferman D, Moseley L. Analysis scripts and supplementary files: Was that painful or non-painful? The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context. Figshare, 2018. DOI: 10.6084/m9.figshare.6561743.Manuscript citationMadden V, Kamerman P, Bellan V, Catley M, Russek L, Camfferman D, Moseley L. The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context. Journal of Pain [in press].Manuscript abstractIn experiments on pain perception, participants are frequently exposed to non-painful and painful stimuli, yet the conventional pain-rating scales lack a non-painful range and a clear point of transition from non-painful to painful events. The Sensation and Pain Rating Scale (SPARS) is a 0-100 scale that assesses the full stimulus intensity range, extending from no sensation (rating: -50) to worst pain imaginable (rating: +50), and it explicitly identifies pain threshold (rating: 0). Here, we tested the SPARS in two experiments using laser heat stimuli to establish its stimulus-response characteristics (Experiment 1, n = 19, 13 stimulus intensities applied 26 times each across a 1-4J range), and to compare it to 0-100 scales that access non-painful (0: no sensation, 100: painful) and painful (0: not painful, 100: worst pain imaginable) events (Experiment 2, n = 7, 9 stimulus intensities applied 36 times each across a 1.5-4.5J range). Despite high inter- and intra-individual variation, we found a reasonably consistent curvilinear stimulus-response relationship (the curve flattens around pain threshold), with stable response characteristics across the range of the scale. SPARS ratings tended to be lower than the 0-100 pain rating scale in the noxious stimulus intensity range, and greater than the 0-100 non-painful sensation scale in the non-noxious stimulus range; likely reflecting differences in scale dimensionality. The SPARS overcomes limitations of scale range inherent in conventional pain rating scales and, as such, is well suited to experimental studies in which distinguishing between painful and non-painful events is a priority.USING DOCKER TO RUN THE SPARS ANALYSIS SCRIPTSThese instructions are for running the analysis on your local machine.You need to have Docker installed on your computer. To do so, go to docker.com (https://www.docker.com/community-edition#/download) and follow the instructions for downloading and installing Docker for your operating system. Once Docker has been installed, follow the steps below, noting that Docker commands are entered in a Terminal Window (Linux and OSX/macOS) or command prompt Window (Windows). Windows users also may wish to install GNU Make (http://gnuwin32.sourceforge.net/downlinks/make.php) (required for the `make` method of running the scripts) and Git (https://gitforWindows.org/) version control software (not essential). Download the latest imageEnter: `docker pull kamermanpr/docker-spars:v1.1.2`Download the repositoryDownload the compressed zip file from GitHub (kamermanpr/SPARS (https://github.com/kamermanpr/SPARS), or from figshare [DOI: 10.6084/m9.figshare.6561743 (https://doi.org/10.6084/m9.figshare.6561743)]. Run the containerEnter: `docker run --name spars -d -p 8787:8787 -e USER=user -e PASSWORD=password kamermanpr/docker-spars:v1.1.2`Login to RStudio Server- Open a web browser Window and navigate to: `localhost:8787`- Use the following login credentials: - Username: _user_ - Password: _password_ Prepare the SPARS directoryOn the Files tab in the bottom right panel of RStudio, click on the 'Upload' button, navigate to the downloaded zip file, and upload the file (it will self extract).The SPARS directory comes with the outputs for all the analysis scripts in the /outputs directory (html and md formats). However, should you wish to run the scripts yourself, there are several preparatory steps that are required:1. Acquire the data. The data required to run the scripts have not been included in the repo because participants in the studies did not consent to public release of their data. However, the data are available on request from Tory Madden (torymadden@gmail.com) or Peter Kamerman (peter.kamerman@gmail.com). We will send you a zip file with the data. Using the directory tree in the Files tab of RStudio, open the SPARS directory. Repeat the upload procedure described above, but upload the zipped data file we supplied you with into the SPARS directory.2. In the SPARS directory, double-click on the SPARS.Rproj file, and follow the prompts (RStudio will reload).3. Clean the /outputs and /figures directories by entering `make clean` in the Terminal tab in bottom right panel of RStudio.Run the SPARS analysis scriptsTo run all the scripts (including the data cleaning scripts), enter `make` in the Terminal tab.To run individual RMarkdown scripts (\*.Rmd files) 1. Generate the cleaned data using one of the following methods: - Enter `make data-cleaned/SPARS_A.rds` and then `make data-cleaned/SPARS_B.rds` in the Terminal tab; - Enter `source('0A-clean-data.R')` and then `source('0B-clean-data.R')` in the Console tab in bottom left panel of RStudio. - Open 0A-clean-data.R and 0B-clean-data.R scripts through the File tab, and then click the 'Source' button on top of the panel on the top left of RStudio for each script. 2. Run the individual script using one of the following methods: - Enter `make outputs/.html` in the Terminal tab; - Open the relevant \*.Rmd file through the File tab, and then click the 'knit' button on the top of the panel on the top left of RStudio. Shutting downOnce done, log out of RStudio and enter the following into a Terminal to stop the Docker container: `docker stop spars`. If you then want to remove the container, enter: `docker rm spars`. If you also want to remove the Docker image you downloaded, enter: `docker rmi kamermanpr/docker-spars:v1.1.2`
Lorimer Moseley - One of the best experts on this subject based on the ideXlab platform.
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Analysis scripts and supplementary files: Was that painful or non-painful? The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context.
2018Co-Authors: Peter Kamerman, Victoria Madden, Valeria Bellan, Mark Catley, Leslie Russek, Danny Camfferman, Lorimer MoseleyAbstract:DESCRIPTIONThis repository contains analysis scripts (with outputs), figures from the manuscript, and supplementary files for two studies on the properties of the Sensation and Pain Rating Scale (SPARS). All analysis scripts (and their outputs -- /outputs subdirectory) are found in SPARS.zip, while PDF copies of the analysis outputs that are cited in the manuscript as supplementary material are found in the relevant supplement_*.pdf. Note: Participant consent did not provide for the publication of their data, and hence neither the original nor cleaned data have been made available. However, we do not wish to bar access to the data unnecessarily and we will judge requests to access the data on a case-by-case basis. Examples of potential use cases include independent assessments of our analyses, and secondary data analyses. Please contact Peter Kamerman (peter.kamerman@gmail.com), Dr Tory Madden (torymadden@gmail.com, or open an issue on the GitHub repo (https://github.com/kamermanpr/SPARS/issues).BIBLIOGRAPHIC INFORMATIONRepository citationKamerman P, Madden V, Bellan V, Catley M, Russek L, Camfferman D, Moseley L. Analysis scripts and supplementary files: Was that painful or non-painful? The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context. Figshare, 2018. DOI: 10.6084/m9.figshare.6561743.Manuscript citationMadden V, Kamerman P, Bellan V, Catley M, Russek L, Camfferman D, Moseley L. The Sensation and Pain Rating Scale (SPARS) performs well in the experimental context. Journal of Pain [in press].Manuscript abstractIn experiments on pain perception, participants are frequently exposed to non-painful and painful stimuli, yet the conventional pain-rating scales lack a non-painful range and a clear point of transition from non-painful to painful events. The Sensation and Pain Rating Scale (SPARS) is a 0-100 scale that assesses the full stimulus intensity range, extending from no sensation (rating: -50) to worst pain imaginable (rating: +50), and it explicitly identifies pain threshold (rating: 0). Here, we tested the SPARS in two experiments using laser heat stimuli to establish its stimulus-response characteristics (Experiment 1, n = 19, 13 stimulus intensities applied 26 times each across a 1-4J range), and to compare it to 0-100 scales that access non-painful (0: no sensation, 100: painful) and painful (0: not painful, 100: worst pain imaginable) events (Experiment 2, n = 7, 9 stimulus intensities applied 36 times each across a 1.5-4.5J range). Despite high inter- and intra-individual variation, we found a reasonably consistent curvilinear stimulus-response relationship (the curve flattens around pain threshold), with stable response characteristics across the range of the scale. SPARS ratings tended to be lower than the 0-100 pain rating scale in the noxious stimulus intensity range, and greater than the 0-100 non-painful sensation scale in the non-noxious stimulus range; likely reflecting differences in scale dimensionality. The SPARS overcomes limitations of scale range inherent in conventional pain rating scales and, as such, is well suited to experimental studies in which distinguishing between painful and non-painful events is a priority.USING DOCKER TO RUN THE SPARS ANALYSIS SCRIPTSThese instructions are for running the analysis on your local machine.You need to have Docker installed on your computer. To do so, go to docker.com (https://www.docker.com/community-edition#/download) and follow the instructions for downloading and installing Docker for your operating system. Once Docker has been installed, follow the steps below, noting that Docker commands are entered in a Terminal Window (Linux and OSX/macOS) or command prompt Window (Windows). Windows users also may wish to install GNU Make (http://gnuwin32.sourceforge.net/downlinks/make.php) (required for the `make` method of running the scripts) and Git (https://gitforWindows.org/) version control software (not essential). Download the latest imageEnter: `docker pull kamermanpr/docker-spars:v1.1.2`Download the repositoryDownload the compressed zip file from GitHub (kamermanpr/SPARS (https://github.com/kamermanpr/SPARS), or from figshare [DOI: 10.6084/m9.figshare.6561743 (https://doi.org/10.6084/m9.figshare.6561743)]. Run the containerEnter: `docker run --name spars -d -p 8787:8787 -e USER=user -e PASSWORD=password kamermanpr/docker-spars:v1.1.2`Login to RStudio Server- Open a web browser Window and navigate to: `localhost:8787`- Use the following login credentials: - Username: _user_ - Password: _password_ Prepare the SPARS directoryOn the Files tab in the bottom right panel of RStudio, click on the 'Upload' button, navigate to the downloaded zip file, and upload the file (it will self extract).The SPARS directory comes with the outputs for all the analysis scripts in the /outputs directory (html and md formats). However, should you wish to run the scripts yourself, there are several preparatory steps that are required:1. Acquire the data. The data required to run the scripts have not been included in the repo because participants in the studies did not consent to public release of their data. However, the data are available on request from Tory Madden (torymadden@gmail.com) or Peter Kamerman (peter.kamerman@gmail.com). We will send you a zip file with the data. Using the directory tree in the Files tab of RStudio, open the SPARS directory. Repeat the upload procedure described above, but upload the zipped data file we supplied you with into the SPARS directory.2. In the SPARS directory, double-click on the SPARS.Rproj file, and follow the prompts (RStudio will reload).3. Clean the /outputs and /figures directories by entering `make clean` in the Terminal tab in bottom right panel of RStudio.Run the SPARS analysis scriptsTo run all the scripts (including the data cleaning scripts), enter `make` in the Terminal tab.To run individual RMarkdown scripts (\*.Rmd files) 1. Generate the cleaned data using one of the following methods: - Enter `make data-cleaned/SPARS_A.rds` and then `make data-cleaned/SPARS_B.rds` in the Terminal tab; - Enter `source('0A-clean-data.R')` and then `source('0B-clean-data.R')` in the Console tab in bottom left panel of RStudio. - Open 0A-clean-data.R and 0B-clean-data.R scripts through the File tab, and then click the 'Source' button on top of the panel on the top left of RStudio for each script. 2. Run the individual script using one of the following methods: - Enter `make outputs/.html` in the Terminal tab; - Open the relevant \*.Rmd file through the File tab, and then click the 'knit' button on the top of the panel on the top left of RStudio. Shutting downOnce done, log out of RStudio and enter the following into a Terminal to stop the Docker container: `docker stop spars`. If you then want to remove the container, enter: `docker rm spars`. If you also want to remove the Docker image you downloaded, enter: `docker rmi kamermanpr/docker-spars:v1.1.2`