BRIGHTS

BRIGHTS Version 1 Description Analytical data Funder Grant Central Finance and Contracting Agency BRIGHTS Researchers Organizations LICENSE:CC-BY-4.0 DOI: - 17/12/2025 1. Main Info Title of DMP: BRIGHTS Description: Analytical data Researchers: Organizations: Contact: Suna Edgars ([email protected]) 2. Funding Funding organizations: Central Finance and Contracting Agency Grants: BRIGHTS Project: 3. License License: CC-BY-4.0 Access Rights: Public Publication Date: 4. Templates Descriptions Characterization data for developed luminphores and devices This dataset contains characterization data for developed luminphores and devices, developed within research project Binary ionic organic systems for next generation solid state lighting sources (BRIGHTS) Template: LCS FARP Data Management Plan | BRIGHTS LICENSE:CC-BY-4.0 DOI: - 17/12/2025 Type: Dataset 1 Data Summary 1.1 Data Summary 1.1.1 What is the purpose of the data collection/generation? The data is collected to identify and characterize the developed luminophores and LEC devices 1.1.2 Type of data generated/collected Experimental data 1.1.3 Format of data generated/collected Others FID, CSV, PDF 1.1.4 Expected size of the data – give expected size and choose unit of measurement 2-10 GB 1.1.5 Are you re-using this data set? No 1.1.6 If No, please describe, if you have considered re-using any of existing data? we have not considered 1.1.8 To whom the data might be useful ("data utility")? Other relevant remarks) To those skilled in the art Data Management Plan | BRIGHTS LICENSE:CC-BY-4.0 DOI: - 17/12/2025 2 Research Data Management 2.1 Metadata and documentation 2.1.1 Will data be attributed with standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers – DOI)? Yes DOI 2.1.2 Do you plan to provide metadata for your data? Yes 2.1.3 Will you use any metadata standard? Dublin Core 2.1.4 Will search keywords be provided that optimize possibilities for re-use? Yes 2.1.5 Will you follow any naming conventions for keywords? Yes 2.1.6 Will you provide clear version numbers? Yes 2.2 Making data openly accessible 2.2.1 What type of access the data generated/produced will have (choose one)? restricted, request access (access is restricted, but request with collaboration proposal could be submitted to the authors) Public disclosure of technical data prior to filing patent applications can irreversibly compromise patentability in many jurisdictions. Restricting access ensures that: Novel device architectures, materials compositions, fabrication processes, and performance data remain confidential until appropriate IP protection is secured. The project preserves freedom to operate and maximizes the strength, scope, and enforceability of future patent claims. Accidental disclosure through premature data sharing is prevented. Data Management Plan | BRIGHTS LICENSE:CC-BY-4.0 DOI: - 17/12/2025 2.2.2 Will you apply embargo period to access for your data? Yes Comment: Public disclosure of technical data prior to filing patent applications can irreversibly compromise patentability in many jurisdictions. Applying embargo ensures that: Novel device architectures, materials compositions, fabrication processes, and performance data remain confidential until appropriate IP protection is secured. 2.2.3 Will data, associated metadata, documentation and code be made accessible with means of a repository? Yes Zenodo https://zenodo.org Zenodo is a general-purpose, open-access research data repository operated by CERN and supported by the European Commission. It is widely used across disciplines, including chemistry and materials science. Accepts research data and outputs: datasets, software, figures, reports, presentations, and publications. Provides persistent identifiers (DOIs) for deposited records, enabling citation and long-term referencing. Supports access control: records can be open, embargoed, or restricted (closed access with metadata publicly visible). Not a controlled vocabulary: Zenodo stores and disseminates data; it does not define standardized scientific keywords or ontologies. FAIR-aligned: supports findability and reuse through metadata, licensing, and versioning. 2.2.4 What methods or software tools are needed to access the data? MestreNova, Excel, PDF viewer 2.2.5 Will documentation about the software needed to access the data included? Data Management Plan | BRIGHTS LICENSE:CC-BY-4.0 DOI: - 17/12/2025 No 2.2.6 Is it possible to include the relevant software (e.g. in open source code)? No 2.3 Making data interoperable 2.3.1 Are the formats open to software applications and to recombination with different data sets? Yes FIDs, csv and PDF are open and widely supported , enabling compatibility with various software. 2.3.2 Will you use data standard vocabulary/taxonomy to make your data interoperable? No 2.4 Increase data re-use 2.4.1 How will the data be licensed to permit the widest re-use possible? Indicate the license planned to use Creative Commons Attribution 4.0 Creative Commons Attribution 4.0 (CC BY 4.0) is suitable because it permits unrestricted reuse, redistribution, and adaptation of data for any purpose, including commercial use, while requiring only proper attribution to the original creators. 2.4.2 Are the data usable by third parties, in particular after the end of the project? Yes 28/04/2028 2.4.5 Are data quality assurance processes provided? No Data Management Plan | BRIGHTS LICENSE:CC-BY-4.0 DOI: - 17/12/2025 3 Resources and Security 3.1 Allocation of resources 3.1.1 How will costs be covered for making data FAIR during project realization? Euro Project Budget Allocation: allocate part of the project budget for data curation, documantation, and long term preservation 3.1.2 Who will be responsible for data management in your project? Edgars Suna (orcid: 0000-0002-3078-0576) 3.1.3 How will costs be covered for making data FAIR after project realization? Euro Project Budget Allocation: allocate part of the project budget for data curation, documantation, and long term preservation 3.2 Data security 3.2.1 What security measures will be used for data security? • Encryption • Firewall • Passwords Data security is ensured through a combination of encryption, firewalls, and passwords. Encryption protects data by converting it into a coded format that can only be accessed with authorized decryption keys, safeguarding information both in storage and during transmission. Firewalls act as a barrier between internal networks and external threats, controlling traffic to prevent unauthorized access and block malicious activity. Passwords restrict access to authorized users, and when combined with strong policies or multi-factor authentication, they further reduce the risk of unauthorized access. Together, these measures create a layered security approach that protects the integrity, confidentiality, and availability of sensitive research data. Data Management Plan | BRIGHTS LICENSE:CC-BY-4.0 DOI: - 17/12/2025 3.3 Ethical aspects 3.3.1 Are there any ethical or legal issues that could have an impact on data collection and sharing? No 3.3.2 Have you got/will you get permission from ethics committee to collect and process data (if applicable)? No 3.3.3 Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data, if applicable? No 3.3.4 Will data collected/generated include personal data/ sensitive information? No Powered by Data Management Plan | BRIGHTS LICENSE:CC-BY-4.0 DOI: - 17/12/2025