DMP: Communication on meat consumption

Data management plan (DMP) Meat Com Study MCS 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 10.5281/zenodo.17769419 2 MCS DMP version 1.0 Project details Project Coordinator Principal Investigator Clovis Gay, [email protected], ORCID iD: 0009-00046397-9671, TU Wien, ROR: ror.org/04d836q62, Project Leader Contact person (responsible for data management and DMP) Clovis Gay, [email protected], ORCID: 0009-00046397-9671, TU Wien, ROR: ror.org/04d836q62 Contributors Start date 2025-11-01 End date 2026-01-31 Funder Funding programme, grant number Internal project number List of acronyms DMP data management plan RDM research data management … … … … … … … … … … … … MCS 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 6 2. DOCUMENTATION AND DATA QUALITY 7 2a Data organisation, metadata and documentation 7 2b Data quality control 7 3. STORAGE AND BACKUP DURING RESEARCH PROCESS 8 3a Storage and backup facilities 8 3b Data security and protection of sensitive data 8 4. LEGAL AND ETHICAL REQUIREMENTS 8 4a Personal data 8 4b Intellectual property rights and rights of use 8 4c Ethical issues 8 5. DATA SHARING AND LONG-TERM PRESERVATION 9 5a Data publication and access conditions 9 5b Long-term preservation and deletion of data 10 6. RDM RESPONSIBILITIES AND RESOURCES 10 6a RDM-roles and responsibilities 10 6b Resources 10 4 MCS 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) MCS 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 Survey Structured text, Images, Plain text, Source code .csv .py .png .txt 1 - 5 GB no P2 Social Media Standard office documents, Networkbase d data .txt 1 - 5 GB no Description for "Survey": The dataset will consist of raw survey data in CSV format, a Python script used for data processing and statistical calculations, and the graphical outputs generated from the analysis. These files collectively form a coherent dataset enabling full reproducibility of the study A CSV file (.csv) will be generated as part of the survey on meat consumption within the population. It will contain the raw responses from approximately 1,000 participants. Each column will correspond to a specific survey variable (e.g., gender, age group, consumption frequency, etc.). The file will consist exclusively of unprocessed data, prior to any cleaning, transformation, or analysis. A Python script (.py) will be generated to extract the necessary information for analyzing participants’ responses, including calculations such as consumption percentages by age group and other relevant indicators. A README file (.txt) will also be produced, showing the python version used, documenting the code structure and explaining the purpose of each section to ensure transparency and facilitate reproducibility. It will also include the GDPR regulations applicable to the collection of questionnaire data. Finally, a processed-data CSV file (.csv) will be created to compile the results derived from the script. This file will serve as the basis for generating visual outputs. The corresponding graphics will be included in the dataset in PNG format, illustrating the key findings of the analysis. Description for "Social Media": This dataset compiles accessible social media posts and online publications related to meat consumption. It includes content from various platforms (e.g., Instagram, X/Twitter, Facebook, YouTube) as well as relevant online statements made by public figures, organisations, and institutions. Each data contains metadata such as the URL, publication date, account name, textual content, and a brief categorisation of the communication strategy. An accompanying README file provides detailed documentation of the data collection methods, including the search strategy, selection criteria, and procedures followed to obtain consent when required from the original content creators. It also outlines how the dataset is structured and the considerations taken to ensure respectful and compliant data reuse. In the event that an original publication becomes unavailable, the README will be updated to include, when possible, the reason for its removal along with a description of the content and its relevance to the research. 6 MCS DMP version 1.0 Reused datasets dataset ID title source rights (e.g. license) contains sensitive data R1 Meat Consumption and Sustainability https://doi.org/10.1146/annure v-resource-111820-032340 no Description for "Meat Consumption and Sustainability": This article “Meat Consumption and Sustainability" is published in the Annual Review of Resource Economics. It compiles the key figures, indicators, and conceptual elements presented in the article, which examines global meat consumption patterns and their environmental, social, and health implications. The resource synthesizes evidence on the environmental footprint of meat production, the role of technological innovations, and the need for dietary shifts in high-income countries. The content is available under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing reuse as long as proper attribution is provided. 1b Data generation and reuse Methods and software used for data generation and reuse GENERATED AND REUSED RESEARCH DATA A new dataset will be generated through a survey designed to assess public attitudes, consumption habits, and perceptions regarding meat consumption. The raw responses will be collected in a CSV file. A Python script will be developed to process the data and prepare the data for further analysis. A derived CSV file containing cleaned and processed data will be generated for producing visual outputs (graphics). Outputs used for analysis (graphics) will be exported as PNG files and included in the dataset. A dedicated dataset will be created to document public statements and communication strategies used by influential figures, organisations, and institutions on social media regarding meat consumption. This dataset will primarily include URLs, textual content, publication metadata (e.g., date, account name), and a structured description of the messaging approach (e.g., emotional appeal, scientific argumentation). Data will only be collected from publicly accessible posts and in accordance with platform policies or with the personnal consent of the publisher. Then existing open-access dataset obtained online will be reused to provide contextual information on meat consumption and its environmental, social, or economic implications. This dataset has been selected for its relevance to the project and for its open licence allowing reuse within the scope of the study. METHODS ANS SOFTWARE USED The survey will be administered using an online questionnaire tool (Google Forms), which will export responses in CSV format. Excel will be used to create graphics relevant for analysis. Excel that contain these graphics will be exported in CSV format and each graphic in PNG. Python (with libraries pandas and numpy) will be used for data cleaning, transformation, statistical analysis. Scripts will be documented in a text file to facilitate reproducibility. Social media data will be collected manually or via API-compliant methods where possible, strictly following the terms of service of each platform and according to the consent of the publisher Each generated dataset will include a README file describing the structure, variables, and methods of generation to ensure reusability and transparency. MCS DMP version 1.0 7 2. Documentation and data quality 2a Data organisation, metadata and documentation The data will be organised using a clear and consistent folder structure that separates raw data, processed data, scripts, outputs, and documentation (README). Each dataset (survey data, social media data, reused external data) will be stored in dedicated subfolders such as: data_raw – original, unmodified data (survey CSV, external datasets, initial social media extractions) data_processed – cleaned and transformed files used for analysis data_scripts – Python scripts used for processing and visualisation data_outputs – generated figures, tables, and diagrams (PNG) data_documentation – README files and metadata following Science Europe recommendations Descriptive and consistent naming conventions will be applied to all files. In addition to the datasets, an XML file will be produced as the final report of the project. It will link meat consumption data with the different types of communication strategies and their respective roles and impacts on the population. This XML report will reference the datasets used throughout the project to support the analyses and will be connected to them through metadata, including data-mapping elements that explain precisely how each piece of information can be traced back to the corresponding dataset. Finally, a PDF generated from the XML file will be created to provide readers with a clear, visual, and easily accessible overview of the project’s results without requiring any additional actions. These two files will be accompanied by a “global” README file. This README will include the project name, a complete list of all files (including each dataset) with a brief description, the software used throughout the project, the project contact information, as well as the licences associated with each dataset or deposited file. The TU Wien repository will be linked to the team’s GitHub environment to ensure that the project follows the established naming conventions and that versioning is carried out properly. This integration will provide a coherent working framework for the entire team. The README files will contain all necessary metadata in line with Science Europe’s recommendations, ensuring that the datasets are findable, accessible, interoperable, and reusable. These files will include, among others, the following types of metadata: Descriptive metadata, such as the author, creation date, and a summary explaining the purpose and relevance of the dataset within the research. Technical metadata, detailing the software, versions, and tools used, as well as any information required to reproduce or reuse the processing steps. Provenance metadata, specifying the origin of each data element (externally sourced or internally generated), the associated licences, and the applicable conditions of use. The README files will be updated as needed throughout the duration of the research and publication process to ensure accurate and up-to-date documentation. 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. To ensure the project meets FAIR principles, the README file will include all information necessary to validate the provenance of the data, such as the DOIs of external datasets and detailed explanations of the internally created datasets, including the raw data they contain. In addition, the documentation will provide all elements required to enable reuse under the same conditions, including the software used, their versions, the processing scripts, and any other resources needed to reproduce the analytical workflow. 2b Data quality control Data quality checks will be done, e.g. checks of consistency of labels, logical errors in the data, data curation, and version control. 8 MCS DMP version 1.0 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 Clovis Gay (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 (Survey), R1 (Meat Consumption and Sustainability), P2 (Social Media) 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. P1 (Survey), R1 (Meat Consumption and Sustainability), P2 (Social Media) will be stored on TUcloud: TUcloud is a sync&share service provided by Campus IT for TU Wien members. It runs on Campus IT servers and offers features known from public cloud systems, such as Dropbox, for example, the exchange of data with authorised persons. Deleted files can be recovered within 180 days. 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 R1 writing reading only reading only 4. Legal and ethical requirements 4a Personal data In this project, we will process personal data (see section 1a) P1 (Survey) and P2 (Social Media) will contain personal data. We will ensure compliance with data protection laws, by gaining informed consent for processing personal data and by anonymisation of personal data for preservation and/or sharing. 4b Intellectual property rights and rights of use The following individual(s) hold rights and control access to the project data: Project leader 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. MCS DMP version 1.0 9 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 2026-01-01 TU Wien Research Data DOI CC-BY-4.0 P2 Open 2026-01-01 TU Wien Research Data DOI 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: No specialised software or hardware is required to access or reuse the data. All processes can be performed using the tools and software listed in the README files, which are widely available and compatible with standard computer systems.