Data management plan (Final)

Ref. Ares(2025)6384903 - 05/08/2025 D9.4 – Data Management Plan (final) February 2025 Authors: Serra-Castells, Carlos; Gallego-Valadés, Alfonso; Garcés-Ferrer, Jorge This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 101036838. Ref. Ares(2025)8678911 - 13/10/2025 A2C – Deliverable D9.4v1.0 Page 2 І 60 Technical references Project Acronym Agro2Circular Project Title TERRITORIAL CIRCULAR SYSTEMIC SOLUTION FOR THE UPCYCLING OF RESIDUES FROM THE AGRIFOOD SECTOR Project Coordinator Fuensanta Monzó CETEC [email protected] Project Duration October 2021 – March 2025 (42 months) Deliverable No. D9.4 Dissemination level* PU Work Package WP 9 - Project management and coordination activities Task T9.3 - Ethical issues management and Data Protection issues Lead beneficiary Partn22 (UVEG) Contributing beneficiary/ies All partners Due date of deliverable 28th February 2025 Actual submission date 27th February 2025 * PU = Public PP = Restricted to other programme participants (including the Commission Services) RE = Restricted to a group specified by the consortium (including the Commission Services) CO = Confidential, only for members of the consortium (including the Commission Services) Document history V Date Comments v0.1 07/02/2025 First draft of document v0.2 13/02/2025 Revised version based on the comments of Marcello Bardellini – ICONS and Juan Agüera -CETEC A2C – Deliverable D9.4v1.0 Page 3 І 60 v1.0 27/02/2025 First final version, approved by the WP leader and the project coordinator, (will be) submitted to EC. v1.1 First draft based upon first final version v2.0 Second final version, approved by the WP leader and the project coordinator, (will be) submitted to EC. Document Distribution Log Version Date Distributed to v0.1 07/02/2025 Marcello Barcellini (ICONS), Juan Agüera (CETEC) V0.2 13/02/2025 Fuensanta Monzo (CETEC) Verification and approval Name Date Verification Final Draft by WP leader Fuensanta Monzó (CETEC) 27/02/2025 Approval Final Deliverable by coordinator Fuensanta Monzó (CETEC) 27/02/2025 A2C – Deliverable D9.4v1.0 Page 4 І 60 Disclaimer and acknowledgement This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036838 Disclaimer This document reflects only the views of the author(s) the European Research Executive Agency (REA) is not responsible for any use that may be made of the information it contains. Whilst efforts have been made to ensure the accuracy and completeness of this document, the A2C consortium shall not be liable for any errors or omissions, however caused. A2C – Deliverable D9.4v1.0 Page 5 І 60 Table of contents 1 Executive summary ........................................................................... 13 2 Introduction ........................................................................................ 14 3 Data Summary ................................................................................... 15 3.1 The A2C Project ........................................................................................................... 15 3.2 Project structure and data generation...................................................................... 16 3.2.1 Master dataset register ................................................................................................ 23 3.3 Data re-use ................................................................................................................... 24 4 FAIR data ........................................................................................... 28 4.1 Making data Findable .................................................................................................. 28 4.1.1 Metadata ...................................................................................................................... 28 4.1.2 File naming .................................................................................................................. 29 4.1.3 Responsibility and quality gates ................................................................................... 30 4.2 Making Data Openly Accessible ................................................................................ 31 4.2.1 Repository selection .................................................................................................... 31 4.2.2 Data access ................................................................................................................. 32 4.2.3 Metadata Availability and Preservation ...................................................................... 33 4.2.4 Compliance with Open Access Requirements............................................................ 33 4.3 Making data interoperable .......................................................................................... 37 4.3.2 Consortium Commitments for Data Interoperability .................................................... 38 4.3.3 File formats in A2C ..................................................................................................... 39 4.4 Increase data re-use ................................................................................................... 40 4.4.1 Long-term use of the data ........................................................................................... 40 4.4.2 Data Quality Control .................................................................................................... 41 4.4.3 Licensing and Access Conditions ............................................................................... 42 5 Other Research Outputs .................................................................... 43 5.1 Scope and categories ................................................................................................. 43 5.2 FAIR Management ....................................................................................................... 43 5.3 Licensing and IP .......................................................................................................... 44 A2C – Deliverable D9.4v1.0 Page 6 І 60 5.4 Storage, security and long-term preservation .......................................................... 45 5.5 Roles and responsibilities .......................................................................................... 45 6 Allocation of resources ...................................................................... 46 6.1 Costs for making data fair ......................................................................................... 46 6.2 Responsibilities ........................................................................................................... 47 6.3 Long-term preservation plan ....................................................................................... 48 7 Data Security ..................................................................................... 50 7.1 Security measures implemented ............................................................................... 50 7.2 Storage and long-term preservation ......................................................................... 51 8 Ethical aspects .................................................................................. 52 8.1 Compliance with Ethical and Legal Standards ....................................................... 52 8.2 Data Sharing and Cross-Border Considerations .................................................... 52 8.3 Ethical Oversight and Project Deliverables ............................................................. 53 8.4 Informed Consent and Anonymisation ..................................................................... 53 9 Conclusions ....................................................................................... 54 ANNEX I. Master Dataset Register ........................................................ 55 A2C – Deliverable D9.4v1.0 Page 7 І 60 List of Tables Table 1 - RA 1 data information ......................................................................................... 16 Table 2 - RA 2 data information ......................................................................................... 17 Table 3 - RA 3 data information ......................................................................................... 18 Table 4 - RA 4 data information ......................................................................................... 18 Table 5 - RA5 data information .......................................................................................... 19 Table 6 - RA 6 data information ......................................................................................... 19 Table 7 - RA 7 data information ......................................................................................... 20 Table 8 - Preferred Data Formats for Sharing, Reuse, and Long-Term Preservation in Agro2Circular .............................................................................................................. 40 A2C – Deliverable D9.4v1.0 Page 8 І 60 List of abbreviations • A2C: Agro2Circular • APC: Article Processing Charge • DIS: Data Integration System • DMP: Data Management Plan • DOI: Digital Object Identifier • DPO: Data Protection Officer • DSA: Data Sharing Agreement • EC: European Commission • EG: Ethylene Glycol • FAIR: Findable, Accessible, Interoperable and Re-usable • FNC: File Naming Convention • F&V: Fruits & Vegetables • GA: General Assembly • GDPR: General Data Protection Regulation • LCA: Life Cycle Assessment • PHBV: Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) • PE/PET: Polyethylene/Polyethylene Terephthalate • PLA: Polylactic Acid • PID: Persistent Identifier • RA: Research Action • TPA: Terephthalic Acid • WP: Work Package A2C – Deliverable D9.4v1.0 Page 9 І 60 Glossary • Access authorization - A control mechanism that restricts data access to specific users based on authentication criteria and established permissions. • Accessibility of data - A FAIR principle stating that data should be available and accessible to authorised users with clear access conditions and authentication mechanisms where necessary. • Anonymisation - The process of removing or modifying personal information in data to prevent the direct or indirect identification of individuals. • Archiving of data - The process of storing and preserving data in structured and accessible formats for long-term availability. • Article Processing Charges (APCs) - Fees paid to publishers to make scientific publications freely accessible under open access policies. • Biodegradability - The ability of a material to decompose in the environment through microbial activity without generating harmful waste. • Circular economy - An economic model focused on minimising waste and maximising resource efficiency through reuse, recycling, and sustainable production processes. • Confidentiality of data - A principle ensuring that sensitive or private information is protected from unauthorised access and only available to approved users. • Controlled access data - Data that are subject to restrictions due to confidentiality, intellectual property rights, or ethical considerations, requiring approval for access. • Data availability - The principle ensuring that data are accessible for use at the required time and in the appropriate format. • Data cleaning - The process of identifying and correcting errors, inconsistencies, or duplicates in a dataset to improve data quality. • Data compatibility - The ability of different datasets to be integrated and used together without conflicts in format, structure, or meaning. • Data confidentiality - A data protection measure ensuring that information remains private and accessible only to authorised users. A2C – Deliverable D9.4v1.0 Page 16 І 60 • Developing a systemic circular model to facilitate the territorial implementation, replication, and scalability of the A2C solution. • Maximising project impact and facilitating replication and scalability, ensuring long-term sustainability and knowledge transfer. 3.2 Project structure and data generation A2C has brought together 40 international and interdisciplinary partners working on seven RAs, structured across different work packages: • RA 1: A2C Specifications, Residues Management and Data Integration System (DIS). The project has defined product specifications, compliance strategies, recommendations on water and energy efficiency, and residue management plans. The A2C DIS has been developed to manage project data. Data collected Waste analysis data, water analysis data, energy analysis data, raw material analysis data, process data (spreadsheets, pictures, diagrams). Origin of data Generated from studies on waste characterisation, resource consumption, and management strategies. Collection methods Scientific research, desk research, consultation with stakeholders, co-creation sessions. Data process Scientific methods, statistical analysis of results, evaluation of optimal conditions for resource management. Type of data generated Datasets of individual microdata, diagrams, technical datasheets, secondary documents. Purpose/utility of the data To define product specifications, establish residue management plans, ensure compliance with regulatory standards, and develop the A2C DIS. Table 1 - RA 1 data information • RA 2: A2C Technologies for Recycling Organic Agri-Food Waste. The project has optimised extraction technologies, purification and stabilisation of bioactive compounds, and assessed their antioxidant and antimicrobial properties. A bioprocess has also been tested for the production of biodegradable plastic using organic agri-food waste as a feedstock. A2C – Deliverable D9.4v1.0 Page 17 І 60 Data collected Process data, experimental measurements, material analysis and process data (spreadsheets, pictures, diagrams). Origin of data Generated through upcycling and upscaling studies on bioactive extraction and biodegradable plastic production. Collection methods Scientific research, process development, process optimisation. Data process Scientific methods, quantitative analysis, evaluation of biotransformation efficiency. Type of data generated Diagrams, technical datasheets, formulations, datasets of individual microdata. Purpose/utility of the data To optimise extraction and purification routes for bioactive compounds and assess their applicability in food, cosmetic, and nutraceutical formulations. To optimise the bioproduction process of biodegradable plastic and assess its applicability in the agrifood industry for biodegradable applications (packaging, mulching). Table 2 - RA 2 data information • RA 3: Technologies for Sorting and Recycling Multilayer Plastics. This RA has developed pre-treatment processes, improved sorting technologies, separated aluminium from multilayer materials, optimised enzymatic depolymerisation of PE/PET, and enhanced decontamination for mechanical recycling. Data collected Experimental measurements, material analysis and process data (spreadsheets, pictures, diagrams). Origin of data Generated through recycling process studies, including sorting and depolymerisation trials. Collection methods Scientific research, process development, process optimisation. Data process Scientific methods, lab-scale simulation, collection of key performance indicators. Type of data generated Diagrams, technical datasheets, formulations, secondary documents. A2C – Deliverable D9.4v1.0 Page 18 І 60 Purpose/utility of the data To develop and optimise pre-treatment, sorting, enzymatic depolymerisation, decontamination technologies and bioproduction of building blocks for multilayer plastic upcycling. Table 3 - RA 3 data information RA 4: Upcycling and Scaling of Recycled Agri-Food Materials. The project has tested the application of extracted bioactive compounds and building blocks in cosmetic, food, and nutraceutical formulations and developed pilot-scale extraction processes and bioproduction of building blocks. Data collected Formulations and validation data for cosmetics, food, and nutraceuticals (spreadsheets, surveys), data from scale-up processes and extract & building block validation (spreadsheets, diagrams, technical datasheets). Origin of data Generated from pilot-scale extraction and validation of bioactive compounds. Generated from pilot-scale building blocks bioproduction and validation in cosmetic formulations. Collection methods Scientific research, process development, process optimisation. Data process Scientific methods, statistical analysis of formulations, validation of optimal extraction parameters. Type of data generated Diagrams, technical datasheets, formulations, datasets of individual microdata. Purpose/utility of the data To validate the effectiveness of bioactive compounds and building blocks in new formulations and assess their stability and functional properties at pilot scale. Table 4 - RA 4 data information • RA 5: A2C Technologies for Upcycling Recycled Plastic Materials and biodegradable plastic materials. The focus has been on obtaining high-value products from recycled plastics and biodegradable plastics, ensuring material properties meet industrial requirements. A2C – Deliverable D9.4v1.0 Page 19 І 60 Data collected Experimental measurements, material analysis and process data (spreadsheets, pictures, diagrams). Origin of data Generated from biodegradability and recyclability tests on processed materials. Collection methods Scientific research, development of new processes, process optimisation. Data process Scientific methods, quantitative analysis of material properties. Type of data generated Diagrams, technical datasheets, formulations. Purpose/utility of the data To obtain high-value products from recycled plastics and biodegradable plastic materials while ensuring material performance meets industry standards. Table 5 - RA5 data information • RA 6: Demonstration of the A2C Technological Solution. Pilot tests have been carried out in southern Spain, integrating and validating the developed technologies in industrial environments. Several prototypes have been manufactured, and business models have been defined. Data collected Process data, business and financial data. Origin of data Generated from upcycling and upscaling studies at pilot sites. Collection methods Scientific research, process development, process optimisation. Data process Scientific methods, qualitative and quantitative analysis, life cycle assessment (LCA; LC costing, social LCA). Type of data generated Datasets of individual microdata, textual data (from transcriptions), secondary documents. Purpose/utility of the data To integrate and validate developed technologies in industrial settings and establish business models for future scaling. Table 6 - RA 6 data information • RA 7: Adoption, Replication, and Scalability of the A2C Systemic Solution. This RA has established the A2C Systemic Solution Model, assessing its transferability A2C – Deliverable D9.4v1.0 Page 20 І 60 through multidimensional impact analysis, cost evaluations, and identification of key enablers and challenges. Data collected Socio-demographic data, environmental data, institutional data, economic/market data. Origin of data Generated from study sites and stakeholder engagement activities. Collection methods Surveys, interviews, focus groups, stakeholder consultation, cocreation sessions, desk research. Data process Scientific methods, qualitative and quantitative analysis, policy evaluation frameworks. Type of data generated Datasets of individual microdata, textual data (from transcriptions), secondary documents. Purpose/utility of the data To define the A2C Systemic Solution Model, assess its scalability, and analyse economic, social, and environmental impacts. Table 7 - RA 7 data information Diversity of Data Generated Data in A2C has been generated across a wide range of formats and types, aligning with the technical, scientific, and economic objectives of the project. The most commonly used formats include: • Structured datasets (Excel, CSV, SQL databases) • Technical reports and documentation (Word, PDF) • Laboratory outputs (experimental data files, .dat, .raw) • Visual materials (JPEG, PNG, TIFF, schematic diagrams) • Process monitoring data (sensor logs, process tracking files) Each RA has contributed specific types of data, reflecting the distinct focus of each work package: • RA 1: Data on waste characterisation, resource consumption, and residue management strategies, contributing to monitoring frameworks for circularity indicators. A2C – Deliverable D9.4v1.0 Page 21 І 60 • RA 2: Experimental datasets on bioactive extraction and purification processes, and biodegradable plastic bioproduction supporting traceability models and certification efforts. • RA 3: Technical data on plastic sorting, depolymerisation, and recycling and upcycling trials, aiding in industrial standardisation of recycling processes. • RA 4: Process data from cosmetic, food, and nutraceutical formulations, validating bioactive and building blocks stability and efficacy. • RA 5: Measurements of biodegradability and material properties in recycled and biodegradable plastics, contributing to industry certification. • RA 6: Business and financial datasets from pilot testing of circular economy solutions, supporting cost-benefit assessments. • RA 7: Socio-demographic, institutional, and environmental data used in policy evaluations and systemic solution adoption studies. Purpose of Data Generation The primary objectives behind data generation in A2C include: • Evaluating circularity and sustainability metrics (RA 1, RA 7) • Enhancing technological processes for upcycling materials (RA 2, RA 3, RA 5) • Developing models for traceability, scalability, and economic feasibility (RA 6, RA 7) • Generating scientific outputs such as reports, methodologies, and standards (RA 1, RA 4) • Informing policy frameworks and regulatory developments in circular economy (RA 7) The project has produced a range of scientific deliverables, including standardisation protocols, predictive models, and industry guidelines, which are relevant not only to the consortium but also to external stakeholders. Volume of Data Generated The scale of data generated within A2C varies significantly depending on the research focus: • Small to medium-sized datasets (a few MB to 3 GB): o Circularity monitoring frameworks (RA 1) o Technical reports and policy recommendations (RA 7) o Traceability models for extracted bioactives and produced building blocks (RA 2) A2C – Deliverable D9.4v1.0 Page 22 І 60 • Large datasets (over 1 TB): o Laboratory analyses of biodegradability and recyclability (RA 5) o High-resolution imaging and process monitoring in plastic recycling/upcycling (RA 3) o Large-scale economic models for business case evaluation (RA 6) The largest datasets stem from laboratory experiments, industrial process trials, and computational modelling, particularly in bioplastic and biotechnological developments. Relevance and Potential External Applications Although some datasets will remain restricted within the project, many have broader applicability for key stakeholders: • Academic and research institutions: o Laboratory data and experimental methodologies from RA 2, RA 3, and RA 5 can support further research in biotechnology, recycling, and material science. • Industry and private sector: o Process optimisation data from RA 4 and RA 6 could aid companies in food, cosmetic, and plastic industries to develop sustainable products. • Policy and regulatory bodies: o Socio-economic and environmental impact assessments (RA 7) can inform policy development in circular economy and waste management. While raw data may remain internal for confidentiality and commercial reasons, the project ensures that final results and key findings are made available through scientific publications, policy reports, and open-access repositories. Other research data In addition to the data presented in previous paragraphs, the A2C Consortium developed internal databases (such as stakeholder databases including organizations that could have interests and impacts on the project results) derived from the communication and dissemination activities to be executed throughout the project. Such databases are to provide an overview of stakeholders’ relevant information (e.g. names and institutional email addresses), information on the newsletter subscribers, and data on registration to events via the project website. In addition, other types of data (such as subscribers to the project newsletter or online users’ navigation data) have been collected from the A2C website and other platform’s analytics (like LinkedIn and YouTube) and monitoring tools (Matomo), A2C – Deliverable D9.4v1.0 Page 23 І 60 however including no sensitive data and in line with the Privacy Notice published on the website. These data will not be shared. Challenges in Data Management and Dissemination Despite the high potential of the datasets generated, certain barriers remain regarding data sharing and dissemination: • Ensuring compliance with confidentiality agreements for commercially sensitive data. • Standardising data formats and documentation to facilitate reuse by external actors. • Balancing open-access publication with industry confidentiality requirements. To address these challenges, A2C has implemented data governance mechanisms and FAIR data principles, ensuring that generated knowledge is structured, accessible, and reusable where possible. 3.2.1 Master dataset register A2C maintains a consolidated Master Dataset Register, which enumerates all datasets generated under the project and ensures their traceability, accessibility, and compliance with FAIR and open-access principles. The complete register is provided in Annex I. Each dataset entry is assigned a unique identifier following the scheme A2C-DS[NN], and includes the essential metadata elements required under Horizon Europe’s FAIR data management framework. The information recorded covers, at minimum, the following: Dataset ID, Title, Authors and Responsible Partner(s), Linked Publication DOI, Dataset DOI, Repository, Community, Access Level, Licence, Format and File Size, Work Package/Deliverable, Short Description, Date of Deposit, Contact Person, and Notes. The current register comprises eight datasets that have been deposited in FAIR-compliant repositories (seven in Zenodo and one in Mendeley Data). All datasets are openly accessible, except for two which are available under a non-commercial licence (CC BYNC) due to restrictions associated with experimental reuse conditions. Each dataset includes a README file describing its content, structure, and variables to facilitate reuse, as well as metadata and references to linked publications where applicable. This register serves as the project’s authoritative source for tracking research data outputs, ensuring persistent identifiers (DOIs), standardised licences, and verifiable provenance for each dataset deposited. A2C – Deliverable D9.4v1.0 Page 24 І 60 3.3 Data re-use The reuse of pre-existing data has played a relevant role in the A2C project, supporting various research and development activities. Prior to the project’s implementation, several partners leveraged previous datasets to facilitate analysis, optimisation, and planning. The reuse of data can be categorised into key areas: The reuse of pre-existing data has played a central role in the A2C project, providing the legal, technical, environmental, and socio-economic foundations for the design, implementation, and replication of the proposed circular economy solutions. A2C has systematically reused data from external public sources, regulatory frameworks, previous projects, and partners’ inventories to ensure compliance, scalability, and comparability with existing standards and practices. Types of Data Re-used. The pre-existing data varied in type and format, supporting different research and development activities: • Waste and agricultural production data: Annual data on agro-food residues, disaggregated by fruit and vegetable (F&V) type and seasonality, were reused to characterise the availability of raw materials for valorisation. An initial inventory was carried out in the Region of Murcia, complemented by data from consortium partners on multilayer plastic residues (e.g. aseptic bags, agricultural films) and agro-food residues (e.g. citrus, apple, grape, cauliflower, broccoli, artichoke). These datasets constituted the baseline for the Digital Information System (DIS) and for the pilot activities. • Regulatory and compliance data: EU regulations and standards were reused to define the technical requirements of the products under development. Key sources included Regulation (EU) 2022/1616 on recycled plastics in food contact materials, Regulation (EU) 2015/2283 on Novel Foods, Regulation (EC) 1935/2004 and Regulation (EU) 10/2011 on food contact materials, and cosmetics legislation. These data ensured that valorisation routes and products complied with health and safety requirements from the outset. • Standards for traceability and quality: Standards such as EN 15343:2007 (traceability and recycled content in plastics) and EN 15347:2007 (characterisation of A2C – Deliverable D9.4v1.0 Page 25 І 60 plastic waste), as well as methodological concepts like Critical Tracking Events (CTE) and Key Data Elements (KDE), were reused in the design of the DIS. • Environmental and sectoral data: Contextual data on climate (e.g. precipitation levels in Spain) and sectoral indicators (e.g. energy consumption in the plastics industry) were reused to inform waterand energy-efficiency recommendations (D1.7) and to feed Life Cycle Assessment (LCA) models. • Life Cycle Inventory (LCI) databases: Data from Ecoinvent v3.10 were reused to model unit processes for LCA and eLCC (e.g. electricity mix, ultrapure water, enzyme markets). Benchmarking data on equivalent functional products (e.g. virgin LDPE pellets) were also reused to compare the environmental and economic performance of A2C products. • Socio-economic and labour market data: Reports such as the Murcia Labour Market Report 2021 and the PSILCA v3 database were reused for socio-economic assessments and social life cycle analysis (sLCA). • Natural capital valuation data: OECD carbon rates, regional agricultural statistics, and Eurostat data on food waste generation were reused to monetise environmental externalities and assess ecosystem services preserved through A2C processes. • Historical project data: Biomass rich in PHBV previously produced in the EU project SCALIBUR was reused to meet a project milestone by supplying 500 g of recovered PHBV to CETEC for bioplastic development. • Scientific and grey literature: Previous academic publications, industry reports, and project deliverables were reused to establish theoretical frameworks, support benchmarking, and identify circular business models and barriers. Formats of Re-used Data. The reused datasets were available in heterogeneous formats, including: • Regulatory and technical documentation (.pdf, .docx). • Structured databases and tables (.csv, .xls). • Scientific and technical reports from EU projects and partners. • Literature and grey reports (digital and print). A2C – Deliverable D9.4v1.0 Page 32 І 60 institutional Zenodo account to ensure indefinite availability. Zenodo supports OAI-PMH harvesting, ensuring metadata is indexed by OpenAIRE and other aggregators. Repository selection criteria. Repositories used by partners must: (i) issue PIDs (DOIs or Handles); (ii) provide stable landing pages; (iii) support standard metadata (e.g., Dublin Core/JSON-LD); (iv) offer clear licence options (including CC0 for metadata); and (v) implement back-ups and preservation policies consistent with trusted status. 4.2.2 Data access Openness policy. A2C follows an “open by default, closed by exception” approach. All datasets required to validate published results will be made openly available unless sharing would: (a) breach legal obligations (e.g., GDPR for personal data), (b) conflict with contractual obligations (e.g., third-party background/IP, non-disclosure), or (c) undermine legitimate interests of specific beneficiaries (e.g., trade secrets, foreground IP under exploitation) as permitted by the Grant Agreement. Embargo policy. Where short delays are needed: • For publication: embargo up to 6 months (SS&H up to 12 months) to allow journal submission and peer review. • For IP protection: embargo up to 18 months aligned with patent filing timelines. Any longer embargo requires GA approval, with written justification recorded in the Dataset Inventory. Embargo end-dates are included in the repository record and lifted automatically where supported. Access protocol. Open datasets are provided via free, standardised protocols (HTTPS) from trusted repositories (e.g., Zenodo, institutional repositories). Download does not require proprietary software; when specialised tools are needed (e.g., DSC/TGA/HPLC raw formats), a README/software note (vendor/tool name, version, file types, viewer/converter options) is included. Identity verification. Requesters of restricted datasets must authenticate via repository SSO (institutional email or ORCID) and accept the DUA/terms. Dataset owners may request additional verification (affiliation letter or ORCID-verified email) for sensitive cases. Approval decisions and access logs are retained by the repository/institutional system for audit. Data Access Committee. A consortium-level DAC is not required. Requests are handled by the dataset owner named in the DOI record. Where requests involve personal data (even if pseudonymised) or raise ethical issues, the dataset owner consults the partner A2C – Deliverable D9.4v1.0 Page 33 І 60 DPO using the workflow defined in D9.2/D10.2 before granting access. 4.2.3 Metadata Availability and Preservation Metadata availability across the project also varies. Some partners have committed to making metadata openly accessible under a CC0 licence, ensuring unrestricted reuse, while others have not provided clarity on their metadata policies. In certain cases, metadata will only be available within scientific publications, limiting its usability outside academic contexts. Long-term metadata preservation remains a challenge. While some partners guarantee that data will remain available in their repositories indefinitely, there is no unified strategy to ensure that metadata remains accessible even if the datasets themselves become unavailable. Regarding software dependencies, most datasets can be accessed using standard tools such as Microsoft Office, Open Office, and Adobe Reader. However, some datasets require specialised laboratory software, which could pose accessibility issues unless appropriate documentation and references are provided. 4.2.4 Compliance with Open Access Requirements As per Article 29.2 of the Grant Agreement, all beneficiaries must ensure open access to peer-reviewed scientific publications related to A2C results. This includes: • Depositing a machine-readable electronic copy of the published version or final peerreviewed manuscript in an open repository. • Ensuring open access via the repository at the latest on publication, or within six months (twelve months for social sciences and humanities). • Providing bibliographic metadata in a standard format, including references to “European Union (EU)” and “Horizon 2020,” project name, acronym, grant number, and publication details. To enhance long-term accessibility, all public deliverables of the A2C project, which do not fall under scientific publications with a DOI or publisher platform, will be uploaded to the European Commission's CORDIS platform upon approval. This ensures that project outputs remain accessible beyond the duration of the project, mitigating potential risks related to the long-term availability of the A2C website. Furthermore, research data needed to validate scientific findings are deposited in FAIRcompliant repositories, primarily Zenodo and Mendeley Data, in line with the FAIR and A2C – Deliverable D9.4v1.0 Page 34 І 60 Open Data policy of Horizon Europe. Each dataset includes a DOI, licence, metadata, and README file as recorded in the Master Dataset Register (Annex I). Datasets that cannot be made openly available are clearly identified and justified (e.g. IPR, confidentiality, or GDPR restrictions). The following research outputs are expected to be made openly accessible through publications, CORDIS, or both: • Task 1.1 A2C requirements (public deliverable). • Task 1.2 A2C methodologies, including compliance pre-dossiers. • Task 1.3 A2C energy management plan (three public deliverables) • Task 1.4 Residue management plan (three public deliverables). • Task 1.5 Beta version of the DIS • Tasks 2.2 & 2.3 Scientific papers on bioactive compound extraction and purification • Task 5.1.1 Scientific papers on building blocks bioproduction with yeast • Tasks 5.1.2 to 5. Technical publications on PHBV and carotenoids production, compound transformation, validation, and plastic end-life scenarios. • Tasks 6.2 -6.3 Demonstrator integrations, DIS and process protocols (public deliverables). • Task 6.4 Natural capital assessment A2C – Deliverable D9.4v1.0 Page 35 І 60 Task 7.1 Public Engagement and Community-Based Schemes for Sustainable Systemic Circular Economy • D7.1 Public Engagement (Draft) – Preliminary version of the public engagement approach, defining strategies for stakeholder participation. • D7.2 Public Engagement Strategy & Summary of Activities and Results (Status Report) – Mid-project report detailing implemented engagement actions. • D7.3 Public Engagement Strategy & Summary of Activities and Results (Final) – Final consolidated report on public engagement methodologies and impact. • D7.4 Community-Based Innovation Schemes – Analysis of pilot initiatives, including the food waste collection system in Alhama de Murcia. Task 7.2 Evaluation Framework • D7.5 Evaluation Framework and Methodology – Development of a structured framework for assessing the replicability and scalability of A2C circular economy business models. Task 7.3 Environmental Assessment, LCA, and A2C Circularity Monitoring • D7.6 Environmental Assessment, LCA, and A2C Circularity Monitoring (Draft) – Initial assessment of environmental externalities and key circularity indicators. • D7.7 Environmental Assessment, LCA, and A2C Circularity Monitoring (Final) – Finalised environmental evaluation, incorporating a full LCA. Task 7.4 Social and Economic Analysis • D7.8 Socioeconomic and Sociocultural Analysis Report (Draft) – Baseline study on the economic and social impacts of the A2C systemic solution. • D7.9 Socioeconomic and Sociocultural Analysis Report (Final) – Comprehensive assessment of socioeconomic effects, integrating stakeholder feedback and policy implications. Task 7.5 Multidimensional Model for Adoption, Replicability, and Scalability of A2C Systemic Solution • D7.10 A2C Systemic Solution Model and Self-Assessment Tool – A structured framework for evaluating territorial conditions and readiness for A2C adoption. • D7.11 A2C Circular Action Plan Report – Roadmap for implementing A2C systemic circular economy strategies at a territorial level. A2C – Deliverable D9.4v1.0 Page 36 І 60 Task 7.7 Validation of the A2C Multidimensional Model Through Cross-Fertilisation with Other Clusters • D7.12 Policy Briefs (Draft) – Initial policy recommendations based on preliminary findings and early stakeholder engagement. • D7.13 Policy Briefs (Final) – Finalised policy briefs incorporating validation results and cross-sectoral learnings. • D7.14 Financing Recommendations (Draft) – Preliminary analysis of financial mechanisms supporting A2C replication and adoption. • D7.15 Financing Recommendations (Final) – Comprehensive set of financing recommendations, addressing public procurement and funding gaps. • D7.16 Report on Potential for Adoption and Scale-Up – Strategic analysis of enablers and barriers for scaling the A2C systemic solution. • D7.17 Lombardy Action Plan – Specific recommendations for implementing A2C in the Lombardy region, based on regional needs and stakeholder inputs. • D7.18 Lithuania NUTS-2 Action Plan – Tailored roadmap for A2C adoption in Lithuania, aligned with territorial circular economy strategies. Task 8.1 A2C Outreach Strategy • D8.1 Dissemination and Communication Strategy – Defines the outreach strategy, including stakeholder mapping, communication campaigns, key messages, and dissemination tools. Task 8.4 Outreach at European Level • D8.2 Report on Dissemination and Communication (Draft) – Mid-project report assessing communication and dissemination actions, outreach performance, and stakeholder engagement. • D8.3 Report on Dissemination and Communication (Final) – Final report summarising the impact of A2C dissemination activities and the effectiveness of outreach efforts. Task 8.6 Training, Knowledge Transfer and Guidelines for Promoting A2C Circular Model • D8.7 Toolkit of Guidelines and Training Materials (Final) – Finalised training toolkit, incorporating feedback from participants and lessons learned during the project. Task 8.8 Standardisation Activities A2C – Deliverable D9.4v1.0 Page 37 І 60 • D8.8 Standardisation Landscape and Applicable Standards – Overview of existing and emerging standards relevant to A2C technologies and methodologies. • D8.9 Contribution to Standardisation (Draft) – Initial report detailing A2C's engagement with standardisation bodies and contributions to ongoing standardsetting processes. • D8.10 Contribution to Standardisation (Final) – Final report outlining A2C’s impact on standardisation, including technical inputs, regulatory considerations, and future recommendations. In line with the Horizon 2020 Open Access policy, A2C has ensured the availability of scientific and industrial research outputs through open-access publications, repositories, and the project website. Research findings have been disseminated in peer-reviewed scientific journals and industry publications, facilitating knowledge transfer to the academic community, policymakers, and relevant industrial stakeholders. The project has ensured that both scientific publications and associated datasets are accessible under appropriate open licences (typically CC BY 4.0), thus meeting Horizon Europe’s requirements on open access, transparency, and reproducibility. 4.3 Making data interoperable Interoperability is fundamental to ensuring that research data can be efficiently accessed, processed, and integrated across different platforms and disciplines. In line with Horizon Europe guidance, A2C datasets have been structured to follow community-endorsed interoperability best practices. Specifically, the consortium has adopted principles from the FAIR Data Guidelines, the Research Data Alliance (RDA) recommendations, and sector-specific standards in the agri-food and recycling domains (e.g. ISO 15216 for microbiological data, EN 13432 for biodegradability). To achieve interoperability, the project has committed to: • Syntactic interoperability: Adoption of structured, machine-readable formats such as CSV, XML, JSON, and DDI XML, ensuring compatibility across repositories. • Semantic interoperability: Alignment of project-specific terminologies with recognised ontologies such as AGROVOC (FAO) for agri-food terminology and ECHA’s controlled vocabularies for chemicals and polymers. • Search interoperability: Use of Dublin Core metadata standards, enabling cross- A2C – Deliverable D9.4v1.0 Page 38 І 60 dataset queries and discovery. 4.3.1 Current Status of Interoperability in A2C The level of interoperability across A2C datasets varies, but a common baseline has now been established: • File formats: Partners are required to deposit datasets in interoperable formats (CSV, XML, JSON, TXT for tabular/text data; TIFF/PNG for images; MP4/OGG for video). Proprietary formats (e.g. .sav, .dta, .docx) are permitted only when accompanied by an open equivalent. • Semantic mapping: While some datasets originally relied on internal vocabularies (e.g. in circularity indicators monitoring), these have now been mapped to broader ontologies. For example, waste classification terms developed in A2C are linked to the European Waste Catalogue (EWC), and polymer descriptors are aligned with ISO/TR 10358. • References: Datasets referencing previous research, ISO/EN standards, or external repositories (e.g. Addgene for biological materials) now include qualified references, clarifying the nature of the link (e.g. “Dataset X provides validation data for Method Y”). Although interoperability was initially uneven, the project has consolidated a minimum standard across all research areas (RAs), ensuring that final datasets meet the requirements for syntactic and semantic compatibility. 4.3.2 Consortium Commitments for Data Interoperability To strengthen interoperability, the following commitments have been implemented: • Adoption of controlled vocabularies and ontologies: All datasets must include metadata aligned with AGROVOC, ECHA vocabularies, or ISO/EN standards. Where project-specific terms are unavoidable (e.g. bespoke circularity indicators), mappings to community standards are mandatory. • Publication of project-specific vocabularies: Any new vocabularies developed in A2C (e.g. classification schemes for agri-food residues) will be openly published in the consortium’s Zenodo community under a CC0 licence, enabling their reuse, refinement, or extension. • Cross-referencing to external data and standards: Datasets will systematically A2C – Deliverable D9.4v1.0 Page 39 І 60 include qualified references (e.g. “this dataset reuses methodology from ISO 14855-1” or “this dataset validates results from project X”) to ensure contextual integration. • Community best practices: Interoperability practices follow the RDA recommendations on metadata standards and the FAIRsharing.org registry of standards. 4.3.3 File formats in A2C To align with interoperability principles, A2C datasets have been structured in machinereadable and widely accepted formats. The following table provides an overview of the preferred formats for data sharing, reuse, and preservation within the project: Type of data Recommended formats Acceptable Formats Quantitative tabular data with extensive metadata (Variable labels, code labels, and defined missing values) SPSS portable format (.por), Delimited text with setup files (SPSS, Stata, SAS), DDI XML metadata files Proprietary formats: SPSS (.sav), Stata (.dta), MS Access (.mdb/.accdb) Quantitative tabular data with minimal metadata (Column headings, variable names) CSV (.csv), Tab-delimited (.tab), Delimited text with SQL definitions MS Excel (.xls/.xlsx), OpenDocument Spreadsheet (.ods), dBase (.dbf) Qualitative textual data Rich Text Format (.rtf), Plain text (.txt), XML (.xml), HTML (.html) MS Word (.doc/.docx), NVivo, ATLAS.ti A2C – Deliverable D9.4v1.0 Page 40 І 60 Digital Image Data TIFF uncompressed (.tif), JPEG (.jpeg, .jpg) if original format TIFF (.tiff), PDF (.pdf), RAW image formats (.raw), Photoshop (.psd) Digital audio data FLAC (.flac), MP3 (.mp3) if original format AIFF (.aif), WAV (.wav) Digital video data MPEG-4 (.mp4), OGG video (.ogv, .ogg), Motion JPEG 2000 (.mj2) AVCHD video (.avchd) Documentation and scripts RTF (.rtf), PDF/A (.pdf), XHTML (.xhtml), OpenDocument Text (.odt), Plain text (.txt) MS Word (.doc/.docx), MS Excel (.xls/.xlsx), XML (.xml) Table 8 - Preferred Data Formats for Sharing, Reuse, and Long-Term Preservation in A2C By following these guidelines and applying structured formats, A2C has strengthened the interoperability of research data, facilitating its exchange across disciplines and ensuring long-term usability. 4.4 Increase data re-use Maximising the reusability of research data is a central objective within A2C. Ensuring that datasets can be accessed, validated, and applied beyond the project’s lifetime increases their scientific, technological, and societal impact. The consortium has therefore adopted a set of binding commitments to provide structured documentation, standardised licences, and long-term preservation policies that guarantee usability by third parties. 4.4.1 Long-term use of the data The long-term availability of A2C data is shaped by a combination of scientific, regulatory, and commercial factors. While several partners have committed to open access dissemination through structured documentation and repositories, others will maintain controlled access to protect sensitive information. Decisions regarding data accessibility have been guided by the following criteria: A2C – Deliverable D9.4v1.0 Page 41 І 60 • Completion of data collection and processing: Data must be finalised, verified, and free from inconsistencies before being made accessible. • Quality assurance measures: Internal validation and peer review procedures ensure data integrity and reliability. • Exploitation and licensing considerations: The reuse of data is aligned with partners’ exploitation strategies, ensuring that commercial and scientific interests are appropriately balanced. The project’s General Assembly (GA) has played a central role in defining access policies, balancing open access principles with confidentiality and intellectual property concerns. Discussions have focused on determining appropriate licensing models, with some partners opting for Open Access licences, while others have defined more restricted conditions depending on agreements established in the Consortium Agreement. Additionally, A2C ensures that all data intended for reuse comply with Article 28.1 of the Grant Agreement, which requires that, for up to four years after the project’s completion, beneficiaries take measures to facilitate: • Further research activities beyond A2C’s scope. • Development of new products, processes, and services. • Contributions to standardisation activities. To maximise long-term usability, several partners have committed to sharing their datasets with regional actors, industry stakeholders, and scientific communities where appropriate. However, access to specific datasets will depend on agreements made during the project’s final stages to ensure data protection and ethical compliance. 4.4.2 Data Quality Control High-quality data is essential for ensuring reliable project outcomes and facilitating reuse beyond A2C. Data quality control has been implemented at various stages, from data collection and processing to validation and documentation. However, approaches to quality control vary across partners, reflecting different methodological and disciplinary backgrounds. Key measures adopted across the consortium include: • Documentation of data processing and validation: Some partners have integrated structured documentation such as README files, variable definitions, A2C – Deliverable D9.4v1.0 Page 48 І 60 6.3 Long-term preservation plan The A2C consortium adopts the following minimum consortium-wide preservation policy: 1. Public datasets and public research outputs. o Where kept: Zenodo (A2C community) and/or institutional repositories. o For how long: Minimum 5 years beyond project end (already ensured); thereafter indefinite availability through CETEC’s institutional Zenodo account or successor repository. o Who decides: Owner proposes; DMP lead validates; Coordination ensures transfer/mirroring if needed. o Resources: Repository hosting; minimal curation time at owner/DMP lead. 2. Restricted/confidential datasets (IPR/commercial/GDPR/safety). o Where kept: Secure institutional storage (two-site backup recommended) with a public metadata stub (DOI) in Zenodo describing the asset, access conditions, and justification. o For how long: Minimum 10 years beyond project end or the legally/contractually required period (whichever is longer). o Who decides: Owner proposes retention; GA validates in case of conflict; DPO confirms GDPR compatibility. o Resources: Institutional OPEX (storage, security, audits); designated contact for access requests. 3. Administrative datasets containing personal data (e.g., stakeholder lists, raw web logs). o Where kept: Internal secure systems only; no public sharing of raw personal data. o For how long: As per institutional GDPR retention schedules; delete or anonymise at end of purpose. o Who decides: Owner with DPO approval; DMP lead records status in the register. o Resources: Minimal storage; GDPR-compliant deletion workflows. 4. Physical outputs (pilot materials, reagents/samples). o Where kept: Owner’s facilities with inventory control and spec sheets archived; metadata record (DOI) public. A2C – Deliverable D9.4v1.0 Page 49 І 60 o For how long: ≥5 years for records; physical stocks until depletion or end-oflife. o Access: MTA/NDA and safety review; “not available” status recorded if depleted. o Resources: Local storage & safety management. A2C – Deliverable D9.4v1.0 Page 50 І 60 7 Data Security Data security has been a central priority of A2C to prevent unauthorised access, breaches, and loss of information. Security provisions covered physical protection, network and system safeguards, access controls, anonymisation protocols for personal data, and robust recovery/back-up mechanisms. These measures ensured that sensitive and high-value data were securely stored, transferred, and preserved in line with GDPR and Horizon Europe standards. 7.1 Security measures implemented Security provisions were applied at multiple levels across the consortium: • Physical security. Controlled access to facilities, restricted entry to server rooms, and access logs for sensitive documentation. Transportation of sensitive data was avoided, except in strictly controlled circumstances. • Network security. Sensitive datasets were kept on servers without external network access. Firewalls, intrusion detection systems, and secure protocols (VPN, SSL/TLS) were applied to protect remote connections. Systems were patched and updated regularly. • System and file security. Access was role-based, protected with strong password policies and multifactor authentication. Sensitive files were encrypted. Security software was updated regularly, and obsolete data were deleted using certified secure-deletion protocols to prevent recovery. • Protection of personal data in qualitative research. Personal data collected for recruitment in workshops, focus groups, and interviews were encrypted and stored securely, with access limited to the responsible staff. Anonymisation was systematically applied to transcripts and recordings to ensure individuals could not be identified. All personal data related to recruitment were securely deleted upon project completion, in compliance with GDPR and institutional retention schedules. • Data recovery and back-up procedures. In addition to protection measures, partners implemented redundant storage and back-up policies to ensure recovery in case of system failure or breach. These included: A2C – Deliverable D9.4v1.0 Page 51 І 60 o Scheduled daily or weekly backups of datasets to secure institutional servers. o Mirroring of critical datasets across at least two physically separate storage sites. o Use of repository-level disaster recovery features in Zenodo and institutional repositories. o Annual verification of backup integrity and test recovery drills to ensure operational continuity. These measures collectively ensured that no single point of failure could compromise the integrity or availability of A2C datasets. 7.2 Storage and long-term preservation During the project, datasets were stored on secure institutional servers, in trusted repositories (Zenodo, Mendeley Data), and in approved institutional cloud systems. For internal document sharing, Google Drive was used but not for storing sensitive or commercially valuable datasets. For long-term preservation, the consortium has established a unified approach: • Open/public datasets and outputs are deposited in the A2C Zenodo community, which CETEC commits to maintain for at least five years beyond project end. After this period, all deposits are migrated to CETEC’s institutional Zenodo account to guarantee indefinite availability and curation. • Restricted/confidential datasets are preserved in institutional secure servers with dual backup and metadata records (DOIs) deposited in Zenodo to ensure discoverability, even if access is controlled. • Trusted repositories. All long-term storage relies on repositories and infrastructures recognised as trusted (Zenodo, CEN repositories, VTT and university institutional repositories), which meet European Commission recommendations for security, redundancy, and accessibility. Through this consolidated policy, the consortium ensures that all project datasets— whether public or restricted—are preserved in environments that guarantee secure storage, long-term curation, and recoverability, eliminating inconsistencies across partners. A2C – Deliverable D9.4v1.0 Page 52 І 60 8 Ethical aspects Ethical considerations have been integrated into the A2C data management framework to ensure responsible collection, storage, and dissemination of data. While approaches have varied across partners, the project has adhered to recognised ethical and legal standards, ensuring compliance with relevant regulations throughout its duration. 8.1 Compliance with Ethical and Legal Standards A2C has followed the ethical guidelines established in the EU General Data Protection Regulation (GDPR) and other applicable frameworks governing data protection, privacy, and research integrity. The project has maintained a structured approach to ethical compliance, with key principles including: • Protection of sensitive information: Data containing commercially or ethically sensitive elements have been handled in accordance with appropriate security and confidentiality measures. • Long-term data preservation: While some partners have allocated specific resources for long-term storage, others have defined their strategies progressively, ensuring that any retained data comply with ethical and regulatory requirements. • Alignment with ethical best practices: The project has upheld principles of research integrity, data security, and participant confidentiality, applying appropriate safeguards to ensure compliance with ethical and legal obligations. 8.2 Data Sharing and Cross-Border Considerations No cross-border data sharing has taken place among partners in different countries, meaning that Data Sharing Agreements (DSAs) between universities, research centres, and technological partners have not been required. Each organisation has remained responsible for ensuring that its data management practices comply with national and institutional guidelines. A2C – Deliverable D9.4v1.0 Page 53 І 60 8.3 Ethical Oversight and Project Deliverables Ethical issues within A2C have been regulated through dedicated project deliverables, ensuring alignment with legal requirements and best practices throughout the project lifecycle: • D9.2 – Ethics Handbook: This document defines the ethical guidelines governing the project, ensuring that all partners adhere to principles of good scientific practice, data privacy, and confidentiality. It also includes a sample confidentiality agreement and consent form, which have been the basis for participant engagement in research activities. • D10.2 – POPD – Requirement No. 2: This deliverable outlines compliance measures related to the protection of personal data, providing a structured approach to ethical and legal obligations in the project. These deliverables have provided a framework for ethical compliance, ensuring that all partners are aware of and adhere to the established principles throughout the research process. 8.4 Informed Consent and Anonymisation All research activities involving human participants (e.g., workshops, focus groups, interviews) have followed strict ethical protocols. Measures implemented include: • Informed consent: Participants have been informed about the purpose of data collection, storage, and use, with explicit consent obtained before any data processing. • Anonymisation: Identifiable personal data have been removed or pseudonymised to ensure privacy. • Access control: Personal data collected for recruitment purposes have only been accessible to designated project staff and have been securely deleted upon project completion. These measures have ensured compliance with ethical standards while balancing data accessibility with privacy and security requirements. Throughout the project, all partners have acted in accordance with these principles, ensuring the responsible handling of data in line with both legal obligations and best practices in research ethics. A2C – Deliverable D9.4v1.0 Page 54 І 60 9 Conclusions This final version of the Agro2Circular (A2C) Data Management Plan consolidates all procedures implemented to ensure that data generated within the project are handled in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the Horizon Europe Open Science policy. The document reflects the project’s full data lifecycle -from generation to long-term preservationand includes a comprehensive Master Dataset Register (Annex I), which enumerates all datasets deposited in FAIRcompliant repositories (Zenodo and Mendeley Data). Each dataset record includes a DOI, metadata, README file, and a defined access level, ensuring traceability, interoperability, and long-term availability. The project has achieved full compliance with open access requirements for both scientific publications and underlying research data, ensuring that results are discoverable, properly documented, and accessible to the research community, industry, and policymakers. Two datasets are subject to non-commercial reuse licences due to justified restrictions linked to intellectual property or reuse limitations, as recorded in the dataset register. Data security and ethical compliance have been ensured throughout the project via GDPR-aligned procedures, anonymisation protocols, and institutional safeguards. Storage and preservation strategies have been harmonised across partners, guaranteeing the durability and integrity of both open and restricted datasets beyond the project’s completion. Overall, A2C has established a coherent and replicable framework for data governance in applied circular economy research, balancing open access with the protection of commercially sensitive information A2C – Deliverable D9.4v1.0 Page 55 І 60 ANNEX I. Master Dataset Register This annex provides the complete register of datasets generated under the Agro2Circular (A2C) project and made publicly available in trusted repositories in accordance with the Horizon Europe FAIR and Open Access policies. Each record includes the dataset identifier (DOI), description, linkage to the associated peer-reviewed publication, responsible partner, access conditions, licence, and repository location. All datasets include README files and, where applicable, data dictionaries describing variables, measurement units, and structure. Dataset A2C-DS01 Dataset Title PHBV cycle of life using waste as a starting point: from production to recyclability Authors / Responsible Partner García-Chumillas S.; Guerrero-Murcia T.; Nicolás-Liza M.; Monzó Sánchez M.F.; Simica A.G.; Simó-Cabrera L.; Martínez-Espinosa R.M. (University of Alicante; CETEC) Linked Publication DOI 10.3389/fmats.2024.1405483 Dataset DOI 10.5281/zenodo.17248814 Repository Zenodo Community Agro2Circular Access Level Open Licence CC BY 4.0 Format / File Size ZIP · 10.1 kB Work Package / Deliverable WP5 Short Description Supporting material for review article on PHBV synthesis, properties and recyclability; comparative tables. Date of Deposit 2 October 2025 Contact Person [email protected] Notes Metadata and tables included; README file provided. Dataset A2C-DS02 Dataset Title Sustainable food packaging using modified kombuchaderived bacterial cellulose nanofillers in biodegradable polymers Authors / Responsible Partner Zirbs R.; Koreshkov M.; Takatsuna Y.; Fritz I.; Bismarck A.; Reichelt E. (BOKU University; University of Vienna) Linked Publication DOI 10.1039/D4SU00168K Dataset DOI 10.5281/zenodo.17280161 Repository Zenodo A2C – Deliverable D9.4v1.0 Page 56 І 60 Community Agro2Circular Access Level Open Licence CC BY 4.0 Format / File Size ZIP · 264.1 MB Work Package / Deliverable WP3 Short Description Experimental results supporting publication on nanofillers in biopolymers; quantitative and qualitative characterisation data. Date of Deposit 6 October 2025 Contact Person [email protected] Notes SEM/TEM images available upon request; README included. Dataset A2C-DS03 Dataset Title Sustainable food packaging using modified kombuchaderived bacterial cellulose nanofillers in biodegradable polymers Authors / Responsible Partner Zirbs R.; Koreshkov M.; Takatsuna Y.; Fritz I.; Bismarck A.; Reichelt E. (BOKU University; University of Vienna) Linked Publication DOI 10.1039/D4SU00168K Dataset DOI 10.5281/zenodo.17280161 Repository Zenodo Community Agro2Circular Access Level Open Licence CC BY 4.0 Format / File Size ZIP · 264.1 MB Work Package / Deliverable WP3 Short Description Experimental results supporting publication on nanofillers in biopolymers; quantitative and qualitative characterisation data. Date of Deposit 6 October 2025 Contact Person [email protected] Notes SEM/TEM images available upon request; README included. Dataset A2C-DS04 Dataset Title Carotenoid production by Haloferax mediterranei using starch residues from the candy industry as a carbon source Authors / Responsible Giani M.; Pire C.; Martínez-Espinosa R.M. (University of Alicante) A2C – Deliverable D9.4v1.0 Page 57 І 60 Partner Linked Publication DOI 10.1016/j.crbiot.2024.100265 Dataset DOI 10.5281/zenodo.17306035 Repository Zenodo Community Agro2Circular Access Level Open Licence CC BY 4.0 Format / File Size RAR (XLSX) · 18.0 kB Work Package / Deliverable WP5 · D5.2 Short Description Data on pigment concentration, biomass and carotenoid composition in H. mediterranei grown on starch residues. Date of Deposit 9 October 2025 Contact Person [email protected] Notes Includes full experimental methodology in README. Dataset A2C-DS05 Dataset Title Production of Poly(3-hydroxybutyrate-co-3hydroxyvalerate) (PHBV) by Haloferax mediterranei using candy industry waste as raw materials Authors / Responsible Partner Simó-Cabrera L.; García-Chumillas S.; Cánovas V.; Monzó Sánchez M.F.; Pire C.; Martínez-Espinosa R.M. (University of Alicante; CETEC) Linked Publication DOI 10.3390/bioengineering11090870 Dataset DOI 10.5281/zenodo.17306238 Repository Zenodo Community Agro2Circular Access Level Open Licence CC BY 4.0 Format / File Size ZIP · 17.5 kB Work Package / Deliverable WP5 · D5.2 Short Description Experimental data on PHBV production from industrial candy waste; yield analysis and controls. Date of Deposit 9 October 2025 Contact Person [email protected] Notes README and metadata provided. Dataset A2C-DS06 Dataset Title Exploring yeast biodiversity and process conditions for optimising ethylene glycol conversion into glycolic acid Authors / Senatore V.G.; Branduardi P. (University of Milano-