D9.4 Data Management Plan (DMP)

Ref. Ares(2017)2503178 - 17/05/2017

PROJECT DELIVERABLE REPORT

Project Title: Zero-defect manufacturing strategies towards on-line production management for European FACTORies FOF-03-2016 - Zero-defect strategies at system level for multi-stage manufacturing in production lines

Deliverable number D9.4 Deliverable title Data Management Plan (DMP) Submission month of deliverable M6 Issuing partner 7- DATAPIXEL Contributing partners All partners Dissemination Level (PU/PP/RE/CO): PU Project coordinator Dr Dionysis Bochtis Tel: +302421096740 Email: [email protected] Project web site address http://www.z-fact0r.eu/

Document Information “This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723906”

FOF-03-2016 Z-Fact0r - 723906

Filename(s) Z-Fact0r_D9.4 Owner Z-Fact0r Consortium Distribution/Access Z-Fact0r Consortium Quality check EPFL, ATLANTIS, CETRI, CERTH, DATAPIXEL

Report Status Final

Revision History Version Date Responsible Description/Remarks/Reason for changes 1.0 10.03.17 DATAPIXEL Report draft write-up 1.1 15.03.17 ALL PARTNERS Inclusion of partners’ contributions 1.2 21.03.17 DATAPIXEL Second revised draft 1.3 27.03.17 DATAPIXEL Inclusion of ATLANTIS contribution 1.4 31.03.17 ALL PARTNERS Inclusion of partner´s contribution 2 1.5 31.03.17 DATAPIXEL Internal Review Contribution of the Dissemination and 1.6 13.04.17 CETRI Exploitation Manager and WP9 leader 1.7 24.04.17 DATAPIXEL Third revised draft 1.8 03.05.17 DATAPIXEL Inclusion of final contribution of the partner´s 1.9 09.05.17 DATAPIXEL Final version for peer-review process 1.91 10.05.17 EPFL Peer Review ATLANTIS, Final version for technical manager & 1.99 11.05.17 CERTH coordinator review 2.0 16.05.17 DATAPIXEL Final Review ready for submission to the EC

FOF-03-2016 Z-Fact0r - 723906

Contents

1 Summary ...... 5 2 Introduction ...... 5 2.1.1 Participation in the pilot on open research data ...... 6 2.1.2 Building a DMP in the context of H2020 ...... 6 2.2 Z-Fact0r Data Management Plan (DMP) ...... 7 2.2.1 General description ...... 7 2.2.2 Activities of Data Management Plan ...... 8 2.2.3 Register on numerical datasets generated or collected in Z-Fact0r ...... 8 2.2.4 Metadata for Data Management ...... 9 2.2.5 Data description ...... 10 2.2.6 Policies for access, sharing and re-use ...... 62 2.3 Data currently being produced in Z-Fact0r ...... 64 3 Data Management related to Zero-defects Manufacturing ...... 65 4 Data Management Portal (FREEDCAMP) ...... 65 4.1 FREEDCAMP portal functionalities ...... 65 4.2 Data Backup: Private Area of the Project Website ...... 68 4.3 Open Access Section ...... 68 5 Future Work ...... 68 5.1 Roadmap of actions to update the DMP ...... 69 6 Conclusions...... 70 7 Glossary ...... 70 8 Bibliography ...... 71

FOF-03-2016 Z-Fact0r - 723906

Abbreviations

DMP Data Management Plan D9.4 Deliverable 9.4 ORD Pilot Open Research Data Pilot OA Open Access EU European Union EC European Commission CCS Consortium and the Commission Services H2020 Horizon 2020 R&D Research and Dissemination ODF Open Document Format ODT Open Document Text WP Work Package GA Document of Grant Agreement CA Document of Consortium Agreement IPR Intellectual Property Rights M6 Month 6 DEM Dissemination and Exploitation Manager KPI Key Performance Indicator RCA Root Cause Analysis i-Like Intelligent Lifecycle Data and Knowledge DSS Decision Support System ES-DSS Early Stage-Decision Support System KMDSS Knowledge Management and Decision Support System GD&T Geometrical Dimensions and Tolerances MP Measurement Plan CERIF Common European Research Information Format PLC Programmable Logic Controllers DCE Dissemination, Communication and Exploitation RDI Research Data Information QIF Quality Information Framework DB Database

FOF-03-2016 Z-Fact0r - 723906

1 Summary This report focuses on the preparation of the DMP for Z-Fact0r project. DMP provides an analysis of the main elements of data management policy that will be used throughout the project with regard to all datasets that will be generated. In particular, DMP will define how this data will be managed and shared by the project partners, and also, how this information will be curated during as well as preserved after the project duration.

DMP of Z-Fact0r project describes the life cycle of all modelling and observation data collected and processed during the project, giving an overview of available research data, access and data management as well as terms of use. The DMP reflects the current state of the discussions, plans and ambitions of the partners, and it will be updated and augmented with new datasets and results during the lifespan of Z-Fact0r project.

The ORD Pilot of the EC aims to improve and maximize access and reuse of research data generated by projects focusing on encouraging good data management as an essential element of research best practice. Following the recommendation of the EC, Z-Fact0r project is participating in the ORD Pilot and DMP is included as D9.4 deliverable (M6) of WP9 DCE that has been prepared during the first 6 months of the project.

2 Introduction The amount of data generated is continuously increasing while use and re-use of data to derive new scientific findings is relatively stable. This information would be useful in the future if the data is well documented according to accepted and trusted standards which enable the recognition of suitable data by negotiated agreements on standards, quality level and sharing practices. For this purpose, DMP defines strategies to preserve and store data over the defined period of time in order to ensure their availability and re-usability after the end of Z-Fact0r project.

According to the Guidelines of ORD Pilot in H2020, research data refers to information, in particular facts or numbers, collected to be examined and considered and as well as basis for reasoning, discussion, or calculation. The overall objective of Z-Fact0r project is to develop zero- defect manufacturing strategies for on-line production. Z-Fact0r aims to contribute to the eradication of defects in manufacturing, providing better quality of products, increasing flexibility, and reducing production costs. Thus, research activities are more focused on the production process and tools than on production of research or observation of data, so the amount of research data which will be produced within the project is limited, at least at this stage of the project.

FOF-03-2016 Z-Fact0r - 723906

2.1.1 Participation in the pilot on open research data The EC is running a flexible pilot under H2020 called the ORD pilot. The ORD pilot aims to improve and maximize access and re-use of research data generated by H2020 projects and takes into account the need to balance openness and protection of scientific information, commercialization and IPR, privacy concerns, security as well as data management and preservation issues. The 2017 work programme of ORD pilot has been extended to cover all the thematic areas of Horizon 2020.

Following the recommendation of the EC, Z-Fact0r project is participating in the ORD Pilot and DMP is D9.4 deliverable (D.9.4) due M6 of the project. The DMP of Z-Fact0r project has been prepared by taking into account the document template of the “Guidelines on DMP in H2020”.

This document will be updated and augmented with new datasets and results, according to the progress of the activities of the Z-Fact0r project. Also, the DMP will be updated to include possible changes in the consortium composition and policies over the course of the project.

The procedures that will be implemented for data collection, storage, access, sharing policies, protection, retention and destruction will be according to the requirements of the national legislation of each partner and in line with the EU standards.

2.1.2 Building a DMP in the context of H2020 The EC provided a document with guidelines for project participating in the pilot. The guidelines address aspects like research data quality, sharing and security. Following these guidelines, DMP will be developed with aim to provide a consolidated plan for Z-Fact0r partners in the data management plan policy that the project will follow.

The consortium will comply with the requirements of Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. The consortium will preserve the right to privacy and confidentiality of data of the survey participants, by providing them two documents: The Participant Information Sheet and the Consent Form. These documents will be sent electronically and will provide information about how the answers will be used and what the purpose of the survey is.

The participants will be assured that their answers will be used only for the purposes of the specific survey. The voluntary character of participation will be stated explicitly in the Consent Form. Before conducting the survey, the consortium will examine and follow the requirements of the national

FOF-03-2016 Z-Fact0r - 723906 legislation in line with the EU standards, whether the proposed data collection requires special local/national ethical/legal permission.

An ethical approach will be adopted and maintained throughout the fieldwork process. The responsible partners will assure that the EU standards regarding ethics and Data Management are fulfilled. Each partner will proceed with the survey according to the provisions of the national legislation that are adjusted in line with the respective EU Directives for Data Management and ethics.

The consortium will follow a transparent recruitment process for the engagement of stakeholders and inclusion/exclusion criteria for all the surveys will be explained in the Participant Information Sheet.

Each partner will send an invitation (by mail) to participants/third parties that have neither the role in Z-Fact0r project nor professional relationship with the consortium to participate in the survey. The consortium will also examine whether personal data will be collected and how to secure the confidentiality in such a case.

The Steering Committee of the project will also ensure that EU standards are followed. The issue of informed consent for all survey procedures, all participants will be provided with a Participant Information Sheet and Consent Form to provide informed consent. The default position for all data relating to residents and staff will be anonymous.

2.2 Z-Fact0r Data Management Plan (DMP) 2.2.1 General description

This document outlines the first version of the project’s DMP. The DMP is presented as D9.4 public deliverable (Month 6) of WP9, DCE.

The main purpose of DMP is to provide an analysis of the main elements of data management policy that will be used by the consortium with regard to all the datasets that will be generated by the project (e.g. numerical, images, etc.).

This document describes the Research Data with the metadata attached, and presents an overview of datasets to be produced by the project, their characteristics and the management processes to make them discoverable, accessible, assessable, usable beyond the original purpose, and disseminated between researchers. It also introduces the specifications of the dedicated Data Management Portal developed by the project in the context of the ORD Pilot, allowing the efficient management of the project’s datasets and providing proper OA on them for further analysis and

FOF-03-2016 Z-Fact0r - 723906 reuse. In addition, the DMP of Z-Fact0r project reflects the current status of discussion within the consortium about the data that will be produced.

2.2.2 Activities of Data Management Plan The DMP is a dynamic document, updated throughout the whole project lifecycle. The final version of this report will be delivered by the end of the project, reflecting on lessons learnt and describing the plans implemented for sustainable storage and accessibility of the data, even beyond the project’s lifetime.

A Knowledge Management system will be developed, which incorporates in a structured way, the technical and business knowledge created during the project. The activities of the Z-Fact0r concerning the data management are planned as follows:

- Knowledge management – to be led by the DEM, in which the DMP will be delivered. - A knowledge management document will be created, based on DMP, describing how the acquired data and knowledge will be shared and/or made open, and how it will be maintained and preserved. The identifiable project data will be provided in a manner to define the relevant knowledge, increase partners’ awareness, validate the result, and timeframe of actions. - Technology watch - All partners will be responsible for periodically updating the knowledge management system with outcomes of research work conducted by other groups and any new patents/patent applications, i.e. to ensure that ongoing relevant technological developments and innovations are identified, analysed, and hopefully built upon during the course of the project.

2.2.3 Register on numerical datasets generated or collected in Z-Fact0r The goal of the DMP is to describe numerical model or observation datasets collected or created by Z-Fact0r during the runtime of the project. The register on numerical datasets has to be understood as a living document, which will be updated regularly during the project´s lifetime.

The operational phase of the project started in October 2016, so there is no dataset generated or collected until delivery date of this DMP (M6). However, this is not a fixed document so it will be updated and augmented with new datasets and results during the duration of Z-Fact0r project.

The information listed below reflects the conception and design of the individual partners in the different work packages at the beginning of the project. The data register will deliver information according to the information detailed in Annex 1 of the GA document:

FOF-03-2016 Z-Fact0r - 723906

 Dataset reference and name: identifier for the dataset to be produced.  Dataset description: descriptions of the data that will be generated or collected, its origin or source (in case it is collected), nature and scale and 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 sharing: 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 CCS) or public. In case a dataset cannot be shared, the reasons for this should be mentioned (e.g. ethics, 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.

2.2.4 Metadata for Data Management An initial plan of research data has been explored in Annex 1 of the GA. The dataset list is provided in the table below, while the nature and details of each dataset are presented in the next section.

Table 1. Research data that will be collected and generated during Z-Fact0r.

Research Data Partners Data structures with production machine signatures (healthy and deteriorated ATLANTIS conditions) Machine Deterioration thresholds for predicting production of defected products ATLANTIS RCA data structures for identifying the root cause of a defect in upstream stages CERTH/ITI

FOF-03-2016 Z-Fact0r - 723906

Data from the comparative assessment (i.e. with and without Z-Fact0r) in the 3 use MICROSEMI, cases: difference in production cost/waste/scrap, in detection efficiency, in single- INTERSEALS, stage production defect rate, in average multistage production defect rate, in DURIT production output quality (qualified output / total output produced), in defect propagation to downstream stages Defect detection efficiency data: false alarm rate, precision, recall, F-Measure ALL PARTNERS Defect prediction efficiency data: positive prediction rate ALL PARTNERS Discrete Event Modelling – cost function generation to optimize production with BRUNEL green scheduling Validation and verification of KPIs to assess the direct impact of system level to BRUNEL, EPFL the final cost Context aware models and associated algorithms EPFL Additive manufacturing methodologies for rework and repair CETRI Improved functionalities of i-LiKe knowledge management and DSS suite HOLONIX

Partners will characterize their research data and associated software and/or used in the project whether these are discoverable, accessible, assessable and intelligible, useable beyond the project’s life and interoperable. In specific, research data can be discovered by means of an identification mechanism such as Digital Object Identifier and accessible by defining modalities, the scope of the action, establish the licenses and define the IPR. Otherwise research data will be assessable and intelligible allowing third parties to make assessments. Also, the dataset will be useable beyond the original purpose for which it was collected or usable to third parties after the collection of the data for long periods (repositories, preservation and curation). Finally, research data will offer interoperability to specific quality standards and allow data exchange between researchers, institutions, organizations, countries, re-combinations with different datasets, data exchange, compliant with available software applications.

2.2.5 Data description In order to collect the information about the research data that will be generated in different activities of the Z- Fact0r project, we have elaborated a template to be completed by the consortium partners. This template includes the following information items:

 Dataset reference and name: name, homepage, publisher, maintainer  Dataset description: description, provenance, usefulness, similar data, re-use and integration  Standards and metadata: metadata description, vocabularies and ontologies  Data sharing: license, URL dataset description, openness, software necessary, repository

FOF-03-2016 Z-Fact0r - 723906

 Archiving and preservation: preservation, growth, archive, size

2.2.5.1 Dataset per partner All partners have identified the data that will be produced in the different project activities;

DS.CETRI.Z-Repair_AM_processing Data Identification Dataset description Formulation of the inks or paste for additive manufacturing repairing processes & printing/deposition protocols. Source Various characterization techniques, e.g. microscopy, viscometer, printing station. Partners activities and responsibilities Partner owner device CETRI Partner in charge of CETRI data collection Partner in charge of CETRI data analysis Partner in charge of CETRI data storage WPs and tasks T2.4 in WP2 Standards Info about metadata The metadata include: (Production and a) The characteristics of the materials to be deposited. storage dates, places, b) The user requirements as obtained by the end users. and documentation) Standards, Format, No standards apply. The format will be in the form of Estimated volume of spreadsheets and images (TIFF of JPG). Estimated volume is < data. 10 MB. Data exploitation and sharing Data exploitation The results of the study have the potential to be exploited by (purpose/use of the CETRI along with the Z-Fact0r end users MICROSEMI, data analysis) DURIT and INTERSEALS, as well as by SIR, towards the implementation of integrating new processes in their production lines.

FOF-03-2016 Z-Fact0r - 723906

Data access policy / In general, the data will be confidential with the exception of Dissemination level possible future publications in the case the consortium permits such activities. Data sharing, re-use CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users and distribution (MICROSEMI, DURIT, INTERSEALS). Each set will be shared with the individual partners in the form of raw data and complete reports in order to receive feedback during the project implementation. Embargo periods No

Archiving and preservation (including storage and backup) Data storage (including Data will be stored into a computer and an external hard disc and backup) will be send frequently to the individual end-users. The data will be stored permanently in a computer in CETRI facilities.

DS.CETRI.Z-Repair_laser_processing Data Identification Dataset description Measurements of the laser source couples to measurements of the processed surface. Source Laser source. Laser Power Meter Microscopy. Partners activities and responsibilities Partner owner device CETRI Partner in charge of CETRI data collection Partner in charge of CETRI data analysis Partner in charge of CETRI data storage WPs and tasks T2.4 in WP2 Standards Info about metadata The metadata include: (Production and a) The type/origin of the processed material. b) The conditions of the experiments.

FOF-03-2016 Z-Fact0r - 723906 storage dates, places, and documentation) Standards, Format, No standards apply. The format will be in the form of Estimated volume of spreadsheets and images (TIFF of JPG). Estimated volume is < data. 10 MB. Data exploitation and sharing Data exploitation The results of the study have the potential to be exploited by (purpose/use of the CETRI along with the Z-Fact0r end users MICROSEMI, data analysis) DURIT and INTERSEALS, as well as by SIR, towards the implementation of integrating new processes in their production lines. Data access policy / In general, the data will be confidential with the exception of Dissemination level possible future publications in the case the consortium permits such activities. Data sharing, re-use CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users and distribution (MICROSEMI, DURIT and INTERSEALS). Each set will be shared with the individual partners in the form of raw data and complete reports in order to receive feedback during the project implementation. Embargo periods No

DS.DURIT. Production line. Demo 3. Data Identification Dataset description Data collected:  Dimensions/shapes/surface and 3D details  Superficial defects like cracks

FOF-03-2016 Z-Fact0r - 723906

Source The data will be collected by different sensors and imaging devices such as cameras. Installed in the production line after green machining and after finishing operations. Ideally installed in machine for real time inspection although very difficult to implement at the current time. Partners activities and responsibilities Partner owner device DURIT Partner in charge of DURIT data collection Partner in charge of DURIT data analysis Partner in charge of DURIT data storage WPs and tasks The data are going to be collected in WP5 and WP6. Standards Info about metadata The dataset will be accompanied by information regarding: (Production and  Drawings and sequence of operations.  Batch of material used. storage dates, places,  Operators involved. and documentation)  Date, time.  Temperature and relative humidity in the metallurgy section. Standards, Format, Our tests will be in a specific type of pieces. The volume of data Estimated volume of depends of the quantity of order. data. Data exploitation and sharing Data exploitation Production process recognition and help during the different (purpose/use of the production phases, avoiding mistakes. Support of quality checks data analysis) and production batches recalls Data access policy / The full dataset will be confidential and only the members of Dissemination level the consortium will have access on it. Furthermore, if the dataset or specific portions of it (e.g. metadata, statistics, etc.) are to become of widely OA, a data management portal will be created that should provide a description of the dataset and link to a download section. Of course, these data will be

FOF-03-2016 Z-Fact0r - 723906

anonymized, so as not to have any potential ethical issues with their publication and dissemination.

Data sharing, re-use Data sharing is dependent of DURIT, DURIT´s customers and and distribution partner requirements. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including All information belongs to the industrial partner that owns the backup) shop floor. All data will respect the partner policies. All data has to be stored till the end of life/warranty of the produced component. Probably also stored at DURIT servers at the cloud.

DS.EPFL.01_KMDSS Data Identification Dataset description Z-Fact0r Knowledge Management and Decision Support System Dataset. Source Device Manager, Event Manager, Semantic Context Manager, Z- Fact0r Repository. Partners activities and responsibilities Partner owner device The device will be owned by Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT), where the data collection will be performed.

Partner in charge of Various partners related to the specific event and/or operation. data collection Partner in charge of Various partners related to the specific event and/or operation. data analysis Partner in charge of EPFL will store data related to KMDSS (various partners can data storage handle the rest of the data). WPs and tasks The data will be collected within the activities of WP2, WP3 and WP4. Standards

FOF-03-2016 Z-Fact0r - 723906

Info about metadata Indicative metadata include: Input from the Sensor Network (Production and (through the Device Manager), the overall model of Production storage dates, places, activities (through Z-Fact0r Repository), shop-floor events data and documentation) from the Event Manager, and context – aware knowledge stemming from the Semantic Context Manager (Ontology). Standards, Format, Data can be available in XML or JSON format. Estimation of the Estimated volume of volume of data cannot be predicted in advance of a real use of data. the technology at the shop floor level. Data exploitation and sharing Data exploitation The collected data will be used for better understanding of the (purpose/use of the processes and activities evolving in the shop-floor which will data analysis) provide actionable knowledge in the form of a set of recommendations to (i) supervise and provide feedback for all the processes executed in the production line, (ii) evaluate performance parameters and responding to defects, keeping historical data, (iii) send efficiently alarms to initiate actions, filter out false alarms, increase confidence levels (through previously acquired knowledge) of early defect detection and prediction, etc. Data access policy / Accessible to Z-Fact0r consortium members including the Dissemination level commission services as defined in the Z-Fact0r GA.

Data sharing, re-use The sharing of this data is yet to be decided together with the and distribution industrial partners.

Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Data will be stored in a dedicated repository. backup)

DS.EPFL.02.SemanticContextManager Data Identification

FOF-03-2016 Z-Fact0r - 723906

Dataset description Context-aware shop-floor analysis and semantic model for the annotation and description of the knowledge to represent manufacturing system performance. Source Z-Fact0r repository. Partners activities and responsibilities Partner owner device Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT) Partner in charge of EPFL data collection Partner in charge of EPFL data analysis Partner in charge of EPFL data storage WPs and tasks The data will be collected within the activities of WP3 and in particular T3.5. Standards Info about metadata Data from Z-Fact0r repository (data concerning machines, (Production and workers, actors, activities and processes, production data logs, storage dates, places, etc.). and documentation) Standards, Format, Generated output will be the semantic enrichment of shop-floor Estimated volume of data for representation of processes, actors, alarms, actions, data. work-pieces/products, etc., e.g. as RDF Triplets. Standards: W3C-OWL, RDF. Less than 2GB. Data exploitation and sharing Data exploitation Data is required for the Z-Fact0r ontology development. (purpose/use of the Ontology describes semantic models. The ontology will be used data analysis) in order to drive the semantic framework. Furthermore, it will be used for data integration, visualization, inferencing /reasoning. The ontology will describe the basic entities of the project and model relevant structures of multi-stage manufacturing processes. Data access policy / Accessible to Z-Fact0r consortium members including the Dissemination level commission services as defined in the Z-Fact0r GA.

FOF-03-2016 Z-Fact0r - 723906

Data sharing, re-use The Ontology will be uploaded in a server where it will be and distribution accessible to Z-Fact0r consortium members including the commission services. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Data will be stored in a dedicated repository. No expiry date – revisions will be kept. backup)

DS.HOLONIX.ProductionManagement Data Identification Dataset description Collections of data from industrial partner’s production plant, operators, and elaborated data from other Z-Modules. These collections of data contain information about machine conditions, plant conditions, process KPIs of an Industrial production plant. Source Industrial partners’ production plant with its operators and other Z-Modules. Partners activities and responsibilities Partner owner device Industrial partners Partner in charge of Industrial partner with support of HOLONIX presumably data collection Partner in charge of HOLONIX and Z-Modules data analysis Partner in charge of HOLONIX data storage WPs and tasks T3.2 in WP3 Standards Info about metadata A set of RESTful APIs will be released with documentation of (Production and how to require data from datasets. storage dates, places, and documentation)

FOF-03-2016 Z-Fact0r - 723906

Standards, Format, No estimation has been done so far. Estimated volume of data. Data exploitation and sharing Data exploitation Support on the monitoring of production machine, production (purpose/use of the performance and process both for operators and other data analysis) monitoring modules. Data access policy / Collections of data of Production management module should be Dissemination level accessible only for Z-Fact0r consortium partners only.

Data sharing, re-use Data sharing should not be possible with users outside of the and distribution project. A set of RESTFul APIs will be implemented to share data between partners. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Physical place to store production data has still to be decided, data backup) will be stored at least for all the duration of the project.

DS.HOLONIX.Repository Data Identification Dataset description The repository is a collection of dataset coming from various sources including sensors, operator notes, production line installed at industrial partners of the project as well as data incoming from Z-modules as results of their calculation. Source Z-Fact0r industrial partner’s production plant and Z-modules. Partners activities and responsibilities Partner owner device Z-Modules’ responsible partner and industrial partners. Partner in charge of Z-Modules and industrial partners. data collection Partner in charge of Various Z-modules data analysis

FOF-03-2016 Z-Fact0r - 723906

Partner in charge of HOLONIX data storage WPs and tasks T3.2 Standards Info about metadata A set of RESTful APIs will be released with documentation of (Production and how to require data from datasets. storage dates, places, and documentation) Standards, Format, JSON will be data exchange format between Repository and Z- Estimated volume of Modules. No estimation of data volume has been done so far. data. Data exploitation and sharing Data exploitation The datasets collected should be used by various modules of the (purpose/use of the project for the pursue of Zero defects production objective. data analysis) Data access policy / Still to be clarified, for the nature of the dataset collected, only Dissemination level the members of consortium should have rights to access to the datasets with appropriate authorization/authentication policy. Data sharing, re-use No discussion about this matter has been done so far, and should and distribution not be shared with entities outside of Z-Fact0r consortium. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Still to be decided where the collected datasets will be stored backup) definitely.

DS.CONFINDUSTRIA.Events&Roadmapping Data Identification Dataset description Info regarding the demand for Zero Defect production and a global matching coming from a Desk Research based on technology brokerage system available at CONFINDUSTRIA. List of potential tradefairs and events.

FOF-03-2016 Z-Fact0r - 723906

List of potential customer/visitors of workshop and potential companies interested in Z-Fact0r technology. Source Internet, specific DBs (we have not selected anyone). Partners activities and responsibilities Partner owner device CONFINDUSTRIA Partner in charge of CONFINDUSTRIA data collection Partner in charge of CONFINDUSTRIA data analysis Partner in charge of CONFINDUSTRIA data storage WPs and tasks WP 7: T7.3 Roadmap for wider adoption and take-up WP 8: T8.2 Adoption Plan for increasing Awareness WP 9: T9.2 To identify the relevant conference or event Standards Info about metadata It will be used the only available and not confidential (public) data (Production and and documentation coming from Z-Fact0r results in order to storage dates, places, define our strategy and desk research and documentation) Standards, Format, -- Estimated volume of data. Data exploitation and sharing Data exploitation It will be used the only available and not confidential (public) data (purpose/use of the and documentation coming from Z-Fact0r results in order to data analysis) define our strategy and desk research Data access policy /  It will be respected the project rules about confidentiality, by Dissemination level using and disseminate the only public data  Our results will be public Data sharing, re-use  Our results will be public, they could be shared and re-used as and distribution a model:  Research method structure  Roadmap structure  Business Network created Embargo periods None

FOF-03-2016 Z-Fact0r - 723906

Archiving and preservation (including storage and backup) Data storage (including Original data and results will be kept in our company server at backup) least until the all project duration and audit period. Results will be also shared with partners and kept in the project repository.

DS.ATLANTIS.ES-DSS Data Identification Dataset description Dataset for insufficient glue detection obtained by cameras and lasers at the glue implementation machine. The camera images will be processed and not saved anywhere, while the metadata of insufficient glue placement will be used for analysis and detection. Data will be used for early detection of failures. The metadata will be able to send notifications and alarms to the responsible control operators and glue workers. Source The dataset will be collected by using cameras and lasers at the glue machine Partners activities and responsibilities Partner owner device The device will be owned to the industry (MICROSEMI), where the data collection is going to be performed. Partner in charge of Various partners related to the specific incident and/or operation. data collection Partner in charge of Various partners related to the specific incident and/or operation. data analysis Partner in charge of ATLANTIS will store data related to ES-DSS (various partners data storage can handle the rest of the data). WPs and tasks The data are going to be collected within activities of WP3 and more specifically within activities of T3.1, T3.2, T3.3 and T3.4. Standards Info about metadata The dataset will be accompanied with a detailed documentation (Production and of its contents. Indicative metadata include:

FOF-03-2016 Z-Fact0r - 723906 storage dates, places, (a) description of the experimental setup (e.g. location, date, etc.) and documentation) and procedure that led to the generation of the dataset, (b) annotated detection of insufficient glue, activity, business process, state of the monitored activity. Standards, Format, The data will be stored at XML format and are estimated to be Estimated volume of 1GB per day. data. Data exploitation and sharing Data exploitation The collected data will be used for the development of the (purpose/use of the activities analysis and incident detection methods of the Z – data analysis) Fact0r project and all the tasks, activities and methods that are related to it. Data access policy / The full dataset will be confidential and only the members of the Dissemination level consortium will have access on it.

Data sharing, re-use The sharing of this data is yet to be decided along with the and distribution industrial partners. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Data will be stored in a DB. RAID and other common backup backup) mechanism will be utilized to ensure data reliability and performance improvement and to avoid data losses.

DS.ATLANTIS.Evaluation Data Identification Dataset description Values of the KPIs for: 1) Technical indicators. 2) User/Stakeholders acceptance. 3) Indicators for accessing the impact of the project on the factories. Source The dataset will be collected from Z-Fact0r industrial partners, technology providing partners and User/Stakeholders - the tool/solution beneficiaries.

FOF-03-2016 Z-Fact0r - 723906

Partners activities and responsibilities Partner owner device The device will be owned by the consortium. Partner in charge of ATLANTIS with the respective and responsible partners per data collection toolkit/task/plant. Partner in charge of ATLANTIS. data analysis Partner in charge of ATLANTIS will store analysed data related to Solution data storage Evaluation. WPs and tasks The data are going to be collected through demonstrations in relevant environment, specifically within T5.3 activity in collaboration with WP6. Standards Info about metadata Collected data from the execution of the demonstrations at the (Production and operational environment of the pilot sites (WP6) as well as the storage dates, places, users’ acceptance and overall impact will be analysed and and documentation) documented - Report on Solution Validation. Standards, Format, Alphanumeric Estimated volume of data. Data exploitation and sharing Data exploitation Solution validation will be synthesised and documented in the (purpose/use of the form of report - deliverable. data analysis) Data access policy / The full dataset will be confidential, the reports will be public. Dissemination level

Data sharing, re-use Data will be shared among involved partners. and distribution Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including To avoid data losses during the project and to ensure data backup) reliability analysed data will be stored for up to two years after the project life by ATLANTIS.

FOF-03-2016 Z-Fact0r - 723906

DS.ATLANTIS.ReverseSupplyChain Data Identification Dataset description Dataset for gathered data during the manufacturing process, obtained by cameras, lasers and other measurement sensors. The camera images will be processed and not saved anywhere while the metadata for all the other sensors will be used for analysis in order to activate the Reverse – flow process in the Reverse Supply Chain. Data will be used for defect detection in the reverse supply chain. The metadata will be able to send notifications and alarms to the responsible machine operators for removal the defected parts, special inspection, return to previous internal tier (upstream stage) or external tier (other production line or external supplier). Standards and prototypes shall be included in the data for comparison with the defected parts and setting acceptance levels. Source Cameras, lasers and measurement instruments at different points of the production lines Partners activities and responsibilities Partner owner device The device will be owned to the industry, where the data collection is going to be performed. Partner in charge of Various partners related to the specific incident and/or operation. data collection Partner in charge of ATLANTIS will analyse the data in order to provide answers and data analysis reliable use of the Reverse Supply Chain. Partner in charge of ATLANTIS will store data related to Reverse Supply Chain data storage (various partners can handle the rest of the data). WPs and tasks The data are going to be collected within activities of WP2 and more specifically within activities of T2.5. Standards Info about metadata The dataset will be accompanied with a detailed documentation (Production and of its contents. Indicative metadata include: storage dates, places, (a) description of the experimental setup (e.g. location, date, etc.) and documentation) and procedure that led to the generation of the dataset,

FOF-03-2016 Z-Fact0r - 723906

(b) annotated detection of a defective part in the production line, the cause of the defect, the acceptable standards and limits of the part, as well as return point in the production process. Standards, Format, The data will be stored at XML format and are estimated to be Estimated volume of 100ΜΒ per day. data. Data exploitation and sharing Data exploitation The collected data will be used for the development of the (purpose/use of the activities analysis and defect detection methods in the production data analysis) lines of the Z – Fact0r project plants and all the tasks, activities and methods that are related to it. The Reverse Supply Chain shall be able to use the data in order to decide whether or not a defective part should return to a previous tier. Data access policy / The full dataset will be confidential and only the members of the Dissemination level consortium will have access on it.

Data sharing, re-use The sharing of this data is yet to be decided along with the and distribution industrial partners Embargo periods None

DS.DATAPIXEL.3DPointcloud Data Identification Dataset description High accuracy and high resolution 3D Pointclouds of scanned parts. The Pointcloud is a list of 3D points, and can be structured and unstructured Source DATAPIXEL 3D Scanner Partners activities and responsibilities Partner owner device DATAPIXEL

FOF-03-2016 Z-Fact0r - 723906

Partner in charge of DATAPIXEL data collection Partner in charge of DATAPIXEL data analysis Partner in charge of DATAPIXEL and Z-Fact0r repository data storage WPs and tasks WP2 and WP3 Standards Info about metadata Metadata includes part identification, date and time of data (Production and collection, equipment. Pointcloud is part of the information storage dates, places, associated with the manufactured parts. and documentation) Standards, Format, ASCII list of X Y Z is the most common format. Typically, Estimated volume of Pointclouds have a size between 100 K to 10M points, or data. 3Mbytes to 300 Mbytes. Data exploitation and sharing Data exploitation Main use will be the automatic detection of defects by two (purpose/use of the methods: CAD based inspection and GD&T analysis. data analysis) Data access policy / Confidential, except parts authorized by the industrial partners. Dissemination level

Data sharing, re-use The data will be shared using the Sensor network manager, and and distribution stored in the repository for further future analysis. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Data will be stored in the Z-Fact0r repository and by the 3D backup) Pointcloud analysis software.

DS.DATAPIXEL.CADModel Data Identification

FOF-03-2016 Z-Fact0r - 723906

Dataset description The CADModel is the description of the surfaces and geometries of the designed part. It is representing the 3D information of the manufactured part model, and will be utilized for CAD based inspection. Source (e.g. which Industrial partner’s CAD modelling software and DATAPIXEL device?) 3D Pointcloud Analysis software. Partners activities and responsibilities Partner owner of the Industrial partners (MICROSEMI, INTERSEALS, DURIT) and device DATAPIXEL. Partner in charge of the Same data collection (if different) Partner in charge of the DATAPIXEL data analysis (if different) Partner in charge of the DATAPIXEL and Z-Fact0r repository data storage (if different) WPs and tasks WP2 and WP3 Standards Info about metadata Metadata includes part identification, date and time of data (Production and generation. CAD Model is part of the information associated with storage dates, places) the manufactured parts. and documentation? Standards, Format, STEP format Estimated volume of data. Data exploitation and sharing Data exploitation Main use will be the automatic detection of deviations based in (purpose/use of the local regions and the extraction of nominal values for GD&T data analysis) analysis. Data access policy / Confidential, except parts authorized by the industrial partners. Dissemination level

FOF-03-2016 Z-Fact0r - 723906

(Confidential, only for members of the CCS) / Public Data sharing, re-use The data will be shared by the 3D Pointcloud Analysis module, and distribution and stored in the repository for further future analysis. (How?) Embargo periods None (if any) Archiving and preservation (including storage and backup) Data storage (including Data will be stored in the Z-Fact0r repository and by the 3D backup): where? For Pointcloud analysis software. how long?

DS.DATAPIXEL.DeviationMaps Data Identification Dataset description The deviation map is a 3D representation of surface deviations calculated between a captured Pointcloud and the reference CAD model. The deviation map is represented as a list of regions with their corresponding deviation. Typically, the regions are polygonal regions with their associated deviation. Source DATAPIXEL 3D Pointcloud Analysis software. Partners activities and responsibilities Partner owner device DATAPIXEL Partner in charge of DATAPIXEL data collection Partner in charge of DATAPIXEL data analysis Partner in charge of DATAPIXEL and Z-Fact0r repository. data storage WPs and tasks WP2 and WP3 Standards

FOF-03-2016 Z-Fact0r - 723906

Info about metadata Metadata includes part identification, date and time of data (Production and generation. Deviation Map is part of the information associated storage dates, places, with the manufactured parts. and documentation) Standards, Format, A polygonal mesh with an associated deviation number in mm. Estimated volume of Most common formats are STL with annotated deviations and data. PLY. Typically, deviation maps have a size between 100 K to 1M polygons, or 10Mbytes to 100 Mbytes. Data exploitation and sharing Data exploitation Main use will be the automatic detection of defects based in local (purpose/use of the deviations. A deviation threshold can be defined to identify data analysis) defects. Data access policy / Confidential, except parts authorized by the industrial partners. Dissemination level

Data sharing, re-use The data will be shared by the 3D Pointcloud Analysis module, and distribution and stored in the repository for further future analysis. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Data will be stored in the Z-Fact0r repository and by the 3D backup) Pointcloud analysis software.

DS.DATAPIXEL.MeasurementPlan Data Identification Dataset description The MP is a definition of the GD& to be measured in the Pointcloud. The MP contains a detailed definition of geometrical elements and the tolerances associated to them. This information is the input to the Geometrical Feature Extraction module of the 3D Pointcloud Analysis software Source DATAPIXEL 3D Pointcloud Analysis software. Normally the MP is extracted from the geometrical information contained in the CAD model Partners activities and responsibilities

FOF-03-2016 Z-Fact0r - 723906

Partner owner device DATAPIXEL Partner in charge of DATAPIXEL data collection Partner in charge of DATAPIXEL data analysis Partner in charge of DATAPIXEL and Z-Fact0r repository data storage WPs and tasks WP2 and WP3 Standards Info about metadata Metadata includes project identification, date and time of data (Production and generation. MP is part of the project information. storage dates, places, and documentation) Standards, Format, The standard format for MP can be QIF or DMO. Estimated volume of data. Data exploitation and sharing Data exploitation Main use will be automatic measurement of dimensions and (purpose/use of the geometries based in nominal values and tolerances. This data analysis) information will be used for defect detection and process analysis. Data access policy / Confidential, except parts authorized by the industrial partners. Dissemination level

Data sharing, re-use The data will be shared by the 3D Pointcloud Analysis module, and distribution and stored in the repository for further future analysis. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Data will be stored in the Z-Fact0r repository and by the 3D backup) Pointcloud analysis software.

DS.DATAPIXEL.MeasurementResults Data Identification

FOF-03-2016 Z-Fact0r - 723906

Dataset description The Measurement Results are the set of measurement values extracted from the Pointcloud based in the MP. Source DATAPIXEL 3D Pointcloud Analysis software. Partners activities and responsibilities Partner owner device DATAPIXEL Partner in charge of DATAPIXEL data collection Partner in charge of DATAPIXEL data analysis Partner in charge of DATAPIXEL and Z-Fact0r repository. data storage WPs and tasks WP2 and WP3 Standards Info about metadata Metadata includes part identification, date and time of data (Production and generation. Measurement Results is part of the information storage dates, places, associated with the manufactured parts. and documentation) Standards, Format, The standard format for MP can be QIF or DMO. Estimated volume of data. Data exploitation and sharing Data exploitation Main use will be the automatic detection of based in geometrical (purpose/use of the deviations. data analysis) Data access policy / Confidential, except parts authorized by the industrial partners. Dissemination level

Data sharing, re-use The data will be shared by the 3D Pointcloud Analysis module, and distribution and stored in the repository for further future analysis. Embargo periods None

Archiving and preservation (including storage and backup)

FOF-03-2016 Z-Fact0r - 723906

Data storage (including Data will be stored in the Z-Fact0r repository and by the 3D backup) Pointcloud analysis software.

DS.CERTH/IRETETH.DataConditioning Data Identification Dataset description Data collected by DATAPIXEL’s laser system or other complementary data sources for defect detection. Source Should be defined by DATAPIXEL. Partners activities and responsibilities Partner owner device DATAPIXEL Partner in charge of DATAPIXEL + the relevant end user (manufacturer) depending data collection on the use case. Partner in charge of IRETETH/CERTH data analysis Partner in charge of TO BE DEFINED data storage WPs and tasks WP2 / T2.1 - T2.2 Standards Info about metadata Should be discussed between DATAPIXEL and (Production and IRETETH/CERTH. storage dates, places, and documentation) Standards, Format, Should be determined by DATAPIXEL. In IRETETH/CERTH Estimated volume of we are open to use different data formats with a preference in raw data. data formats. As far as the volume, the more the better. Ideally, we would like to have hundreds of measurements per product (e.g. 500 per case including defected and non-defected). Data exploitation and sharing Data exploitation This should be discussed within the relevant partners. (purpose/use of the data analysis) Data access policy / This should be discussed within the consortium and approved by Dissemination level the DEM.

FOF-03-2016 Z-Fact0r - 723906

Data sharing, re-use This should be discussed within the relevant partners. and distribution Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including We need to see who is responsible for the data storage task. backup)

DS.SIR.RoboticCellData Data Identification Dataset description Collections of data from SIR robotic deburring cell. These collections of data contain information about machine conditions, algorithms, machine data, plant conditions, process KPIs. Source SIR robotic deburring cell. Partners activities and responsibilities Partner owner device SIR Partner in charge of SIR with support of technological partners involved in the task. data collection Partner in charge of SIR with support of technological partners involved in the task. data analysis Partner in charge of SIR data storage WPs and tasks T2.3 in WP2 Standards Info about metadata -- (Production and storage dates, places, and documentation) Standards, Format, Mainly consisting in MS documents released using the following Estimated volume of formats (.doc, .pptx and .xls files, images for visualizing and data. conceptualizing the use cases will be released as PDF files), UML

FOF-03-2016 Z-Fact0r - 723906

documents, machine algorithms (various type of informatics languages C#, RAPID, etc). Data exploitation and sharing Data exploitation The datasets collected should be used by SIR to achieve the (purpose/use of the objectives of T2.3. data analysis) Data access policy / Confidential Dissemination level

Data sharing, re-use No discussion about this matter has been done so far, and should and distribution not be shared with entities outside of Z-Fact0r consortium. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Documents will be store in FREEDCAMP document backup) management system. Machine data, algorithms and machines backups will be store in the SIR internal repository.

DS.SIR.IndustrialPartnersData Data Identification Dataset description Collections of data from industrial partners. These collections of data contain information about machine conditions, plant conditions, process KPIs of an Industrial production plant. Source Industrial partners’ production plant, internal reports, operators. Partners activities and responsibilities Partner owner device Industrial partners. Partner in charge of Industrial partner with support of SIR. data collection Partner in charge of Task leader data analysis Partner in charge of Task leader and SIR data storage WPs and tasks WP1

FOF-03-2016 Z-Fact0r - 723906

Standards Info about metadata -- (Production and storage dates, places, and documentation) Standards, Format, Mainly consisting in MS documents released using the following Estimated volume of formats (.doc, .pptx and .xls files, images for visualizing and data. conceptualizing the use cases will be released as PDF files) and UML documents. The metadata standard proposed is the CERIF.

Data exploitation and sharing Data exploitation Only for members of the CCS. (purpose/use of the data analysis) Data access policy / The information leading to the preparation of the following Dissemination level deliverable might be confidential as the following deliverables are marked as confidential:  D1.1 Z-Fact0r User requirements DURIT M3  D.1.3 Z-Fact0r system architecture EPFL M5  D1.5 Report on Z-Fact0r strategy implementation and risk analysis EPFL M18 Data sharing, re-use For the time being data are expected to be used internally as input and distribution by the other WPs. However, D1.2 Report on the analysis of SoA, existing and past projects initiatives due by CERTH at M2 and D1.4 Z-Fact0r Use Cases due by INTERSEALS at M6 are expected to be released publicly. Embargo periods None

Archiving and preservation (including storage and backup) Data storage (including Documents are stored in FREEDCAMP document management backup) system. Data and documents will be up to five years after the project completion. Revisions will be stored in the FREEDCAMP document management system.

FOF-03-2016 Z-Fact0r - 723906

2.2.5.2 Dataset per task In addition, data information that will be generated in the different tasks, has been identified by the different partners;

Task: T1.1- T1.5, T2.1-T2.5, T6.1-T6.3 WP: WP1 + WP2 + WP6 WP Leader: SIR Author: G. Tinker (MICROSEMI)

1) Scope  State the purpose of the data generation/ collection Main aims for MICROSEMI are for the improvement of the dispense process and its analysis. Other opportunities for the system and the data might be learning how much glue might be needed for a new size die (prediction) and checking LCP panels for surface defects prior to dispensing.  Explain the relation to the objectives of the project/WP/Task The data being collected will enable the KPIs to be monitored and to generate history for prediction and correction of the process. 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) MICROSEMI preference for data would be: o xls for sensor history data o xls for a volumetric measurement of the glue dispensed o jpeg images of the surface  Is the data generated or collected from other sources under certain terms and conditions? TBC – not believed to be a requirement at this stage  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. TBC – Possibilities of either Pointcloud clouds from DATAPIXEL or micro – profilometry data generated by CERTH or both may need to be utilised  State the expected size of the data (if known) Currently unknown but Good IT infrastructure at MICROSEMI means Data size should not be a constraint  Standards None 3) Ownership  Is another organization contributing to the data development? TBC – If the answer does end up being yes, it will be a member of the Z-Fact0r project 4) Reuse of existing data  Specify if existing data is being re-used (if any) No Data is currently being collected other than the process improvement project that has already been completed. 5) Data use

FOF-03-2016 Z-Fact0r - 723906

 How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful Much depends on: Who wants access? How often they want access? How big the files are? MICROSEMI does have an FTP site – terms of access to this will have to be agreed by MICROSEMI and the members of the Z-Fact0r project. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? TBC – this depends a little on the data being collected and if it is deemed sensitive. 7) Storage and disposal  How will this data be stored? Probably on a local PC with the option to back up data (depending on size) to the MICROSEMI servers at Caldicot.  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: T1.1 -T1.5 WP: 1 USER REQUIREMENTS – SPECIFICATIONS – USE CASE ANALYSIS WP Leader: SIR Author: Marcello Pellicciari (SIR)

1) Scope  State the purpose of the data generation/ collection Qualitative and quantitative data will be produced: I. WP1 data generated and collected are aimed at defining both the user and system requirements and use cases (T.1.1 and T.1.4) II. Bibliographic and data-based information (e.g. Cordis) for T.1.2 State of the art to analyse new, live and past projects, initiatives in the field. III. Workflow and UML diagrams, blue prints will be generated to design the Z- Fact0r architecture (T.1.3) IV. Report on Z-Fact0r strategy and risk analysis (T.1.5) to monitor the status of the manufacturing process in real time.  Explain the relation to the objectives of the project/WP/Task Data are related to all tasks WP1. (See above) 2) Types  Are the data digital/hard copies or both? Digital data and documents will be produced.  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Data are preserved in their incoming format, Files generated and used will be mainly consisting in MS documents released using the following formats (.doc, .pptx and .xls files, images for visualizing and conceptualizing the use cases will be released as PDF files).  Is the data generated or collected from other sources under certain terms and conditions?

FOF-03-2016 Z-Fact0r - 723906

No  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Unified Modelling Language will be used  State the expected size of the data (if known) Not yet a clear idea.  Standards Not at the moment. 3) Ownership  Is another organization contributing to the data development? To date no other external organization is contributing to the data development activities of WP1. 4) Reuse of existing data  Specify if existing data is being re-used (if any) For the time being data are expected to be used internally as input by the other WPs. However, D1.2 Report on the analysis of SoA, existing and past projects initiatives due by CERTH at M2 and D1.4 Z-Fact0r Use Cases due by INTERSEALS at M6 are expected to be released publicly. 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful D1.2 Report on the analysis of SoA, existing and past projects initiatives due by CERTH at M2 and D1.4 Z-Fact0r Use Cases due by INTERSEALS at M6 are expected to be released publicly. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? The information leading to the preparation of the following deliverable might be confidential as the following deliverables are marked as confidential: o D1.1 Z-Fact0r User requirements DURIT M3 o D.1.3 Z-Fact0r system architecture EPFL M5 o D1.5 Report on Z-Fact0r strategy implementation and risk analysis EPFL M18 7) Storage and disposal  How will this data be stored? Documents are stored in FREEDCAMP document management system. Content creator upload the relevant file.  How long is it required to keep the data? Expire date. Will revisions be kept? Data and documents will be up to five years after the project completion. Revisions will be stored in the FREEDCAMP document management system.

Task: T.1-USER REQUIREMENTS WP: WP1 + WP6 WP Leader: SIR Author: E. Soares (DURIT)

FOF-03-2016 Z-Fact0r - 723906

1) Scope  State the purpose of the data generation/ collection Automated quality control, with high accuracy level and predictive system for defect generation based on online continuous monitoring.  Explain the relation to the objectives of the project/WP/Task The data collected will enable to detect probability or trends that lead to defects that normally result in scrapping the parts. 2) Types  Are the data digital/hard copies or both? Both  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) o xls for sensor history data o jpeg images of the defects  Is the data generated or collected from other sources under certain terms and conditions? Possibly collected by sensors at an bench top apparatus.  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Optical and physical sensors to be studied.  State the expected size of the data (if known) A few MB per type of part. Perhaps 1 GB per day.  Standards 3) Ownership  Is another organization contributing to the data development? Only partners from Z-Fact0r 4) Reuse of existing data  Specify if existing data is being re-used (if any) No Data is currently being collected 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful System software, cloud where DURIT servers are stored and some local pc. data will be used mainly by Quality Department 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? Partners from Z-Fact0r can have access during the project. In our premises access is limited to quality operators. 7) Storage and disposal  How will this data be stored? Probably on a local PC + DURIT servers at the cloud.  How long is it required to keep the data? Expire date. Will revisions be kept? five years minimum

Task: T1.4, T6.2 WP: WP1 (User Requirements), WP6 (Demonstration activities)

FOF-03-2016 Z-Fact0r - 723906

WP Leader: SIR, INTERSEALS Author: Pierino Izzo (INTERSEALS) 1) Scope  State the purpose of the data generation/ collection  Explain the relation to the objectives of the project/WP/Task The INTERSEALS actual data are generated and exploited to plan and manage the Customer orders, to planning the production, getting feedback by the production phase, managing of the maintenance. The Z-Fact0r will useful for all the objectives of the software itself: Z-DETECT, Z- PREDICT, Z-PREVENT, (Z-REPAIR), Z-MANAGER. This data could then be connected to the INTERSEALS ERP and Quality Data Management. 2) Types  Are the data digital/hard copies or both? The data are digital.  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files). They could be of two kinds: .xls, SQL formats, emails.  Is the data generated or collected from other sources under certain terms and conditions? For Z-Fact0r, there isn’t this possibility.  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. The data for Z-Fact0r will be generate by:  FT-IR (Infrared spectroscopy): for material checking.  System control (CoMo by Kistler) that concentrate the data from the sensor cavities pressure.  Injection moulding machine parameters: these data can be achieved by the connection to the PLC of the injection Machine (the PLC can be Siemens, Omron, Moog).  Data from visual and dimensional checking machine (DATAPIXEL will be involved in this)  Data from the worker that work beside the production cell and will communicate with the software using Augmented Reality.  State the expected size of the data (if known) At the moment, our servers are of about 150Gbyte.  Standards SQL server 3) Ownership  Is another organization contributing to the data development? No, we have all the competence to generate and manage the data. 4) Reuse of existing data  Specify if existing data is being re-used (if any) The existing data are re-used as really useful:  for making quotations  for process study  quality control

FOF-03-2016 Z-Fact0r - 723906

 traceability  claim answer 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful See point 4 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? The data could be accessible after signing the INTERSEALS NDA. 7) Storage and disposal  How will this data be stored? Workstation server, SQL server  How long is it required to keep the data? Expire date. Will revisions be kept? At least for 6 months for the dynamic production data and five years for the static data.

Task: Task 1.3 WP: WP 1 WP Leader: SIR Author: EPFL

1) Scope  State the purpose of the data generation/collection Development of the architecture of the Z-Fact0r system (i.e. functional view, information view, deployment view, etc.) & the definition and description of the main components  Explain the relation to the objectives of the project/WP/Task A complete description of the modules included in the detailed view is provided in order to point out the responsibilities of each module and their interactions with the global System Architecture 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Emails, doc files, .vpp files etc.  Is the data generated or collected from other sources under certain terms and conditions? No  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Visual Paradigm V14.0 for component diagrams  State the expected size of the data (if known) Not known  Standards UML for component diagrams 3) Ownership  Is another organization contributing to the data development?

FOF-03-2016 Z-Fact0r - 723906

ALL Z-Fact0r partners 4) Reuse of existing data  Specify if existing data is being re-used (if any) No 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful As described in GA document. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? As described in GA document. Accessible to Z-Fact0r consortium members including the commission services. Based on further discussions and agreement between partners, part of data (e.g. overall approach and architecture etc.) could be published in the form of an article or conference proceedings for dissemination purposes. 7) Storage and disposal  How will this data be stored? All the collected info/data will be delivered in the deliverable D1.3  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project for further research; but it will be with consent of the consortium members in case the data is to be accessed and used for the purpose of academic exercise (e.g. teaching and publications)

Task: Task 1.5 WP: WP 1 WP Leader: SIR Author: EPFL 1) Scope  State the purpose of the data generation/ collection Monitoring the application of the various Z-Fact0r strategies and risk analysis will determine how well & successful the implementation of the strategies will be in the use cases, aligned with the project objectives.  Explain the relation to the objectives of the project/WP/Task Data collected and generated will support part, machine and process level itself continuous monitoring in real time. Actions of correctness will be suggested in case of error occurrence. Re-evaluations of the deployed strategies will be conducted. Also, manufacturing equipment, part and process status measurement analysis will be adapted to provide the means for process validation. Z-Fact0r strategies developed according to objectives. 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Data types could be: .doc files, emails, SQL DB programs, .XML files etc.  Is the data generated or collected from other sources under certain terms and conditions?

FOF-03-2016 Z-Fact0r - 723906

 How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Machine sensors, network infrastructure /middleware (device manager)/ shop-floor (Z- Fact0r repository for machine processes, part condition and worker’s actions)  State the expected size of the data (if known) Not known  Standards 3) Ownership  Is another organization contributing to the data development? ALL Z-Fact0r partners 4) Reuse of existing data  Specify if existing data is being re-used (if any) Data will be reused for corrective actions on the deployed strategies and actions will be suggested based on correlations by the automatic decision support mechanism. 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful As described in GA document. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? Confidential, only for members of the consortium, Commission Services 7) Storage and disposal  How will this data be stored?  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: Task 2.2 WP: 2 WP Leader: EPFL Author: IRETETH/CERTH

1) Scope  State the purpose of the data generation/ collection Data needed for formulation of data driven model.  Explain the relation to the objectives of the project/WP/Task Defect prediction from process inputs, correlated to Z-DETECT Module. 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Can accept data in any format (*.csv, *.xls, etc). Data output will be in the form of matlab files, (*.mat, *.m, etc).  Is the data generated or collected from other sources under certain terms and conditions?

FOF-03-2016 Z-Fact0r - 723906

Data are collected from the manufacturing processes (end users’ collection systems)  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. State the expected size of the data (if known) Standards 3) Ownership  Is another organization contributing to the data development? No 4) Reuse of existing data  Specify if existing data is being re-used (if any) No 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful As described on GA document. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? Confidential, only for members of the consortium, CCS. 7) Storage and disposal  How will this data be stored? How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: Task 2.5 WP: WP 2 WP Leader: EPFL Author: EPFL 1) Scope  State the purpose of the data generation/ collection Supervise and provide feedback for all the processes executed in the production line, evaluating performance parameters and responding to defects, keeping historical data. Send efficiently alarms to initiate actions, filter out false alarms, increase confidence levels (through previously acquired knowledge) of early defect detection and prediction.  Explain the relation to the objectives of the project/WP/Task KMS refers to an information and communication technology system for managing knowledge in organizations for supporting creation, capture, storage and dissemination of information. Facilitate the adoption of risk-based thinking (in line with ISO 9001:2015) at enterprise level by supporting faster and better decision making at shop-floor. Link the 5 intertwined zero-defect strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, Z- REPAIR and Z-MANAGE). Implement the designed Z-MANAGE strategy and interface with MES and/or other high level manufacturing systems in-place.

 Provide the inference engine a second layer of autonomous decision support in relation to the 5 Z-Fact0r strategies.  Update the monitoring and inspection conditions and constraints of the ES-DSS.

FOF-03-2016 Z-Fact0r - 723906

 Define rights for data sharing and exchanging internally with various enterprise systems and decision making units, as well as externally with customers and suppliers 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Data should be available at XML or JSON format.  Is the data generated or collected from other sources under certain terms and conditions?  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. It needs inputs from the Sensor Network (through the Device Manager), the overall model of Production activities (through Z-Fact0r Repository) and context – aware knowledge stemming from the Semantic Context Manager (Ontology)  State the expected size of the data (if known) Estimation of the volume of data can be done only by the source.  Standards 3) Ownership  Is another organization contributing to the data development? Yes 4) Reuse of existing data  Specify if existing data is being re-used (if any) No 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful Reaction to Incident detection, re-adaptation of the production processes and notifying components of Z-Fact0r which has subscribed for these events. 6) Dissemination Level of Data Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? As described on GA document. 7) Storage and disposal  How will this data be stored? In a main KM server and also into local terminals when appropriate indications have been disseminated.  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: T2.5 / T3.4 WP: WP2 / WP3 WP Leader: CERTH / EPFL Author: Ziazios Konstantinos (ATLANTIS)

FOF-03-2016 Z-Fact0r - 723906

1) Scope  State the purpose of the data generation/ collection o Early stage decision support system o Reverse supply chain system  Explain the relation to the objectives of the project/WP/Task o Data will be used to model the early stage DSS. o Models for the supply chain 2) Types  Are the data digital/hard copies or both? Digital.  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) o Images o CSV o JSON o Binary  Is the data generated or collected from other sources under certain terms and conditions? Collected from sensors.  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. o From laser scanning o From user input o Batch files  State the expected size of the data (if known) Several GBs per day.  Standards Not known at this stage. 3) Ownership  Is another organization contributing to the data development? Only partners of the consortium 4) Reuse of existing data  Specify if existing data is being re-used (if any) No 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful o Used for modelling o To visualise processes o Create visual KPI’s 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? o Avoiding storage of sensitive data. Stored encrypted always. o Limited access to confidentiality data. 7) Storage and disposal  How will this data be stored?

FOF-03-2016 Z-Fact0r - 723906

o On cloud only for the training / modelling period. o No production data will store outside the shop floor.  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: T2.1, T2.2, T3.1 WP: WP2 / WP3 WP Leader: CERTH / EPFL Author: Toni Ventura (DATAPIXEL)

1) Scope  State the purpose of the data generation/ collection o High accuracy and high resolution 3D Pointcloud of scanned parts. o CAD Model by description of the surfaces and geometries of the designed part. o Deviation map of a 3D representation of surface deviations calculated between a captured Pointcloud and the reference CAD model. o MP, definition of the GD&T to be measured in the Pointcloud.  Explain the relation to the objectives of the project/WP/Task Data generation of WP2 and WP3 will be connected with Z-DETECT activities, in particular with T2.1, T2.2 and T3.1. 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) ASCII list of X Y Z, STEP format, STL with annotated deviations and PLY and QIF or DMO.  Is the data generated or collected from other sources under certain terms and conditions? Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud analysis software.  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. DATAPIXEL 3D Scanner, 3D Pointcloud Analysis software and Industrial partner’s CAD modelling software.  State the expected size of the data (if known) Typically, Pointcloud have a size between 100 K to 10M points, or 3Mbytes to 300 Mbytes and deviation maps have a size between 100 K to 1M polygons, or 10Mbytes to 100 MBytes.  Standards Metadata includes part identification, date and time of data generation, collection, equipment. Pointcloud, CAD model, deviation map and measurement results are part of the information associated with the manufactured parts. Also, MP is part of the project information. 3) Ownership  Is another organization contributing to the data development? Only partners of the consortium 4) Reuse of existing data  Specify if existing data is being re-used (if any)

FOF-03-2016 Z-Fact0r - 723906

No 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful Main use will be the automatic detection of defects by two methods: CAD based inspection and G&T analysis. Also, for automatic measurement of dimensions and geometries based in nominal values and tolerances and local deviations. This information will be used for defect detection and process analysis. The data will be shared using the Sensor network manager, 3D Pointcloud Analysis module and stored in the repository for further future analysis 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? Confidential, except parts authorized by the industrial partners. 7) Storage and disposal  How will this data be stored? Data will be stored in the Z-Fact0r repository and by the 3D Pointcloud analysis software.  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: T3.2 WP: WP3 WP Leader: CERTH Author: Simone Parrotta (HOLONIX)

1) Scope  State the purpose of the data generation/ collection Within this task data from sensors will be integrated and stored in Z-Fact0r. Retrieved data will came from sensors and systems from industrial partners.  Explain the relation to the objectives of the project/WP/Task The task will define and develop the middleware and related tools for the Z-Fact0r sensor data integrations. 2) Types  Are the data digital/hard copies or both? Digital data will be stored in Cloud Based DB.  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) XML, JSON, CSV  Is the data generated or collected from other sources under certain terms and conditions? Proper term and conditions will be defined later during the project  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Data will be collected from: new sensors placed in the shop floor to support the processes monitoring; PLC; legacy systems.  State the expected size of the data (if known)  Standards

FOF-03-2016 Z-Fact0r - 723906

XML, JSON, CSV 3) Ownership  Is another organization contributing to the data development? Z-Fact0r industrial partners will provide confidential data regarding their processes. 4) Reuse of existing data  Specify if existing data is being re-used (if any) 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? They will be used internally for system testing and validation.  Outline the data utility: to whom will it be useful 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? Data confidentiality/ sensitive have been already mentioned in the GA. Processes data from industrial consortium partners should be kept confidential based on internal company policies. 7) Storage and disposal  How will this data be stored? Data will be stored within Z-Fact0r repository.  How long is it required to keep the data? Expire date. Will revisions be kept? At least five years after the project ends.

Task: Task 3.5 WP: WP 3 WP Leader: CERTH Author: EPFL

1) Scope  State the purpose of the data generation/collection Data is required for the Z-Fact0r ontology development. Ontology describes semantic models. The ontology will be used in order to drive the semantic framework. Furthermore, it will be used for data integration, visualization, inferencing/reasoning.  Explain the relation to the objectives of the project/WP/Task Context-aware shop-floor analysis and semantic model for the annotation and description of the knowledge to represent manufacturing system performance. The ontology will describe the basic entities of the project and model relevant structures of multi-stage manufacturing processes. 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Required input will be data from Z-Fact0r repository (data concerning machines, workers, actors, activities and processes, production data logs, etc.), e.g. in XML, CSV, etc. Generated output will be the semantic enrichment of shop-floor data for representation of processes, actors, alarms, actions, work-pieces/products, etc., e.g. as RDF Triplets

FOF-03-2016 Z-Fact0r - 723906

 Is the data generated or collected from other sources under certain terms and conditions? Data from Z-Fact0r repository (data concerning machines, workers, actors, activities and processes, production data logs, etc.)  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Data will be stored in a dedicated repository  State the expected size of the data (if known) Less than 1GB  Standards W3C-OWL, RDF 3) Ownership  Is another organization contributing to the data development? Z-Fact0r End-users (MICROSEMI, INTERSEALS, DURIT) 4) Reuse of existing data  Specify if existing data is being re-used (if any) No 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful The ontology will be used in order to drive the semantic framework. Furthermore, it will be used for data integration, visualization, inferencing/reasoning. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? Accessible to Z-Fact0r consortium members including the commission services 7) Storage and disposal  How will this data be stored? The Ontology will be uploaded in a server where it will be accessible to Z-Fact0r consortium members including the commission services  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: T4.1 - T4.3 WP: 4 WP Leader: Brunel University London Author: Brunel

1) Scope  State the purpose of the data generation/ collection The purpose of the data collection and generation is to facilitate the building of the event- based model, green scheduler using the KPIs, implementation of the scheduler and extracting cost functions. The raw data will be collected from plants and the output will provide the metrics for process and control optimisation to minimise defect.  Explain the relation to the objectives of the project/WP/Task

FOF-03-2016 Z-Fact0r - 723906

The fulfilment of the tasks will lead to achieving: An event-base modelling platform and green scheduler to identify the key parameters that influence and have the largest effect of creation of defects in the productions process as well as energy consumption and carbon emissions. They will assist in customisation of the measurement of the KPIs for each industrial partner in the consortium, and build the framework for implementing control and optimisation solutions to minimise defect. 2) Types  Are the data digital/hard copies or both? Mainly digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) Mainly DB driven files that can be converted to CSV, JSON, TXT, HTML, and SML  Is the data generated or collected from other sources under certain terms and conditions? The agreed T&C of the consortium  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. PLC, SCADA, Production Management Systems, Internet, and project Intranet.  State the expected size of the data (if known) Large but not known at this stage.  Standards Control Area Network, TCP/IP. 3) Ownership  Is another organization contributing to the data development? Members of the consortium. 4) Reuse of existing data  Specify if existing data is being re-used (if any) N/A 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful Members of the consortium, in addition the results of the R&D project will be disseminated according to the consortium agreement in the form of conference, journal, specialist magazine/website outlets. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? N/A within the sensitive and project oriented data will remain within the boundaries of the consortium 7) Storage and disposal  How will this data be stored? In local data storages defined and design specifically for the project. Brunel University SERG Laboratories will have a dedicated storage and computing facility for the project. The data will then be stored and utilised in accordance with the T&C of the consortium agreement.

 How long is it required to keep the data? Expire date. Will revisions be kept?

FOF-03-2016 Z-Fact0r - 723906

Duration of the project, and potentially five years after the completion of the project for further research; but it will be with consent of the consortium members in case the data is to be accessed and used for the purpose of academic exercise (e.g. teaching and publications)

Task: Task 4.2 WP: WP 4 WP Leader: BRUNEL Author: EPFL 1) Scope  State the purpose of the data generation/ collection For the validation and verification of the KPI models, i.e. Productivity, Efficiency, Quality (Customer Satisfaction), Environmental Impact, and Inventory levels.  Explain the relation to the objectives of the project/WP/Task 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) .docs & formats for discrete event simulation (descriptive) models using off-the-shelf simulation packages  Is the data generated or collected from other sources under certain terms and conditions?  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Already installed actuators and sensors will be used for monitoring and evaluating the KPIs.  State the expected size of the data (if known) N/A  Standards 3) Ownership  Is another organization contributing to the data development? 4) Reuse of existing data  Specify if existing data is being re-used (if any) 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful Based on the prediction of the expected results and depending on the measurements received for the evaluation of the KPIs through the use-cases utilization they will be fine- tuned in order for afterwards on-line & real-time application of them. Corrective actions will be considered for the production line based on the results received. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? The final deliverable report associated with this task will be public. All the other data will be Accessible to Z-Fact0r consortium members. 7) Storage and disposal  How will this data be stored?

FOF-03-2016 Z-Fact0r - 723906

 How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project

Task: Task 4.4 WP: WP 4 WP Leader: BRUNEL Author: EPFL 1) Scope  State the purpose of the data generation/ collection Design and development of the cost functions for each of the KPIs.  Explain the relation to the objectives of the project/WP/Task Models will be industry specific and will be defined as monetary loss functions due to loss of Productivity (OEE, OLE, Resource Utilisation), Efficiency (energy consumption per produced unit), Quality (process and product quality loss models), Environmental Loss (emissions of pollutants per produced unit), and Inventory (storage, and work-in-process). 2) Types  Are the data digital/hard copies or both? Digital  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) .xls , .doc files  Is the data generated or collected from other sources under certain terms and conditions?  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Financial data from end-users.  State the expected size of the data (if known)  Standards 3) Ownership  Is another organization contributing to the data development? Z-Fact0r end-users. 4) Reuse of existing data  Specify if existing data is being re-used (if any) No 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful At a second stage, a validation and verification process of the direct observations and experiments on the Shop-floor and direct measurement of costs against system state and contrastively with the simulated ones. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? 7) Storage and disposal  How will this data be stored?

FOF-03-2016 Z-Fact0r - 723906

 How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: T7.3, T8.2, T9.2 WP: 7-8-9 WP Leader: INOVA+, CETRI Author: CONFINDUSTRIA

1) Scope  State the purpose of the data generation/ collection Regularly, Data generation and Collection is an important requirement aimed to develop activities/tasks, to allow Analysis, to measure performances and to measure the achievement of own objectives.  Explain the relation to the objectives of the project/WP/Task Data generation, and, in particular, data collection will be useful in order to develop market analysis and Customer Adoption Plan. We also recognize the importance of these options, generate and collecting data, by thinking to the planned workshops, in which results achieved will be shared (in respect to the privacy needed). 2) Types  Are the data digital/hard copies or both? Mainly digital.  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected .xls files, .ppt files, emails, .doc files  Is the data generated or collected from other sources under certain terms and conditions? Since we will have to use available info and results from other WP we will respect the required IP protection, confidentiality of data.  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Data will be collected from website, available DB and we will use the only not confidential results/information that we could share in order to develop our tasks (Roadmapping, Customer Adoption plan and DCE).  State the expected size of the data (if known) Unknown  Standards 3) Ownership  Is another organization contributing to the data development? 4) Reuse of existing data  Specify if existing data is being re-used (if any) We will use existing data coming from web, available surveys or other WPs. 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful For their definitions and objective, our tasks and results will be useful for the all project partners and some of them for some potential customers, since our tasks imply Partners and customers sharing.

FOF-03-2016 Z-Fact0r - 723906

6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? No confidentiality. 7) Storage and disposal  How will this data be stored? Z fact0r Shared digital folders.  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

Task: 8.1-8.5 & 9.1-9.6 WP:8,9 WP Leader: CETRI Author: Dr Souzanna Sofou (CETRI)

1) Scope  State the purpose of the data generation/ collection i) Website: project dissemination and product innovation delivery. ii) Innovation Management Strategy: Form the IM strategy for the ultimate use and dissemination of project Results. iii) Innovation Management Roadmap: Design and implement WPs 8 and 9. iv) DMP Questionnaire: Manage Research Data, MetaData, before and after project duration. v) Deliverables: [D.8.1-D.8.5], [D9.1-D9.5]: as explained in the vi) W. vii) Publications: dissemination and communication activities. viii) Z-Fact0r leaflet: communication activity for project wider acceptance. ix) Z-Fact0r poster: communication activity for project wider acceptance.

 Explain the relation to the objectives of the project/WP/Task As explained above 2) Types  Are the data digital/hard copies or both? Digital: i) Website ii) Innovation Management Strategy iii) Innovation Management Roadmap iv) DMP Questionnaire v) Deliverables: [D.8.1-D.8.5], [D9.1-D9.5] vi) Publications (hard copies may also be sent for journal & conference publications) Hard Copies: vii) Z-Fact0r leaflet viii) Z-Fact0r poster  What types of data will the WP generate/collect? Specify the types and formats of data generated/collected (for example .xls files, .ppt files, emails, .doc files) i) Website developed by wordpress

FOF-03-2016 Z-Fact0r - 723906

ii) Innovation Management Strategy .ppt and .pdf file iii) Innovation Management Roadmap: .xls file iv) DMP Questionnaire: .doc file v) Deliverables: [D.8.1-D.8.5], [D9.2-D9.5] doc files, .pdf files website vi) Publications .doc files, .pdf files vii) Z-Fact0r leaflet .cdr file, .ppt file, .pdf file viii) Z-Fact0r poster .cdr file, .ppt file, .pdf file  Is the data generated or collected from other sources under certain terms and conditions? i), iii), Data taken also from the GA. iv) Data collected from participants. v) Data generated during project duration, data from other deliverables will be used. vi) Research Data generated. vii) Data taken also from the GA.  How is generated/collected? Specify the origin of the data and instruments/tools that will be used. Not applicable for WP8 and WP9.  State the expected size of the data (if known) For digital files: less than 100Mb. For Hard Copies: Z-Fact0r leaflet: print on both sides A4 size, Z-Fact0r poster: print on one side, according to conference restrictions.  Standards 3) Ownership According to the ownership model.  Is another organization contributing to the data development? According to the ownership model. 4) Reuse of existing data  Specify if existing data is being re-used (if any) Data from other WP´s might be used in dissemination and communication files. 5) Data use  How will this data be exploited and/or shared/made accessible for verification and re-use? Outline the data utility: to whom will it be useful All WP8 and 9 data will be useful to the consortium for the ultimate use and dissemination of project results. 6) Dissemination Level of Data  Confidentiality/ Sensitive data. If data cannot be made available, explain why. Who will have access? Website Public Innovation Management Private, the DEM has created the file, only the project partners Strategy have access Innovation Management Private, the DEM has created the file, only the project partners Roadmap: have access DMP Questionnaire: Private, Only the project partners have access Deliverables: [D.8.1- D8.1, D8.2, D8.3, D8.5, D9.2, D9.3, D9.5: Confidential D.8.5], [D9.1-D9.5]: D8.4, D9.1, D9.4: Public Publications Public, dissemination rules apply

FOF-03-2016 Z-Fact0r - 723906

Z-Fact0r leaflet Public, dissemination rules apply Z-Fact0r poster Public, dissemination rules apply 7) Storage and disposal  How will this data be stored? All digital files will be stored in FREEDCAMP All hard copy files will be stored by the DEM as well as all consortium parties.  How long is it required to keep the data? Expire date. Will revisions be kept? Duration of the project, and potentially five years after the completion of the project.

2.2.5.3 Dataset per WP Data information of the partners has been used to define the complete RDI that will be generated in each WP;

WP1 User requirements, specifications, use case analysis WP leader: SIR Objective: Development of the architecture of the Z-Fact0r system and the definition and description of the main components. A complete description of the modules included in the detailed view in order to point out the responsibilities of each module and their interactions with the global System Architecture. Qualitative and quantitative data generated and collected are aimed at defining both the user and system requirements and use, prepare the bibliographic and data- based information, design the workflow and UML diagrams and report on Z-Fact0r strategy and risk analysis to monitor the status of the manufacturing process in real time. Data description: The data being collected will enable the KPI’s to be monitored and to generate history for prediction and correction of the process. Digital data and documents will be preserved in their incoming format, files generated and used will be mainly consisting in MS documents released using the following formats (.doc, .pptx, .vpp and .xls files, emails, SQL DB programs, images for visualizing and conceptualizing the use cases will be released as PDF files). Instrument and tools: Unified Modelling Language, TBC for either Pointclouds or micro– profilometry data. Machine sensors, network infrastructure /middleware (device manager)/ shop- floor (Z-Fact0r repository for machine processes, part condition and worker’s actions).

Production-process monitoring, detect life-cycle management WP leader: WP2 and remanufacturing EPFL Objective: Data generation will be needed for formulation of data driven model for the defect prediction from process inputs, correlated to Z-DETECT Module. Supervise and provide feedback for all the processes executed in the production line, evaluating performance parameters and responding to defects, keeping historical data. Send efficiently alarms to initiate actions, filter

FOF-03-2016 Z-Fact0r - 723906 out false alarms, increase confidence levels (through previously acquired knowledge) of early defect detection and prediction. Data description: Data generated will be digital and any format data can be accepted (.CSV, .XLS, etc). However, data output will be in the form of matlab files, (.XLM, .JSON, .MAT, .M, etc). Instrument and tools: Inputs from the Sensor Network (through the Device Manager), the overall model of Production activities (through Z-Fact0r Repository) and context – aware knowledge stemming from the Semantic Context Manager (Ontology) will be necessary. Terms & Conditions of data generated: Data are collected from the manufacturing processes (end users’ collection systems).

Data management and early stage DSS for inspection and WP3 WP leader: CERTH control Objective: Data is required for the Z-Fact0r ontology development. Ontology describes semantic models for the annotation and description of the knowledge to represent manufacturing system performance. The ontology will be used in order to drive the semantic framework. Furthermore, it will be used for data integration, visualization, inferencing/reasoning. Data from sensors will be integrated and stored in Z-Fact0r. Retrieved data will came from sensors and systems from industrial partners. Data description: Digital data will be stored in Cloud Based DB and required input will be data from Z-Fact0r repository (data concerning machines, workers, actors, activities and processes, production data logs, etc.). Generated output will be the semantic enrichment of shop-floor data for representation of processes, actors, alarms, actions, work-pieces/products, etc., e.g. RDF Triplets, .CSV, .XML, .JSON. Instrument and tools: Data will be collected from new sensors placed in the shop floor to support the processes monitoring; PLC; legacy systems. Data re-use: The ontology will be re-used in order to drive the semantic framework. Furthermore, it will be used for data integration, visualization, inferencing/reasoning.

WP4 System modelling for fast forward cost functions WP leader: BRUNEL Objective: The purpose of the data collection and generation is to facilitate the building, validation and verification of the KPI models, implementation of the scheduler and extracting cost functions.

FOF-03-2016 Z-Fact0r - 723906

The raw data will be collected from plants and the output will provide the metrics for process and control optimisation to minimise defect. Data description: Data generate will be mainly digital and DB driven files can be converted to CSV, XLS, TXT, HTML and XML. Instrument and tools: Data and instruments that will be used are PLC, SCADA, Production Management Systems, Internet, and project Intranet. Already installed actuators, sensors and financial data from end-users will be used for monitoring and evaluating the KPIs. Data re-use: Data reutilization will be fine-tuned in order for afterwards on-line & real-time application of them and corrective actions will be considered for the production line based on the results received.

WP5 Integration & Testing Validation WP leader: ATLANTIS Objective: Diverse set of technologies will be developed and all s/w and h/w components and platforms will be integrated throughout a predefined integration methodology. The technology validation plan is to be defined and executed while applying corrective design and re- implementation on all detected errors. Furthermore, the methodology validation throughout the demonstration in relevant environments will be used as well as the evaluation data and feedback that are going to be collected, analyzed and documented. Data description: Data generated will be made available from the Z-Fact0r components that will be integrated into the complete system. Multiple formats will be available, however, all compatible to commonly agreed standards, most probably Business to Manufacturing Markup Language (B2MML) in XML form. Moreover, from the evaluation part of the WP data will come out in XLS format. Instrument and tools: Data will be provided by the Z-Fact0r components that form the 5 strategies. For this WP, primary, non-analysed data are not considered, rather than the results of their analysis by the Z-Fact0r tools and components. Data related to end user evaluation will be most probably collected using online forms and questionnaires, that will allow transfer into XLS files. Data re-use: The collected data will be evaluated and considered in order to reach a better understanding of the processes and activities evolved in the shop-floors. Combined with the input from the technical validation plan this will lead to fine-tuning of the Z-Fact0r components and the lessons learned could be transformed into actionable knowledge.

FOF-03-2016 Z-Fact0r - 723906

WP6 Demonstration activities WP leader: INTERSEALS Objective: Automated quality control, with high accuracy level and predictive system for defect generation based on online continuous monitoring. The data collected will enable to detect probability or trends that lead to defects that normally result in scrapping the parts. Data description: Data generate will be digital and hard copies and data format will be .xls for sensor history data and volumetric measurement and .jpeg images of the defects, and also SQL formats, emails, etc. Instrument and tools: Possibly collected by sensors at a bench top apparatus and optical and physical sensors to be studied. FT-IR (Infrared spectroscopy): for material checking. System control (CoMo by Kistler) that concentrate the data from the sensor cavities pressure. Injection moulding machine parameters: achieved by the connection to the PLC of the injection Machine (the PLC can be Siemens, Omron, Moog). Data from visual and dimensional checking machine. Data from the worker that work beside the production cell and will communicate with the software using Augmented Reality. Data re-use: The existing data are re-used as really useful for making quotations, for process study, quality control, traceability and claim answer.

WP 7, Valorization, market replication, WP leader: INNOVA, CETRI 8 & 9 dissemination/ communication/exploitation Objective Data generation and collection is an important requirement to develop activities/tasks, allow analysis, measure performances and measure the achievement of WP objectives as: Website (dissemination and innovation delivery), DMP Questionnaire (Manage Research Data, MetaData, before, during and after project duration), Innovation Management Strategy (for the ultimate use and dissemination of results) and Roadmap (planning WP7, design and implement WPs 8 and 9), Publications (dissemination and communication activities), Z-Fact0r leaflet and poster (communication activity wider acceptance) and Market analysis and Customer Adoption Plan. Data description: Data generated will be digital for Website, Innovation Management Strategy, Innovation Management Roadmap, DMP Questionnaire, Deliverables and Publications (hard copies may also be sent for journal & conference publications), and hard copies in the case of Z- Fact0r leaflet and poster. Format of data generated will be .xls, .ppt, .pdf, .doc and .cdr files and emails.

FOF-03-2016 Z-Fact0r - 723906

Instrument and tools: Data will be collected from website and available DB, with only non- confidential results/information that could be shared for the development of WP activities (Road- mapping, Customer Adoption plan and DCE).

In conclusion, data and data management-related challenges under Z-Fact0r are identified and addressed mainly within WP1 (T1.1, T1.4), WP2 and WP3. As described in the proposal, data to be used in the project will include: on-line (nearly) real-time and historical data related to (i) the product (desired specifications; quality inspection results, etc.); (ii) the production equipment and environment (e.g. temperature, pressure, vibrations, etc.); (iii) manufacturing/ production and maintenance (e.g. capacity, planning, etc.). Sources of these data will be: existing sensors and actuators (such as sensors embedded in production machinery, quality inspection equipment, etc.), as well as new novel sensors and actuators (such as laser scanning, visual and/or IR cameras, non- contact profilometers, etc.), and enterprise systems. The type of sensors/ actuators and data to be used will be defined and finalized per Z-Fact0r use case on the basis of the required metrics at product and workstation level at single manufacturing stage, and also at multiple stage. Additionally, non-research data collected related to Innovation Management like the IPR registry that includes the IP strategy per Result are confidential and are only stored in the FREEDCAMP repository and the website private area, with no access rights for members outside the consortium.

2.2.6 Policies for access, sharing and re-use Data generated during Z-Fact0r project will be confidential. Ownership and management of intellectual property and access will be limited to the project consortium partners. For this purpose, policies for access, sharing, and re-use have been established:

2.2.6.1 Partners Background Partners have identified their background for the action (data, know-how or information generated before they acceded to the Agreement), which will be accessible to each other partners to implement their own tasks (under to legal restrictions or limits previously defined in the CA). The partners should be able to access, mine, exploit, reproduce and disseminate the data. This should also help to validate the results presented in scientific publications. The partner´s background, acquired prior to the starting date of the project, will remain the sole property of the originating partner, provided that it was presented in the CA.

2.2.6.2 Data Ownership and Access The full dataset will be confidential and only the members of the consortium will have access on it. Special consideration will be taken for the project dissemination dataset (e.g. leaflet, brochures,

FOF-03-2016 Z-Fact0r - 723906 posters, etc.) that will be considered as public information. As described in GA, data generated are expected to be used internally as input by the other WPs. All the partners will have free-access to the results generated during the project, the information needed for implementing their own tasks under the action and for exploiting their own results. Also, this information will be available to EU institutions, bodies, offices or agencies, for developing, implementing or monitoring EU policies, however such access rights are limited to non-commercial and non-competitive use.

Regarding ORD Pilot, data that will be generated in the OA will be decided during the course of the project and can include; final peer-reviewed scientific research articles that will be published in the online repository after the publication, research data including data underlying publications, curated data and/or raw data and public deliverables of the project (described in GA). If any document or dataset are decided to become of OA, a special section into the data management portal (FREEDCAMP) will be created that should provide a description of the item and link to a download section. Of course, these data will be anonymized, so as not to have any potential ethical issues with their publication and dissemination.

2.2.6.3 Naming rules All data files will be saved using a standardized, consistent file naming protocol agreed by the project partners, which will include relevant metadata to ensure their accessibility. The metadata standard proposed is the CERIF.

2.2.6.4 Storage Information Documents of the dataset will be stored at the data management portal (FREEDCAMP) created and maintained by CERTH/ITI, while links to the portal will exist at the Z-Fact0r website. The Data Management Portal developed by the project (FREEDCAMP) in the context of the ORD Pilot, allows the efficient management of the project’s datasets and provides the proper OA of them for further analysis and reuse.

The dataset will remain at the data management portal for the whole project duration, as well as for at least 2 years after the end of the project.

Finally, after the end of the project, the portal is going to be accommodated with other portals at the same server, so as to minimize the needed costs for its maintenance.

2.2.6.5 Data sharing and dissemination Data will be reused for corrective actions on the deployed strategies and actions will be suggested based on correlations by the automatic decision support mechanism. Research data results will be

FOF-03-2016 Z-Fact0r - 723906 disseminated according to the CA in the form of conference, articles in a journal, specialist magazine/website outlets or conference proceedings for dissemination purposes.

All patent applications and all other publications will require prior agreement in respect to content and the publication media. To this end, each partner should notify the consortium members about the content and material they wish to publish/disseminate and a 21 days’ evaluation period will be provided as stated in the CA.

2.2.6.6 IPR management and security As an innovation action close to the market, Z-Fact0r project covers high-TRL technologies and aims at developing marketable solutions. The project consortium includes nine industrial partners from the private sector, in particular, CETRI, ATLANTIS (Technical Management), HOLONIX, DATAPIXEL, SIR, INOVA, MICROSEMI (demonstrator/end user), INTERSEALS (demonstrator), and DURIT (demonstrator). Those partners obviously have Intellectual Property Rights on their technologies and data, on which their economic sustainability is at stake. Consequently, the Z-Fact0r consortium will protect that data and get approval of concerned partners before every data publication.

The data management portal will be equipped with authentication mechanisms, so as to handle the identification of the persons/organizations that download them, as well as the purpose and the use of the downloaded dataset.

2.2.6.7 Data expire date Copyright statements of the Z-Fact0r project will protect any written material produced during its lifetime. As described in GA, the information and data supplied by all project partners and documents produced during the project will be protected for a period of five years after the project completion unless there are agreements between the partners.

After the end of the project, the partners should keep for five years the original documents, digital and digitalized documents, records and other supporting documentation in order to prove the proper implementation of the action and the costs they declare as eligible.

2.3 Data currently being produced in Z-Fact0r

This version of the DMP does not include the actual metadata about the data being produced in Z- Fact0r because there is no dataset generated or collected until delivery date of this deliverable (M6). Further details will be provided in the next updated version.

FOF-03-2016 Z-Fact0r - 723906

3 Data Management related to Zero-defects Manufacturing The quality and performance data of the Manufacturing enterprises will be considered private and will only be available after granting permission. On the other hand, the research data about modelling procedures, KPI validation, event modelling, inspection and real-time quality control, as well as the system optimization, which will be collected/generated during Z-Fact0r will be distributed freely.

4 Data Management Portal (FREEDCAMP) The Data Management Portal, a web based portal name as “FREEDCAMP”, is being used within the Z-Fact0r project for the purposes of the management of the various datasets that will be produced by the project, as well as, for supporting the exploitation perspectives for each of those datasets. FREEDCAMP Portal will need to be flexible in terms of the parts of datasets that are made publicly available. Special attention is going to be given on ensuring that the data made publicly available violates neither IPR issues related to the project partners, nor the regulations and good practices around personal data protection.

4.1 FREEDCAMP portal functionalities

The FREEDCAMP Portal is accessed through a web based platform which enables its users to easily access and effectively manage the various datasets created throughout the development of the project.

Regarding the user authentication, as well as the respective permissions and access rights, the following three user categories are foreseen:

- Admin; the Admin has access to all of the datasets and the functionalities offered by the DMP and is able to determine and adjust the editing/access rights of the registered members and users (OA area). Finally, the Admin is able to access and extract the analytics, concerning the visitors of the portal.

- Member; when someone successfully registers to the portal and is given access permission by the Admin, she/he is then considered as a “registered Member”. All the registered members will have access to and be able to manage most of the collected datasets.

Knowledge sharing and public documents, apart from the admin and the registered members, as OA area will be available for users who will not need to register and they will have access to some specific datasets, as well as to project outcomes.

Figure 1 shows the Login page of the FREEDCAMP portal.

FOF-03-2016 Z-Fact0r - 723906

Figure 1. Login Page of the FREEDCAMP Portal

FREEDCAMP portal will be easily and effectively managed by the members. A variety of graphs, pie charts etc. is going to be employed for helping members to easily understand and elaborate the data. In particular, the architecture of the portal presents special interfaces organized to comply the information.

All tasks and datasets available in the DMP will be accompanied by a short description of the item (Figure 2 and 3).

FOF-03-2016 Z-Fact0r - 723906

Figure 2. Data access page of the FREEDCAMP portal.

Figure 3. File access of the FREEDCAMP Portal.

Dataset will be structured in three different folders into FREEDCAMP portal; Tasks, Discussion, Files. Draft documents and deliverables, and other data will be uploaded on specific tasks folders, and final version documents will be uploaded into the file section of appropriate folder.

In addition, technical and progress meetings will be scheduled in the FREEDCAMP portal calendar (Figure 4).

FOF-03-2016 Z-Fact0r - 723906

Figure 4. Calendar access of the FREEDCAMP Portal.

4.2 Data Backup: Private Area of the Project Website

As described in Deliverable 9.1, the website private area can be used by all the partners:

i) for storage of files and confidential deliverables ii) for providing feedback on work in progress iii) for exchanging information about upcoming events, conferences, etc.

The website Private Area is only accessible by the consortium partners using a username and password, and will be used as a backup repository to store data that are either confidential or data that will be made public after a release date that has been identified by the data owner.

4.3 Open Access Section

A special free access section into the data management portal (FREEDCAMP) will be created to upload the documents, data and datasets and other information that are decided to become of OA. The description of the item and the link to a download section will be available into this section. Of course, these data will be anonymized, so as not to have any potential ethical issues with their publication and dissemination.

5 Future Work A spreadsheet has been created that will be used throughout the project for the continuous logging of data and datasets, as well as the related information that has been previously presented. Figure 5 shows the spreadsheet of Z-Fact0r data and datasets.

FOF-03-2016 Z-Fact0r - 723906

Figure 5. Spreadsheet of Z-Fact0r data and datasets.

Additionally, the release date of the datasets that will be available in the open research data pilot will be defined in this xls.

More specifically, after the IP protection route has been defined for each “result” in the IPR registry currently being developed, dissemination actions will take place for some of them (for example for those for which a patent application has been submitted). As soon as a dissemination action is complete, data can be uploaded in the open research data pilot.

5.1 Roadmap of actions to update the DMP

This deliverable is a dynamic document and will be updated and augmented throughout the whole project lifecycle with new datasets and results according to the progress of the activities of the Z- Fact0r project. Also, the DMP will be updated to include possible changes in the consortium composition and policies over the course of the project.

FOF-03-2016 Z-Fact0r - 723906

For that purpose, the final version of this report will be delivered 6 month before the end of the project (M36), reflecting on lessons learnt and describing the plans implemented for sustainable storage and accessibility of the data, even beyond the project’s duration.

6 Conclusions This report includes the DMP and describes the RDI that will be generated during Z- Factor project and the challenges and constraints that need to be taken into account for managing it. In addition, it describes the updated procedures and the infrastructure used in the project to efficiently manage the produced data, named as FREEDCAMP Portal. The DMP is identified as starting point for the discussion with the community about the Z-Fact0r data management strategy and reflects the procedures planned by the work packages at the beginning of the project.

An elaborated questionnaire has been distributed between the consortium partners, asking them what kind of data they were expecting to produce and collect during the project. From this information, it has become clear that currently only work packages 1, 2 and 3, are planning to generate or collect data that can be classified as relevant information according to the definition of the European Commission. Nonetheless, DMP is not a fixed document and it can be the case that this situation evolves during the lifespan of the project. Thus, the DMP will be updated and augmented with new datasets and results twice during project lifetime with the Project Periodic Reports.

Regarding storage information, documents generated during the project will be stored in FREEDCAMP Portal which is the document management system of the project. This information, data and documents produced during the project will be protected for a period of two years after the project completion, as it is described in GA.

7 Glossary Participant Information Sheet The information sheet is an important part of recruiting research participants. It ensures that the potential participants have sufficient information to make an informed decision about whether to take part in your research or not (http://www.kcl.ac.uk/innovation/research/support/ethics/training/infosheet.aspx). Consent Form A form signed by a participant to confirm that he or she agrees to participate in the research and is aware of any risks that might be involved. Metadata

FOF-03-2016 Z-Fact0r - 723906

Metadata is data that describes other data. Meta is a prefix that in most information technology usages means "an underlying definition or description." Metadata summarizes basic information about data, which can make finding and working with particular instances of data easier. (http://whatis.techtarget.com/definition/metadata) or http://www.data- archive.ac.uk/media/54776/ukda062-dps-preservationpolicy.pdf Repository A digital repository is a mechanism for managing and storing digital content. Repositories can be subject or institutional in their focus. (http://www.rsp.ac.uk/start/before-youstart/ what-is-a- repository/)

8 Bibliography - Guidelines on Data Management in Horizon 2020, Version 2.0, 30 October 2015: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oapilot- guide_en.pdf - Guidelines on OA to Scientific Publications and Research Data in Horizon 2020, Version 2.0, 30 October 2015: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oapilot- guide_en.pdf - Webpage of European Commission regarding OA: http://ec.europa.eu/research/science-society/open_access