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-#+TITLE: Scientific Data Repository : 1D conduction radiation coupled heat transfer in an absorbing medium : Monte Carlo functionnal modeling  
+#+TITLE: Scientific Data Repository : 1D conduction radiation coupled heat transfer in an absorbing medium : Monte Carlo functionnal modeling
 #+AUTHOR: Olivier Farges
 #+DATE: 
 
 * Introduction
 
-This repository serves as a centralized hub for scientific data
-related to the project Star-CoRad.
-The primary goal is to share and
-disseminate datasets that accompany research publications or
-standalone datasets that may be of interest to the scientific community.
+This repository serves as a centralized hub for scientific data related to the
+project Star-CoRad. The primary goal is to share and disseminate datasets that
+accompany research publications or standalone datasets that may be of interest
+to the scientific community.
 
 * Table of Contents
   - [Authors](#authors)
@@ -28,27 +27,27 @@ For a list of contributors, please refer to [AUTHORS.org](AUTHORS.org).
 
 * Publication
 
-The publication associated with this dataset is titled "Functional estimation of radiative
-source term with Monte Carlo method in coupled heat transfer
-problem", DOI: 10.1016/j.jqsrt.2024.108894.
+The publication associated with this dataset is titled "Functional estimation of
+radiative source term with Monte Carlo method in coupled heat transfer problem",
+DOI: 10.1016/j.jqsrt.2024.108894.
 
 * Computational Code
 
-The computational code in this repository is referred to as
-*Star-CoRad*, SWHID: swh:1:dir:9bc63cd78b653d0825c6b80de1bbb43d2d26af28. 
+The computational code in this repository is referred to as *Star-CoRad*, SWHID:
+swh:1:dir:9bc63cd78b653d0825c6b80de1bbb43d2d26af28.
 
 * Dataset Overview
 
-The data in this repository encompasses simulation results obtained
- with the Star-CoRad code. It includes but is not limited to raw
- simulation data and processed results.
+The data in this repository encompasses simulation results obtained with the
+ Star-CoRad code. It includes but is not limited to raw simulation data and
+ processed results.
 
 * Data Description
 
 Provide detailed information about the structure and content of the
-dataset. Include information such as data format, units, and any
-relevant metadata. If applicable, specify the data collection methods
-and instruments used. 
+dataset. Include information such as data format, units, and any relevant
+metadata. If applicable, specify the data collection methods and instruments
+used.
 
 Directory structure:
 
@@ -79,8 +78,8 @@ Directory structure:
 
 ** Prerequisites
 
-Before using this project, make sure you have the
-following software and dependencies installed: 
+Before using this project, make sure you have the following software and
+dependencies installed:
 
 1. **Gnuplot:**
    - Version 5.2.8 or later is recommended.
@@ -124,14 +123,15 @@ Install the required Python libraries using the following command:
 
 6. **Arara:**
    - Arara is a TeX automation tool often used with LaTeX.
-   - Installation instructions can be found on the [Arara GitHub repository](https://github.com/cereda/arara).
+   - Installation instructions can be found on the [Arara GitHub
+     repository](https://github.com/cereda/arara).
 
 *Note: It is recommended to set up a virtual environment for Python to
 manage dependencies more effectively.* 
 
-If you encounter any issues with the installation or have specific
-version requirements, refer to the official documentation for each
-tool for troubleshooting and detailed instructions. 
+If you encounter any issues with the installation or have specific version
+requirements, refer to the official documentation for each tool for
+troubleshooting and detailed instructions.
 
 
 ** Compiling the Project
@@ -141,9 +141,8 @@ To compile the project, use the following command:
 ~make scripts~
 ~make plots~
 
-Alternatively, an interactive graphic can be obtained in the form of a
-web application. This graph resembles more data than that used in the
-graphs. 
+Alternatively, an interactive graphic can be obtained in the form of a web
+application. This graph resembles more data than that used in the graphs.
 
 ~make scripts~
 ~make webapp~
@@ -744,11 +743,6 @@ These two options can be combined using the command :
 
    plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
 
-   #def update_title(attrname, old, new):
-   #    plot.title.text = text.value
-
-   #text.on_change('value', update_title)
-
    def update_data(attrname, old, new):
 
        # Get the current slider values
@@ -787,8 +781,9 @@ These two options can be combined using the command :
 
 * License
 
-The data in this repository is licensed under the [GNU General Public License (GPL)](COPYING).
-See the [COPYING](COPYING) file for he full text of the license.
+The data in this repository is licensed under the [GNU General Public License
+(GPL)](COPYING).  See the [COPYING](COPYING) file for he full text of the
+license.
 
 * Citation
 
@@ -797,13 +792,13 @@ publication or provide a citation format.
 
 * Acknowledgment
 
-The authors would like to thank the CNRS joint laboratory Canopée and Saint–Gobain Research Paris for initiating
-the project and funding the post-doctoral support, as well as
-the star-engine library for enabling the development of
-the coupled heat transfer algorithms thanks to the computational Monte Carlo
-Libraries.
-High Performance Computing resources were partially provided by the EXPLOR centre hosted
-by the University de Lorraine (Project :  2020EMPPX2210).
+The authors would like to thank the CNRS joint laboratory Canopée and
+Saint–Gobain Research Paris for initiating the project and funding the
+post-doctoral support, as well as the star-engine library for enabling the
+development of the coupled heat transfer algorithms thanks to the computational
+Monte Carlo Libraries.  High Performance Computing resources were partially
+provided by the EXPLOR centre hosted by the University de Lorraine (Project :
+2020EMPPX2210).
 
 * Contact