Mattermost is an open-source communication platform similar to slack that is widely used at CERN. The CERN Anonymized Mattermost Dataset includes Mattermost data from January 2018 to November 2021 with 20794 CERN users, 2367 Mattermost teams, 12773 Mattermost channels, 151 CERN buildings, and 163 CERN organizational units. The data set states the relationship between Mattermost teams, Mattermost channels, and CERN users, and holds various information such as channel creation, channel deletion times, user channel joining and leave times, and user-specific information such as building and organizational units. To hide identifiable information (e.g. Team Name, User Name, Channel Name, etc.), the dataset was anonymized. The anonymization was done by omitting some attributes, hashing string values, and removing connections between users/teams/channels.
Dataset License: CC BY-NC Creative Commons Attribution Non-Commercial Licence
Dataset Link: CERN Anonymized Mattermost Data | Zenodo
@dataset{jakovljevic_igor_2022_6319684,
author = {Jakovljevic, Igor and
Wagner, Andreas and
Gütl, Christian and
Pobaschnig, Martin and
Mönnich, Adrian},
title = {CERN Anonymized Mattermost Data},
month = mar,
year = 2022,
publisher = {Zenodo},
version = 1,
doi = {10.5281/zenodo.6319684},
url = {https://doi.org/10.5281/zenodo.6319684}
}
Retrieve Mattermost Data (mmdata.json) from Zenodo.
Install the cdhf package by
$ pip install cdhfInclude the cdhf package:
from cdhf.data import DataCreate the Data object to and load the data set:
data = Data("path/to/mmdata.json/file")
data.load_all()You can find examples on how to work with the data at the cdhf-examples repository.
API documentation is available at https://mpobaschnig.github.io/cdhf/.
If you happen to mention or use this project as part of one of your scientific works, please cite the following paper:
- Jakovljevic, I., Pobaschnig, M., Gütl, C. and Wagner, A., 2022. Privacy Aware Identification of User Clusters in Large Organisations based on Anonymized Mattermost User and Channel Information. In: DATA ANALYTICS 2021, The Tenth International Conference on Data Analytics.
@inproceedings{DataAnalytics2022,
author = { Jakovljevic, I., Pobaschnig, M., Gütl, C. AND Wagner, A. },
year = { 2022 },
month = { 11 },
title = { Privacy Aware Identification of User Clusters in Large Organisations based on Anonymized Mattermost User and Channel Information }
}
- Jakovljevic, I., Gütl, C., Wagner, A. and Nussbaumer, A. Compiling Open Datasets in Context of Large Organizations while Protecting User Privacy and Guaranteeing Plausible Deniability. In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022)
@article{Data22,
author={Igor Jakovljevic. and Christian Gütl. and Andreas Wagner. and Alexander Nussbaumer.},
title={Compiling Open Datasets in Context of Large Organizations while Protecting User Privacy and Guaranteeing Plausible Deniability},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA,},
year={2022},
pages={301-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011265700003269},
isbn={978-989-758-583-8},
issn={2184-285X},
}
Contributors from the following institutions were involved in the development of this project:
In case you would like to visually explore the CERN Mattermost dataset without any programming you can use Collaboration Spotting X.
It is a web-based visual network analytics application which includes various convenient features which enable exploration of network datasets on the fly.
To get started with exploring the CERN Mattermost dataset read the instructions of CSX.
We would like to express our gratitude to CERN, for allowing us to publish the dataset as open data and use it for research purposes.