D1.2 Data Management Plan

HORIZON –CL4-2022-TWIN-TRANSITION-01-06 HORIZON Innovation Actions Grant Agreement No.: 101092295 Circular and Dynamic Manufacturing Supply Chain Orchestration and OptimiSation D1.2 Data Management Plan Report Identifier: D1.2 Work-package: WP1 Task: T1.4 Responsible Partner: European Dynamics Luxembourg SA (ED) Version Number: 1.0 Due Date 29/02/2024 Document Date: 08/03/2024 Distribution Security: PU Deliverable Type: DMP Keywords: Data management, FAIR data, IPR management, Datasets Project website: https://circuloos.eu/ D1.2 Data Management Plan 2 | 67 Legal Disclaimer CIRCULOOS is an EU project funded by the Horizon Europe (HORIZON) research and innovation programme under grant agreement No. 101092295. The information and views set out in this deliverable are those of the author(s) and do not necessarily reflect the official opinion of the European Union. The information in this document is provided “as is”, and no guarantee or warranty is given that the information is fit for any specific purpose. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein. The CIRCULOOS Consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials subject to any liability which is mandatory due to applicable law. Copyright notice © Copyright by the CIRCULOOS Consortium This document contains information that is protected by copyright. All Rights Reserved. No part of this work covered by copyright hereon may be reproduced or used in any form or by any means without the permission of the copyright holders. D1.2 Data Management Plan 3 | 67 Table of Contents Table of Contents........................................................................................................................................................................3 List of Figures...............................................................................................................................................................................5 List of Tables.................................................................................................................................................................................6 Executive Summary...................................................................................................................................................................9 1 Introduction...................................................................................................................................................................10 1.1 Project Introduction....................................................................................................................................10 1.2 Deliverable Purpose....................................................................................................................................10 1.3 Data Protection Legislative Framework............................................................................................ 11 1.4 The Data Management Plan (DMP)......................................................................................................11 1.5 Data Management Strategy......................................................................................................................13 1.6 Open Research Data Pilot.........................................................................................................................14 1.7 Objectives of the Data Management Plan..........................................................................................16 1.8 CIRCULOOS Data Management Plan....................................................................................................17 2 Data Set identification...............................................................................................................................................19 2.1 Data set identification by partner organisations........................................................................... 20 2.1.1 Partners’ data identification...................................................................................................22 3 Data Summary..............................................................................................................................................................46 3.1 Reasons for collecting and generating data within CIRCULOOS activities......................... 46 3.2 Data types and formats collected and generated in CIRCULOOS............................................48 3.3 Data characterisation: re-use of existing data, data origins and size....................................48 3.4 Types and formats of data that CIRCULOOS will generate/collect........................................ 49 3.5 Information types in the CIRCULOOS project..................................................................................50 3.5.1 PUBLIC.............................................................................................................................................50 4 FAIR Data........................................................................................................................................................................53 4.1 Making data Findable, including provisions for metadata.........................................................54 4.1.1 Metadata......................................................................................................................................... 54 4.1.2 Naming conventions..................................................................................................................55 4.2 Making data openly Accessible.............................................................................................................. 55 4.3 Making data Interoperable...................................................................................................................... 56 D1.2 Data Management Plan 4 | 67 4.4 Increasing data Re-use...............................................................................................................................56 5 Allocation of resource............................................................................................................................................... 58 5.1 Estimation of cost.........................................................................................................................................58 5.2 Responsibilities for data management...............................................................................................58 6 Data security................................................................................................................................................................. 59 7 Legal framework and guidelines..........................................................................................................................60 7.1 Personal Data Management.....................................................................................................................60 7.1.1 Important GDPR provisions................................................................................................... 60 8 Intellectual Property Rights (IPR) Management...........................................................................................63 8.1 Definitions.......................................................................................................................................................63 8.1.1 Applicable legislation................................................................................................................ 63 8.2 IPR Management in the CIRCULOOS Project....................................................................................64 9 Ethical Aspects............................................................................................................................................................. 65 10 Conclusion......................................................................................................................................................................66 D1.2 Data Management Plan 5 | 67 List of Figures Figure 1:Open Access strategy for publications and research data.....................................................................17 Figure 2: FAIR Data Principle..............................................................................................................................................54 D1.2 Data Management Plan 6 | 67 List of Tables Table 1: FAIR Data Principles.............................................................................................................................................14 Table 2: Data Identification template............................................................................................................................. 20 Table 3: ED Data identification..........................................................................................................................................22 Table 4: Dutch Pilot 3A - 1st Platform - Plennid - Houthub (Sustainable Sourced Fresh Timber Platform (SSFTP)..................................................................................................................................................................... 24 Table 5: Leather recycling pilot – leather waste platform of supply chain (Khoani Ltd. – B&A Ltd. – ITI Hungary Ltd.)..................................................................................................................................................................... 26 Table 6: Plastic material recycle/remanufacture (Thermolympic S.L. – Contenedores Lolo S.L – Canonical Robots S.L )........................................................................................................................................................... 27 Table 7: Data identification - Open calls.......................................................................................................................29 Table 8: MWCB Data Identification..................................................................................................................................32 Table 9: SUPSI Data identification....................................................................................................................................34 Table 10: INN Data identification.....................................................................................................................................36 Table 11: Thermolympic (PILOT1 Plastic recycling)...............................................................................................38 Table 12: FIW Data identification.....................................................................................................................................41 Table 13: ALA Data identification.....................................................................................................................................42 Table 14: CUT Data identification.....................................................................................................................................43 Table 15: Research Data.......................................................................................................................................................47 Table 16: Types and formats of data...............................................................................................................................49 Table 17: CIRULOOS Public Deliverables......................................................................................................................50 Table 18: CIRCULOOS Sensitive Deliverables............................................................................................................. 51 D1.2 Data Management Plan 7 | 67 Abbreviations Acronym Description AI Artificial Intelligence CE Circular Economy CSV Comma Separated Values D Deliverable DMP Data Management Plan DoA Description of Actions DOIs Digital Object Identifiers EC European Commission EPC European Patent Convention ERP Enterprise Resource Planning EU European Union EXDs Experiments for Demonstration FAIR Findable, accessible, interoperable and re-usable data GA Grand Agreement GDPR General Data Protection Regulation GIS Geographic Information System GRETA Green Targets IoT Internet of Things IPR Intellectual Property Rights JPEG Joint Photographic Expert Group JSON JavaScript Object Notation KPI Key Performance Indicators LCA Life Cycle Assessment LCI Life Cycle Inventory M Month MSMEs Manufacturing SMEs OA Open Access ORDP Open Research Data Pilot PEDR Plan for Exploitation and Dissemination of Results PNG Portable Network Graphics RAMP Robotic and Automation MarketPlace REST Representational state transfer RDMS Relational Database Management Systems TBD To Be Determined SCDT 3D Digital Twin of Supply chain / production / products SCODT AI and Data-driven Supply Chain Optimization SMEs Small and medium manufacturing enterprises S-LCA Social Life Cycle Assessment SSFTP Sustainable Sourced Fresh Timber Platform TXT Text only URI Uniform Resource Identifier D1.2 Data Management Plan 8 | 67 URL Uniform Resource Locator WP Work Package D1.2 Data Management Plan 9 | 67 Executive Summary The Data Management Plan (DMP) is a document that outlines the procedures and methodologies of data treatment during the project and how data will be used and shared after the project ends. The DMP has to describe the observed data that are collected and processed during the lifespan of the project, while providing the overview of available research data, access procedures, data management and terms of use. The first version of the CIRCULOOS DMP that is presented in this document, reflects the current state of the discussions, plans and ambitions of partners. It includes the preliminary scenario of data set definitions and will be updated and implemented with new datasets and results in the following months. Partners have been asked to identify and characterize the data they are going to use and have been involved in a brainstorming session aimed at discussing the data utility. Moreover, CIRCULOOS strives to enhance access and reusability of research data, with a particular focus on achieving a balance between openness and safeguarding scientific information. Given the involvement of workers in the technical activities foreseen in the project, technical and organisational measures that safeguard the rights and freedoms of the data subjects/research participants will be developed and informed consent procedures in regard to data processing will be designed, in conformity to the GDPR requirements and guidelines. The DPM is a live document that will be kept update all along the project duration. D1.2 Data Management Plan 16 | 67 various projects’ databases’ information about the latest projects’ outputs, together with reports and demo materials. Figure 1:Open Access strategy for publications and research data 1.7 Objectives of the Data Management Plan CIRCULOOS will deliver the tools to enable Manufacturing SMEs (MSMEs) become full members of the Circular Manufacturing value chain. The project will ensure that these tools orchestrate and continuously optimise the supply-chain end-to-end and integrate planning and execution monitoring to enable transparent and on-time communications. The project will ensure that the kits are widely distributed to a wide audience of MSMEs and midcaps in Europe. The adoption of these tools will be facilitated with the use of systems like ERP, PLM, CAD, data sharing platforms used with suppliers, external database containing LCI and LCIA information, as well as IoT sensors and wearable devices, robots, and other factory data sources. Considering the whole amount of data that CIRCULOOS is expected to generate, the purpose of the DMP is to define the proper management of project research data and of data subjects in compliance with the EC recommendations and national and international regulations and guidelines on the use of data. The Plan is intended as a roadmap illustrating how data arisen from project research lines will be treated throughout the project lifetime and beyond, once it will be finished. CIRCULOOS DMP will provide a vehicle for conveying information to and setting expectations for the project team during the different stages of the project. The plan will be a living document that is periodically reviewed according to the new data gathered, the needs and any changes in protocols (e.g. metadata, QA/QC, storage) and policies. D1.2 Data Management Plan 17 | 67 1.8 CIRCULOOS Data Management Plan CIRCULOOS project is the result of a successful proposal (number 101092295) submitted on 10 Oct 2023 to the European Commission. The consortium has signed the Grant Agreement and thus jointly undertaken to execute the project. The mandate of the project is to execute the work described in accordance with the signed contract (Grant Agreement) and its annexes, within budgetary and schedule constraints. This document is related to what is described in the Description of Actions (DoA) (CIRCULOOS, 2023) as deliverable D1.2 Data Management Plan and is related to T1.4 Data Management Plan. The CIRCULOOS ecosystem is organized around a platform offering added value services to MSMEs who are willing to share information coming from their production systems and human resources. Open management and distribution of data is thus relevant to the generation of services while, on the other hand, data security and data ownership have to be carefully addressed. This aspect of the platform will be reflected in the data management strategy both during project duration, as well as in the following platform exploitation activities. In particular, it is of paramount importance to ensure an appropriate quality management of data collection and sharing in-line with European privacy and data protection regulations when pilot-related datasets or data deriving from human beings are dealt with. Furthermore, attention will be paid to research data management starting from the beginning of the project so to make sure that results can be findable, accessible, interoperable and re-usable (FAIR). This includes careful screening of relevant standards (and appropriate selections that ensure the widest possible compatibility and outreach), but also developing models for data sharing, while protecting privacy of users and providing security of IP and business of the involved companies. A first analysis allows the identification of the following categories of data that will be relevant for the CIRCULOOS project: Individual data: users’ data have to be shared with the platform in order to build the customized service packages and do matchmaking between different entities (users or companies); experiments that include the use of cobots may require some human monitoring activity to provide realtime support and enhance human-robot interaction. The profile may contain, on the one hand, static info such as psychophysiological features or skills and preferences; on the other hand, dynamic and physiological data collected real-time during the execution of activities. Production system data: in order to offer real-time support for an improved quality control, a more agile reconfigurability, or for a symbiotic interaction between operators and the production system, also data from the production system have to be shared with the platform. Supply Chain Data: multi-level data flow across the supply chain partners, supporting the reuse of materials in novel products, the extension of the life-cycle of finished products (remanufacturing), and data-driven decisions for collaboration of parties offering matching services in the most dynamic and efficient way. Assessment data: the dashboard of KPIs collected from the concluded project are elaborated to both understand if the single solution has been successfully implemented and derive statistics and trends useful to improve the design of future solutions and, hence, the provided service. Registry data: data providing information about the registered companies. D1.2 Data Management Plan 18 | 67 The DMP of the CIRCULOOS project has been prepared following the template provided by the European Commission of the “Guidelines on Data Management in HORIZON”.10 In the following chapters, there will be a preliminary version of the data sets that will be generated within the project, even if only a few partners were already able to provide information on the data they expect to generate. Also, general information about data (types, format, re-use, origins, size, etc); data strategies (FAIR data); allocation of resources and data security are provided. 10 Horizon-Europe-Data-Management-Plan-Template.pdf (enspire.science) D1.2 Data Management Plan 19 | 67 2 Data Set identification The CIRCULOOS DMP describes the observed data that are collected and processed during the lifetime of the project while providing the overview of available research data, access policies, data management and terms of use. The DMP reflects the current state of the discussions, plans and ambitions of the partners. It includes the preliminary scenario of data set definitions and expected results and will be updated and implemented with new datasets and results during the lifespan of CIRCULOOS. Moreover, CIRCULOOS project takes part to the Open Research Data Pilot (ORDP) in HORIZON that aims to improve access to and re-use of research data with a special focus on the need to balance openness and protection of scientific information. This version of the DMP contains a preliminary dataset where information listed below reflects the conception and design of the different work packages by the individual partners at the beginning of the project. The data register will deliver information according to information detailed in Annex 1 (Part A) of the Grant Agreement Document (GA): Data set reference and name: identifier for the data set to be produced. Data set description: descriptions of the data that will be generated or collected, its origin or source (in case it is collected), nature, scale, to whom it could be useful and whether it underpins a scientific publication. Information on the existence (or not) of similar data and the possibilities for integration and reuse. Partners’ activities and responsibilities: partner owner of the device, in charge of the data collection, data analysis and/or data storage, and WPs and tasks it is involved. Standards and metadata: reference to existing suitable standards of the discipline. If these do not exist, an outline on how and what metadata will be created. Format and estimated volume of data. Data exploitation and share: description of how data will be shared, including access procedures and policy, embargo periods (if any), outlines of technical mechanisms for dissemination and necessary software and other tools for enabling re-use, and definition of whether access will be widely open or restricted to specific groups. Identification of the repository where data will be stored, if already existing and identified, indicating in particular the type of repository (institutional, standard repository for the discipline, etc.) and if this information will be confidential (only for members of the Consortium and the Commission Services) or public. In case a dataset cannot be shared, the reasons for this should be mentioned (e.g., ethical, rules of personal data, intellectual property, commercial, privacy-related, security related). Archiving and preservation (including storage and backup): description of the procedures that will be put in place for long-term preservation of the data. Indication of how long the data should be preserved, what is its approximated end volume, what the associated costs are and how these are planned to be covered. Such data can be anonymised for statistical or other dissemination purposes and shared with open access, which could be further analysed and provide the possibility to extract information and knowledge from them. Each dataset can be accompanied by several metadata (e.g. type, gender, age, etc.) which can support various kinds of historical data analysis. D1.2 Data Management Plan 20 | 67 2.1 Data set identification by partner organisations All partners need to identify the data that will be produced in the different project activities they are involved in and must provide an overview on the nature and details for each dataset. To do so partners have been asked to fill a table with data they expect to treat in their activities according to the WPs and tasks they are involved in. The table template to be used is shown in Table 2. Table 2: Data Identification template Data Identification Data set description Type of data: qualitative or quantitative? Order of magnitude Describe the existing or intended data, indicating their origin, nature and order of magnitude. Motivate the creation of new data sets and their added value. Provenance of data: sources Describe whether the data come from interviews, surveys or are extracted from disciplinary archives, databases and / or other projects, devices, machines... Nature and formats of data Describe nature and format of data: a) text documents (DOC, ODF, PDF, TXT, etc.); b) images (JPG, GIF, SVG, PNG, TIFF); c) video / film (MPEG, AVI, WMV, MP4); d) audio recordings (MP3, WAV, AIFF, OGG, etc); e) structured data (HTML, JSON, TEX, XML, RDF); f) tables (CSV, ODS, TSV, XLS, SAS, Stata, SPSS portable); g) source codes (C, CSS, JavaScript, Java, etc.); h) configuration data (INI, CONF, etc.) i) database (Microsoft Access, MySQL, Oracle, etc.) New data set value Motivating the creation of the new dataset, defining its added value for the scientific community or other recipients Audio-visual material In case of video, indicate the duration (the data is useful for planning the costs design and archiving) Partners Activities & Responsibilities Partner owner of the device producing the data Partner of the project owner of the device/software producing the data Partner in charge of the data collection (if different) D1.2 Data Management Plan 21 | 67 Partner in charge of the data analysis (if different) Partner in charge of the data storage (if different) WPs and tasks Standards and Metadata Metadata standards and data documentation Describe the type of metadata with reference to standards and documentation Methodology for data collection/generation Describe the methodologies of data collection and production during the research process. 1. Who and how it collects data; 2. Who and how it structures and stores them; 3. Who and how he processes them; 4. Who and how he distributes them. Refer to regulations or practices in force in the scientific community of reference Data exploitation & sharing Data exploitation (purpose/use of the data analysis) How can data be exploited? For what purpose? Will data be exploited in their raw form coming from the dataset or will data analysis be exploited? Data ownership Who is the owner of the data? Is another organization contributing to the data development? Are you re-using some pre-existing data? Suitability for sharing Public/confidential/limited access Data utility How will this data shared/made accessible for verification and reuse? Open research data pilot Can data be uploaded in an open research data pilot? When? Embargo periods (if any) Archiving & preservation (including storage and backup) Managing, storing and curating data Please describe the modality of: storage; backup; transmission; D1.2 Data Management Plan 22 | 67 data processing in the short and medium term, with references to practices, standards and regulations where applicable. Data Storage Please indicate: where data will be stored if the conservation concerns the whole collected data or only part of them, for how long data will be stored 2.1.1 Partners’ data identification The following tables report partners’ feedback on data that will be generated during the project’s activities. The data analysis follows the structure presented in Table 2 Table 3: ED Data identification Data Identification Data set description Type of data: qualitative or quantitative? Order of magnitude Registration data for companies that participate in RAMP. Some personal data will be collected from representative personnel for verification purposes (contact points). The data is collected during the registration process and the users are required to consent to the storage and processing of their data. The data consists of textual information in the magnitude of <1Mb. A minimal set of user’s behaviour inside the marketplace may be collected to support matchmaking and the creation of new business opportunities between the registered users. Provenance of data: sources Provided by the humans (during the registration process). Nature and formats of data Raw text data, formatted in JSON files. New data set value 1. Understand the users’ needs and improve/ adjust the functionality of RAMP 2. Matchmake end-users to solution providers 3. Inform end-users about the ‘offering’ 4. Validate the offering of solution providers in the Marketplace Audio-visual material N/A Partners Activities & Responsibilities Partner owner of the device producing the data ED (RAMP) Partner in charge of the data collection (if different) Partner in charge of the data D1.2 Data Management Plan 23 | 67 analysis (if different) Partner in charge of the data storage (if different) WPs and tasks WP3 Standards and Metadata Metadata standards and data documentation N/A Methodology for data collection/generation Form is presented to user during the registration process. Participants of Open Calls will be required to register to RAMP Data exploitation & sharing Data exploitation (purpose/use of the data analysis) See New Data Set value above Data ownership The users own the data. They can retrieve / remove the data by sending a request to RAMP administrators Suitability for sharing limited access to other RAMP residents (see Questionnaire below Data utility N/A Open research data pilot No Embargo periods (if any) Archiving & preservation (including storage and backup) Managing, storing and curating data Data is securely stored and accessible in ED’s ISO27001:2013certified infrastructure. End-to-end encryption for data in transit. Data in transit is protected against active (e.g., replays, traffic injection) and passive attacks (e.g., eavesdropping), thus ensuring data integrity Data Storage Data is securely stored and accessible in ED’s ISO27001:2013certified infrastructure. Data is deleted once the users request to leave RAMP. Data coming from the Open Calls are expected to stay at least for the duration of the individual Open Call projects. D1.2 Data Management Plan 24 | 67 Table 4: Dutch Pilot 3A - 1st Platform - Plennid - Houthub (Sustainable Sourced Fresh Timber Platform (SSFTP) Data Identification Data set description Type of data: qualitative or quantitative? Order of magnitude No existing data will be used. For pilot A, Local timber hub - (Sustainable Sourced Fresh Timber Platform (SSFTP) will create demand and supply data. With this platform we are coordinating a market for locally sourced fresh wood. Important parameters are: 1. Locally sourced supply -Reduction of transportcosts -Characteristics of supply (quantities, location, quality, ect) -Argis data from municipalities 2. demand coordination -Market insights -Functional characteristics of demand (application, quantities, durability ect) -Market development (creating awareness) -Interviews, marketresearch, dataanalysis and deskstudies Provenance of data: sources Our data is collected with databases like ArcGis and Airtable. Also interviews and surveys are necessary for market insight and demanded parameters. Desk-research and physical research will give important characteristics of supply. Nature and formats of data Although the precise format of the data is unknown at this time, the following formats should be anticipated: a) text documents (DOC, PDF, TXT, etc.); b) images (JPG, PNG, TIFF); d) audio recordings (MP3 ); e) structured data (unknown) f) tables (CSV) Arcgis data: ●Geodatabase rasters. ●TIFF (. tif) ●Esri Grid. ●CRF raster (. CRF) ●ERDAS IMAGINE (. img) New data set value Match making “Supplydemand”. We use the dataset to get knowledge about the characteristic/parameters that the end-users want to see in the platform. Such as: -Buzzwords for searching specific application of source -insights in harverst moment (pre-ordering based on Gis) -Insights in marketprice (price setting) D1.2 Data Management Plan 25 | 67 Audio-visual material N/A Partners Activities & Responsibilities Partner owner of the device producing the data (unknown at the moment) Partner in charge of the data collection (if different) Partner in charge of the data analysis (if different) Partner in charge of the data storage (if different) WPs and tasks WP3 Standards and Metadata Metadata standards and data documentation Unknown Methodology for data collection/generation Plennid is collecting, structuring, processing, distributing data about fresh locally timber streams. We need guidance and from Circuloos partners. Data exploitation & sharing Data exploitation (purpose/use of the data analysis) See New Data Set value above Data ownership Plennid Suitability for sharing Limited access, but data will always be available on platform (No extract of dataset possible) Data utility Unknown but we strive towards Pre-ordering possibilities Open research data pilot Developing an algorithm which can provide an optimal match making in the locally fresh timber market. Data will be open-source through the platform to provide research information (No extract of full dataset possible) Embargo periods (if any) N/A Archiving & preservation (including storage and backup) Managing, storing and curating data Safe and user friendly storage needed D1.2 Data Management Plan 32 | 67 request access to the information for research purposes. A ‘Data Management Plan’ will be developed as indicated in WP6 detailing what data the project will generate, whether and how it will be exploited or made accessible for verification and re-use. Data ownership CIRCULOOS Consortium; records are kept for reasons of accountability to the EU. Suitability for sharing Confidential; access only to EU representatives. Data utility N/A Open research data pilot No Embargo periods (if any) The data is stored for accountability purposes to the EU. Archiving & preservation (including storage and backup) Managing, storing and curating data F6S Data Storage F6S Table 8: MWCB Data Identification Data Identification MWCB Data set description Type of data: qualitative or quantitative? Order of magnitude MWCB plan to collect data from the following sources: newsletters, video – interviews, website contact forms, events – Networking (online and in-person), social media (LinkedIn,) and blogs. MWCB plans to collect names and e-mails from interested third parties in different formats (.csv, .txt, .xlsx, .pdf, .doc). Provenance of data: sources Provided by the humans (during the registration process).. Nature and formats of data Textual (.txt, .docx, .pdf), Tabular (.csv, .xlsx), Video (.mp4, .ogv) New data set value The dataset is used to inform and engage identified target groups about the project and its outcomes according to the impact maximisation plan. Furthermore, it serves to attract interested parties, encouraging their participation in open calls and other planned activities such as the trainings. Audio-visual material yes Partners Activities & Responsibilities Partner owner of the device producing the data MWCB D1.2 Data Management Plan 33 | 67 Partner in charge of the data collection (if different) Partner in charge of the data analysis (if different) Partner in charge of the data storage (if different) WPs and tasks WP6 Standards and Metadata Metadata standards and data documentation N/A Methodology for data collection/generation Data from interviews, trainings, website contact form, events, social media, and blog information will be stored on MWCB servers. The collection of personal data will be gathered according to MWCB privacy policy (https://mobileworldcapital.com/privacy-policy/) and will follow guidelines agreed with the consortium. Data exploitation & sharing Data exploitation (purpose/use of the data analysis) See New Data Set value above Data ownership MWCB Suitability for sharing The data gathered by MWCB from the activities of Impact Maximization (WP6) will not be shared with any other third parties outside MWCB to comply with current regulations. Data utility MWCB also fulfil their commitment to storing them and therefore take all the necessary measures to prevent any loss, unauthorized processing or access or alteration thereto, as established in the applicable data protection regulations. Open research data pilot N/A Embargo periods (if any) N/A Archiving & preservation (including storage and backup) Managing, storing and curating data The data will be stored on MWCB servers with access to members of the team only. This information will not be shared with any other partner or third party. The data will be stored in MWCB servers with access to members of the [project’s name] team only. MWCB undertakes to use and process users’ personal data while respecting their confidentiality and to avail of them in accordance with the purpose for which they were initially collected D1.2 Data Management Plan 34 | 67 Data Storage The collection of personal data will be gathered according to MWCB privacy policy (https://mobileworldcapital.com/privacypolicy/). Table 9: SUPSI Data identification Data Identification SUPSI Data set description Type of data: qualitative or quantitative? Order of magnitude Qualitative and quantitative data will be sourced from SUPSI, with a rigorous scientific approach employed for data acquisition. Quantitative data will be derived through the calculations performed in GRETA, a customized application developed by SUPSI for comprehensive sustainability evaluations. These data involve sustainability indicators (social, environmental, economic, and circular economy metrics) set with specific units. The magnitude of these indicators may exhibit variance, necessitating potential normalization for comparative analysis. Qualitative data will be harnessed from the utilization of quantitative data in the formulation of sustainability reports, derived from both pilot studies and open calls. Provenance of data: sources Both quantitative and qualitative data will be generated from GRETA. The inputs required to produce GRETA results can be sourced from interviews, surveys, disciplinary archives, or databases. However, the data products are systematically derived from calculations and analyses conducted within the GRETA software environment. Nature and formats of data Nature and format of data: a) text documents (PDF) for reports; b) structured data (JSON) as exchanged messages obtained exploiting the REST API exposed by GRETA. New data set value Compiling a list of sustainability indicators involving social, environmental, economic, and CE provides a significant added value for the scientific community and other stakeholders by contributing to the expansion of existing databases focused on sustainability. Audio-visual material None. D1.2 Data Management Plan 35 | 67 Partners Activities & Responsibilities Partner owner of the device producing the data SUPSI Partner in charge of the data collection (if different) SUPSI for LCI data and process characterization and ED as RAMP manager Partner in charge of the data analysis (if different) SUPSI Partner in charge of the data storage (if different) ED as RAMP manager WPs and tasks WP3 - Sustainability and LCA Assessment tools WP5 - DEM - Demonstrator, pilot, prototype Standards and Metadata Metadata standards and data documentation DOI, author’s name list, keywords Methodology for data collection/generation 1. Data Collection: This process involves researchers, analysts, and experts. These individuals use GRETA to calculate relevant sustainability data pertaining to different sectors, such as wood, plastic, and leather. In particular, the data collection carried out by sustainability experts will be fundamental in order to model processes and products from a sustainability point of view, creating the customization spaces on which the users will be able to generate their product alternatives. 2. Structuring and Storage: Once collected, the data are structured and stored within the GRETA software. 3. Processing: Data processing occurs within the GRETA software environment. The data from pilots undergo thorough analysis to provide the sustainability indicators. 4. Distribution: The processed data, along with the qualitative findings in the form of reports, are disseminated through RAMP. Through RAMP, stakeholders, policymakers, and the scientific community can access and use the data generated by GRETA for further research, decision-making, and policy formulation. Data exploitation & sharing D1.2 Data Management Plan 36 | 67 Data exploitation (purpose/use of the data analysis) The data provided can be exploited in different ways: in their report form they can be used to understand conceptual insights. Alternatively, the quantitative data can be applied in further studies by the scientific community, and comparative assessment’s data can inform decision-making processes. Reports can also be accessed by various sectors to improve sustainability practices, expanding the impact beyond the scientific community. Overall, data analysis and interpretation, whether in raw form or through reports, serve diverse purposes, from understanding concepts to guiding practical improvements across sectors. Data ownership SUPSI Suitability for sharing Limited access for specific platform users Data utility Publications, Deliverables, RAMP, Sustainability certification Open research data pilot Yes Embargo periods (if any) Archiving & preservation (including storage and backup) Managing, storing, and curating data Please describe the modality of: Storage: GRETA leverages on different DBs with different natures according to the nature of data. Backup: GRETA cluster is running on SUPSI virtual machine which is daily backup; Transmission: all data exchanged with third-party applications is in JSON format and is exchanged over a secure channel (HTTPS); data processing in the short and medium term: short term for the basic functionalities such as assessment and comparison (assessment usually takes less than a second). Some advisory functionalities (still under development) might require a long time. with references to practices, standards and regulations where applicable. Data Storage Please indicate: · where data will be stored: GRETA environment and RAMP platform · if the conservation concerns the whole collected data or only part of them: the whole data set in GRETA, part of them in RAMP platform · for how long data will be stored: unlimited time (or at least for the period of the project). Table 10: INN Data identification Data Identification INN D1.2 Data Management Plan 37 | 67 Data set description Type of data: qualitative or quantitative? Order of magnitude INN collected qualitative data from the pilot partners during the completion of T2.1 where the pilot partners provided details about their production processes, goals and vision of the project, along with potential cooperation and production optimisation opportunities. Provenance of data: sources The data had been collected and edited on miro boards, excel files and in D2.1. All collected data is from online interviews with the pilot and task partners. Nature and formats of data Nature and format of data: a) text documents: D2.1 deliverable b) images (JPG): provided by the pilot partners c) video: provided by pilot partners, stored on ProofHub d) tables (XLS): filled by INN and pilot partners, describing use cases New data set value The collected data is used to analyse the pilots in more depth and provide an overview to the technical team. New data can be collected regarding follow-up specifications. Audio-visual material N/A Partners Activities & Responsibilities Partner owner of the device producing the data N/A Partner in charge of the data collection (if different) INN Partner in charge of the data analysis (if different) INN Partner in charge of the data storage (if different) INN, ED as owner of ProofHub project space WPs and tasks WP2 Standards and Metadata Metadata standards and data documentation N/A Methodology for data collection/generation Methodologies of data collection and production during the research process: 1. INN and SUPSI collected qualitative data during interviews with pilot partners 2. INN stores the collected data in ProofHub 3. INN and SUPSI analysed and documented the information 4. The collected data will not be shared with any third parties INN handles personal data according to its privacy policy: https://innomine.com/privacy-policy/ and according to the agreed guidelines with the consortium. D1.2 Data Management Plan 38 | 67 Data exploitation & sharing Data exploitation (purpose/use of the data analysis) The collected data is used to analyse the pilots in more depth and provide an overview to the technical team. The data is not used in its raw form, the interview answers have been structured and categorised, to provide a well-defined input for T2.2 and T2.3. Data ownership INN, ED as owner of ProofHub cloud project space Suitability for sharing Confidential, the collected data is for internal use only Data utility INN fulfil their commitment to storing the data and therefore take all the necessary measures to prevent any loss, unauthorized processing or access or alteration thereto, as established in the applicable data protection regulations. All documents, meeting memos, txt and xls files are stored in ProofHub. Open research data pilot N/A Embargo periods (if any) N/A Archiving & preservation (including storage and backup) Managing, storing and curating data All documents, meeting memos, txt and xls files are stored in ProofHub. This information will not be shared with any other third party. Data Storage Data storage is according to ProofHub’s and INN’s privacy policy. Table 11: Thermolympic (PILOT1 Plastic recycling) Data Identification – PILOT1 Plastic recycling D1.2 Data Management Plan 39 | 67 Data set description Type of data: qualitative or quantitative? Order of magnitude Machine configuration, homologation, production and logistic data for comparative purposes with the newly created recipes. For Thermolympic and his customers, the opening of multiple configuration possibilities for using recycled material within normal productions while guarantees the dimensional, mechanical and technical characteristics. Once the quality and stability of the process is guaranteed with full traceability: Raw material origin certificate from the supplier. Production order serial releases with parameters, recipe mixture, quality controls, training matrix and shift data. SPC controls This will trace through the production chain creating a declaration of conformance that met all the regulations from customer specific requirements. With that objective met, the main focus will be moved to logistics with Lolo and local suppliers where additional data will be used and shared for the supply chain analysis like: Customer specific requirements with the criteria to be met for each new material included into the system. Data form the existing costs for including new materials on existing lines (homologation, functional test, energy costs, production cycle impacts…) Logistic insights and costs (Km/€) with comparative data for economic viability and supplier relay. Production planification and capabilities on all supply chain for a JIT or batched production. Provenance of data: sources The data will come basically from production lines, so most of them will relate their origins from IOT devices, local Apps and MES/ERP systems on the company. Nature and formats of data Until the relationship within the consortium platform where this can change it will be: -Text documents (DOCX, ODF, PDF, TXT) -Tables (XLS) -Images (JPG, GIF, SVG, PNG, TIFF); -Mail messages (.msg). -Structured data on DataBases (SQL Server and Oracle DB) -Structured data on the apps (HTML, JSON,XML); New data set value -Cover the gaps between the current production systems for enable the use of recycled materials in a heavily restricted environment. Audio-visual material -Training material for awareness and new methodologies. Partners Activities & Responsibilities Partner owner of the device producing the data TBD: -Thermolympic will be the owner for production and endcustomer data. -Contenedores Lolo will be the owner for recycling data produced from the material separation, and the origin/traceability certificates. D1.2 Data Management Plan 40 | 67 Partner in charge of the data collection (if different) - Partner in charge of the data analysis (if different) - Partner in charge of the data storage (if different) - WPs and tasks - Standards and Metadata Metadata standards and data documentation - Methodology for data collection/generation -Thermolympic production, quality, data is automatically collected through IOT or HMI devices, structured into tables and stored into databases and distributed through service apps. -Contenedores Lolo data is manually collected, stored into their facilities (paper and digital data) and distributed through their internal channels. Data exploitation & sharing Data exploitation (purpose/use of the data analysis) Data value explanation. Data ownership Customer data should be anonymized for project purposes. All the data will be owned by their creator (or authorized entities) unless agreements are reached. Suitability for sharing Limited access. To be defined which data and how can be shared. Data utility TBD. Open research data pilot Can data be uploaded in an open research data pilot? When? It can be upload into open research’s, but anonymized according a data protection act. Embargo periods (if any) N/A Archiving & preservation (including storage and backup) Managing, storing and curating data Storage: Digital mostly, into defined servers or storage services. Backup: Local backups for servers, critical data with backup on cloud systems like (ACENS) Transmission: TBD, secure protocols to be checked. Data Storage The data will be stored in local servers for digital data and internal storages for paper ones. The conservation plan is for three years on direct query for digital data and then will be archived into xml for preservation and recovery purposes (planned maintenance for the next 20 years). Paper data will be stored for 6 years. D1.2 Data Management Plan 41 | 67 Table 12: FIW Data identification Data Identification – FIWARE Data set description Type of data: qualitative or quantitative? Order of magnitude N/A Provenance of data: sources N/A Nature and formats of data FIWARE actions are cross-cutting to the rest of participants. Heterogeneous interfaces, data sources, and datasets will evolve so that seamless interoperability is achieved through the use of the Open Standard NGSI-LD API and harmonized open data model templates (Smart Data Models) New data set value N/A Audio-visual material N/A Partners Activities & Responsibilities Partner owner of the device producing the data - ED/RAMP, WP3 Module Owners, WP4 Data Sharing Framework developers, WP5 (hosted Pilots + FSTP experiments) Partner in charge of the data collection (if different) - Partner in charge of the data analysis (if different) - Partner in charge of the data storage (if different) - WPs and tasks -WP5→ Execution of Demonstrators Standards and Metadata Metadata standards and data documentation -NGSI-LD -Smart Data Models Methodology for data collection/generation N/A Data exploitation & sharing Data exploitation (purpose/use of the data analysis) N/A Data ownership N/A Suitability for sharing N/A Data utility N/A Open research data pilot N/A Embargo periods (if any) N/A Archiving & preservation (including storage and backup) Managing, storing and curating data N/A Data Storage N/A D1.2 Data Management Plan 48 | 67 3.2 Data types and formats collected and generated in CIRCULOOS A non-exhaustive list of types and formats of data was provided to partners (see Table 2) in order to define the most common data types and format that will be generated within the project. According to the consortium the main formats are: structured data (HTML, JSON, TEX, XML, RDF); tables (xlsx files) containing textual data (strings) and quantitative and qualitative information, including: timestamp, barcode of the battery pack, charge, residue, forces and data that describes the location of the battery in the pack (face, cell, point); data concerning the companies (manufacturing SMEs, automation solution and consultancy services providers (from the RAMP); personal data about companies’ profiles involved in the WP1 surveys, RAMP and CIRCULOOS Open calls; images (jpg or png) for training (dataset) and from pilots; raw text data (Json format) for module validation; PDF/ Word file corresponding to a filled-in template; database (Microsoft Access, MySQL, Oracle, etc). Information regarding data types and formats are still provisional and will be more precise in the next version of the DMP. Regarding the CIRCULOOS platform, it will be interfaced with different kinds of Internet of Things (IoT) devices and robots as well as factory information systems such as demand & supply management systems and human resources databases. Each IoT device or robot typically produces raw data in a proprietary format which may vary over time even within the same device and a similar degree of data format volatility can be expected of information systems. Thus, to acquire data from those environments, a plethora of diverse data formats will need to be understood by the platform, at least at its boundary where information is exchanged with external systems. Moreover, new formats may have to be accommodated as shop floors are connected to the platform. 3.3 Data characterisation: re-use of existing data, data origins and size Some partners will reuse existing data. Regarding the demonstration activities within WP5, generally some already existing images and information coming from pilots manufacturing floors and machines, including images of defects, products and production lines, could be reused. In the Dutch pilot 3A some GIS data from the municipality of Rotterdam may be used. Such datasets may be used to reveal future supply of virgin material. This provides the possibility to align demand. Leather recycling pilot does not use secondary data. Plastic recycling pilot does not use secondary data. SUPSI plans to reuse existing data and methodologies developed by their own team for sustainability assessments (LCA, LCC, s-LCA and CE). This includes algorithms, codes and archetypes previously developed for this purpose. Additionally, we use commercial databases (such as Ecoinvent) for comprehensive data coverage. PILOT1: Plastic recycling plans to use historical data retrieved from THER actual production lines and processes. Digitalized data are available from 2015 until now, with additional quality and processes data being stored since 2020. D1.2 Data Management Plan 49 | 67 CUT plans to re-use existing historical data from pilots and experiments for the needs of SCDT and SCOPT modules. Some partners will use data of existing ‘residents’ that is companies already registered in RAMP. This data is provided by companies which register to the Marketplace, which is a mandatory step for the participation to the Open Calls. On general, the size of these data (textual description) is expected to be lower than 1 MB. 3.4 Types and formats of data that CIRCULOOS will generate/collect To fulfil the purpose of the data collection/generation, the CIRCULOOS project will collect and generate the following types and formats of data: Table 16: Types and formats of data Data/Data Source Data type Data format Data origin Surveys, workshops/living labs data, validation cycles data Electronic document Word document (.doc,.docx) Excel document (.xls/.xlsx) Pdf document WP2, WP3, WP4, WP6 Hardcopy Paper/website surveys/Ramp surveys Deliverables Electronic document Word document (.doc,.docx) Excel document (.xls/.xlsx) Pdf document All WPs Hardcopy Paper/Website Website public reports Electronic document Word document (.doc/.docx) Pdf document Excel document (.xls/.xlsx) .csv files .txt All WPs Video files Electronic document .mov, .mpeg, .avi, .mp4, etc. All WPs Audio files Electronic document .mp3, .wav, etc. All WPs Images Electronic document .jpg, .png, .gif, etc. All WPs Software Source Code Source Code WP3, WP4 Signed documents (eg. Consent forms, information sheets, attendance lists, Consortium Agreement, etc.) Electronic document Word document (.doc,.docx) Excel document (.xls/.xlsx) Pdf document WP1, WP2, WP5, WP6, WP7 D1.2 Data Management Plan 50 | 67 Hardcopy paper Presentations Electronic document Powerpoint document All WPs Hardcopy paper Network and system related data Electronic document WP3, WP4, WP5, WP6 3.5 Information types in the CIRCULOOS project Following the “Guidance Guidelines for the classification of research results” of the European Commission, the deliverables have three types of classification: PUBLIC, SENSITIVE, and RESTREINT UE/EU RESTRICTED. 3.5.1 PUBLIC In Table 17, CIRULOOS deliverables are classified as PUBLIC. Table 17: CIRULOOS Public Deliverables Number Deliverable Title D1.1 Project Management Handbook D1.2 Data Management Plan M6 D1.3 Data Management Plan M30 D1.4 Data Management Plan M42 D2.2 CMRA Specification M9 D2.3 CMRA Specification M18 D3.1 Supply Chain Orchestrator M12 D3.2 Supply Chain Orchestrator M24 D3.3 3D Digital Twin of supply chain/production/ products M12 D3.4 3D Digital Twin of supply chain/production/ products M24 D3.5 Sustainability and LCA Assessment tools M12 D3.6 Sustainability and LCA Assessment tools M24 D3.7 AI and Data-driven supply chain Optimisation M12 D3.8 AI and Data-driven supply chain Optimisation M24 D3.9 CV-based system for composition detection M12 D3.10 CV-based system for composition detection M24 D1.2 Data Management Plan 51 | 67 D4.1 Data sharing layer M1 D4.2 Cyber secure data sharing layer M21 D4.4 First version of Integrated Platform D4.5 Final version of Integrated Platform D5.1 Early versions of 3 Pilot Demonstrators D5.2 Final version of 3 Pilot Demonstrators D5.4 Demonstration of EXDs D5.5 EXDs scaled up demonstrations D5.6 Lessons Learnt, feedback and recommendations handbook D6.2 Periodical dissemination retrospect and follow-up action plan M12 D6.3 Periodical dissemination retrospect and follow-up action plan M30 D6.4 Periodical dissemination retrospect and follow-up action plan M42 D6.5 Open Calls report M12 D6.6 Open Calls report M24 D6.7 Open Calls report M42 D6.11 Report on training activities and platform M18 D6.12 Report on training activities and platform M42 SENSITIVE In Table 18 , CIRCULOOS deliverables are classified as SENSITIVE. Table 18: CIRCULOOS Sensitive Deliverables Number Deliverable Title D2.1 Ambitious Scenarios for the pilots D2.4 Technical components specification D4.3 Integration plan and infrastructure D5.3 Summary of EXDs and individual execution plans D6.1 Initial Plan for the Exploitation and Dissemination of Results D6.8 Market Analysis and Standardisation Plan M12 D6.9 Business plan (tools and platform, EXDs, and partners) M30 D1.2 Data Management Plan 52 | 67 D6.10 Business plan (tools and platform, EXDs, and partners) M42 D7.1 OEI - Requirement No. 1 D1.2 Data Management Plan 53 | 67 4 FAIR Data The principles of FAIR (Findable Accessible Interoperable Reusable) data have been established by a set of different stakeholders like academia, industry, funding agencies, and scholarly publishers. According to Wilkinson et al. (2016)4, the FAIR Data Principles are a set of guiding principles in order to make data Findable, Accessible, Interoperable and Reusable (Figure below). Figure 2: FAIR Data Principle The FAIR Data Principles clearly provide a concise and measurable set of parameters that should be respected to ensure the availability and reusability of data for further research purposes by third parties that are not part of the project. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. The mentioned principles do not necessarily propose an explicit technology, standard or implementation solution, forego implementation choices and promote the maximum usage of data. CIRCULOOS project complies with the principal priorities as defined below: - The publications will be made available through the project’s website, and the data produced will be discoverable with metadata and identifiable and locatable by the Digital Object Identifiers (DOIs). The metadata will include: title, data types/formats and software, data collection method and dates, geographic coverage, language, data processing details, funding details, ethics clearance details, a project abstract, keywords, and licensing. - Data needed for the collaborative tools are expected to include a unique ID and a timestamp allowing for proper indexing and handling when stored. No specific standards or metadata have been identified for the time being for the datasets. - The data from the surveys, workshops/living labs, and the validation cycles will not be published as primary data (data that is collected directly from the data source) due to privacy and security concerns. More detailed information on how to make data findable, accessible, interoperable and re-usable will result from discussions among partners and will be included in the 2nd version of the DMP which is expected by M30. D1.2 Data Management Plan 54 | 67 4.1 Making data Findable, including provisions for metadata Most of the data produced within the project are discoverable with metadata and identifiable and locatable by means of a standard identification mechanism. To make their identification easier, partners will decide upon a common naming procedure, taking into account internal project conventions. The conventions that will be followed will be further discussed in the upcoming months: some partners already rely on their internal company system for data identification and their model could be adopted by the whole consortium as the common project methodology. Data regarding companies will be available in RAMP, other data derived from these companies since sensitive will be securely stored and accessible in ED’s ISO27001:2013-certified infrastructure (ED). Dutch Pilot 3A&B we need some help here, as we have no knowledge about this terminology. Data regarding residual leather will be available in KHOANI’s webpage, other data derived from the user companies since sensitive will be securely stored and accessible in a cloud storage. Concerning the SUPSI’s team, the publication of reports and deliverables on open-access platforms will ensure the broad accessibility. Additionally, metadata will be used to describe data following the platform requirements and standards such as authorship, keywords, and DOIs if published. RAMP will also be used for increasing visibility and accessibility. PILOT1 Plastic recycling: Data is available and classified in our systems according to different parameters (job order/machine, customer, timestamps, workers…), to be defined with the consortium how to access it. Shareable data will be published through different platforms like our webpage, Linkedin, and through partners. CUT will also publish on open-access platforms, following the platform requirements. RAMP will also be used for increasing visibility and accessibility. Regarding the CIRCULOOS platform, data are stored and manipulated in accordance with the NGSI standard. NGSI is a protocol developed by OMA to manage Context Information. It provides operations like managing the Context Information about Context Entities, for example the lifetime and quality of information and accessing (query, subscribe/notify) to the available Context Information about Context Entities. NGSI extends well-known Web standards, such as Representational state transfer (REST) and Linked Data7, to develop an ontology and interoperability framework for IoT. In particular, entities and relationships which constitute the system data (the so-called IoT “context” in NGSI parlance) become Web resources, each identified by a unique Uniform Resource Identifier (URI) and retrievable through an HTTP call by constructing a suitable Uniform Resource Locator (URL) from the resource’s URI. Those Web resources are made available through a Web service, the FIWARE Context Broker, which provides a standard mechanism for clients to discover what resources are available in the IoT context that it manages. Regarding pilots, it still needs to be defined but mainly information related to the source, the type of piece and the defect could be made available. It will be agreed with other partners in the next months 4.1.1 Metadata Metadata provides additional information that helps data consumers better understand the meaning of data, its structure, and to clarify other issues, such as rights and license terms, the organization that D1.2 Data Management Plan 55 | 67 generated the data, data quality, data access methods and the update schedule of datasets. Generally speaking, metadata will have a twofold nature: descriptive, therefore giving information on the data discovery and identification (titles, author, keywords) and administrative outlining when and how data was created, file type and other technical information, and who can access it (dataset name, version, description, format, License, keywords). CIRCULOOS platform adopts the NGSI data standards. NGSI makes provisions to model plain metadata as well as complex relationships between metadata items. 4.1.2 Naming conventions To (i) enhance data searchability and discoverability, and (ii) provide clues to the content, status, and versioning of the files, each set of data produced (dataset, deliverables, etc…) will be named in a uniform way and will include a table with a version control. The recommendations to name the documents of the project are as follows: Choose easily readable identifier names (short and meaningful); Do not use acronyms that are not widely accepted; Do not use abbreviations or contractions; Avoid language-specific or non-alphanumeric characters; Add a two-digit numeric suffix to identify new versions of one document. Dates should be included back to front and include the four-digit years: YYYYMMDD. For deliverables: Project’s name - Dx.y - [Name of the deliverable as described in the DoA] being x - work package assigned to the deliverable y - the number of deliverables within the work package i.e.: D.1.2 - Data management plan M6. For datasets: Project’s name - WP [Work Package number] P [Pilot number; pilot activity number] - T [Task number; description of the activity] e.g., WP1 Task 1.4 Data Management Plan. Easy-to-use search keywords will be used in CIRCULOOS to optimise the reuse of data by interested stakeholders. The metadata standards employed by CIRCULOOS provide opportunities for tagging the data collected/generated and its content with keywords. In general, the keywords will comprise terms related to the topics addressed, such as energy efficiency, energy renovations, smart contracting, innovative business models, fair energy transition, capacity building in the energy sector, green currency, smart renovations, energy efficiency policies, as well as keywords specific to the project, such as CIRCULOOS, Horizon Europe, etc. The keywords will accurately reflect the content of the datasets and avoid words used only once or twice within them. 4.2 Making data openly Accessible According to Art. 17 of the Grant Agreement, the beneficiaries must ensure open access to peer-reviewed scientific publications relating to their results. They must ensure that: D1.2 Data Management Plan 56 | 67 at the latest at the time of publication, a machine-readable electronic copy of the published version or the final peer-reviewed manuscript accepted for publication, is deposited in a trusted repository for scientific publications. immediate open access is provided to the deposited publication via the repository, under the latest available version of the Creative Commons Attribution International Public Licence (CC BY) or a licence with equivalent rights; for monographs and other long-text formats, the licence may exclude commercial uses and derivative works (e.g. CC BY-NC, CC BY-ND) and information is given via the repository about any research output or any other tools and instruments needed to validate the conclusions of the scientific publication. manage the digital research data generated in the action (‘data’) responsibly, in line with the FAIR principles and by taking all of the following actions All publications will be made available through ArXiv (https://arxiv.org/) repository or OpenAIRE (https://www.openaire.eu/). The dataset obtained under WP3, WP4, WP5 and WP6 might contain classified information related to the existing mechanisms/processes/infrastructure, hence these datasets could be classified. 4.3 Making data Interoperable According to the consortium, most of the data produced in the project will be interoperable, and data exchange and re-use between researchers, institutions, organisations, countries, etc. will be favoured. However, partners still have to define which metadata vocabularies, standards and methodologies will be used to this end. Generally, interoperable data will use a formal, standard, accessible, shared, and broadly applicable language for knowledge representation, vocabularies that follow FAIR principles and standard data-storage methods. On the contrary, non-interoperable data will use vocabularies, standards and methodologies that will primarily answer to internal efficiency and safety requirements. 4.4 Increasing data Re-use As already shown before some data generated within the project will be prepared to be reused by third parties including academia and industry stakeholders. In the event that some project data will be licensed to permit their re-use by the external stakeholders, Creative Commons or GNU licenses will be used. As previously mentioned, the data that will be made available for individuals that are external to the project will be included in the project deliverables marked as public. Such data will be made public, through the project’s website and open access repositories for scientific publications such as Zenodo and other Institutional Repositories connected with OpenAIRE, as soon as they will be approved by the project officer on the Funding and Tenders Platform of the EC. With regards to the access rights to third parties, according to Art. 9.8 of the Consortium Agreement, it shall, as far as Needed for the Exploitation of the Party’s own Results, comprise the right to grant in the normal course of the relevant trade to end-user customers buying/using the product/services, a sublicense to the extent as necessary for the normal use of the relevant product or service to use the Object Code alone or as part of or in connection with or integrated into products and services of the Party having the Access Rights D1.2 Data Management Plan 57 | 67 D1.2 Data Management Plan 64 | 67 - Directive 96/9/EC14 of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases. - Directive 2009/24/EC15 of the European Parliament and of the Council of 23 April 2009 on the legal protection of computer programs. - Directive 2004/48/EC16 of the European Parliament and of the Council of 29 April 2004 on the enforcement of intellectual property rights. - National laws on patent, design, trademark and copyright protection. 8.2 IPR Management in the CIRCULOOS Project The CIRCULOOS Consortium Agreement expressly stipulates the rules related to the management of IP rights and distinguishes, on the one hand, the IP rights that are held by the partners prior to their accession in the Consortium Agreement and are needed for the Project (Background) and, on the other hand, the IP rights that are held by the partners during the lifetime of the Project (Results). Section 8 Results and Section 9 Access Rights of the CIRCULOOS Consortium Agreement include all relevant clauses that have been agreed between the partners and refer to the IPR management. In Attachment 1 of the CIRCULOOS Consortium Agreement, the Parties have identified and agreed on the Background for the Project and have also, where relevant, informed each other that access to specific Background is subject to legal restrictions or limits. Therefore, we aim to work methodically to classify CIRCULOOS IPRs and define: - the treatment of existing IPRs (background), - the management of joint ownership. Partners will keep record of their contributions which are protected by IP Law and potentially Trade Secrets. This will permit the Consortium to discern the share of each owner in relation to the results of a joint effort. CIRCULOOS Partners aim to reach a point where exploitation of results will become possible, - the protection and management of the results of CIRCULOOS (foreground), - the exploitation and dissemination of the results of CIRCULOOS (foreground), - the protection of know how created during CIRCULOOS. Furthermore, section 4 and section 5 of the CIRCULOOS Consortium Agreement provide for the responsibilities of the partners and their liability towards each other (including for the management of IP rights). All partners may settle any disputes in accordance with the clause 11.8 of the CIRCULOOS Consortium Agreement. To effectively achieve the objectives regarding the overall management of IPRs, a cumulative IPR Control form will be circulated by the coordinator. D1.2 Data Management Plan 65 | 67 9 Ethical Aspects Work package 7 (Deliverables 7.1) of the CIRCULOOS project deals with the ethics requirements with which CIRCULOOS’s objectives, methods, processes, tasks and results must comply. These ethics requirements mainly relate to the processing of personal data and, with that, with data protection. Therefore, in this deliverable, requirements that deal specifically with the procedures with respect to data protection will not be covered. In order to ensure that all ethical aspects are considered and that the CIRCULOOS project is compliant with all legal requirements and ethical issues, a general strategy has been designed based on the Ethics Requirements that were defined by deliverables from WP7. This strategy involves an ad hoc monitoring process of the project development by applying the privacy-by-design approach through a methodological design based on a “Socio-legal Approach.” This is a risk-based approach to privacy and data protection issues in line with the new General Regulation for Data Protection (GDPR). Ethics Requirements that were drawn by the EC, during the ethics check process before the signature of the GA, have been considered by the consortium; to address all these requirements the CIRCULOOS beneficiary Trilateral Research Limited, with its ethical and legal expertise will provide general guidelines to the project and the consortium on all aspects, covering data protection, privacy issues, research participants’ safety, etc. D1.2 Data Management Plan 66 | 67 10 Conclusion The initial DMP for CIRCULOOS is presented and delivered in this document describing how acquired data and knowledge will be shared and/or made open as well as how data will be maintained and preserved during and after the timeline of the CIRCULOOS project. This deliverable defines any kind of information including scientific publications, white papers, OpenSource code, open datasets, anonymous interview results, or mock-up datasets used for gathering customer feedback that may be used or generated from the project. The collected datasets in the current version of the report are research data, related to the project’s work packages and are managed according to their level of availability (public or Consortium). The CIRCULOOS Data Management also follows the Guidelines on FAIR Data Management in Horizon 2020, i.e., data must be findable, accessible, interoperable, and reusable. All partners will be responsible to periodically update information on their research and subject data. The current report will be a living document throughout the project. The DMP will be updated whenever significant changes arise, such as (but not limited to) new data, new innovations, patent filings, changes in the consortium members and others. Formal updates to the DMP will be submitted at specific project milestones, currently scheduled for M30 and M42. 67 | 67