BREADCRUMB Project DMP

This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 DELIVERABLE 6.2 Data Management Plan - 1 D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 1 of 61 Project title BRinging Evidence-bAseD food Chain solutions to prevent and RedUce food waste related to Marketing standards, and deliver climate and circularity co Benefits Project acronym BREADCRUMB Call topic HORIZON-CL6-2023-FARM2FORK-01 Type of action HORIZON-RIA Coordinator EIGEN VERMOGEN VAN HET INSTITUUT VOOR LANDBOUWEN VISSERIJONDERZOEK - EV ILVO Project number 101136701 Project start date 01/01/2024 Duration 36 months URL https://www.breadcrumb-project.eu/ D6.2 – DATA MANAGEMENT PLAN - 1 Due date 31/03/2024 Delivery date 30/03/2024 Work package WP6 Responsible Author(s) Capwell Forbang Echo (EV-ILVO), Anna Twarogowska (EV-ILVO), Sofie De Man (EV-ILVO), Rani Van Gompel (EV-ILVO), Karima Abunada (EV-ILVO). Contributor(s) Reviewer(s) Sasa Straus (ITC), Laura Morcillo (PNO) Version v1.0 Dissemination level Public D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 2 of 61 DISCLAIMER The content of the publication herein is the sole responsibility of the publishers and it does not necessarily represent the views expressed by the European Commission or its services. While the information contained in the documents is believed to be accurate, the authors(s) or any other participant in the BREADCRUMB consortium make no warranty of any kind with regard to this material including, but not limited to the implied warranties of merchantability and fitness for a particular purpose. Neither the BREADCRUMB Consortium nor any of its members, their officers, employees or agents shall be responsible or liable in negligence or otherwise howsoever in respect of any inaccuracy or omission herein. Without derogating from the generality of the foregoing neither the BREADCRUMB Consortium nor any of its members, their officers, employees or agents shall be liable for any direct or indirect or consequential loss or damage caused by or arising from any information advice or inaccuracy or omission herein. COPYRIGHT MESSAGE © BREADCRUMB Consortium, 2024-2026. This deliverable contains original unpublished work except where clearly indicated otherwise. Acknowledgment of previously published material and of the work of others has been made through appropriate citation, quotation, or both. Reproduction is authorised provided the source is acknowledged. VERSION AND AMENDMENTS HISTORY Version Date (DD/ MM/ YYYY) Created /Amended by Changes 0.1 29/03/2024 Capwell Forbang Echo First draft 0.2 04/04/2024 Laura Morcillo Review 1.0 15/04/2024 Capwell Forbang Echo Minor changes and styling D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 3 of 61 TABLE OF CONTENTS TABLE OF CONTENTS ..................................................................................................................................... 3 EXECUTIVE SUMMARY ................................................................................................................................... 8 1 INTRODUCTION ....................................................................................................................................... 9 1.1 MAIN AIMS OF BREADCRUMB ............................................................................................................ 9 1.2 FROM BREADCRUMB OUTPUTS TO IMPACT ...................................................................................... 11 1.3 DATA MANAGEMENT IN BREADCRUMB ............................................................................................ 12 2 FAIR DATA .............................................................................................................................................. 14 2.1 MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA ...................................................... 14 2.2 MAKING DATA ACCESSIBLE ............................................................................................................. 16 2.2.1 Open data .................................................................................................................................... 16 2.2.2 Closed data .................................................................................................................................. 16 2.2.3 Assigning a repository for Open Access of data in the project .................................................... 17 2.3 MAKING DATA INTEROPERABLE ..................................................................................................... 18 2.4 ENHANCING DATA RE-USE ................................................................................................................ 19 2.4.1 Licensing ...................................................................................................................................... 19 2.4.2 Re-use pathways ......................................................................................................................... 20 2.4.3 Data quality assurance ................................................................................................................ 20 3 DATA OVERVIEW ................................................................................................................................... 22 3.1 DATA GENERATED AND ITS PURPOSE ................................................................................................... 22 3.2 DATA TYPE AND FORMATS................................................................................................................... 24 3.3 DATA PROVENANCE ............................................................................................................................ 30 3.4 DATA UTILISATION .............................................................................................................................. 31 3.5 RE-USE OF DATA ................................................................................................................................ 32 3.6 PRACTICALIZING THE FAIR PRINCIPLES PER WP ................................................................................. 33 4 ALLOCATION OF RESOURCES ............................................................................................................ 36 4.1 WHAT WILL THE COSTS BE FOR MAKING DATA AND OTHER RESEARCH OUTPUTS FAIR IN YOUR PROJECT? 36 4.2 HOW WILL THESE COSTS BE COVERED? ............................................................................................... 36 4.3 WHO WILL BE RESPONSIBLE FOR DATA MANAGEMENT IN YOUR PROJECT? ............................................. 36 4.4 HOW WILL LONG-TERM PRESERVATION BE ENSURED? .......................................................................... 37 5 DATA SECURITY .................................................................................................................................... 38 5.1 WHAT PROVISIONS ARE OR WILL BE IN PLACE FOR DATA SECURITY? ...................................................... 38 5.2 WILL THE DATA BE SAFELY STORED IN TRUSTED REPOSITORIES FOR LONG TERM PRESERVATION AND CURATION? .................................................................................................................................................... 39 6 ETHICS .................................................................................................................................................... 40 D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 4 of 61 6.1 ARE THERE, OR COULD THERE BE, ANY ETHICS OR LEGAL ISSUES THAT CAN HAVE AN IMPACT ON DATA SHARING? ...................................................................................................................................................... 40 6.2 WILL INFORMED CONSENT FOR DATA SHARING AND LONG TERM PRESERVATION BE INCLUDED IN QUESTIONNAIRES DEALING WITH PERSONAL DATA ............................................................................................ 41 7 ANNEX ..................................................................................................................................................... 42 7.1 ANNEX 1: BREADCRUMB DELIVERABLE QUALITY REVIEW TOOL ............................................................ 42 7.2 ANNEX 2: INFO SHEET ANONYMISING OR PSEUDONYMISING DATA .......................................................... 45 7.2.1 Why anonymise or pseudonymise? ............................................................................................. 45 7.2.2 What is anonymisation or pseudonymisation? ............................................................................ 46 7.2.3 Storage of the data ...................................................................................................................... 47 7.2.4 When to anonymise or pseudonymise? ...................................................................................... 48 7.2.5 How to anonymise or pseudonymise? ......................................................................................... 48 7.3 ANNEX 3: TEMPLATE INFORMATION SHEET AND INFORMED CONSENT .................................................... 54 7.4 ANNEX 4: TEMPLATE FOR AN APPROVAL OF COMESSH FROM EV ILVO ............................................... 57 D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 5 of 61 LIST OF FIGURES FIGURE 1: BREADCRUMB AT A GLANCE .................................................................................................. 10 FIGURE 2: OVERVIEW OF BREADCRUMB IMPACT PATHWAYS............................................................. 11 FIGURE 3: FLOW OF DATA IN BREADCRUMB PROJECT ........................................................................ 13 LIST OF TABLES TABLE 1: LIST OF DESIGNATED DATA PROTECTION STEWARDS (DPSS) .......................................... 12 TABLE 2: FIRST QUALIFIER THAT REFERS TO THE DATA COLLECTION METHOD ............................ 18 TABLE 3: DELIVERABLE OVERVIEW ACCORDING TO THE BREADCRUMB GRANT AGREEMENT ... 25 TABLE 4: IMPLEMENTATION OF THE FAIR PRINCIPLE PER WP ............................................................ 33 D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 6 of 61 Glossary of terms and acronyms used Acronym/Term Description ABM Agent-Based Model AI Artificial Intelligence CC0 Creative Commons no rights reserved CC BY Creative Commons Attribution License CERN Conseil Européen pour la Recherche Nucléaire ComESSH Commission for Ethics in Social Science and Humanities CS Case Study CSV Comma-separated values D Deliverable DDMMYYYY Day and Month in 2 numbers, Year in 4 numbers e.g. 31042024 DM Data Management DMP Data Management Plan DMT Data Management Team Docx Microsoft Word text document DOI Digital Object Identifier DPS Data Protection Steward EU European Union FAIR Findable, Accessible, Interoperable, and Reusable FLW Food Loss & Waste FW Food Waste GDPR General Data Protection Regulation GIT Global Information Tracker HE Horizon Europe IP1, 2, 3, … Impact Pathway 1 ISO International Standards Organisation D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 7 of 61 JRC Joined Research Center M Month MS Member States IDI In-depth Interviews FGI Focus Group Interview MOA Motivation, Opportunity, Ability MP3, MP4 Motion Pictures Expert Group Audio Layer 3, 4 MS Microsoft O1 Objective 1 OEI Other Ethics Issues ORD Office of Research and Development PID Personal Identifiable Data R1, 2, 3, … Result 1 R&I Research & Innovation RAR Roshal Archive format SSL/TLS Secure Sockets Layer/Transport Layer Security TXT Text USB Universal Serial Bus WP Work Package T Task ZIP Compressed file TCB Technical Coordination Board SEN Sensitive PU Public D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 8 of 61 EXECUTIVE SUMMARY The Data Management Plan – 1 is a first of three planned reports that provides a comprehensive overview of the key aspects addressed in the BREADCRUMB project, focusing on data management, FAIR principles implementation, resource allocation, data security, and ethical considerations. The BREADCRUMB project aims to facilitate impactful research outcomes through effective data management practices. By ensuring the transparency, accessibility, and interoperability of data, the project seeks to maximize the utility and reusability of research outputs. Emphasizing the principles of Findability, Accessibility, Interoperability, and Reusability (FAIR), the DMP strategies for making data more discoverable, accessible, and usable. This includes provisions for metadata, open and closed data access, repository assignment, and pathways for data re-use. The data overview section further delineates the types, formats, provenance, and utilization of data generated within the project. By understanding the nature and purpose of the data, stakeholders can better leverage it for research purposes and ensure compliance with FAIR principles. Additionally, effective resource allocation is crucial for implementing FAIR data practices. This section outlines the anticipated costs, funding sources, responsible parties for data management, and strategies for long-term preservation to ensure the sustainability of the project's data infrastructure. Moreso, addressing concerns about data security, the DMP highlights provisions in place to safeguard data integrity and confidentiality. Trusted repositories and protocols for long-term preservation and curation are emphasized to mitigate risks associated with data storage and sharing. Ethical considerations are paramount in data-intensive research projects. The DMP acknowledges potential ethical and legal implications of data sharing, emphasizing the importance of informed consent and adherence to privacy regulations in handling personal data. In conclusion, the BREADCRUMB project as highlighted in the DMP is committed to promoting transparency, accessibility, and ethical integrity in data management practices. By adhering to FAIR principles, allocating resources effectively, ensuring data security, and upholding ethical standards, the project aims to maximize the impact of its research outcomes while safeguarding the rights and privacy of individuals involved. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 15 of 61 Versioning Version refers to saving new copies of files, so previous versions can be referred to. This practice of tracking and managing changes made to a file over time ensures that a record of changes is being made to the files and to give the new files a unique name. Each time a file is modified, saved, or updated, a new version is created, allowing users to access previous iterations of the file if needed. This process helps in maintaining a history of changes, facilitating collaboration, and ensuring accountability in projects or document management systems. A unique version number should be assigned to each version of a document depending on whether the changes are significant (major) or not (minor). In the BREADCRUMB project, a strict versioning of the project files and documents will be followed: ✓ The author of the document will ensure the current version number is identified on the appropriate place on the first page of the document. ✓ The first draft of a document will be version 0.1, while subsequent drafts will be an increase of “0.1 ” in the version number (e.g., 0.2, 0.3, 0.4, 0.9, 0.10, etc.). ✓ The first final version of a document will be Version 1.0, while Subsequent final documents will have an increase of “1.0” in the version number (1.0, 2.0, etc.) ✓ The second final version of a document after reviewing by a partner will be Version 1.1, 1.2, etc. Metadata Provenance metadata shows the source and history of an object. All BREADCRUMB datasets must have the following provenance metadata defined in the Data Protocol (D1.1): dataset PID dataset description, dataset date of deposit (data range), dataset author(s), dataset venue(internal and external), dataset embargo, HE funding statement, project name, project acronym, project number, D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 16 of 61 licensing terms, PID for the authors, PID for the authors’ organizations, PID for the grant, PID for related publications and other outputs. In addition, the data protocol (D1.1) defines additional datatype-specific metadata, enumerating all mandatory metadata to be collected by responsible partners. Alongside the data from the project, relevant metadata will be deposited in repositories identified in D1.1. If the repository offers such an option, keywords for metadata will be available. Apart from the project's metadata, relevant metadata as specified in the D1.1 will be deposited in the specified repositories (D1.1). If the repository offers the option for harvesting and indexing metadata, then metadata from the project and its various outputs will be indexed and available for harvesting. 2.2 Making data ACCESSIBLE 2.2.1 Open data All data compiled on the project website will be made publicly available. This includes: ✓ Information on existing food marketing standards, as well as anonymized data from case studies, previously approved by the data owner through informed consent. ✓ Data concerning the MOA model and Role Playing Games, when applicable and approved by the data owner through informed consent. ✓ Details of events and activities, such as venue, date, type of demonstrations, and participating actors. ✓ Deliverables, reports, scientific papers, capacity-building materials, and communication and dissemination materials. Accessing the website requires standard tools: a web browser and, for accessing project documents, MS Office or Linux. Datasets will be anonymized whenever possible. If only pseudonymization is feasible, the dataset will not be publicly accessible due to GDPR regulations. Sensitive stakeholder data held by consortium partners (e.g., email and contact lists) for communication and dissemination purposes must be stored with password-based user authentication on partner institution servers. 2.2.2 Closed data Sensitive data, including details of data collection (e.g., date, format, location), must not be openly accessible. Any information that could identify a natural person, or 'Data Subject,' will be anonymized before storage on servers. Business-related data, such as company ownership and property details, are considered sensitive D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 17 of 61 and must be removed during data anonymization. For further details on informed consent procedures, see the Ethics section. 2.2.3 Assigning a repository for Open Access of data in the project The openly available data will be accessible via a free and standardized access protocol. Per the grant agreement, BREADCRUMB will ensure that openly available data is accessible through an open repository. The consortium partners have chosen ZENODO for this purpose due to its platform's safety, trustworthiness, citability, immediate access, flexibility for open or closed access, versioning capabilities, GitHub integration, and usage statistics tracking. This repository will include information accompanying any research output. BREADCRUMB will hence, use ZENODO repository as a main tool for making research data findable in accordance with the HE Open Access mandate. This is based on the recommendation of several project partners who have knowledge of the functional and usage of ZENODO repository in previous projects. There is flexibility for project partners to select other trusted repositories while taking into account the specified guidelines. All preferred repositories were recorded in D1.1. However, if partners prefer another repository, they have to inform the project coordinator. This was discussed at the kick-off meeting. ZENODO, operated by the Conseil Européen pour la Recherche Nucléaire (CERN), is a portal built upon the widely recognized Global Information Tracker (GIT) version control system and the Digital Object Identifier (DOI) system. Aligned with the FAIR principles, it serves as an ideal choice for this purpose. ZENODO facilitates the discovery, access, reuse, and interoperability of datasets, in line with the requirements of Office of Research and Development (ORD) projects. Its repository services are provided free of charge, allowing researchers to share and preserve various research outputs, including datasets, images, presentations, publications, and software, regardless of size or format. Through established practices like mirroring and regular backups, both the digital data and associated metadata are safeguarded. Each uploaded dataset is allocated a unique DOI, ensuring its distinct identification, traceability, and citability. For publicly available data on ZENODO, the platform's metadata standards can be applied. The security settings of ZENODO are summarized as follows: ✓ Versions: Data files undergo versioning, while records do not. Uploaded data is archived as a Submission Information Package, and derivatives are created without altering the original content. Records can be withdrawn from public view, but both data files and records are preserved. ✓ Replicas and file preservation: All data files are stored primarily in the CERN Data Centres in Geneva, with additional replicas in Budapest. These files are maintained in multiple replicas within a distributed file system, which undergo nightly backups onto tape. ✓ Retention period: Items remain in the repository for its lifetime, with ZENODO CERN's host laboratory defining a minimum repository lifespan of the next 20 years. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 18 of 61 ✓ Functional preservation: ZENODO does not guarantee the ongoing usability and comprehensibility of deposited objects over time. ✓ Fixity and authenticity: Each data file is stored with an MD5 checksum of its content, and regular checks are conducted against these checksums to ensure the integrity of the file content. ✓ Succession plans: In the event of repository closure, ZENODO commits to migrating all content to suitable alternative institutional and/or subject-based repositories. The dataset PID is allocated by the data repository. 2.3 Making data INTEROPERABLE In BREADCRUMB, each data file/set should be identified by a unique name, as proposed and elaborated in D1.1 Data Protocol. The naming convention adheres to the following rules: ✓ A data file/dataset name consists of several parts (qualifiers) connected by periods, ✓ Each qualifier must begin with an alphabetic character (A-Z) or a special character ($,#,@), ✓ Each qualifier may contain alphabetic characters (A-Z), digits (0-9), a hyphen (-), or the special characters ($, #, @), ✓ Each qualifier should be as short and meaningful as possible, ✓ Each data file/set name should be as short and meaningful as possible, ✓ Data file/set name should include at least two qualifiers; the first qualifier refers to the data collection method/technique and starts with a capital letter (Table 2) Table 2: First qualifier that refers to the data collection method Data collection method/technique Qualifier Focus Group Interview FGI In-Depth Interview IDI Online Survey Survey Social Simulation SocSim Role Playing Games RPG Qualitative Desk Research Qual Quantitative Desk Research Quant ✓ If multiple files are produced with the use of the same method/technique in the same case study/task (e.g., multiple in-depth interviews), the first qualifier is appended with a unique number (e.g., IDI01, SocSim01), D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 19 of 61 ✓ The second quantifiers will indicate the food sector. For example F&V for fruit and vegetable, C for cereals, M for Meat, E for eggs and F for fish. ✓ The third qualifiers denote the case study, and/or the related tasks that produce the dataset. This third qualifier is completed with a hyphen and an indication of the location or origin of the dataset. For example CSx-Y where x = case study number and Y = country code. It could also be related to a task in the BREADCRUMB project. In which case, TX.Y = T1.1 for example followed meaning task title. Proposed dataset names for all data types generated in BREADCRUMB can be found in Table 1 in deliverable 1.1 Data protocol. The BREADCRUMB project data ontology is described and mapped in D1.1 Data protocol. Wherever possible, the data will include qualified references 1 to other data. Grouping the data collection methods and techniques enables us to standardize provenance metadata tailored to the collection approach. BREADCRUMB has several dedicated tasks and processes to guarantee the data quality: D1.1 Data protocol and D2.2 Case Study Plans, deliverable quality review procedure. 2.4 Enhancing data RE-USE 2.4.1 Licensing The BREADCRUMB Research data will be licensed under the latest version of CC BY (attribution required) or CC0 (public domain), or an equivalent license. Our standard preference is for CC BY 4.0. However, we may also consider Share-Alike and Non-Commercial Share-Alike licenses for specific portions of the datasets if the Consortium decides to make those parts public. ✓ Under the Attribution license, users must appropriately credit the dataset, provide a link to the license, and indicate any changes made. ✓ The Non-commercial license prohibits commercial use of the dataset by others, while ✓ Share Alike license requires derivative works to be licensed under the same terms as the original data. Metadata associated with the dataset will be openly available and licensed under a public domain dedication, such as Creative Commons Public Domain Dedication CC0, whenever the data repository allows for such an option. Regardless of the level of protection applied to the empirical data collected in the project, provenance metadata in BREADCRUMB Research must be open under CC0 or an equivalent license, ensuring that 1 A qualified reference is a cross-reference that explains its intent. For example, X is regulator of Y is a much more qualified reference than X is associated with Y, or X see also Y. The goal therefore is to create as many meaningful links as possible between (meta)data resources to enrich the contextual knowledge about the data. (Source: https://www.go-fair.org/fair-principles/i3-metadata-include-qualified-references-metadata/) D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 20 of 61 legitimate interests or constraints are safeguarded. To aid in identifying and collecting relevant metadata, tools tailored to different method types have been prepared for project partners (D1.1). Upon completion of the project, all simulation code within WP 3 will be open-sourced under an Apache License 2.0. This license balances being open source while permitting commercial users to develop extensions or visualizations without being obligated to open source their additions. 2.4.2 Re-use pathways Documentation needed for the validation of data analysis, and facilitate data re-use in the project will be provided in the following way: WP1: The Evidence Search Plan/Methodology (Word) for Task 1.3 will provide information on how and what data was obtained during research and interviews, and the internet or journal sources for already publiclyavailable resources on food marketing standards and food loss and food waste generation. The subsequent and remaining task in WP1 will rely solely on the data provided in T1.3. UNIBO: metadata, methodology report, codes, file listing libraries/packages used for running the simulation. UCPH: Data will be stored in open registry which defiantly facilitate data re-use. Original data will also be made available for reference. All open datasets, deliverables, and scientific publications will be uploaded to ZENODO. Public deliverables will also be accessible on the official BREADCRUMB website at " https://www.breadcrumb-project.eu/ ". These resources will be linked to the OpenAIRE community to maximize their discoverability. Uploaded files will be appropriately tagged with metadata according to ZENODO standards, as well as tagged with the identifier "BREADCRUMB." The dataset embargo requires anonymization and obtaining specific participant permission to share potentially redacted transcripts. It may contain personal data, in which case anonymization or sharing only aggregated data would be necessary. Personal data, sensitive data, or data pertaining to companies must be protected. The data will only be made available to external stakeholders after the project, ensuring that it does not compromise the legitimate interests of the beneficiaries. An embargo period may be implemented if the data, or certain portions of it, will be utilized in published articles in "Green" open access journals. The European Commission recommends a maximum embargo period of 6 months in such cases. 2.4.3 Data quality assurance BREADCRUMB has several dedicated tasks to guarantee the data quality. They include: ✓ D1.1 Data Protocol, D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 21 of 61 ✓ D2.2 Case Study Plans -1 and subsequent updates ✓ Deliverable Quality Review template ✓ Data Management Plans ✓ Data management through institutional data protection stewards (DPS) D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 22 of 61 3 DATA OVERVIEW 3.1 Data generated and its purpose BREADCRUMB strives to improve the understanding of how food marketing standards influence food loss and waste (FLW) generation. This knowledge is crucial to improve the effectiveness of decision-making and engagement of food chain actors, towards zero food waste. To achieve this aim, the project will pursue five detailed objectives (O), and one overall administrative objective. Objective 1: To establish a holistic view of the existing food marketing standards in the EU and their interrelations, by placing special emphasis on the least documented ones, that is: (i) private standards, and (ii) standards adopted at the level of specific MS, and to identify the marketing standards which are most relevant to FW generation. Corresponding WPs: WP1 Main data sources: desktop research supplemented by in-depth interviews (IDI) will be the main data collection and re-use methods. Data in WP1 will be collected to bet an inventory of private and public marketing standards in EU27. Objective 2: To create an empirical evidence base, by fusing existing (O1) and project-generated data, to provide estimates of the FW generated due to marketing standards in the supply chains of five targeted food commodities (fruits & vegetables, meat, eggs, cereals, and fish). Corresponding WP: WP2 Main data sources: desktop research and 16 project case studies implementing surveys, IDI, and focus group interviews (FGI). Further details in D1.1 Data Protocol and D2.3 Case Study Plans - 1. Objective 3: To understand and model: (i) the underlying mechanisms through which, marketing standards lead to FW generation, and (ii) the trade-offs between the objective of FW reduction and other objectives pursued by marketing standards. This will be structured to validate solutions (re-balancing) that alleviate the negative impacts of marketing standards to FW, while balancing the trade-offs with their other objectives. Corresponding WP: WP3 D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 23 of 61 Main data sources: existing knowledge on Motivation, Opportunity and Ability (MOA) approaches, empirical data collection through social simulation Objective 4: To improve market access and business potential of foods that do not meet marketing standards but are still safe to eat (suboptimal foods), by: (i) guiding food businesses in selecting appropriate marketing channels and business models, and assisting them in quantifying their business value; (ii) fostering change in consumers’ and businesses’ attitudes towards sub-optimal foods, through nudging marketing cues. Corresponding WP: WP4. Main data sources: analytical and report input from WP1 (D1.2: Conceptual Framework, D1.3: Inventory of private and public marketing standards in EU27, and D1.4 FW-relevant marketing standards), WP2 (D2.1 EU estimates of FW generated due to marketing standards, D2.3 Case study estimates of FW generated due to marketing standards, and D2.6 Estimates of FW generated due to marketing standards, including FW coefficients), and WP4 ( D4.1 Typology of suboptimal food valorisation options). Objective 5: To effectively manage the upscaling of the project results by: (i) developing operational guidelines and policy recommendations on how to prevent/reduce FW due to marketing standards, and thereby contributing to environmental sustainability and circularity of the food system; (ii) formulating a Code of Conduct balancing commercial and social value from suboptimal foods, and thereby contributing to economic sustainability and food poverty reduction; (iii) developing a strategy for the exploitation of key project results by the project partners (individually and jointly); (iv) undertaking appropriate dissemination and communication actions to maximise the project’s impact; (v) establishing formal agreements (MoUs) with relevant projects to achieve impact synergies. Corresponding WP: WP5. Objective 6: To ensure ✓ the effective administration of project activities according to EU rules, and to sound project management practices; ✓ that all project outcomes are delivered according to the agreed timeand resource-planning; ✓ that all potential risks are identified at an early stage, and appropriate mitigation actions are taken; ✓ a sound ethical treatment of participants and to meet laws and regulations regarding data management. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 24 of 61 Corresponding WP: WP6 3.2 Data type and formats BREADCRUMB has a complex and multidisciplinary nature requiring various types of data. These data will come from several sources, stored in various formats. The gathered data will be analysed or processed resulting into a set of deliverables (Table 3) which will be used in communication and dissemination activities. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 31 of 61 WP4 where primary data will also be collected from consumers in 27 EU, the D1.1 elaborates on its provenance. Secondary data will be collected from multiple sources, for multiple tasks in different WPs including official databases, surveys, reports, scientific studies and other European projects and for multiple levels: European, national, regional and local. The data origin will be explained in each deliverable. The use of existing data or the generation of new data will be clearly identified in each deliverable produced. The ethical clearance of all data, both new and existing, will be addressed by each WP leader and will be clearly reported. The gathered data will be analysed or processed resulting into a set of deliverables and used in communication and dissemination activities. All BREADCRUMB datasets must have a dataset persistent Identifier (PID), assigned by the indicated data repository. 3.4 Data utilisation BREADCRUMB datasets will be potentially used by three main groups: the consortium partners, the scientific community and the end-users. This last group is a very diverse group, and considering the five food sectors targeted in the project (fruits and vegetables, cereals, eggs, meat and fish), they include: food producers, food businesses, food processing, retail, policy makers (EU, National and Regional governments and local authorities), educational institutions, citizens/consumers/households/, schools. At different levels, the data users during the course of the project will primarily be the 21 consortium partners, who have specified and expected data needs. All information aimed at the scientific community (project reports, deliverables, publications) will be centralised on the BREADCRUMB project website https://www.breadcrumb-project.eu/ In addition, the results and insights for external stakeholders and other end users will be made publicly available on the project website. Given the fact that each consortium partner has a network, a non-determined number of institutions, enterprises and organisations interested in European food system issues especially food marketing standards and food waste generation may also be potential users of the data generated and insights from the project. Furthermore, the Dissemination, Exploitation, and Communication Plans (D5.1, D5.2 and D5.3) will detail how to reach these end-users. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 32 of 61 Furthermore, we will collaborate with the sister project ROSETTA, other FLW-related projects such as ZeroW, CHORIZO, SISTERS, etc. and the JRC to discuss possible joint communications for similar end-users. The resulting approach, if necessary, will be described in an update of the DMP. 3.5 Re-use of data Data re-use will be present, particularly in the collection and analyses of information on existing food marketing standards (WP1), the collection and analyses of food waste estimates due to marketing standards (WP2), and the use of MOA approaches in the modelling work (WP3). Previous experiences and research will serve as data sources and will be collected through (i) the involved partners; (ii) a screening of existing projects and databases, (iii) collaboration with JRC, and sister project ROSETTA. The access to sensitive raw data collected on existing food marketing standards, and on estimates of food waste due to these marketing standards, interviews and similar primary data generated within the project, will be limited to the project partners that work directly with these data. This will be ensured by storing the sensitive raw data in separate restricted access folders on the MS Teams/SharePoint-platform. The non-confidential data generated within the BREADCRUMB project will be available for re-use by the consortium partners after the project ends and will be stored on the project SharePoint hosted by EV-ILVO. The duration of kept data will be discussed in the next Technical Coordination Board (TCB) meeting. The publicly available data will be stored on the BREADCRUMB website (https://www.breadcrumb-project.eu/ ) for at least 3 years after the project ends. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 33 of 61 3.6 Practicalizing the FAIR principles per WP Table 4: Implementation of the FAIR principle per WP WP Partner Expected size of data Description Reference to other data Re-use of data Purpose of data WP1 VLTN Data (generated from desktop research and >200 interviews from food chain stakeholders in EU27 and from the 16 project case studies) on existing private and Member-State food marketing standards An evidence search plan and methodology has been developed in the framework of T1.3 will provide information on how and what data was obtained during the desktop research and interviews, and the internet or journal sources. T1.4 will rely fully on the data gotten from T1.3 Yes Collection and analyses of data on private and Member-State food marketing standards Collection and re-use of data on existing private and Member-State food marketing standards to define hypotheses on in T1.4: (i) the links and the cause-effect intensity (in qualitative terms) between specific food marketing standards and FW; (ii) the links between different food marketing standards. Based on these, a preliminary identification of FW-relevant marketing standards will be developed WP2 UCPH Data generated from desktop research and interviews on estimates of FW as a results of marketing standards in the 27 EU countries. A minimum of 200 stakeholders will be reached. An aligned evidence search plan and methodology as that of T1.3 will be employed in T2.1 to collect information through desktop research and in-depth interviews on estimates of FW due to marketing standards Yes Collection and analyses of data on FW estimates due to food marketing standards To estimate FW in 27 EU countries for five food sectors due to marketing standards D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 34 of 61 ILVO 16 case studies, sample sizes are detailed within the D1.1 Data Protocol and D2.2 Case Study plans -1 No The storage of results of evidence-based analyses of existing food marketing standards and their potential to generate FW (estimates) Collection of data on 16 case studies (further details in D1.1 Data Protocol and D2.2 Case Study Plans - 1) WP3 UNIBO Modelling, role playing games and social simulation: possible to specify in M24 Metadata, methodology report, codes, file listing libraries/packages used for running the simulation No The use of MOA approaches in the modelling work Existing knowledge MOA approaches, empirical data collection through social simulation WP4 MC Qualitative data generated through interviews of stakeholders (number to be determined in M15) Desktop qualitative research data on the typology of suboptimal foods. One specific food product from each of the targeted food commodities will be selected based on: (i) the high influence of marketing standards (based on the results of T1.4), and (ii) the food product’s strong potential business Yes Analytics in WP1, WP2, WP3 will feed or guide the data to be collected and analysed AINIA Qualitative and Quantitative data generated from Survey on consumer acceptance of suboptimal foods generated Yes Analytics in WP1, WP2, WP3 will feed or guide the data to be collected and analysed D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 35 of 61 approximately 500 consumers in 27 EU countries through online survey. through online survey which illtarget 500 consumers WP5 PNO Relatively in small size Some Excel files, Word files, templates, logos which are internally shared within the consortium. Public information is stored on the website and in the newsletters. N/A Gather data relating to business and upscaling strategies for exploiting project results that are innovative. Also, to provide templates to the partners in the consortium to professionalize the working methods e.g. templates for deliverables, presentations, … Some documents also have the purpose to collect info from the partners for example, dissemination, exploitation and communication. WP6 EV-ILVO Relatively in small size Some Excel files, Word files, templates, which are internally shared within the consortium. No N/A The documents will only be used for internal project management for example contact list, meeting minutes, … and for communication with external stakeholders. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 36 of 61 4 ALLOCATION OF RESOURCES 4.1 What will the costs be for making data and other research outputs FAIR in your project? Costs for data storage is to be kept at a minimum in BREADCRUMB using free services and tools, like ZENODO and project MS Teams site. Still, some costs may still incur. The current foreseen costs for making data FAIR includes: ✓ Fees associated with the publication of scientific articles containing project’s research data in “Gold” Open access journals. The cost sharing, in case of multiple authors, shall be decided among the authors on a case-by-case basis. ✓ Project website development and operation: budget of PNO. The requirement is to maintain the website 6 months after the completion of the project. ✓ Data archiving at ZENODO and on other online data base: free of charge. ✓ Copyright licensing with Creative Commons: free of charge. Costs related to open access to research data in Horizon 2020 are eligible for reimbursement under the conditions defined in the BREADCRUMB Grant Agreement, in particular Article 6 (“Eligible Cost”) and Article 6.2. (“D.2 Costs of other goods and services”), but also other articles relevant for the cost category chosen. Project beneficiaries will be responsible for applying for reimbursement for costs related to making data accessible to others beyond the BREADCRUMB Consortium. To be eligible, costs must be: a) purchased specifically for the action and in accordance with Article 10.1.1 (best value for money) or, b) contributed in-kind against payment and in accordance with Article 11.1 (rules for in-kind contributions for against payment). 4.2 How will these costs be covered? Other costs may incur during the project’s lifetime and will be evaluated as eligible or not as specified in the Grant Agreement. Each partner is responsible for the data they produce. Any fee incurred for Open Access through scientific publication of the data will be the responsibility of the data owner (authors) partner(s). 4.3 Who will be responsible for data management in your project? Data manager of BREADCRUMB is EV-ILVO. Capwell Echo Forbang is the DPS of EV-ILVO. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 37 of 61 4.4 How will long-term preservation be ensured? The default long-term preservation will be through ZENODO. ZENODO states that: "Items will be retained for the lifetime of the repository. This is currently the lifetime of the host laboratory CERN, which currently has an experimental programme defined for the next 20 years at least." Since uploading to ZENODO is free of charge this is expected to keep overall retention costs small. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 38 of 61 5 DATA SECURITY 5.1 What provisions are or will be in place for data security? Raw data will not be shared because it is subject to privacy laws. Pseudonymised data will be shared with all members of BREADCRUMB utilizing security measures but will not be made public. However, in accordance with the HE policy data will be made available when anonymisation is possible. Anonymisation involves techniques that can be used to convert personal information into anonymised data; data void of personally identifiable details. . See Annex 7.2 for guidelines on anonymising and pseudonymising data. At the level of the BREADCRUMB project partners: The BREADCRUMB project uses MS Teams, an online collaboration platform. On this platform, a dedicated project site has been established, accessible only by the partner representatives in the Consortium. Furthermore, the coordinator is thinking of creating a folder to collect and store datasets, allowing for stricter access control than the main project site, when needed. The following security settings on the BREADCRUMB MS Teams have been established: ✓ Access level: Restricted to project members only. Further access restrictions on specific folders are enabled. ✓ Encryption with SSL/TLS protects data transfer between partners and the MS Teams site. ✓ Threat management, security monitoring, and file-/data integrity prevents and/or registers possible manipulation of data. After conducting the interview, personal data should be anonymised as soon as possible (e.g. anonymised written report where there is no longer any link to the participant)/pseudonymised (e.g. transcription of interview where directly identifiable elements have been replaced by a code). See Annex 7.2 for guidelines on anonymising and pseudonymising data. When storing/processing data, identification and analysis data should be stored separately with a common code. In this way, access to the identification data can be strictly limited and monitored. Only the person, who has the key to the code, can retrieve the person behind the code number. The key is kept on protected and backed-up network drives of institutions themselves in folders to which only the relevant researchers of the BREADCRUMB project have access. See Annex 7.2 for guidelines on anonymising and pseudonymising data. The analyses are conducted only based on pseudonymised data. The pseudonymised data are stored on protected and backed-up network drives of the institutions themselves and in encrypted folders on BREADCRUMB MS Teams (where it should be checked whether everyone needs access to pseudonymised data). See Annex 7.2 for guidelines on anonymising and pseudonymising data. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 39 of 61 Outside the BREADCRUMB project: If necessary, data transfer to and from end-users (including transfer of sensitive data if allowed) is performed encrypted, either sent by encrypted ZIP or RAR files, or by download directly as web-based services from servers. In any case strong password is required for accessing transferred datasets and passwords must be sent separately from the dataset (preferably using also different channels of communication e.g. WhatsApp). 5.2 Will the data be safely stored in trusted repositories for long term preservation and curation? The default long term preservation will be through ZENODO. ZENODO states that: "Items will be retained for the lifetime of the repository. This is currently the lifetime of the host laboratory CERN, which currently has an experimental programme defined for the next 20 years at least." Since uploading to ZENODO is free of charge this is expected to keep overall retention costs small. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 40 of 61 6 ETHICS 6.1 Are there, or could there be, any ethics or legal issues that can have an impact on data sharing? The proposed work in BREADCRUMB will fully comply with the regulations set out in Article 14 of the Grant Agreement, which states that all activities must be carried out in compliance with: ✓ Ethical principles (including the highest standards of research integrity, e.g. the principles of the European Charter for Researchers and The European Code of Conduct for Research Integrity), and ✓ Applicable international, EU, and national laws (in particular the GDPR). To ensure this, BREADCRUMB works with the Commission for Ethics in Social Science and Humanities (ComESSH) at ILVO who’s president Prof. Fleur Marchand will act as Ethics Mentor of the BREADCRUMB project. ComESSH will advise the project on a regular basis throughout the project, and provide ethical approval and clearance on data collection protocols (see annex 7.4). The specific issues that the commission will provide advice on are listed below. Humans: The project will implement qualitative and quantitative studies by involving the stakeholders in interviews, surveys, case studies and experiments. BREADCRUMB has the help of ComESSH. ComESSH carries out the ethical evaluation of research in the domain of 'social research' that is, "Research with human participants not covered by the Experiments Act of May 7, 2004". The ethics advisor (Fleur Marchand) will advise use on the procedures and criteria that will be used to identify/recruit research participants, but also the specific target groups. Personal Data: Understanding people's behaviour is key for BREADCRUMB. While the project aims to better understand the relationship between food waste and marketing standards, and identify solutions to alleviate the negative impacts on food waste, the other key objective is to improve market access to enhance the business potential for food that is safe to eat, but does not meet specific marketing standards. In this respect, information about consumers’ attitudes – in particular as regards sub-optimal food - is paramount. Understanding the expanse of consumer preferences for certain foods cannot be fully accomplished unless an integrated gender perspective as well as intersectional analysis is incorporated into the project. BREADCRUMB integrates both throughout its work, keeping in mind that gender and intersectional differences can affect the design of and response to marketing standards, affecting individual food choices, usage, and waste. Specifically, the project will: D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 47 of 61 With anonymisation, you process your raw data until all options for (re)identification are irreversibly removed. There is no longer any link to the data subject. The data subject can no longer be (re)identified. This is why both direct identifiers and indirect identifiers must be removed or replaced. • Direct identifiers: data that allow you to directly identify the data subject. For example: name, address, telephone number, e-mail address, IP address, recognisable image of a data subject, voice of a data subject, etc. • Indirect identifiers: based on a combination of data it is possible to (re)identify the data subject. For example: the combination of farm type, municipality business seat and age. The GDPR legislation no longer applies. For example: anonymised written report of focus group discussion. With pseudonymisation, you process your raw data until you can no longer link the pseudonymised data (= analysis data) to the data subject without using the data subject's additional data. You store the link between the identity of the data subject and the pseudonym (= identifying data) in a separate file: the key file. Access to the key file is highly restricted and only accessible for a limited number of people. Analyses are done only on the basis of the pseudonymised data. The GDPR legislation does still apply because the pseudonymised data is linkable to the data subject on the basis of the key file. Example: transcription of interview where directly identifiable elements have been replaced by a pseudonym (=code). List of interviewees' names and codes used in the pseudonymised transcript are kept in highly restricted key file. 7.2.3 Storage of the data 1 Process the obtained personal data on protected and backed-up network disks of the partner organisations themselves (cf. e.g. L-disk of ILVO-T&V115), in folders to which only the researchers involved in the BREADCRUMB project have access. 2 After conducting the interview, personal data should be anonymised as soon as possible (e.g. anonymised written report where there is no longer any link to the participant)/pseudonymised (e.g. transcription of interview where directly identifiable elements have been replaced by a code) 3 When storing/processing data, identification and analysis data should be stored separately with a common code. In this way, access to the identification data can be strictly limited and monitored. Only the person, who has the key to the code, can retrieve the person behind the code number. The key is kept D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 48 of 61 on protected and backed-up network drives of institutions themselves in folders to which only the relevant researchers of the BREADCRUMB project have access. 4 The analyses are conducted only on the basis of pseudonymised data. The pseudonymised data are stored on protected and backed-up network drives of the institutions themselves and in encrypted folders on BREADCRUMB MS TEAMS (where it should be checked whether everyone needs access to pseudonymised data). 7.2.4 When to anonymise or pseudonymise? When anonymisation is not possible or not desirable in function of your intended purpose of your scientific research, pseudonymisation is brought forward from the GDPR to protect your personal data. For example: in the context of follow-up research, you want to be able to ask questions of the interviewee later on. Sometimes, in the context of research, it is important that you do not anonymise or pseudonymise the recording. For example: consider a practice video or a video testimonial to promote and disseminate best farming practices and innovations. In each case, however, it is important that you inform the data subjects about this in the informed consent and that the data subjects have given their explicit consent to this. 7.2.5 How to anonymise or pseudonymise? Dataset with only direct identifiers Anonymise (See Table 2 of annex section) • Strip data of direct identifiers (name, address, phone number, email address, etc). (See Table 5) o Direct identifiers are removed (e.g. omit columns in Excel). or o Direct identifiers are masked: completely or partially overwrite with (*/x) or o A random code is given to each data subject. E.g. Farmer51, Farmer07, Farmer04, ... or • Aggregate data (e.g. average, sum, ...). Never figures from less than 5 farms. (See Table 6) • Raw dataset to be deleted. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 49 of 61 Pseudonymisation (See Table 2 of annex section) • Direct identifiers (name, address, phone number, email address, ...) from raw dataset are replaced by a pseudonym using: o Sequential counter: a sequential number (and possibly a prefix) is used as a pseudonym. E.g. farmer01, farmer02, farmer03, ... (See Table 4) or o Random Number Generator: a random number (and possibly a prefix) is used as a pseudonym. • Create separate key file containing a link between analysis data and personal data based on pseudonym. (See Table 3 of annex section) • The raw dataset is preferably deleted (or has severely restricted access). The researcher preferably performs analyses on the pseudonymised data (= analysis data). The analysis data are restricted to authorised researchers who need the data to conduct the study for which the data were obtained. The key file has highly restricted access (= identifying data). • At the end of the study - e.g. if pseudonymised data should not be kept as part of follow-up research - it is easy to anonymise the pseudonymised data. Change the pseudonym by a random code and permanently delete the raw dataset - if not already done - and the key file. (See Table 5) D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 50 of 61 Table 1: Raw data Names farmers Email address Housing type Eastern bunnies Average number of Easter eggs per Easter bunny per year Noah Peeters [email protected] Enriched cage 461 Arthur Janssens ajanssen[email protected]m Outdoor system 409 Louis Maes [email protected] Outdoor system 380 Olivia Jacobs [email protected] Vrije uitloop 355 Emma Willems [email protected] Vrije uitloop 340 Louise Dubois [email protected] Enriched cage 392 … … … Table 2: Key file (= identification details) Pseudonym Names farmers Email address Farmer01 Noah Peeters [email protected] Farmer02 Arthur Janssens ajanssen[email protected]m Farmer03 Louis Maes [email protected] Farmer04 Olivia Jacobs [email protected] Farmer05 Emma Willems [email protected] Farmer06 Louise Dubois [email protected] … … … Table 3: Pseudonymised data (=analysis data). Based on pseudonym and key file, the farmer can be re-identified Pseudonym Housing type Eastern bunnies Average number of Easter eggs per Easter bunny per year Farmer01 Enriched cage 461 Farmer02 Outdoor system 409 Farmer03 Outdoor system 380 Farmer04 Organic system 355 Farmer05 Organic system 340 Farmer06 Enriched cage 392 … … Random code Housing type Eastern bunnies Average number of Easter eggs per Easter bunny per year Farmer51 Enriched cage 461 Farmer07 Outdoor system 409 Farmer04 Outdoor system 380 D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 51 of 61 Dataset with direct and indirect identifiers Anonymise Removing identifiers from data o Identifiers are removed (e.g. omit columns in Excel). or o Identifiers are masked: overwrite fully or partially with '*' or 'X'. or o A random code is given to each person involved. (E.g. Farmer51, Farmer07, Farmer04, ...) Or • Generalising data: here, the data become less precise, specific to the intended purpose of scientific research. If there is too much loss of information in function of intended purpose, then anonymising is not the appropriate security measure and you should pseudonymise the dataset. For example: o Replacing a year of birth with an age or age range o Replace a location (e.g. municipality business seat) with a less precise location (e.g. province, agricultural region, ... business seat) o Replacing a specific farm type (e.g. specialised greenhouse vegetable farms) with a less specific farm type (e.g. specialised horticultural farm) Or • Setting upper and lower limits: group values of data above or below certain limits to avoid that data subjects can be identified based on outliers. For example o Age range: '65 years and above' and the age range: 'less than 35 years’ o Class of business size of '<25,000 euros' and business size of '>=500,000 euros’ Or • Data perturbation: rounding, adding noise, replacing real values with simulation values or group averages rounding numeric values and dates. The degree of value change determines anonymisation. When data accuracy is essential, data perturbation should not be applied. Or • Aggregate data (average, sum, ...). Never figures from less than 5 companies. The raw dataset are removed. To verify that qualitative data has been properly anonymised, you can e.g. read the anonymous report from the data subject's point of view or have the anonymous report read by the data subject. If there are no more links to the data subject, the GDPR no longer applies. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 52 of 61 Pseudonymisation • Direct identifiers (name, address, phone number, email address, ...) from raw dataset are replaced by a pseudonym. • Create a separate key file containing a link between analysis data and personal data based on the pseudonym. • As a function of intended purpose of scientific research o Separate indirect identifiers from analysis data and add to the key file or o Do not separate indirect identifiers from analysis data because they are of necessary importance for the analysis of the dataset and in consideration of the intended purpose: ▪ Generalise: in this process, the data become less precise, specific. (Possibly not desired in the context of research.) For example: ✓ Replacing a date of birth with an age or age range ✓ Replacing an address of place of business with a less specific location: e.g. municipality or province or agricultural region, ... in which the place of business is located ✓ Replacing a specific farm type (e.g. specialised greenhouse vegetable farms) with a less specific farm type (e.g. specialised horticultural farm) Or ▪ Setting upper and lower limits: group values of data above or below certain limits to avoid that data subjects can be identified based on outliers. (May not be desirable in the context of research.) For example: ✓ Age class: '65 years and above' and the age class: 'less than 35 years’ ✓ Class business size of '<25,000 euros' and business size of '>=500,000 euros’ ▪ Data perturbation: rounding, adding noise, replacing real values with simulation values or group averaging rounding numeric values and dates. When data accuracy is essential, perturbation should not be applied. • When pseudonymising/anonymising transcripts, the above techniques can also be used. Substitutions can be indicated with square brackets e.g. [Farmer01] has a specialised Easter bunny farm in [Limburg] and says that keeping Easter bunnies in free range is better for animal welfare. [Farmer02] has a specialised Easter bunny farm in [West Flanders] and says that keeping Easter bunnies in enriched cage is less labour intensive. • The raw dataset is preferably deleted (or has severely restricted access). The researcher preferably performs analyses on the pseudonymised data (= analysis data). The analysis data are D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 53 of 61 restricted to authorised researchers who need the data to conduct the study for which the data were obtained. The key file has highly restricted access (= identifying data). • At the end of the study - e.g. if pseudonymised data should not be kept in the context of follow-up research - you can consider - if possible/if desirable - anonymising the pseudonymised data. From the point of view of the data subject, consider whether the data subject is re-identifiable from the pseudonymised data. o If re-identifiable, the dataset is not sufficiently pseudonymised. GDPR legislation continues to apply. Data must be deleted at the end of the study, unless the data subject has given permission for the personal data to be retained for possible followup research. o If not re-identifiable, then the dataset is sufficiently pseudonymised. Change the pseudonym by a random code. Permanently delete the raw dataset - if not already done - and the key file. The GDPR legislation no longer applies to anonymous data. The anonymised data may be retained for follow-up research but can no longer be linked to specific individuals/firms. Longitudinal research at company level is therefore not possible with anonymised data. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 54 of 61 7.3 Annex 3: Template information sheet and informed consent INFORMATION SHEET Dear participant, You are invited to voluntarily participate in BREADCRUMB’s research activity, "Title of Study”. Before you agree to participate in this study, it is important that you read this information form carefully. If anything is not clear, please do not hesitate to ask questions, contact information can be found at the bottom of this document. Purpose of the project BREADCRUMB aims to provide an empirical evidence-based understanding and purpose of food marketing standards, along with their influence on the generation of food waste (FW). Its goal is to suggest interventions that strike a balance between the aim of FW reduction and other standards-related objectives, while assisting food chain participants in maximizing the commercial viability of less-than-optimal food products. To achieve these goals, the project will address 16 case studies in five different food commodities (fruit & vegetable, meat, eggs, cereals, and fish). Process description: What happens next? Briefly describe the research activity (e.g., survey, interview, observation, etc.), and the purposes for data collection and data processing. Use the example below, as per case of activity, and adjust the text accordingly. Our purpose with the [name the activity, i.e., survey, interview, observation] is to gain more detailed information and deeper insight into [include here the objectives of the activity] For this purpose, the following personal data will be collected: [e.g., gender, age, email, location data, etc.] What does participation involve for you? Describe the methods (online/paper-based survey, interview, observation, etc.), what type of information will be collected and how the information will be recorded (electronically, on paper, sound/video recording), also including the expected duration of the activity. Use the example below, as per case of activity, and adjust the text accordingly. If you chose to take part in the research activity, this will involve that you [fill in an online survey, answer to some questions of an interview, etc.]. Your participation is expected to last approximately [add here the Template: Information Sheet for BREADCRUMB This is a template for informed consent when collecting and processing personal data in BREADCRUMB. It can be used for surveys, observation, interviews, sound recording, etc. When you provide your own text, please, use clear and simple language, headings, and bullet points, active (not passive) language, and avoid foreign words. Please change all text market in yellow. D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 55 of 61 duration - XX hours/minutes]. The [survey, interview, etc.] includes questions about [describe the most important questions/topics]. Your answers will be recorded [electronically, using pen and paper, sound recording, etc.]. For the case of interviews, please also add the following text as is: In case translation between different languages is needed, the interview may last a little longer. If you permit, the interview will be recorded. If you are not comfortable with sound recording, detailed notes will be taken.” Your participation in this study is voluntary, and you have the right to withdraw at any time without penalty. It involves participating in in-depth interviews, surveys, and focus group interviews to gather insights and perspectives related to food marketing standards and food waste prevention in various sectors Fruit & vegetable, meat, eggs, cereals, and fish. Potential benefits or risks of participation Your involvement contributes to advancing scientific understanding, developing evidence-based solutions to combat food waste, and benefiting society. There are no direct benefits from your participation and there are no foreseeable risks in the participation. Participation is voluntary Your participation is completely voluntary, and there will be no negative consequences for declining to participate or withdrawing from the study at any time. You have the right to refuse to answer any questions you are uncomfortable with or to skip any sections. If you chose to participate, you can withdraw your consent at any time without giving a reason. There will be no negative consequences for you if you chose not to participate or later decide to withdraw. After withdrawal your data will be deleted. Confidentiality Your identity and responses will remain confidential. Only the researcher and authorized personnel will have access to the data collected during the study. Your personal information will be stored securely and will not be disclosed to anyone outside of the research team without your explicit consent. The project will end in December 2026. All project data will be stored only for the minimum period required to complete the research activities, which is XX months/years, and according to the accounting rules that apply under Horizon 2020, no longer than five years from the end of the project, when it will be deleted. Contact information If you have questions or concerns about the project BREADCRUMB, or want to exercise your rights, contact our Data Protection Officer: [insert name of the data protection officer at the institution responsible for the data collection] Kind regards, Responsible for data collection (DPO) [Signature] CONSENT FORM D6.2 – Data Management Plan - 1 This project has received funding from the European Union´s Horizon Europe research and Innovation programme under the agreement No. 101136701 Page 56 of 61 Selecting “I Agree” below indicates that: - You have received and read the information in the BREADCRUMB Information sheet; - You understand the procedures described above and the expected duration of the storage of the data; - You have been given the opportunity to ask questions; - You voluntarily agree to participate, and you are free to withdraw at any time without giving a reason and without consequences; - You understand that your personal information will be treated and handled in accordance with the provisions of the EU General Data Protection Regulation (Reg. 2016/679); - You are at least 18 years of age. o I Agree Participant's Signature: _________________________ Date: _______________ Researcher's Signature: _________________________ Date: _______________