DMP: Do pandemics have a benefit on the environment? Analyzing the Relation between Covid-19 mobility Restrictions and CO2 emmisions

Data management plan (DMP) Do pandemics have a benefit on the environment? Analyzing the Relation between Covid-19 mobility Restrictions and CO2 emmisions COV_MOB Version Effective date Description of document/changes 1.0 30/11/2025 First version of the DMP – created for the start of the project Level of distribution This DMP is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). It is publicly available under 10.5281/zenodo.17672942 2 COV_MOB DMP version 1.0 Project details Project Coordinator Principal Investigator Tibor Cus, [email protected], ORCID iD: 0009000436694902, TU Wien, ROR: ror.org/04d836q62, Project Leader Contact person (responsible for data management and DMP) Tibor Cus, [email protected], ORCID: 0009000436694902, TU Wien, ROR: ror.org/04d836q62 Contributors Start date 2025-11-01 End date 2026-01-31 Funder Self funded Funding programme, grant number Internal project number 1 List of acronyms DMP data management plan RDM research data management COV_MOB DMP version 1.0 3 Content INHALTSVERZEICHNIS INTRODUCTION 4 Science Europe practical guide, FAIR data 4 Relevant Policies and Guidelines 4 1. DATA DESCRIPTION 5 1a Lists of datasets that will be reused or produced 5 1b Data generation and reuse 5 2. DOCUMENTATION AND DATA QUALITY 5 2a Data organisation, metadata and documentation 5 2b Data quality control 6 3. STORAGE AND BACKUP DURING RESEARCH PROCESS 6 3a Storage and backup facilities 6 3b Data security and protection of sensitive data 6 4. LEGAL AND ETHICAL REQUIREMENTS 7 4a Personal data 7 4b Intellectual property rights and ownership 7 4c Ethical issues 7 5. DATA SHARING AND LONG-TERM PRESERVATION 7 5a Data publication and access conditions 7 5b Long-term preservation and deletion of data 8 6. RDM RESPONSIBILITIES AND RESOURCES 9 6a RDM-roles and responsibilities 9 6b Resources 9 4 COV_MOB DMP version 1.0 Introduction Science Europe practical guide, FAIR data A DMP is a structured document that keeps record of what research data is created and what happens to that data during and after a project. It helps with planning the research process and defining responsibilities in a research project involving several researchers or institutions. For writing this DMP, we followed the recommendations of Science Europe as they reflect the guidelines agreed upon by the major funders in Europe. To make our data FAIR, they generally will be treated according to the following criteria: We will make our data findable, by uploading it to a data repository that provides a persistent identifier and adding relevant metadata. We will make our data accessible by providing open access to data, wherever possible. In cases, where open access is not possible, we will provide meaningful metadata plus contact information for access requests. We will make our data interoperable by providing and describing data in a way that is common within our domain by using the same file formats, schemas and vocabularies. We will provide good documentation for all our datasets. We will make our data reusable by adding metadata and comprehensive Readme files to all published datasets. The descriptions include details on the methodology used, analytical and procedural information. In case of publication, licenses for code and data will always be assigned and clearly marked. Relevant Policies and Guidelines European Commission’s document on Ethics and Data Protection: https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/ horizon/guidance/ethics-and-data-protection_he_en.pdf Other (e.g. from a project partner) COV_MOB DMP version 1.0 5 1.Data description 1a Lists of datasets that will be reused or produced Produced datasets dataset ID title type format estimated volume contains sensitive data P1 Joined Air Quality and Movement Dataset Other NetCDF 5 - 10 GB no P2 Regression Analysis Results Structured text csv 100 - 1000 MB no P3 Source Code Source code .py 100 - 1000 MB no Description for "Joined Air Quality and Movement Dataset": Final analytical dataset generated from the original movement dataset provided by google and The environmental data provided by the EU. Contains multidimensional scientific data such as temperature, C02 emiission levels, movement index and links them to european countries at a certain time between 1. 1. 2020 and 15. 10. 2022. Description for "Regression Analysis Results": Results of Multivariate Regression Models for each European country. The file contains information about the package/model used, the predictor, coeficients, standard errors, p values and sample size. There will be one csv file generated for each country and all of them will be stored in the same directory. In the same directory there will also be a readme.md file containing metadata describing the structure and naming of the files. All of the files will follow a consistent naming convention which is to be defined. The readmes will follow googles readme style guides. Description for "Source Code": Source code containing all of the data processing steps and the code for the Plotly Visualizations. Documentation is available in the form of a project Level ReadMe.md file. The repository strusture will follow the Cookie cutter data science convention. The repository will contain python 3.12 code, Readme.md files and package management metadata files produced by UV. All of the files will follow a consistent naming convention which is to be defined. The readme will follow googles style guide Technical resources for "Source Code": Cookie Cutter Data Science: https://github.com/drivendataorg/cookiecutter-datascience Googles Style guide: https://google.github.io/styleguide/docguide/READMEs.html Technical resources for "Joined Air Quality and Movement Dataset": NetCDF documentation: https://www.unidata.ucar.edu/software/netcdf 6 COV_MOB DMP version 1.0 Reused datasets dataset ID title source rights (e.g. license) contains sensitive data R1 COVID-19 Mobility Community Reports https://www.google.com/ covid19/mobility/ no R2 European Environment Agency Air Quality Dataset https://eeadmz1-downloadswebapp.azurewebsites.net/ no Description for "COVID-19 Mobility Community Reports": These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Provided by Google LLC under the CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/) license Description for "European Environment Agency Air Quality Dataset": Air quality measurements time series reported by European member countries. Limited to dates between 1. 1. 2020 and 15. 10. 2022 Provided by Pan European (EEA) License CC-BY 4.0 ( https://creativecommons.org/licenses/by/4.0/). Copyright holder: European Environment Agency (EEA). 1b Data generation and reuse Methods and software used for data generation and reuse Research data will be generated by downloading two existing external datasets, which will be reused in accordance with their licence terms. Based on the Original dataset sources, both of the datasets should be available indefinitly. The data will be filtered, cleaned, transformed and then aggregated and joined to produce the final analytical datasets. The project will follow a directory hierarchy separating raw, processed, and final datasets to ensure traceability and data provenance. All processing and analysis will be carried out using Python 3.12 with standard and freely available scientific libraries, including Pandas and NumPy. Full source code and the documentation detailing the steps to run it will be made available on Gitlab to ensure reproducibility. COV_MOB DMP version 1.0 7 2. Documentation and data quality 2a Data organisation, metadata and documentation Data versioning will be managed using DVC (Data Version Control), which will track all dataset states. The original (raw) data will always be stored unchanged. The project will follow a directory hierarchy separating raw, processed, and final datasets to ensure a clear workflow and traceability. Filenames will follow a structured and consistently applied naming convention, which will be fully defined during the project setup. This approach ensures that every transformation step, from initial downloads through filtering and aggregation to the final datasets used for analysis and visualisation, can be reproduced and verified. The final analytics dataset is provided in the NetCDF format, which is self describing meaning that it already contains the metadata needed for understanding the data. For the statistical analysis csv files there will be a ReadMe.md file provided with detailed descriptions located in same directory as the csv files. It will contain information of the column names, data types and the overall structure of the file. The decision to use the readme.md file was made because there don't seem to be any appropritate metadata standards for this field. . This will help others to identify, discover and reuse our data. Additionally, we will provide common metadata such as title, description or keywords when publishing data in open access repositories. In such a case, we will follow the default template provided by the repository, such as Data Cite Metadata or Dublin Core. A far as possible, we will use controlled vocabularies for our data to allow interdisciplinary interoperability and machine-actionability. The source code will be made available using a public gitlab repository hosted at TU Wien. There will be a project level pyproject.toml file containing all of the used package versions as well as a .python-version file containing the used python version. There will also be included a project level readme file containing the steps needed to run the code and reproduce the results, the authors of the repository, the relevant license and also other important contextual assumptions made during implementation. All of the readme files will follow googles readme style guide (https://google.github.io/styleguide/docguide/READMEs.html). 2b Data quality control The following data quality checks will be done: peer review of data. 3. Storage and backup during research process 3a Storage and backup facilities For the duration of the project, storage and backup of data will be ensured by Tibor Cus (acting as the person responsible for data management and DMP) in cooperation with the system operator. The data will be stored on the servers of TU Wien. P3 (Source Code) will be stored on TUgitLab: TUgitLab is an application for managing repositories based on Git provided and managed by Campus IT. Our institute’s administrators will manage GitLab groups, assign project permissions, and appoint external project partners as additional GitLab users. This service is highly available and scalable on the Kubernetes platform. R1 (COVID-19 Mobility Community Reports), R2 (European Environment Agency Air 8 COV_MOB DMP version 1.0 Quality Dataset), P1 (Joined Air Quality and Movement Dataset), P2 (Regression Analysis Results) will be stored on TUcloud-Spaces: TUcloud-Spaces is a sync&share service for projects provided by Campus IT. Only authorised staff members and project partners will have access to the TUcloud-Spaces folders. Deleted files can be recovered within 180 days by using the bin function. R1 (COVID-19 Mobility Community Reports), R2 (European Environment Agency Air Quality Dataset), P1 (Joined Air Quality and Movement Dataset), P2 (Regression Analysis Results) will be backed up on TuFiles daily. TUfiles is a central and readily available network drive with daily backups and regular snapshots provided by Campus IT 3b Data security and protection of sensitive data We pay strict attention to compliance with the relevant institutional and national data protection policies listed in the introduction of this document. At this stage, it is not foreseen to process any sensitive data in the project. If this changes, advice will be sought from the data protection specialist at TU Wien, and the DMP will be updated. Access to data during research: dataset ID selected project members all other project members the public P1 writing reading only reading only P2 writing reading only reading only P3 writing reading only reading only R1 writing reading only reading only R2 writing reading only reading only 4. Legal and ethical requirements 4a Personal data At this stage, it is not foreseen to process any personal data in the project. If this changes, advice will be sought from the data protection specialist at TU Wien, and the DMP will be updated. 4b Intellectual property rights and rights of use The person with the right to control access to data will be Tibor Cus, the Only Member of the Project, who is also the Project Lead 4c Ethical issues No particular ethical issue is foreseen with the data to be used or produced by the project. This section will be updated if issues arise. COV_MOB DMP version 1.0 9