@@ -79,13 +79,14 @@ It also has the Data-Documentation file that provides a detailed physical explan
- README: explains how the notebook works, the files, and the results produced by running it.
- Jupyter Notebook:on the notebook you will find the code necessary to perform the analyzes and histrograms. The code cells are interspersed with the explanations of the commands in markdown cells.
- atlas-data: this folder is generated when the notebook is run, the analysis and results are stored in it.
- notebooks-info: it contains a .csv file that stores the information and description of the analysis that appears when the notebook is running.
#### Git-Zenodo-Assistant folder:
The true functionalities of the Git\&Zenodo Assistant Program come from its packages and subpackages.
- README: contains a detailed explanation of the tutotial.py program. In addition, contains the links and steps to create a GitHub and Zenodo account and to generate the tokens to access the options of the program, which are necessary to git push and upload a file.
The true functionalities of the Git\&Zenodo Assistant Program come from its packages and subpackages. This folder contains the **Git&Zenodo Assistant** program, named *tutorial.py*, and the *requirements.sh* file to install the python libraries needed to run the program. It also contains 4 directories, which correspond to the packages of the program (*git_assistant*, *zenodo_assistant*); a *metadata folder* which contains .csv files with the controlled vocabulary of Zenodo's metadata; a *test_tutorial* folder with a *test.txt* file, to be used for the testing the program, and a *images_tutorial* with screenshots of the GitHub and Zenodo's websites to help you in creating your token.
## Results
The notebook allows the user to have a clearer idea of what is happening as each cell is executed.User interactivity is no longer through direct modifications to the code but through inputs where the user is asked to enter any of the available options. By adding information on the physics and on the generated histograms, the user has the opportunity to access the entire analysis process without leaving the notebook, being able to even see the results.
The notebook allows the user to have a clearer idea of what is happening as each cell is executed.User interactivity is no longer through direct modifications to the code but through inputs where the user is asked to enter any of the available options. By adding information on the physics and on the generated histograms, the user has the opportunity to access the entire analysis process without leaving the notebook, being able to even see the results.
The tutorial.py program works correctly in the ATLAS Virtual Machine terminal.
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@@ -117,3 +118,6 @@ To help ensure the reproducibility of computational results, researchers should
Even though the achievement of the objectives was declared at the beginning, a list of suggestions is provided at the end as proposals to continue this work. Sharing science is a job that never ends.
## Refrence
[ATLAS Open Data](https://atlas-opendata.web.cern.ch/atlas-opendata/)
- National Academies of Sciences, E., Medicine: Reproducibility and Replicability inScience. The National Academies Press, Washington, DC (2019)
