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analysis-dlr-se-waws

This repository is used to analyze the workshops of the DLR internal workshop series on software engineering.

https://github.com/dlr-sc/analysis-dlr-se-waws

Science Score: 75.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ✓
    Institutional organization owner
    Organization dlr-sc has institutional domain (www.dlr.de)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.8%) to scientific vocabulary

Keywords

analysis community jupyter-notebook research-software-engineering
Last synced: 6 months ago · JSON representation ·

Repository

This repository is used to analyze the workshops of the DLR internal workshop series on software engineering.

Basic Info
  • Host: GitHub
  • Owner: DLR-SC
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 358 KB
Statistics
  • Stars: 6
  • Watchers: 4
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Topics
analysis community jupyter-notebook research-software-engineering
Created over 7 years ago · Last pushed over 3 years ago
Metadata Files
Readme Changelog License Citation

README.md

DOI

Analysis of the DLR Knowledge Exchange Workshop Series on Software Engineering

This repository is used to analyze the workshops of the DLR internal workshop series on software engineering. These workshops are two-day events of the DLR software engineering community and focus on different main topics every year. The Juypter notebook gives an overview about the basic workshop data and insights into the participant attendance behavior.

The analysis has been performed on Windows 7 using Python 3 and Jupyter notebook. The analysis code makes use of the libraries pandas (BSD 3-Clause License), matplotlib (Matplotlib License) and seaborn (BSD 3-Clause License).

Using this Jupyter notebook

Preparation

We used Python 3.7.3 on Windows 7. The dependencies are captured in the requirements.txt file which we installed in a Python virtual environment as follows: - python -m venv env - env\Scripts\activate.bat - pip install -r requirements.txt

Currently, there is a bug when activating a virtual environment on Windows operating systems using German localization. Please adapt your env\Scripts\activate.bat as suggested in the bug report until the problem is fixed.

Run the Jupyter notebook

  • Start Jupyter: jupyter notebook
  • Your browser should start and you can select the analysis notebook.

Citation

If you use this work, please cite the specific version using its DOI and the metadata provided in the CITATION file. For more information on the format, please see Citation File Format (CFF).

License

The whole material including data sets and analysis code is licensed under the terms of the MIT License.

Owner

  • Name: DLR Institute for Software Technology
  • Login: DLR-SC
  • Kind: organization
  • Email: opensource@dlr.de
  • Location: Cologne, Berlin, Braunschweig, Oberpfaffenhofen, Bremen

German Aerospace Center (DLR)

Citation (CITATION.cff) 

cff-version: 1.0.3
message: If you use this work, please cite it using the following metadata.
authors:
  - family-names: Schlauch
    given-names: Tobias
    orcid: https://orcid.org/0000-0001-8760-8913
  - family-names: Haupt
    given-names: Carina
    orcid: https://orcid.org/0000-0001-6447-1379
title: Analysis of the DLR Knowledge Exchange Workshop Series on Software Engineering
version: 1.2.0
doi: 10.5281/zenodo.3403991
date-released: 2019-09-10
repository-code: https://github.com/DLR-SC/analysis-dlr-se-waws

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Last synced: 10 months ago

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  • Total issues: 0
  • Total pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: 10 months
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  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.67
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 3
Past Year
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  • Pull requests: 0
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  • Average time to close pull requests: N/A
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  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
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  • Bot issues: 0
  • Bot pull requests: 0
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dependencies (3)

Dependencies

requirements.txt pypi
  • matplotlib ==2.2.4
  • notebook ==5.7.8
  • pandas ==0.24.2
  • seaborn ==0.9.0