DMP: Impact of Seasonal Temperature Variations on Data Center Energy Consumption

Data management plan (DMP) DMP: Impact of Seasonal Temperature Variations on Data Center Energy Consumption TEMP-DC Version Effective date Description of document/changes 1.0 29/11/2025 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 https://doi.org/10.5281/zenodo.17763397 2 TEMP-DC DMP version 1.0 Project details Project Coordinator Principal Investigator Contact person (responsible for data management and DMP) Briana Straut, [email protected] Contributors Briana Straut, [email protected] Start date 2025-11-27 End date 2026-01-31 Funder Funding programme, grant number Internal project number List of acronyms DMP data management plan RDM research data management … … … … … … … … … … … … TEMP-DC 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 6 2a Data organisation, metadata and documentation 6 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 7 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 TEMP-DC 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 § Other (e.g. from a project partner) TEMP-DC 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 Aggregated Temperature-Energy Data Structured text csv 100 - 1000 MB no P2 Analysis Code Source code py 100 - 1000 MB no P3 Chart Images png 100 - 1000 MB no Description for "Aggregated Temperature-Energy Data": Aggregated data linking temperature to energy usage. Description for "Analysis Code": Python script for data merging, calculation and plot generation Description for "Chart": Chart for visualizing data Reused datasets dataset ID title source rights (e.g. license) contains sensitive data R1 Data Centres Metered Electricity Consumption Central Statistics Office Ireland no R2 Dublin Airport Hourly Data Met Éireann no Description for "Data Centres Metered Electricity Consumption": Electricity usage for data centers per quarters Description for "Dublin Airport Hourly Data": Daily weather data 6 TEMP-DC DMP version 1.0 1b Data generation and reuse Methods and software used for data generation and reuse 2. Documentation and data quality 2a Data organisation, metadata and documentation - src/ for the python script - data/ (not synced) for input files - results/ for generated outputs - files will follow the convention YYYYMMDD-Title-Version.ext - the script is version-controlled using Git. We will use the Dublin Core metadata schema provided by the TU Wien repository to ensure FAIRness. - Keywords: Energy Efficiency, Data Centers, Climate Impact, Thermodynamics. - Administrative: License (CC-BY 4.0), Access Rights, and Persistent Identifiers (DOI). 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. A README.md file will accompany the repository. It will include: - Methodology: Explanation of the baseline calculation and aggregation logic. - Data Dictionary: Definitions of CSV columns (e.g., avg_temp_celsius, energy_kwh). - Data Origin: Citations and access links for the raw CSO and Met Éireann datasets. - Dependencies for the Python environment. 2b Data quality control The following data quality checks will be done: calibration 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 Briana Straut (acting as the person responsible for data management and DMP) in cooperation with the system operator. External and internal storage options will be used. P2 (Analysis Code), P1 (Aggregated Temperature-Energy Data), P3 (Chart) will be stored on Institutional Cloud: Institutional Cloud is your institutions cloud service. It runs on your IT department's servers and has every standard cloud functionality. TEMP-DC DMP version 1.0 7 P2 (Analysis Code), P1 (Aggregated Temperature-Energy Data), P3 (Chart), R1 (Data Centres Metered Electricity Consumption), R2 (Dublin Airport Hourly Data) will be stored on Laptop. 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 our institution, 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 P2 writing reading only no access P3 writing reading only no access R1 writing reading only no access R2 writing reading only no access 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 our institution, and the DMP will be updated. 4b Intellectual property rights and rights of use Information regarding rights and control of access to data has yet to be identified. 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. 8 TEMP-DC DMP version 1.0 dataset ID access conditions estimated publication date location for publication (repository) PID license P1 Open 2025-12-01 TU Wien Research Data https://doi.o rg/10.7012 4/cqy0abxx07 CC-BY-4.0 P2 Open 2025-12-01 TU Wien Research Data https://doi.o rg/10.7012 4/cqy0abxx07 MIT P3 Open 2025-12-01 TU Wien Research Data https://doi.o rg/10.7012 4/cqy0abxx07 CC-BY-4.0 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: Python along with its dependencies are needed to run the script, for visualisation of the results no specific tools are required? 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 Environment scientists, infrastructure engineers P2 TU Wien Research Data 10 years P3 TU Wien Research Data 10 years TEMP-DC DMP version 1.0 9 6. RDM responsibilities and resources 6a RDM-roles and responsibilities The Principal Investigator (Briana Straut) 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.