DMP: Relationship Between Temperature and Energy Consumption in Europe

Data management plan (DMP) Relationship Between Temperature and Energy Consumption in Europe Version Effective date Description of document/changes 1.0 28/11/2025 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.17710565. 2 DMP version 1.0 Project details Contact person (responsible for data management and DMP) Martin Folzberger, [email protected], ORCID: 00090006-9196-4412 Start date 30/11/2025 End date 31/01/2025 List of acronyms DMP data management plan RDM research data management DOI digital object identifier 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 rights of use 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 8 6a RDM-roles and responsibilities 8 6b Resources 8 4 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/20212027/horizon/guidance/ethics-and-data-protection_he_en.pdf 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 Research Data Structured text, Images, Source code CSV, text/python, image/png 100 - 1000 MB no Description for "Research Data": Contains spreadsheets with the raw data and processed data in csv format, the source code used for data processing and visualizing as plaintext python files, and pictures of produced graphs in PNG format. Reused datasets dataset ID title source rights (e.g. license) contains sensitive data R1 Energy Efficiency (nrg_ind_eff) https://doi.org/10.2908/NRG_I ND_EFF CC BY 4.0 no R2 ERA5-Land monthly averaged data from 1950 to present https://doi.org/10.24381/cds.68 d2bb30 CC BY 4.0 no 1b Data generation and reuse Methods and software used for data generation and reuse The existing datasets will be preprocessed to ensure compatibility with subsequent analyses. Data handling will be done using Python and commonly used scientific libraries (such as pandas, NumPy, and SciPy). Established statistical methods will be used to analyze the data. The results will be provided in open machine readable format, primarily CSV. Key results will additionally be visualized using appropriate graphs using common libraries (such as Matplotlib and Plotly). 2. Documentation and data quality 2a Data organisation, metadata and documentation The filenames will follow the projects naming convention as defined in File_Naming_Conventions.pdf. Data files include a timestamp of creation. Version control is automated using Git. 6 DMP version 1.0 As there are no domain specific metadata standards applicable, we will provide a README file with an explanation of all values and terms used at project level. 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 inter-disciplinary interoperability and machine-actionability. Documentation will be provided in the README file and will make the workflow transparent and reproducible. This includes a description of all preprocessing steps, analytical methods, and statistical techniques applied during the project. Python scripts used for data cleaning, transformation, and analysis will be made available, along with information on software versions and library dependencies. These materials will enable others to validate the results, replicate the workflow, or adapt it for their own use. 2b Data quality control The following data quality checks will be done: peer review of data and data entry validation. 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 Martin Folzberger (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. P1 (Research Data) 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. 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 no access R1 reading only reading only reading only R2 reading only reading only reading only All incidents will be handled individually by an incident response team that is maintaining the affected service. DMP version 1.0 7 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 following individual(s) hold rights and control access to the project data: The rights to control access to all datasets will lie with the Data Manager, who was identified earlier in the document. 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. 5. Data sharing and long-term preservation 5a Data publication and access conditions As far as possible, obtained datasets will be published in repositories. Details on access conditions, reuse licenses, reasons for restrictions, etc. are collected in the table below. dataset ID access conditions estimated publication date location for publication (repository) PID license P1 Open 2026-02-28 TU Wien Research Data DOI 10.70124/cd0p3gv967 CC-BY-4.0 for data and images MIT for code Repository description: TU Wien Research Data is an institutional repository of TU Wien to enable storing, sharing and publishing of digital objects, in particular research data. It facilitates the funders' requirements for open access to research data and the FAIR principles by making research output findable, accessible, interoperable, and reusable. A DOI is assigned to each dataset published in TU Wien Research Data. This service is developed by the TU Wien Center for Research Data Management and hosted by TU.it. https://researchdata.tuwien.at/ Methods or software needed to access and use data: The processed and published data will be made available in open, non-proprietary formats such as CSV to allow user to open and analyze the files with common open tools. No proprietary or domain-specific software will be required, however, users wishing to replicate the full analysis pipeline may need require some standard scientific Python libraries. All required dependencies and code used during the project will be documented to support reproducibility. 8 DMP version 1.0 5b Long-term preservation and deletion of data dataset ID location for long-term storage minimum retention period (≥ 10 years) foreseeable research uses and/or users P1 TU Wien Research Data 10 years The primary audience are members of the scientific community, specifically researchers in the fields of energy systems, environmental sciences, sustainability studies. The data may also be of interest to policymakers, urban planners, and utility companies, as it can support evidence-based decision-making related to energy efficiency measures and demand forecasting. 6. RDM responsibilities and resources 6a RDM-roles and responsibilities The data manager will direct the data management process overall, with the research assistants responsible for ensuring metadata production, day-to-day cross-checks, back-up and other quality control activities are maintained. 6b Resources There are no costs dedicated to data management and ensuring that data will be FAIR.