DMP: NBA MVP Likelihood Investigator

Data management plan (DMP) NBA MVP Likelihood Investigator Version Effective date Description of document/changes 1.0 24/11/2025 First version of the DMP – created for the start of the project 1.5 25/11/2025 Updated DMP, additional information added 2.0 28/11/2025 Experiment data and repository is uploaded 3.0 29/11/2025 Finalised DMP after FAIR self-assesment plan 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.17714832 2 DMP version 1.0 Project details Project Coordinator Principal Investigator Peter Reti, [email protected]uwien.ac.at, TU Wien, ROR: ror.org/04d836q62, Project Leader Contact person (responsible for data management and DMP) Peter Reti, [email protected]uwien.ac.at, TU Wien, ROR: ror.org/04d836q62 Contributors Peter Reti, [email protected], TU Wien, ROR: ror.org/04d836q62 Start date 2025-10-30 End date 2026-01-30 Funder TU Wien Funding programme, grant number Funded with motivation from the RDM Course Internal project number 12432931 List of acronyms DMP data management plan RDM research data management CSV Comma Separated Values FAIR Findable Accessible Interoperable Reusable … … … … … … … … 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 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 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 § Other (e.g. from a project partner) 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 Historical NBA Data and Player Box Scores Cleaned Structured text .csv < 500 MB no Description for "Historical NBA Data and Player Box Scores Cleaned": The first dataset is the cleaned version of the Historical NBA Data and Player Box Scores dataset from Kaggle. Reused datasets dataset ID title source rights (e.g. license) contains sensitive data R1 Historical NBA Data and Player Box Scores https://www.kaggle.com/datas ets/eoinamoore/historical-nbadata-and-player-boxscores/data MIT License no Description for "Historical NBA Data and Player Box Scores" (R1): This dataset contains player and team box scores and statistics for NBA games from 1947 up to and including the 2025–2026 season. It is updated regularly and provides a comprehensive basis for exploring player performance and team dynamics. In this project, only the parts relevant to the 2025–2026 regular season are used. No additional external dataset (R2) is used in the current project version. Note – As mentioned in the project description initially a second dataset was planned for reuse. R2 would have been a dataset accumulated from the NBA Statistics platform to update the initial dataset R1, however in the meantime the authors of R1 decided to update this dataset for the current season, therefore this R2 dataset is currently not needed. 1b Data generation and reuse Methods and software used for data generation and reuse The research data are processed using Python and standard data-science libraries (pandas, numpy, matplotlib, scikit-learn). The main workflows are implemented in Jupyter notebooks and Python scripts contained in the GitHub repository and in the ZIP archive uploaded to TU Wien Research Data. Reused data from R1 (Historical NBA Data and Player Box Scores) are downloaded from Kaggle and stored locally under data/raw/. The cleaned dataset P1 is generated by a documented preprocessing pipeline (src/groundwork/preprocessing.ipynb), which selects relevant columns and exports the result as a CSV file under data/processed/. 6 DMP version 1.0 All steps are scripted and reproducible; no manual row-by-row editing is performed. 2. Documentation and data quality 2a Data organisation, metadata and documentation The whole project (code, documentation and exemplary processed dataset) is kept in a public GitHub repository. Versioning of scripts, notebooks and small configuration files is handled via Git. Within the project, raw and processed data are clearly separated in different folders (e.g. data/raw/ and data/processed/) and each level is accompanied by explanatory documentation. As there are no domain specific metadata standards applicable, we will provide documentation at three levels: § a project-level README describing the overall goals, folder structure, software requirements and workflow; § a dataset-level description for P1 (Cleaned NBA Player Game Statistics 2025–2026), including variable definitions, provenance and preprocessing steps; § a short README in data/raw/ explaining where the original external datasets (R1) can be downloaded and under which conditions they may be used. This documentation helps others to identify, understand and reuse the data and code. P1 is uploaded together with the rest of the software and folders to the institutional research data platform, and a descriptive record (title, description, keywords, licence information) has been provided there. Where appropriate, we use consistent naming conventions and, as far as possible, controlled vocabularies (e.g. standard team names, season identifiers) to support interoperability and machineactionability. A structured README-based documentation approach is used throughout the project to facilitate future data reuse. 2b Data quality control The following data quality checks will be done: repeated samples or measurements and data entry validation. Verification that key identifier are present and not empty. Basic range checks for core statistics. Reproducibility checks to make sure the notebooks present can be run and the output can be obtained automatically. 3. Storage and backup during research process 3a Storage and backup facilities For the duration of the project, storage and backup of data are ensured by Peter Reti (responsible for data management and the DMP). Working copies of code and small datasets are stored in a version-controlled Git repository (GitHub), which is mirrored locally. The cleaned dataset P1 and the accompanying project files are archived in TU Wien Research Data as a ZIP package for publication and long-term preservation. Raw data from R1 are obtained directly from the external source (Kaggle) and are not redistributed; they can be redownloaded as needed by following the instructions in data/raw/README.md. 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 DMP version 1.0 7 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 R1 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 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 control access over all datasets in this project will be decided solely by 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 DOI:10.701 24/vmf7gnv342 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. 8 DMP version 1.0 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: There are no specific softwares or technologies required as the final data will be available in a csv format. This Data Management Plan File is stored in Zenodo, separately from the dataset and source code. 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 Sport enthusiasts, especially people who are interested in basketball. NBA analysts and scouts along with aspiring data scientists as the data is great for further research. 6. RDM responsibilities and resources 6a RDM-roles and responsibilities The Project Leader, Peter Reti will direct the data management process overall and is 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.