DMP: Influence of Demographic Characteristics In the Sport of Gymnastics

Data management plan (DMP) Influence of demographic characteristics in the sport of gymnastics Version Effective date Description of document/changes 1.0 24/11/2025 First version of the DMP – created for the start of the project 2.0 25/11/2025 Second version of the DMP – additional information 3.0 27/11/2025 Third version of the DMP – correction to fit FAIR principles 4.0 28/11/2025 Final version of the DMP – additional information about data 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.17753970. 2 DMP version 4.0 Project details Project Coordinator Principal Investigator Noemi Gazdik, ORCID iD: 0009-0004-8028-2892, Project Leader Contact person (responsible for data management and DMP) Noemi Gazdik, ORCID: 0009-0004-8028-2892 Contributors Start date 01/10/2025 End date 31/01/2026 Funder The project has no costs. Funding programme, grant number Internal project number List of acronyms DMP data management plan RDM research data management FAIR findable, accessible, interoperable, reusable CSV comma-separated values PID persistent identifier ISO international organization for standardization DMP version 4.0 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 7 3. STORAGE AND BACKUP DURING RESEARCH PROCESS 7 3a Storage and backup facilities 7 3b Data security and protection of sensitive data 7 4. LEGAL AND ETHICAL REQUIREMENTS 8 4a Personal data 8 4b Intellectual property rights and ownership 8 4c Ethical issues 8 5. DATA SHARING AND LONG-TERM PRESERVATION 8 5a Data publication and access conditions 8 5b Long-term preservation and deletion of data 9 6. RDM RESPONSIBILITIES AND RESOURCES 9 6a RDM-roles and responsibilities 9 6b Resources 9 4 DMP version 4.0 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) DMP version 4.0 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 Olympic Data Women's Gymnastics Cleaned Structured text CSV 100 - 1000 MB no P2 Gymnastics Scores and Demography Structured text CSV 100 - 1000 MB no Description for "Olympic Data Women's Gymnastics Cleaned": This dataset is created by filtering the original Athlete Events dataset to include only female artistic gymnastics medallists across all Olympic Games. During preprocessing, all non-gymnastics rows and all non-medallist athletes are removed. An additional derived variable, era, is added to classify each observation as belonging to the perfect 10 era (all Olympics before 2008) or the open-ended scoring era (2008 onwards). This dataset enables analysis of demographic and performance patterns across scoring-system eras. Description for "Gymnastics Scores and Demography": This dataset results from joining the Gymnastics Scores 2022–2023 dataset with demographic attributes (age, height, weight) from the Athlete Event - Olympics dataset. The join is performed on gymnast name and nationality. To ensure data quality and interpretability, gymnasts for whom no demographic information is available are excluded from the final output. The produced dataset therefore contains fully enriched records linking athlete characteristics with international competition scores. Reused datasets dataset ID title source rights (e.g. license) contains sensitive data R1 Athlete Event - Olympics https://www.kaggle.com/code/jl ove5/olympic-data-women-sgymnastics/notebook Apache 2.0 open source license no R2 Gymnastics Scores 2022-2023 https://github.com/ucsas/gym2 024data/blob/main/cleandata/d ata_2022_2023.csv no information available no Description for "Athlete Event - Olympics": This dataset provides historical records of Olympic athletes and their participations in specific events. Each row represents an athlete’s appearance in a particular Olympic Games and event. The dataset includes demographic and physical attributes (e.g. Age, Height, Weight), team and country information (Team, NOC), as well as event metadata (e.g. Year, Season, Sport, Event) and medal outcome (Medal). 6 DMP version 4.0 Description for "Gymnastics Scores 2022-2023": This dataset contains international artistic gymnastics competition results from the 2022–2023 season. Each row corresponds to a single athlete–apparatus performance in a given round of a competition. The main variables include athlete information (FirstName, LastName, Gender, Country), event metadata (Date, Competition, Round, Location, Apparatus) and performance outcomes (Rank, D_Score, E_Score, Penalty, Score). In total, the dataset has 24,434 rows and 14 columns, covering multiple World Cup events and both qualification and final rounds. 1b Data generation and reuse Methods and software used for data generation and reuse Both reused datasets (R1 and R2) will be processed, cleaned, and integrated within a Jupyter Notebook environment using Python (version 3.11 or newer). The workflow includes: • loading both external datasets from the project’s rawdata/ directory; • filtering, harmonising naming conventions, and removing irrelevant entries; • joining datasets using precise matching rules (name + nationality); • deriving new fields (e.g., era, average apparatus scores); • validating demographic completeness; • exploring the two produced dataset and their characteristics; • exporting the two produced datasets (P1 and P2) into the cleandata/ directory as CSV files. All preprocessing steps are implemented as executable and documented Python code. Running the provided Jupyter Notebook from start to finish will automatically regenerate both produced datasets in a deterministic and reproducible manner. Intermediate states are not saved, ensuring clarity and full traceability of how the final datasets were produced. The project uses the uv package manager for environment reproducibility. After installing uv, running uv sync automatically recreates the exact Python environment required to reproduce the datasets. 2. Documentation and data quality 2a Data organisation, metadata and documentation The reusable data is going to be in a rawdata folder in the project folder. The output of the Notebook that produces the clean data (2 .csv files) are going to be placed in the cleandata folder also in the main project folder. This project folder is going to be a part of a public GitHub repository that handles the versioning throughout the project and can be looked at any time in the future. The GitHub repository can be found under https://github.com/noi121/Influence-of-demography-in-gymnastics.git. Each dataset (produced) will be accompanied by a file-level README describing: • variable names and units, • data provenance and retrieval date, • licensing and citation requirements, • preprocessing applied prior to publication. A project-level README will document: • the full workflow, • software dependencies (Python version, libraries), • instructions on how to reproduce the final outputs, • references to metadata standards followed. As there are no domain specific metadata standards applicable, we will provide a README file with an explanation of all values and terms used both at file level for both reusable datasets and at project level. This will help others to identify, discover and reuse our data. DataCite and Dublin Core elements will be used when creating the metadata (e.g. title, description, keywords, contributors, temporal coverage). DMP version 4.0 7 Controlled vocabularies (e.g., ISO country codes, canonical apparatus names) are used wherever applicable to support interoperability. 2b Data quality control The following data quality checks will be done: repeated samples or measurements and data entry validation. To ensure high-quality data, we are going to take the following precautions. Duplicate athlete entries from the reused datasets are going to be removed. Name harmonisation procedures reduce mismatches during joining (e.g., trimming whitespace, consistent casing). Country names will be standardized using ISO 3166-1 alpha-3 codes. Apparatus labels (e.g., Vault, Floor, Beam, Uneven Bars) are going to be normalised to be understandable and readable. For the P2 dataset, incomplete rows (missing age, height, or weight) will be excluded to ensure reliable demographic analyses. After preprocessing, summary statistics (counts, value ranges, missingness reports) are going to be generated automatically in the notebook to allow manual inspection. All transformations will be documented in the code, ensuring transparency and full traceability. 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 Noemi Gazdik (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 (Olympic Data Women's Gymnastics Cleaned), P2 (Gymnastics Scores and Demography) 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 R2 writing reading only reading only All incidents will be handled individually by the project leader who is maintaining all services. 8 DMP version 4.0 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 right to control acces over all datasets has only the 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. 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 2025-11-30 TU Wien Research Data (Test) DOI: 10.70124/s s6ae53p30 CC-BY-4.0 P2 Open 2025-11-30 TU Wien Research Data (Test) DOI: 10.70124/s s6ae53p30 CC-BY-4.0 The experiment’s produced datasets, source code, documentation, and related outputs will be published in the Test TU Wien Research Data Repository. This repository assigns a persistent identifier (DOI) for each deposition and supports open access sharing in a FAIR-compliant manner. All files will be openly accessible under appropriate licences (CC BY 4.0 for data, MIT license 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/ DMP version 4.0 9 For the purpose of this exercise, the produced datasets are going to be published on the Test site of the TU Wien Research Data, under the following URL: https://test.researchdata.tuwien.at/ Methods or software needed to access and use data: There are no specific softwares or technologies required to use the data, all formats are cvs that can be opened in a simple Excel but also by using almost any programming languages and tools. 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 (Test) 10 years Anyone interested in the sport of Women's Artistic Gymnastics. P2 TU Wien Research Data (Test) 10 years 6. RDM responsibilities and resources 6a RDM-roles and responsibilities The Project Leader 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.