DMP: Drones See Signs

DMP version 1.0 Drones See Signs Data Management Plan (DMP) Lead partner: Version: 1.0 Status: Dissemination level: PU: Public Document link: Project code of the funding body Internal project ID DMP version 1.0 2 Deliverable abstract This deliverable is the initial Data Management Plan (DMP) for the Drones See Signs project, delivered in M4. It will be kept updated as a living document. The DMP addresses the relevant aspects of the management of data and other outputs produced by the project according to the principles outlined in the FAIR Data section. DMP version 1.0 3 COPYRIGHT NOTICE This work by parties of the Drones See Signs project is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). The Drones See Signs project is funded by the European Union Horizon Europe programme under Grant Agreement No . DELIVERY SLIP Name Partner/Activity Date From: Moderated by: Reviewed by: Approved by: DOCUMENT LOG Issue Date Comment Author 1.0 30.11.2025 Initial DMP version Chung-Hsuan Ryan Lee TERMINOLOGY Acronym Definition DMP Data Management Plan CSV Comma Separated Values FAIR FAIR-Principles: Findable, Accessible, Interoperable, and Reusable MB Megabyte PDF Portable Document Format WP Work Package ADR Agreement concerning the International Carriage of Dangerous Goods by Road DMP version 1.0 4 ROS Robot Operating System CONTRIBUTORS Role Persons Project Coordinator Principal Investigator Contact person (responsible for data management and DMP) Chung-Hsuan Ryan Lee, [email protected], TU Wien, ROR: ror.org/04d836q62 Contributors Chung-Hsuan Ryan Lee, [email protected], TU Wien, ROR: ror.org/04d836q62, Data Manager DMP version 1.0 5 Content 1. Executive Summary 6 1.1. Introduction 6 1.2. Data Summary 6 1.3. Methods and software used for data generation and reuse 7 1.4. Foreseeable research uses and /or users 7 2. FAIR data 8 2.1. Making data findable, including provisions for metadata 8 2.2. Making data accessible 8 2.3. Making data interoperable 10 2.4. Increase data re-use 10 3. Allocation of resources 11 4. Data security 11 4.1. Storage and backup facilities 12 4.2. Data security and protection of sensitive data 12 4.3. Long-term preservation and deletion of data 13 5. Ethical and legal issues 13 5.1. Personal data 13 5.2. Intellectual property rights and rights of use 13 5.3. Ethical issues 13 DMP version 1.0 6 1. Executive Summary 1.1. Introduction A data management plan (DMP) is a structured document that keeps record of what research data is created or reused 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, I followed the Horizon Europe DMP template. Chung-Hsuan Ryan Lee ([email protected]) will act as the data manager who will be in charge of the actions described by this DMP. 1.2. Data Summary • Will you re-use any existing data and what will you re-use it for? State the reasons if re-use of any existing data has been considered but discarded. • What types and formats of data will the project generate or re-use? • What is the purpose of the data generation or re-use and its relation to the objectives of the project? • What is the expected size of the data that you intend to generate or re-use? • What is the origin/provenance of the data, either generated or re-used? • To whom might your data be useful ('data utility'), outside your project? Produced datasets: dataset ID title type format estimated volume contains sensitive data P1 Scripts Source code Python, Bash 100 - 1000 MB no P2 Docker images Archived data TAR 50 - 100 GB no Description for "Scripts": These are Python and Bash scripts used to launch ROS2 nodes, it will allow the users to control the drone in offboard control mode, publish camera data, and run object detection inference pipelines. The files will be stored in .py .sh format with comments to explain the codes to increase reusability. DMP version 1.0 7 Description for "Docker images": These are containerized environments that contain all dependencies required to make the project work “out of the box”, ensuring reproducibility for potential users. This includes for example, YOLO, ROS2, CUDA libraries, Python packages etc. Reused datasets: dataset ID title source rights (e.g. license) contains sensitive data R1 ADR Signs https://universe.roboflow.com/ derya-logsd/adr-levha CC BY 4.0 no Description for "ADR Signs": This is an existing ADR (hazard) sign dataset from the Roboflow Universe that I plan to re-use. This dataset includes images of ADR signs in the form of JPEG/PNG, bounding box labels and annotations in YOLO-format .txt, and metadata JSON/CSV describing the dataset properties. 1.3. Methods and software used for data generation and reuse The research data will be generated and re-used through a combination of existing ADR sign dataset and newly recorded drone sensor data. Firstly, R1 will be augmented and preprocessed using Roboflow to increase the tolerance. I will then split R1 by a 70/20/10 ratio to be re-used to train, validate, and test YOLO11 object detection models. After training, the best weight from the model is then extracted into the containerized environment to ensure reusability. ROS2 will be used to run nodes for controlling the drone and publishing camera data. Additional data, such as ROS topics like odometry, IMU, depth information, will be generated during drone test flights and stored as ROS2 bag files. Docker will then be used to containerize the full environment to ensure reproducibility. 1.4. Foreseeable research uses and /or users Members of the scientific community, anyone who is interested in the topics of drone research, autonomous drone flight and robot vision. DMP version 1.0 8 2. FAIR data 2.1. Making data findable, including provisions for metadata • Will data be identified by a persistent identifier? • Will rich metadata be provided to allow discovery? What metadata will be created? What disciplinary or general standards will be followed? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how. • Will search keywords be provided in the metadata to optimize the possibility for discovery and then potential re-use? • Will metadata be offered in such a way that it can be harvested and indexed? I will make the data findable by uploading it to a data repository that provides a persistent identifier, and adding relevant metadata, ensuring long-term findability and citation. The repository will provide means for harvesting the metadata, including its machineactionable representation. Each version of the dataset (e.g., model weights, ROS2 bag samples) will be clearly versioned so users can reference or reproduce specific states of the project. A README file with an explanation of all terms used and codes to run at project level will be included. This will help others to identify, discover and reuse the data. Additionally, I will provide common metadata such as title, description, or keywords (e.g., ADR signs, YOLO detection, ROS2, drone mapping, autonomous drone flight, computer vision) when publishing data. In this case, I will follow the default template provided by the repository, such as Data Cite Metadata or Dublin Core. As far as possible, I will use controlled vocabularies for the data to allow inter-disciplinary interoperability and machine-actionability. 2.2. Making data accessible Repository: • Will the data be deposited in a trusted repository? • Have you explored appropriate arrangements with the identified repository where your data will be deposited? • Does the repository ensure that the data is assigned an identifier? Will the repository resolve the identifier to a digital object? Data: • Will all data be made openly available? If certain datasets cannot be shared (or need to be shared under restricted access conditions), explain why, clearly separating legal and contractual reasons from intentional restrictions. Note that in multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if opening their data goes against their legitimate interests or other constraints as per the Grant Agreement. DMP version 1.0 9 • If an embargo is applied to give time to publish or seek protection of the intellectual property (e.g. patents), specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible. • Will the data be accessible through a free and standardized access protocol? • If there are restrictions on use, how will access be provided to the data, both during and after the end of the project? • How will the identity of the person accessing the data be ascertained? • Is there a need for a data access committee (e.g. to evaluate/approve access requests to personal/sensitive data)? Metadata: • Will metadata be made openly available and licenced under a public domain dedication CC0, as per the Grant Agreement? If not, please clarify why. Will metadata contain information to enable the user to access the data? • How long will the data remain available and findable? Will metadata be guaranteed to remain available after data is no longer available? • Will documentation or reference about any software be needed to access or read the data be included? Will it be possible to include the relevant software (e.g. in open source code)? I will make the data accessible by providing open access to data, wherever possible. In cases where open access is not possible, I will provide meaningful metadata plus contact information for access requests. Some datasets cannot be published and even need to be deleted at the end of the project. See section 5 for more details. dataset ID access conditions estimated publication date location for publication (repository) PID license P1 Open 2026-02-03 TU Wien Research Data DOI MIT P2 Open 2026-02-03 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/