“This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723906”
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
Ref. Ares(2017)2503178 - 17/05/2017
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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
1.6 13.04.17 CETRI Contribution of the Dissemination and 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
1.99 11.05.17 ATLANTIS,
CERTH Final version for technical manager & coordinator review
2.0 16.05.17 DATAPIXEL Final Review ready for submission to the EC
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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
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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
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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.
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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
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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
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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:
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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
conditions)
ATLANTIS
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
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Data from the comparative assessment (i.e. with and without Z-Fact0r) in the 3 use
cases: difference in production cost/waste/scrap, in detection efficiency, in single-
stage production defect rate, in average multistage production defect rate, in
production output quality (qualified output / total output produced), in defect
propagation to downstream stages
MICROSEMI,
INTERSEALS,
DURIT
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
green scheduling
BRUNEL
Validation and verification of KPIs to assess the direct impact of system level to
the final cost
BRUNEL, EPFL
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
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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
data collection
CETRI
Partner in charge of
data analysis
CETRI
Partner in charge of
data storage
CETRI
WPs and tasks T2.4 in WP2
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
The metadata include:
a) The characteristics of the materials to be deposited.
b) The user requirements as obtained by the end users.
Standards, Format,
Estimated volume of
data.
No standards apply. The format will be in the form of
spreadsheets and images (TIFF of JPG). Estimated volume is <
10 MB.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
The results of the study have the potential to be exploited by
CETRI along with the Z-Fact0r end users MICROSEMI,
DURIT and INTERSEALS, as well as by SIR, towards the
implementation of integrating new processes in their production
lines.
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Data access policy /
Dissemination level
In general, the data will be confidential with the exception of
possible future publications in the case the consortium permits
such activities.
Data sharing, re-use
and distribution
CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users
(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
backup)
Data will be stored into a computer and an external hard disc and
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
data collection
CETRI
Partner in charge of
data analysis
CETRI
Partner in charge of
data storage
CETRI
WPs and tasks T2.4 in WP2
Standards
Info about metadata
(Production and
The metadata include:
a) The type/origin of the processed material.
b) The conditions of the experiments.
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storage dates, places,
and documentation)
Standards, Format,
Estimated volume of
data.
No standards apply. The format will be in the form of
spreadsheets and images (TIFF of JPG). Estimated volume is <
10 MB.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
The results of the study have the potential to be exploited by
CETRI along with the Z-Fact0r end users MICROSEMI,
DURIT and INTERSEALS, as well as by SIR, towards the
implementation of integrating new processes in their production
lines.
Data access policy /
Dissemination level
In general, the data will be confidential with the exception of
possible future publications in the case the consortium permits
such activities.
Data sharing, re-use
and distribution
CETRI will generate 3 sets of data for the 3 Z-Fact0r end-users
(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
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored into a computer and an external hard disc and
will be send frequently to the individual end-users.
The data will be stored permanently in a computer in CETRI
facilities.
DS.DURIT. Production line. Demo 3.
Data Identification
Dataset description Data collected:
Dimensions/shapes/surface and 3D details
Superficial defects like cracks
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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
data collection
DURIT
Partner in charge of
data analysis
DURIT
Partner in charge of
data storage
DURIT
WPs and tasks The data are going to be collected in WP5 and WP6.
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
The dataset will be accompanied by information regarding:
Drawings and sequence of operations.
Batch of material used.
Operators involved.
Date, time.
Temperature and relative humidity in the metallurgy section.
Standards, Format,
Estimated volume of
data.
Our tests will be in a specific type of pieces. The volume of data
depends of the quantity of order.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Production process recognition and help during the different
production phases, avoiding mistakes. Support of quality checks
and production batches recalls
Data access policy /
Dissemination level
The full dataset will be confidential and only the members of
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
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anonymized, so as not to have any potential ethical issues with
their publication and dissemination.
Data sharing, re-use
and distribution
Data sharing is dependent of DURIT, DURIT´s customers and
partner requirements.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
All information belongs to the industrial partner that owns the
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
data collection
Various partners related to the specific event and/or operation.
Partner in charge of
data analysis
Various partners related to the specific event and/or operation.
Partner in charge of
data storage
EPFL will store data related to KMDSS (various partners can
handle the rest of the data).
WPs and tasks The data will be collected within the activities of WP2, WP3 and
WP4.
Standards
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Info about metadata
(Production and
storage dates, places,
and documentation)
Indicative metadata include: Input from the Sensor Network
(through the Device Manager), the overall model of Production
activities (through Z-Fact0r Repository), shop-floor events data
from the Event Manager, and context – aware knowledge
stemming from the Semantic Context Manager (Ontology).
Standards, Format,
Estimated volume of
data.
Data can be available in XML or JSON format. Estimation of the
volume of data cannot be predicted in advance of a real use of
the technology at the shop floor level.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
The collected data will be used for better understanding of the
processes and activities evolving in the shop-floor which will
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 /
Dissemination level
Accessible to Z-Fact0r consortium members including the
commission services as defined in the Z-Fact0r GA.
Data sharing, re-use
and distribution
The sharing of this data is yet to be decided together with the
industrial partners.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored in a dedicated repository.
DS.EPFL.02.SemanticContextManager
Data Identification
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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
data collection
EPFL
Partner in charge of
data analysis
EPFL
Partner in charge of
data storage
EPFL
WPs and tasks The data will be collected within the activities of WP3 and in
particular T3.5.
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
Data from Z-Fact0r repository (data concerning machines,
workers, actors, activities and processes, production data logs,
etc.).
Standards, Format,
Estimated volume of
data.
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. Standards:
W3C-OWL, RDF. Less than 2GB.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
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.
The ontology will describe the basic entities of the project and
model relevant structures of multi-stage manufacturing
processes.
Data access policy /
Dissemination level
Accessible to Z-Fact0r consortium members including the
commission services as defined in the Z-Fact0r GA.
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Data sharing, re-use
and distribution
The Ontology will be uploaded in a server where it will be
accessible to Z-Fact0r consortium members including the
commission services.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored in a dedicated repository. No expiry date – revisions will be kept.
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
data collection
Industrial partner with support of HOLONIX presumably
Partner in charge of
data analysis
HOLONIX and Z-Modules
Partner in charge of
data storage
HOLONIX
WPs and tasks T3.2 in WP3
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
A set of RESTful APIs will be released with documentation of
how to require data from datasets.
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Standards, Format,
Estimated volume of
data.
No estimation has been done so far.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Support on the monitoring of production machine, production
performance and process both for operators and other
monitoring modules.
Data access policy /
Dissemination level
Collections of data of Production management module should be
accessible only for Z-Fact0r consortium partners only.
Data sharing, re-use
and distribution
Data sharing should not be possible with users outside of the
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
backup)
Physical place to store production data has still to be decided, data
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
data collection
Z-Modules and industrial partners.
Partner in charge of
data analysis
Various Z-modules
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Partner in charge of
data storage
HOLONIX
WPs and tasks T3.2
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
A set of RESTful APIs will be released with documentation of
how to require data from datasets.
Standards, Format,
Estimated volume of
data.
JSON will be data exchange format between Repository and Z-
Modules. No estimation of data volume has been done so far.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
The datasets collected should be used by various modules of the
project for the pursue of Zero defects production objective.
Data access policy /
Dissemination level
Still to be clarified, for the nature of the dataset collected, only
the members of consortium should have rights to access to the
datasets with appropriate authorization/authentication policy.
Data sharing, re-use
and distribution
No discussion about this matter has been done so far, and should
not be shared with entities outside of Z-Fact0r consortium.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Still to be decided where the collected datasets will be stored
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.
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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
data collection
CONFINDUSTRIA
Partner in charge of
data analysis
CONFINDUSTRIA
Partner in charge of
data storage
CONFINDUSTRIA
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
(Production and
storage dates, places,
and documentation)
It will be used the only available and not confidential (public) data
and documentation coming from Z-Fact0r results in order to
define our strategy and desk research
Standards, Format,
Estimated volume of
data.
--
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
It will be used the only available and not confidential (public) data
and documentation coming from Z-Fact0r results in order to
define our strategy and desk research
Data access policy /
Dissemination level
It will be respected the project rules about confidentiality, by
using and disseminate the only public data
Our results will be public
Data sharing, re-use
and distribution
Our results will be public, they could be shared and re-used as a model:
Research method structure
Roadmap structure
Business Network created
Embargo periods None
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Archiving and preservation (including storage and backup)
Data storage (including
backup)
Original data and results will be kept in our company server at
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
data collection
Various partners related to the specific incident and/or operation.
Partner in charge of
data analysis
Various partners related to the specific incident and/or operation.
Partner in charge of
data storage
ATLANTIS will store data related to ES-DSS (various partners
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
(Production and
The dataset will be accompanied with a detailed documentation
of its contents. Indicative metadata include:
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storage dates, places,
and documentation)
(a) description of the experimental setup (e.g. location, date, etc.)
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,
Estimated volume of
data.
The data will be stored at XML format and are estimated to be
1GB per day.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
The collected data will be used for the development of the
activities analysis and incident detection methods of the Z –
Fact0r project and all the tasks, activities and methods that are
related to it.
Data access policy /
Dissemination level
The full dataset will be confidential and only the members of the
consortium will have access on it.
Data sharing, re-use
and distribution
The sharing of this data is yet to be decided along with the
industrial partners.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored in a DB. RAID and other common 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.
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Partners activities and responsibilities
Partner owner device The device will be owned by the consortium.
Partner in charge of
data collection
ATLANTIS with the respective and responsible partners per
toolkit/task/plant.
Partner in charge of
data analysis
ATLANTIS.
Partner in charge of
data storage
ATLANTIS will store analysed data related to Solution
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
(Production and
storage dates, places,
and documentation)
Collected data from the execution of the demonstrations at the
operational environment of the pilot sites (WP6) as well as the
users’ acceptance and overall impact will be analysed and
documented - Report on Solution Validation.
Standards, Format,
Estimated volume of
data.
Alphanumeric
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Solution validation will be synthesised and documented in the
form of report - deliverable.
Data access policy /
Dissemination level
The full dataset will be confidential, the reports will be public.
Data sharing, re-use
and distribution
Data will be shared among involved partners.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
To avoid data losses during the project and to ensure data
reliability analysed data will be stored for up to two years after the
project life by ATLANTIS.
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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
data collection
Various partners related to the specific incident and/or operation.
Partner in charge of
data analysis
ATLANTIS will analyse the data in order to provide answers and
reliable use of the Reverse Supply Chain.
Partner in charge of
data storage
ATLANTIS will store data related to Reverse Supply Chain
(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
(Production and
storage dates, places,
and documentation)
The dataset will be accompanied with a detailed documentation
of its contents. Indicative metadata include:
(a) description of the experimental setup (e.g. location, date, etc.)
and procedure that led to the generation of the dataset,
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(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,
Estimated volume of
data.
The data will be stored at XML format and are estimated to be
100ΜΒ per day.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
The collected data will be used for the development of the
activities analysis and defect detection methods in the production
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 /
Dissemination level
The full dataset will be confidential and only the members of the
consortium will have access on it.
Data sharing, re-use
and distribution
The sharing of this data is yet to be decided along with the
industrial partners
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored in a DB. RAID and other common backup
mechanism will be utilized to ensure data reliability and
performance improvement and to avoid data losses.
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
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Partner in charge of
data collection
DATAPIXEL
Partner in charge of
data analysis
DATAPIXEL
Partner in charge of
data storage
DATAPIXEL and Z-Fact0r repository
WPs and tasks WP2 and WP3
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
Metadata includes part identification, date and time of data
collection, equipment. Pointcloud is part of the information
associated with the manufactured parts.
Standards, Format,
Estimated volume of
data.
ASCII list of X Y Z is the most common format. Typically,
Pointclouds have a size between 100 K to 10M points, or
3Mbytes to 300 Mbytes.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Main use will be the automatic detection of defects by two
methods: CAD based inspection and GD&T analysis.
Data access policy /
Dissemination level
Confidential, except parts authorized by the industrial partners.
Data sharing, re-use
and distribution
The data will be shared using the Sensor network manager, and
stored in the repository for further future analysis.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored in the Z-Fact0r repository and by the 3D
Pointcloud analysis software.
DS.DATAPIXEL.CADModel
Data Identification
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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
device?)
Industrial partner’s CAD modelling software and DATAPIXEL
3D Pointcloud Analysis software.
Partners activities and responsibilities
Partner owner of the
device
Industrial partners (MICROSEMI, INTERSEALS, DURIT) and
DATAPIXEL.
Partner in charge of the
data collection
(if different)
Same
Partner in charge of the
data analysis
(if different)
DATAPIXEL
Partner in charge of the
data storage
(if different)
DATAPIXEL and Z-Fact0r repository
WPs and tasks WP2 and WP3
Standards
Info about metadata
(Production and
storage dates, places)
and documentation?
Metadata includes part identification, date and time of data
generation. CAD Model is part of the information associated with
the manufactured parts.
Standards, Format,
Estimated volume of
data.
STEP format
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Main use will be the automatic detection of deviations based in
local regions and the extraction of nominal values for GD&T
analysis.
Data access policy /
Dissemination level
Confidential, except parts authorized by the industrial partners.
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(Confidential, only for
members of the CCS) /
Public
Data sharing, re-use
and distribution
(How?)
The data will be shared by the 3D Pointcloud Analysis module,
and stored in the repository for further future analysis.
Embargo periods
(if any)
None
Archiving and preservation (including storage and backup)
Data storage (including
backup): where? For
how long?
Data will be stored in the Z-Fact0r repository and by the 3D
Pointcloud analysis software.
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
data collection
DATAPIXEL
Partner in charge of
data analysis
DATAPIXEL
Partner in charge of
data storage
DATAPIXEL and Z-Fact0r repository.
WPs and tasks WP2 and WP3
Standards
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Info about metadata
(Production and
storage dates, places,
and documentation)
Metadata includes part identification, date and time of data
generation. Deviation Map is part of the information associated
with the manufactured parts.
Standards, Format,
Estimated volume of
data.
A polygonal mesh with an associated deviation number in mm.
Most common formats are STL with annotated deviations and
PLY. Typically, deviation maps have a size between 100 K to 1M
polygons, or 10Mbytes to 100 Mbytes.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Main use will be the automatic detection of defects based in local
deviations. A deviation threshold can be defined to identify
defects.
Data access policy /
Dissemination level
Confidential, except parts authorized by the industrial partners.
Data sharing, re-use
and distribution
The data will be shared by the 3D Pointcloud Analysis module,
and stored in the repository for further future analysis.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored in the Z-Fact0r repository and by the 3D
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
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Partner owner device DATAPIXEL
Partner in charge of
data collection
DATAPIXEL
Partner in charge of
data analysis
DATAPIXEL
Partner in charge of
data storage
DATAPIXEL and Z-Fact0r repository
WPs and tasks WP2 and WP3
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
Metadata includes project identification, date and time of data
generation. MP is part of the project information.
Standards, Format,
Estimated volume of
data.
The standard format for MP can be QIF or DMO.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Main use will be automatic measurement of dimensions and
geometries based in nominal values and tolerances. This
information will be used for defect detection and process analysis.
Data access policy /
Dissemination level
Confidential, except parts authorized by the industrial partners.
Data sharing, re-use
and distribution
The data will be shared by the 3D Pointcloud Analysis module,
and stored in the repository for further future analysis.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Data will be stored in the Z-Fact0r repository and by the 3D
Pointcloud analysis software.
DS.DATAPIXEL.MeasurementResults
Data Identification
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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
data collection
DATAPIXEL
Partner in charge of
data analysis
DATAPIXEL
Partner in charge of
data storage
DATAPIXEL and Z-Fact0r repository.
WPs and tasks WP2 and WP3
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
Metadata includes part identification, date and time of data
generation. Measurement Results is part of the information
associated with the manufactured parts.
Standards, Format,
Estimated volume of
data.
The standard format for MP can be QIF or DMO.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Main use will be the automatic detection of based in geometrical
deviations.
Data access policy /
Dissemination level
Confidential, except parts authorized by the industrial partners.
Data sharing, re-use
and distribution
The data will be shared by the 3D Pointcloud Analysis module,
and stored in the repository for further future analysis.
Embargo periods
None
Archiving and preservation (including storage and backup)
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Data storage (including
backup)
Data will be stored in the Z-Fact0r repository and by the 3D
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
data collection
DATAPIXEL + the relevant end user (manufacturer) depending
on the use case.
Partner in charge of
data analysis
IRETETH/CERTH
Partner in charge of
data storage
TO BE DEFINED
WPs and tasks WP2 / T2.1 - T2.2
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
Should be discussed between DATAPIXEL and
IRETETH/CERTH.
Standards, Format,
Estimated volume of
data.
Should be determined by DATAPIXEL. In IRETETH/CERTH
we are open to use different data formats with a preference in raw
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
(purpose/use of the
data analysis)
This should be discussed within the relevant partners.
Data access policy /
Dissemination level
This should be discussed within the consortium and approved by
the DEM.
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Data sharing, re-use
and distribution
This should be discussed within the relevant partners.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
We need to see who is responsible for the data storage task.
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
data collection
SIR with support of technological partners involved in the task.
Partner in charge of
data analysis
SIR with support of technological partners involved in the task.
Partner in charge of
data storage
SIR
WPs and tasks T2.3 in WP2
Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
--
Standards, Format,
Estimated volume of
data.
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), UML
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documents, machine algorithms (various type of informatics
languages C#, RAPID, etc).
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
The datasets collected should be used by SIR to achieve the
objectives of T2.3.
Data access policy /
Dissemination level
Confidential
Data sharing, re-use
and distribution
No discussion about this matter has been done so far, and should
not be shared with entities outside of Z-Fact0r consortium.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Documents will be store in FREEDCAMP document
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
data collection
Industrial partner with support of SIR.
Partner in charge of
data analysis
Task leader
Partner in charge of
data storage
Task leader and SIR
WPs and tasks WP1
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Standards
Info about metadata
(Production and
storage dates, places,
and documentation)
--
Standards, Format,
Estimated volume of
data.
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) and
UML documents. The metadata standard proposed is the CERIF.
Data exploitation and sharing
Data exploitation
(purpose/use of the
data analysis)
Only for members of the CCS.
Data access policy /
Dissemination level
The information leading to the preparation of the following
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
and distribution
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.
Embargo periods
None
Archiving and preservation (including storage and backup)
Data storage (including
backup)
Documents are stored in FREEDCAMP document management
system. Data and documents will be up to five years after the
project completion. Revisions will be stored in the
FREEDCAMP document management system.
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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
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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?
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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)
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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)
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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
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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?
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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?
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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?
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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.
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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)
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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?
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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)
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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
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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
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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
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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?
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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?
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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?
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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.
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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
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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 Strategy
Private, the DEM has created the file, only the project partners have access
Innovation Management Roadmap:
Private, the DEM has created the file, only the project partners have access
DMP Questionnaire: Private, Only the project partners have access
Deliverables: [D.8.1-D.8.5], [D9.1-D9.5]:
D8.1, D8.2, D8.3, D8.5, D9.2, D9.3, D9.5: Confidential D8.4, D9.1, D9.4: Public
Publications Public, dissemination rules apply
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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).
WP2 Production-process monitoring, detect life-cycle management
and remanufacturing
WP leader:
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
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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).
WP3 Data management and early stage DSS for inspection and
control WP leader: CERTH
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.
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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.
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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,
8 & 9
Valorization, market replication,
dissemination/ communication/exploitation WP leader: INNOVA, CETRI
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.
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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,
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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
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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.
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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.
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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).
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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).
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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.
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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.
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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
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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
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