DEEPCUBE EXPLAINABLE AI PIPELINES FOR BIG COPERNICUS DATA - D1.1 PROJECT MANAGEMENT & QUALITY PLAN - DEEPCUBE H2020
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DeepCube
Explainable AI pipelines
for big Copernicus data
D1.1 Project Management &
Quality plan
This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 101004188
Deliverable Information
D1.1 Project Management & Quality plan
Work Package: WP1 Project Management
Lead partner: National Observatory of Athens (NOA)
Alkyoni Baglatzi (NOA), Souzana Touloumtzi (NOA), Ioannis
Author(s):
Papoutsis (NOA), George Stamoulis (UoA)
Due date: 28/02/2021
Version number: 1.0 Status: Final
Dissemination level: Public
Project Number: 101004188 Project Acronym: DeepCube
Project Title: EXPLAINABLE AI PIPELINES FOR BIG COPERNICUS DATA
Start date: 1 January 2021
Duration: 36 months
Call identifier: H2020-SPACE-2020
Topic: DT-SPACE-25-EO-2020
Big data technologies and Artificial Intelligence for Copernicus
Instrument: Research & Innovation Action (RIA)
This project has received funding from the European Union's Horizon 2020
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research and innovation programme under grant agreement No 101004188
Revision History
Revision Date Contributor(s) Description
0.1 01/02/2021 Alkyoni Baglatzi (NOA) Table of Contents
Souzana Touloumtzi (NOA), Ioannis First draft for
0.2 12/02/2021
Papoutsis (NOA), Alkyoni Baglatzi (NOA) internal review
1.0 26/02/2021 Alkyoni Baglatzi (NOA) Final
Quality Control
Role Date Contributor(s) Approved/Comment
Internal review 22/02/2021 George Stamoulis (UoA) Approved
Final Quality Alkyoni Baglatzi (NOA),
23/02/2021 Approved
review Ioannis Papoutsis (NOA)
This project has received funding from the European Union's Horizon 2020
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research and innovation programme under grant agreement No 101004188
Disclaimer
This document has been produced in the context of the DeepCube Project. The DeepCube project
is part of the European Community's Horizon 2020 Program for research and development and is
as such funded by the European Commission. All information in this document is provided ‘as is’
and no guarantee or warranty is given that the information is fit for any particular purpose. The
user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the
European Commission has no liability with respect to this document, which is merely representing
the authors’ view.
This project has received funding from the European Union's Horizon 2020
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research and innovation programme under grant agreement No 101004188
Executive Summary
The aim of this deliverable is to provide all necessary information related to the management of
the project and the quality plan. These include the governance of the project with all related roles
and responsibilities, the means, and processes to execute the day-to-day activities, the
communication within the consortium as well as with external stakeholders and REA, the quality
assurance plan and risk management.
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research and innovation programme under grant agreement No 101004188Table of Contents
Executive Summary ..................................................................................................................................5
1. Introduction ......................................................................................................................................9
2. Description of the Project ...............................................................................................................11
2.1. Project Scope and Objectives................................................................................................................. 11
2.2. Project Overview .................................................................................................................................... 11
2.3. Project Milestones ................................................................................................................................. 12
2.4. Project Deliverables ............................................................................................................................... 13
2.5. Project Work Plan and Breakdown ........................................................................................................ 16
3. Project Management and Governance ...........................................................................................20
3.1. Project Management Strategy ............................................................................................................... 20
3.2. Project Management Structure ............................................................................................................. 20
4. Management Processes and Tools .................................................................................................23
4.1. Deliverable Preparation ......................................................................................................................... 23
4.2. Document formats and naming conventions ........................................................................................ 23
4.3. Reporting to the EC ................................................................................................................................ 24
4.4. Conflict resolution .................................................................................................................................. 25
5. Communication Processes and Tools .............................................................................................26
5.1. Internal Communication and monitoring .............................................................................................. 26
5.1.1. Project Team Directory/Repository ................................................................................................................ 26
5.1.2. Emails and Mailing lists .................................................................................................................................. 28
5.1.3. Online Meetings Platform .............................................................................................................................. 28
5.1.4. Organization of Meetings ............................................................................................................................... 28
5.1.5. Project Meetings ............................................................................................................................................ 28
5.2. External Communication........................................................................................................................ 30
5.3. Communication with REA ...................................................................................................................... 30
6. Quality Assurance Plan....................................................................................................................32
6.1. Quality Assurance Overview .................................................................................................................. 32
6.2. Roles and Responsibilities ...................................................................................................................... 33
6.3. Quality Criteria ....................................................................................................................................... 33
6.4. Deliverable Quality Assurance Processes .............................................................................................. 33
6.5. Milestones and Quality Controls ............................................................................................................ 36
7. Risk Management ...........................................................................................................................38
7.1. Risk Management Introduction ............................................................................................................. 38
7.2. Risk Management Plan .......................................................................................................................... 38
7.3. Identified Risks ....................................................................................................................................... 39
8. Effort and Cost Management..........................................................................................................42
8.1. Effort and Cost Management Overview ................................................................................................ 42
8.2. Effort and Cost Monitoring and Reporting ............................................................................................ 42
9. Conclusions .....................................................................................................................................43
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research and innovation programme under grant agreement No 101004188List of Figures
Figure 1: Diversity and expertise of the DeepCube consortium ....................................................... 12
Figure 2: Work Package interdependencies and connections .......................................................... 17
Figure 3: Relation between Work Packages and Use Cases .............................................................. 18
Figure 4: DeepCube Gantt Chart ....................................................................................................... 19
Figure 5: Project Management Structure.......................................................................................... 20
Figure 6: DeepCube wiki.................................................................................................................... 27
Figure 7: Quality assurance principles............................................................................................... 32
Figure 8: Risk Management Processes .............................................................................................. 38
Figure 9: Risk Assessment Matrix ..................................................................................................... 38
List of Tables
Table 1: DeepCube consortium ......................................................................................................... 12
Table 2: Milestones of DeepCube ..................................................................................................... 12
Table 3:DeepCube List of Deliverables .............................................................................................. 15
Table 4: Work Packages of DeepCube............................................................................................... 16
Table 5: Use Cases of DeepCube ....................................................................................................... 16
Table 6: Deliverable preparation timeplan ....................................................................................... 23
Table 7: Tools and formats recommended to be used in DeepCube................................................ 24
Table 8: DeepCube meetings ............................................................................................................ 29
Table 9: Communication representatives of DeepCube partners..................................................... 30
Table 10:Effort of each partner in the project .................................................................................. 34
Table 11: DeepCube Deliverable Reviewers ..................................................................................... 36
Table 12: Milestone list and quality controls .................................................................................... 37
Table 13:Guidelines for using the Risk Assessment Matrix............................................................... 39
Table 14: Definition of risk levels ...................................................................................................... 39
Table 15: Identified risks for DeepCube ............................................................................................ 41
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research and innovation programme under grant agreement No 101004188Definitions and acronyms
CA Consortium Agreement
EC European Commission
EU European Union
GA Grant Agreement
PC Project Coordinator
WP Work Package
AI Artificial Intelligence
ML Machine Learning
DL Deep Learning
HPC High Performance Computing
EO Earth Observation
SAR Synthetic-Aperture Radar
NOA Εthniko Asteroskopeio Athinon/National Observatory of Athens
MPG Max-Planck-Gesellschaft zur Foerderung der Wissenschaften EV
UVEG Universitat de Valencia
LC Logical Clocks AB
GAEL GAEL SYSTEMS
UoA University of Athens
INFALIA INFALIA PRIVATE COMPANY
MURM Murmuration
TREA Tre Altamira Srl
TERRA Terra Nordeste
STS Survey Technologies
HFS Hellenic Fire Service
PM Project Manager
TM Technical Project Manager
AB Advisory Board
EB Executive Board
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research and innovation programme under grant agreement No 1010041881. Introduction
The DeepCube project leverages advances in the fields of Artificial Intelligence (AI) and Semantic
Web to unlock the potential of big Copernicus data. DeepCube is impact driven; the objective is to
address new and ambitious problems that imply high environmental and societal impact, enhance
the understanding of Earth’s processes, correlated with Climate Change, and feasibly generate
high business value. To achieve that, the project brings together mature and new ICT technologies,
such as the Earth System Data Cube, the Semantic Cube, the Hopsworks platform for distributed
Deep Learning (DL) and a state-of-the-art visualization tool tailored for linked Copernicus data, and
integrate them to deliver an open and interoperable platform that can be deployed in several
cloud infrastructures and High-Performance Computing (HPC), including Data and Information
Access Services (DIAS) environments.
These tools are then used to develop novel DL pipelines to extract value from big Copernicus data.
DeepCube 1) develops novel DL architectures that extend to non-conventional data and problems
settings, such as Interferometric Synthetic-Aperture Radar, social network data, and industrial
data, 2) introduces a novel hybrid modelling paradigm for data-driven AI models that respect
physical laws, and 3) opens-up the DL black box through Explainable AI and Causality. In this
manner, it implements a shift in the use of AI pipelines. This technology stack is showcased in the
following five Use Cases (two business, two on earth system sciences, and one for humanitarian
aid):
• UC1: Forecasting localized extreme drought and heat impacts in Africa.
• UC2: Climate induced migration in Africa.
• UC3: Fire hazard short-term forecasting in the Mediterranean.
• UC4a: Automatic volcanic deformation detection and alerting.
• UC4b: Deformation trend change detection on PSI time-series for critical infrastructure
monitoring.
• UC5: Copernicus services for sustainable and environmentally friendly tourism.
This document is organized in 9 chapters as follows.
Chapter 1 provides a general introduction to the document and the project. Chapter 2 provides
and overview of the project, providing key information regarding objectives, milestones,
contractual deliverables and work plan. Chapter 3 analyses the governance structure of DeepCube.
Chapter 4 describes the management means and processes, with emphasis on deliverables
preparation process, reporting to the European Commission (EC) and procedures for conflict
resolution. Chapter 5 is focusing on the internal communication aspects of the project, data &
information sharing best practices, project meeting, etc. This will be at the heart of DeepCube
project. Chapter 6 is dedicated to the quality assurance plan, discriminating between the various
roles and responsibilities, and defining the processes to ensure that maximum deliverable quality
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research and innovation programme under grant agreement No 101004188is reached. Chapter 7 provides information about tackling risks and challenges, while Chapter 8 is
dedicated to the effort and cost management. Finally, Chapter 9 concludes the document.
This project has received funding from the European Union's Horizon 2020
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research and innovation programme under grant agreement No 1010041882. Description of the Project
2.1. Project Scope and Objectives
DeepCube aims at addressing the Digital Twin Earth challenges, by developing new Artificial
Intelligence architectures, to address new Earth Observation (EO) problems that imply high
environmental and societal impact. Through its novel approach the project will enhance our
understanding of Earth’s processes, with the view to correlating them with the current and future
Climate emergency by generating high business value. The project addresses data driven problems
that require quantitative estimation and forecasting of geophysical variables. It is also exploiting
non-space data and makes use of Interferometric Synthetic-Aperture Radar (SAR) data, which is a
very rich data source not investigated to its full extent when it comes to AI-based applications.
To achieve this, DeepCube utilizes mature and new technologies in the fields of AI and Semantic
Web, as enablers to unlock the capabilities of the large EO data of the Copernicus program. The
overarching objective of the project is to propose and implement a shift in the use of AI pipelines
to solve problems that require big Copernicus data. Specifically, the aims of the project are to:
• Develop novel DL architectures that extend to non-conventional, multifaceted, data and
problems settings, e.g., Interferometric SAR, social network data, industrial data, etc., for
producing new knowledge and creating novel value chains.
• Develop and apply the novel hybrid modelling paradigm where DL models become
“physics-aware”, i.e., data-driven AI models that respect physical laws and include
constraints for improved consistency.
• Open the black box of DL and use Explainable AI and Causality algorithms to develop our
intuition. Interpreting what the models have learned becomes very important, especially
in problems with economical, societal or environmental implications.
2.2. Project Overview
The consortium of DeepCube consists of 9 partners from 6 European countries (Table 1).
No Beneficiary Country
1 Ethniko Asteroskopeio Athinon (NOA) Greece
2 Max-Planck-Gesselschaft zur Foerderung der Wissenschaft Germany
(MPG)
3 Universitat de Valencia (UVEG) Spain
4 Logical Clocks AB (LC) Sweden
5 National and Kapodistrian University of Athens (UoA) Greece
6 Gael Systems (GAEL) France
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research and innovation programme under grant agreement No 1010041887 Tre Altamira Srl (TREA) Italy
8 Infalia Private Company (INFALIA) Greece
9 Murmuration (MURM) France
Table 1: DeepCube consortium
The partners are remarkably diverse and can be grouped into 5 SMEs (TREA, MURM, INFALIA, LC,
GAEL), 2 Research Institutes (NOA, MPG), 2 Universities (UoA, UVEG) and 3 Linked Parties as Users
participating with in-kind contributors (Terra, STS, HFS) (Figure 1).
Figure 1: Diversity and expertise of the DeepCube consortium
2.3. Project Milestones
DeepCube has 8 milestones throughout its lifetime, which are summarised in Table 2.
Milestone
Milestone Title WP Partner Deadline
Number
MS1 Small scale Data Cubes WP3 MPG M6
MS2 1st Version of the DeepCube platform WP2 GAEL M12
MS3 1ST Prototype of the DL architecture WP4 UVEG M12
MS4 Evaluation of the prototype use cases WP5 NOA M20
MS5 Large scale Data Cubes WP3 MPG M24
MS6 2nd Version of the DeepCube platform WP2 GAEL M30
MS7 2nd Prototype of the DL architectures WP4 UVEG M30
MS8 Evaluation of the full-blown UCs WP5 NOA M36
Table 2: Milestones of DeepCube
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research and innovation programme under grant agreement No 1010041882.4. Project Deliverables
The work of the project will be documented in 51 deliverables. Table 3 presents all project’s
Deliverables with the responsible partners and the delivery date.
Del.
WP Del. Deliverable name Partner
Month
1 D1.1 Project Management & Quality plan NOA 2
1 D1.2 Interim Progress Report M6 NOA 6
1 D1.3 Data Management Plan v1 NOA 6
1 D1.4 DWH data use for year 1 NOA 9
1 D1.5 DWH data use for year 2 NOA 9
1 D1.6 Status of Liaison activities v1 UoA 15
1 D1.7 Interim Progress Report M18 NOA 18
1 D1.8 DWH data use for year 2 NOA 21
1 D1.9 DWH data request for year 3 NOA 21
1 D1.10 Data Management Plan v2 NOA 30
1 D1.11 DWH data use for year 3 NOA 30
1 D1.12 Interim Progress Report M30 NOA 30
1 D1.13 Status of Liaison activities v2 UoA 36
DeepCube platform requirements, specs and architecture-
2 D2.1 GAEL 4
v1
2 D2.2 DeepCube technical components-v1 UoA 9
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research and innovation programme under grant agreement No 1010041882 D2.3 DeepCube Platform – v1 GAEL 12
DeepCube platform requirements, specs and architecture-
2 D2.4 GAEL 23
v2
2 D2.5 DeepCube technical components-v2 LC 27
2 D2.6 DeepCube Platform – v2 GAEL 30
3 D3.1 Creation of training datasets, datacubes & ontologies MPG 6
3 D3.2 EO and non-EO data ingestion report – v1 GAEL 15
3 D3.3 Creation of training datasets, datacubes & ontologies -v2 UoA 24
3 D3.4 EO and non-EO data ingestion report – v2 INFALIA 36
4 D4.1 Analytics and DL architectures for the droughts UC-v1 MPG 12
Causal Inference Approach for the Droughts-induced
4 D4.2 UVEG 12
Migration UC
Analytics and DL architectures for fire risk assessment UC -
4 D4.3 NOA 12
v1
Analytics and DL architectures for the deformation
4 D4.4 TREA 12
monitoring UC -v1
4 D4.5 Analytics and DL architectures for the EO4tourism UC -v1 MURM 12
4 D4.6 Analytics and DL architecture for the droughts UC -v2 MPG 30
4 D4.7 Causality Toolbox UVEG 30
Analytics and DL architectures for fire risk assessment UC -
4 D4.8 NOA 30
v2
Analytics and DL architectures for the deformation
4 D4.9 TREA 30
monitoring UC -v2
4 D4.10 Analytics and DL architectures for the EO4tourism UC -v2 MURM 30
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research and innovation programme under grant agreement No 1010041885 D5.1 User Requirements Report - Cycle I TREA 3
5 D5.2 Design and Validation Plan - Cycle I NOA 12
5 D5.3 Use Case Demonstration Report - Cycle I NOA 18
5 D5.4 Use Case Evaluation Report - Cycle I UVEG 20
5 D5.5 User Requirements Report - Cycle II TREA 22
5 D5.6 Design and Validation Plan - Cycle II NOA 27
5 D5.7 Use Case Demonstration Report - Cycle II NOA 34
5 D5.8 Use Case Evaluation Report - Cycle II UVEG 36
6 D6.1 Website & Material NOA 2
6 D6.2 Initial Communication and Dissemination plan NOA 3
6 D6.3 Initial exploitation analysis and IPs MURM 8
6 D6.4 Impact Monitoring report MURM 15
6 D6.5 Mid-term dissemination plan MPG 18
6 D6.6 Exploitation plan and updates on IPs MURM 26
6 D6.7 Updates on impact monitoring activities MURM 30
6 D6.8 Final dissemination and communication activity MPG 36
6 D6.9 Final Exploitation, sustainability, and business plan MURM 36
7 D7.1 POPD - Requirements No. 1 NOA 4
Table 3: DeepCube list of Deliverables
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research and innovation programme under grant agreement No 1010041882.5. Project Work Plan and Breakdown
The workplan of the project is organized in 7 Work Packages and 5 Use Cases presented in Table 4
and Table 5 respectively with the responsible beneficiary and the assigned effort in person
months. The work plan will be conducted in two cycles of development, M1-M20 and M21-M36.
Lead Person Start End
WP WP Title
Beneficiary months month month
WP1 Project Management NOA 28.5 1 36
WP2 DeepCube platform GAEL 87.25 1 30
Big data acquisition and set-up
WP3 MPG 64.50 1 36
of Data Cubes
Scalable AI architectures for big
WP4 UVEG 158.50 4 30
EO and non-EO data
WP5 Use cases and demonstrations NOA 78.0 1 36
Communication, Dissemination
WP6 MURM 35.00 1 36
and Engagement
WP7 Ethics requirements NOA N/A 1 36
Table 4: Work Packages of DeepCube
Use Case
Use Case Title
Leader
UC1: Forecasting localized extreme drought and heat impacts in Africa MPG
UC2: Climate induced migration in Africa UVEG
UC3: Fire hazard short term forecasting in the Mediterranean NOA
UC4a: Automatic volcanic deformation detection and alerting TREA
UC4b: Deformation trend change detection on PSI time-series for critical
NOA
infrastructure monitoring
UC5: Copernicus services for sustainable and environmentally friendly tourism
MURM
Table 5: Use Cases of DeepCube
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research and innovation programme under grant agreement No 101004188WP1 and WP6 have a horizontal character (Figure 2) and have implications or are dependent on
WP2, WP3, WP4, WP5. The WP5 is highly connected to WP2, WP3 and WP4 as it is providing the
user requirements. It is also focusing on the use cases implementation and evaluating the first
cycle of the project, with the view to providing insights for changes and optimizations of the work
conducted in WP2, WP3 and WP4.
Figure 2: Work Package interdependencies and connections
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research and innovation programme under grant agreement No 101004188The WPs and UCs are connected to each other as follows. The UC leaders have vertical
responsibilities across the WPs meaning that they oversee data collection, architecture and model
building and demonstration (Figure 3). The WP leaders are responsible for the coordination,
monitoring and management of the work conducted within the WP as well as ensuring the timely
and of high-quality preparation and submission of deliverables and reports.
Figure 3: Relation between Work Packages and Use Cases
The duration of each WP as well as the connection between WPs is depicted in Figure 4.
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research and innovation programme under grant agreement No 101004188Deep Cube GANTT Work Package Breakdown Structure
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
1
2
3
4
5
6
7
8
9
WP WP Title
WP1 Project Management
D1.3 D1.6 D1.9
Project coordination xx xx xx xx D1.2 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
T1.1 D1.1 D1.4 D1.7 D1.10
T1.2 Quality control xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx D1.8 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
T1.3 Liaison with ICT projects and European AI initiatives xx xx xx xx xx xx xx xx xx xx xx xx xx xx D1.5 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx D1.11
WP2 Deep Cube platform
T2.1 System requirements, specifications and architecture xx xx xx D2.1 xx xx xx D2.4
T2.2 Extend ESDL Data Cube functionalities xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
T2.3 Semantic data cubes xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
D2.2 D2.5
T2.4 Visualization tools for both EO and non-EO data xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
T2.5 Hyper-parameter optimization, Semi-Supervised ML & Unsupervised ML on EO-data xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
M02 M06
Integration and platform development xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
T2.6 D2.3 D2.6
WP3 Big data acquisition and set-up of Data Cubes
T3.1 Ingestion of Copernicus datasets xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
D3.2 D3.4
T3.2 Ingestion of non-EO data and concept extraction xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
T3.3 Creation of training data sets xx xx xx xx xx xx xx xx xx xx xx xx xx
M01 M05
T3.4 Creation of Data Cubes xx xx xx xx xx xx xx xx xx xx xx xx xx
D3.1 D3.3
T3.5 Creation of the DeepCube ontologies and mappings xx xx xx xx xx xx xx xx xx xx xx xx xx
WP4 Scalable AI architectures for big EO and non-EO data
T4.1 Analytics and deep learning architectures for the droughts UC xx xx xx xx xx xx xx xx M03 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx M03
T4.2 Causality for the droughts-induced migration UC xx xx xx xx xx xx xx xx D4.1 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx D4.6
D4.2 D4.7
T4.3 Analytics and explainable deep learning architectures for the fire risk UC xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
D4.3 D4.8
T4.4 Analytics and deep learning architectures for the satellite interferometry UC xx xx xx xx xx xx xx xx D4.4 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx D4.9
Analytics and deep learning architectures for the EO4tourism UC xx xx xx xx xx xx xx xx D4.5 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx D4.10
T4.5
WP5 Use cases & demonstrations
T5.1 Gathering and analysis of requirements xx xx D5.1 xx D5.5
T5.2 Use Case design xx xx xx xx xx xx xx xx D5.2 xx xx xx xx D5.6
T5.3 Use case implementation xx xx xx xx xx D5.3 xx xx xx xx xx xx D5.7
M04 M08
Use Case evaluation xx xx xx xx xx xx
T5.4 D5.4 D5.8
WP6 Communication, Dissemination and Engagement
T6.1 Communication and user engagement xx D6.1 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
D6.2 D6.5 D6.8
T6.2 Dissemination xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx
T6.3 Exploitation and IPR management xx xx xx xx xx xx xx D6.3 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx D6.6 xx xx xx xx xx xx xx xx xx xx
T6.4 Impact monitoring xx xx xx xx xx xx xx xx xx xx xx xx xx xx D6.4 xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx D6.7 xx xx xx xx D6.9
D7.1
WP7 Ethics requirements
D7.2
M Milestone intra work package connections
xx Task Execution inter work package connections
D Deliverable
Figure 4: DeepCube Gantt Chart3. Project Management and Governance
3.1. Project Management Strategy
Project management includes all core activities to ensure the successful completion of the project
within all technical and financial aspects set out in the Grant Agreement. WP1, led by NOA, is
dedicated to the management and coordination of the project to ensure that it stays on track in
terms of scope, costs, resources, and quality. All changes and optimizations essential for
facilitating this goal are always under discussion with the partners and the decisions are taken
based on the partners approval.
Good communication management practices are crucial for ensuring that information reaches the
appropriate partners, and that timely, efficient decisions can be taken. Quality management
contributes to establishing the relevant to the project quality control and quality assurance
activities for ensuring an efficient collaboration among the consortium partners and delivery of
project results. Risk management is necessary for providing the process and techniques for the
evaluation and control of potential project risks, focusing on their precautionary diagnosis and
handling.
3.2. Project Management Structure
The overall organizational structure of DeepCube consists of the Executive Board, the Project
Management Office, the General Assembly, the Advisory Board, and the Project Steering
Committee (Figure 5).
Figure 5: Project Management Structure
The Executive Board (EB) is comprised of the Project Coordinator (PC) Dr. Ioannis Papoutsis and
the Project Management Office (PMO) which consists of: the Project Manager (PM) Ms Alkyoni
Baglatzi, the Quality Manager (QM): Ms Alkyoni Baglatzi, the Technical Manager (TM): Dr. Ioannis
Papoutsis, the Innovation Manager (IM): Mr. Tarek Habib, the Communication & DisseminationManager (CDM): Ms. Souzana Touloumtzi, the AI Liaison Manager (AIM): Prof. Manolis Koubarakis
and the Ethics & IPR Manager (EM): Mr. Ilias Karakatsanis and the Work Package Leaders: WP1:
Ms. Alkyoni Baglatzi (NOA), WP2: Mr. Christophe Demange (GAEL), WP3: Prof. Markus Reichstein
(MPG), WP4: Prof. Gustau Camps-Valls (UVEG), WP5: Ms. Souzana Touloumtzi (NOA), WP6:
Mr.Tarek Habib (MURM), WP7: Ms Alkyoni Baglatzi (NOA).
The Project Coordinator (PC) is the intermediary between the partners and the EC and will perform
all tasks assigned to him as described in the Grant Agreement (GA) and the Consortium Agreement
(CA). The Quality Manager (QM) is responsible for all quality assurance processes.
The Technical Manager (TM) supports the PC to technical decisions, being also responsible for
supervising and providing guidance on the technical progress of project’s assignments. TM must
timely foresee, suggest, and solve any technical risk that could deviate project’s objectives and
sidetrack project’s schedule.
The Innovation Manager (IM) is responsible for: i) monitoring the on-going project work,
identifying the innovation and exploitation potential of the technologies developed; ii) proposing
recommendations for IPR Results protection and projecting an adequate IPR Results protection
plan; iii) identifying and protocoling the intellectual property ownership conditions of the
intermediate and final project results; iv) outlining and keeping updated the exploitation plan for
the individual technologies and the DeepCube as a whole; v) monitoring the publication activities
of the partners and preventing any precipitated disclosure of confidential information that might
negatively affect the exploitation and/or the Results IPR protection; vi) directing the exploitation
and Results IPR protection within the project.
The Communication & Dissemination Manager (CDM) will be responsible for the design,
preparation and implementation of the project Dissemination and Communication Plan, targeting
to create awareness in relevant scientific communities. The CDM coordinates all dissemination
activities and sharing of ideas with external stakeholders and ensures the widest possible exposure
of project’s outcomes to its target groups.
The AI liaison Manager (AIM) is responsible for capturing evolutions and bringing them to the
project, while promoting the project’s assets to the AI community.
The Ethics & IPR Manager (EM) will make sure that activities such as the engagement of citizens
and local actors, as well as the use of social media data, are conducted in an ethical manner,
consider sex and gender considerations and respecting GDPR regulations.
WP Leaders, are responsible for: i) planning the scientific and technical work of the WP, in
coordination with all partners that are involved in this WP; ii) ensuring that the time-schedule is
maintained and indicate any discrepancies to the PC; iii) initiating corrective actions for project
deviations (if required); iv) consolidating partner information and preparing the reports for
submission to the PC; v) ensuring that the objectives and milestones of the whole WP as well as of
the detailed activities within the WP are achieved in time; vi) ensuring that the deliverables are
provided according to the time schedule.
The EB functions as the supervisory body for the proper execution of the project. It monitors and
manages the day-to-day operations and is accountable to the General Assembly (GA).
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research and innovation programme under grant agreement No 101004188The General Assembly (GA) is the ultimate decision-making body that takes decisions concerning
top-level management and strategic issues. It comprises of one representative from each
beneficiary, namely the Project Coordinator (PC): Dr. Ioannis Papoutsis, Representative MPG: Prof.
Markus Reichstein, Representative UVEG: Prof. Gustau Camps-Valls, Representative LC: Mr.
Theofilos Kakantousis, Representative UoA: Prof. Manolis Koubarakis, Representative GAEL: Mr,
Christophe Demange, Representative TREA: Mr. Marco Bianchi, Representative INFALIA: Dr.
Stefanos Vrochidis, Representative MURM: Mr. Tarek Habib.
The Advisory Board (AB) consists of leading experts from the HPC, ICT/AI and EO communities,
namely Prof. Stefanos Kollias, Dr. Yannis Avrithis and Prof. Mihai Datchu, providing their advice
and guidance throughout the implementation phase of the project. Other members will be added
to the AB as the project progresses.
The Project Steering Committee (PSC) consists of the Project Coordinator (PC): Dr. Ioannis
Papoutsis, the Technical Manager (TM): Dr. Ioannis Papoutsis, the Innovation Manager (IM): Mr.
Tarek Habib and the Advisory Board (AB), which is responsible for monitoring the impact of the
project, following its KPIs and providing strategic guidance.
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research and innovation programme under grant agreement No 1010041884. Management Processes and Tools
4.1. Deliverable Preparation
According to the GA, DeepCube has 51 deliverables, each one assigned to the responsible partner.
The partner in charge of the deliverable is responsible for its timely and of high-quality submission
to the PC. After the quality review, the final version of the deliverable is uploaded by the PC to the
EC portal. The deliverable preparation process is depicted in Table 6.
Action Due Date
Table of content sent for feedback 45 days before deadline
Final TOC in place 40 days before deadline
First draft for internal review ready 20 days before deadline
Final draft with internal reviews ready 15 days before deadline
Review by the Quality Manager and provision of feedback to the
10 days before deadline
deliverable leader
Approval of the draft by the PC and preparation of finalized version 5 days before deadline
Table 6: Deliverable preparation timeplan
Any deviations from the time plan should be communicated by the deliverable leader to the PC as
soon as possible. The time plan can be adjusted if previously agreed between the author, the
reviewers, and the PC. The deliverables marked as “public” will be uploaded to the DeepCube
website while the deliverables marked as “confidential”, will be only made available to the EC and
the consortium partners via the project’s repository (DeepCube wiki – section 5.1.1).
4.2. Document formats and naming conventions
A lot of material will be prepared and shared during the implementation of the project. Table 7
shows the recommended formats and tools that shall be used.
Type Format Production Tool Version
“Word 2010 or
Documents .docx Microsoft Word
later”, Google Docs
Any desktop or
Scientific papers .tex Latex
online release
Data in tabular form and “Excel 2010 or
.xlsx Microsoft Excel
graphics later”, Google Docs
Presentations .pptx Microsoft PowerPoint “PowerPoint 2010
or later”, Google
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research and innovation programme under grant agreement No 101004188Docs
Any software tools that
Images .jpeg, .png etc various
can produce images
Any software that can
Portable Document Format .pdf various
produce .pdf files
Any software that can
Compressed files .7z various
produce .7z (7-Zip) files
Table 7: Tools and formats recommended to be used in DeepCube
In order to ease the communication process and the identification of documents and versions all
partners are advised to use some naming conventions based on the principle of self-explanatory
titles and versions. The general file name conventions are as follows:
DeepCube_[name of the document]_Vxy_date_[partner acronym/person name].FileExtension
• The name of the document shall be as concise as possible but also self-explanatory i.e.,
Kick_Off_Project_Meeting_Minutes
• The date should be presented in the form ddmmyyyy i.e., 12032021.
• The partner acronym or person name should be used as defined in the GA i.e., NOA for the
National Observatory of Athens.
4.3. Reporting to the EC
DeepCube has 3 reporting periods which are related to payment requests:
• Reporting Period 1 (RP1) from M1 – M12
• Reporting Period 2 (RP2) from M13 – M24
• Reporting Period 3 (RP3) from M25 - M36
The Periodic reports are being prepared with the contribution of all partners and the overall
responsibility and coordination of the PC. The final repots are to be submitted to the portal by the
PC, within 60 days after the end of the reporting period.
Additionally, there will be 3 Interim Progress Reports documenting the 6 months progress in
between the reporting periods of the project without being related to payment requests. These
are included in WP1 and namely as deliverables:
• D1.2 Interim Progress Report: M6
• D1.7 Interim Progress Report: M18
• D1.12 Interim Progress Report: M30
Similarly, for these reports, the PC is responsible for the coordination and the submission of the
reports. All partners will be asked to contribute based on templates and instructions that will be
circulated by the PC.
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research and innovation programme under grant agreement No 1010041884.4. Conflict resolution
Project and quality management activities as well as the awareness of all partners about their
commitments, will ensure the proper implementation of the project plan and the realization of its
objectives. Decisions will normally be taken by the responsible partners based on the work to be
conducted, as described in the GA. Transparency and a good communication among the project
members are key to avoid challenges and conflicts before they arise.
It is expected though, that during the project, the partners may need to resolve various issues and
reach agreements. The processes to be followed start with informal contacts as a first step such as
an oral discussion or ad-hoc meeting and further on include written notification in terms of email,
minutes etc.
The PC is responsible for the overall resolving of conflicts. The general principle is to solve conflicts
at the lower possible level starting from the task level with strong emphasis on the use of
negotiation skills.
Task leaders and Work Package leaders should notify the PC as soon as possible when conflicts
arise so that intermediate corrections can be proposed. Conflicts that are not being solved on the
PC level, will be communicated to the General Assembly. Any correction measures will be in
accordance with the GA and the CA. Good communication among all involved parties is key point
for resolving any conflicts.
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research and innovation programme under grant agreement No 1010041885. Communication Processes and Tools
5.1. Internal Communication and monitoring
Communication processes and tools form the communication framework of DeepCube which will
serve as a guide for communication throughout the duration of the project and can be adjusted as
communication requirements may change. The PC will take a central and proactive role in ensuring
effective communication on this project and facilitating the seamless implementation of the
workplan. The internal communication regards to the processes and tools that will be used among
the partners of the projects.
5.1.1. Project Team Directory/Repository
A Wiki (http://deepcube-wiki.space.noa.gr/) will be used as the central repository for the project
where all partners will be able not only to share documents but also include written texts, minutes
of meetings etc. The wiki will be linked with google drive spreadsheets and other potential
electronic means that will be used and will be restricted only to the personnel of the project. The
structure of the wiki is presented below in Figure 6.
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research and innovation programme under grant agreement No 101004188Figure 6: DeepCube wiki
This project has received funding from the European Union's Horizon 2020
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research and innovation programme under grant agreement No 101004188For sharing financial documents, google drive folders are set up restricted only to the PC and the
respective partners.
All contact details, are organized in a Contacts Google Spreadsheet1 which is regarded as the
central point of reference and will be always updated when the personnel of the partners changes.
5.1.2. Emails and Mailing lists
Direct email will be used as a common means for sharing information and addressing day-to day
businesses of the project. Mailing lists will be created for five different topics: technical issues,
financial & administrative issues, communication & dissemination issues, project management
board and one including all persons of the consortium. The DeepCube mailing lists are following:
• deepcube.tech@noa.gr (Technical)
• deepcube.admin@noa.gr (Financial/ Administrative)
• deepcube.comm@noa.gr (Communication/Dissemination)
• deepcube.pmb@noa.gr (Project Management Board)
• deepcube.all@noa.gr (All project partners)
It is highly recommended that the mailing lists are only used for topics of interest in each domain.
Due to the dynamic character of this project and the expected changes in personnel, emails will be
added/removed accordingly.
5.1.3. Online Meetings Platform
For the effective communication among the partners, regular online calls will be held. Partners are
free to choose the most appropriate platform i.e. Google Meet, Skype, Webex, Zoom etc. The
online meetings organized by the PC will be held over Zoom.
5.1.4. Organization of Meetings
For the organization of meetings, the online service Doodle2 will be used to define the date and
time of the meeting.
5.1.5. Project Meetings
A number or meetings will be held during the implementation of the project. Table 8, presents the
type of meetings, their schedule, the organizers, participants, location and related documents. It is
expected that due to the COVID-19 pandemic, several of the meetings will be held virtually.
1 https://docs.google.com/spreadsheets/d/1BajyaBYRkVgwJdHPItVDV6GGcxqM6Iyo1U-Vo593D_A/edit#gid=0
2 https://doodle.com/en/
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research and innovation programme under grant agreement No 101004188Meeting Time Organizer Participants Location Deliverables
Kick-off M1 (26- PC All project Virtual event Agenda
meeting 27/01/2021) partners
Meeting
presentations
Minutes - Action
Plan
Plenary Every 6 months PC All project Face-to- Agenda
Project (M6, M12, partners Face/virtual
Meeting
Meetings M18, M24, event
presentations
M30, M36)
Minutes - Action
Plan
Technical 2-3 meetings TPM Technical Face-to- Agenda
Project to be project Face/virtual
Meeting
Meetings organized in partners event
presentations
conjunction
with the Minutes - Action
Plenary project Plan
meetings
Biweekly Every 2 weeks PC All project Online meeting Agenda
Project partners
Minutes - Action
Meetings
Plan
Ad hoc Whenever All project Project Face-to-Face Agenda
meeting needed partners partners meeting/online
Minutes - Action
based on based on meeting
Plan
topic and topic and
need needs
General At least once PC Members of Face-to-Face Agenda
Assembly per year the GA meeting/online
Minutes - Action
Meeting meeting
Plan
Executive Twice a year PC Members fo Face-to-Face Agenda
Board the EB meeting/online
Minutes - Action
Meeting meeting
Plan
Table 8: DeepCube meetings
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research and innovation programme under grant agreement No 1010041885.2. External Communication
For external communications, the consortium will establish its own website and communicate with
external stakeholders by e-mail, social media accounts and social platforms (Twitter, Facebook,
LinkedIn).
All partners are expected to produce high quality presentations and scientific papers for
publication in specialized conferences and journals as well as more simplified press releases
demonstrating the impact of the project for a wide range of readers. In all external communication
tools (including the web) and materials (e.g., leaflets, posters, etc.) a reference to the project and
the European funding will be made, with the project acronym (DeepCube) and the GA number (No
101004188), as required per Article 29.4 of the GA.
These efforts will be pursued throughout the project to raise awareness and ensure high visibility
of the project results. More information about the external communication will be presented in
the Deliverable “D6.1 Initial Communication and Dissemination Plan” to be submitted in M3.
For the better organization of the communication activities, each partner assigns a communication
manager responsible to monitor and implement the communication plan (Table 9).
Partner Name and Surname e-mail
NOA Ms. Souzana Touloumtzi stouloumtzi@noa.gr
MPG Dr. Fabian Gans fgans@bgc-jena.mpg.de
UVEG Dr. Maria Pilles maria.piles@uv.es
LC Mr. Jim Dowling jim@logicalclocks.com
UoA Prof. Manolis Koubarakis koubarak@di.uoa.gr
GAEL Mr. Christophe Demange christophe.demange@gael.fr
TREA Ms Chiara Gervasi chiara.gervasi@tre-altamira.com
INFALIA Ms. Eleni Kamateri ekamater@infalia.com
MURM Mr Tarek Habib tarek.habib@murmuration-sas.com
Table 9: Communication representatives of DeepCube partners
The Project Communication Manager, Ms Souzana Touloumtzi, will coordinate all project
communication activities as well as communicate them to the EC.
5.3. Communication with REA
The PC is the responsible contact point on behalf of the project, for the communication with REA
or the European Commission. He is responsible for keeping the project portal always up to date
i.e., regarding communication activities, milestones reached, deliverables and progress report
submitted etc. Moreover, the PC is responsible for providing any requested information by REA as
well as inform the partners about any information that should be shared from the EC. The partners
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research and innovation programme under grant agreement No 101004188are not supposed to communicate with the EC directly except for there is a certain need that has
been prior discussed and agreed upon with the PC. In all other cases, the PC will communicate any
issues to the EC.
This project has received funding from the European Union's Horizon 2020
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research and innovation programme under grant agreement No 1010041886. Quality Assurance Plan
6.1. Quality Assurance Overview
According to the PMBOK 3 “Quality Assurance is s the process of auditing the quality requirements
and the results from quality control measurements to ensure that appropriate quality standards
and operational definitions are used.”
Quality assurance is a fundamental part of the implementation of the project and will be
performed throughout the duration of the project by all the partners. The quality assurance plan is
based on the plan-do-check-act cycle introduced by W. Edwards Deming.4) (Figure 7)
Act Plan
Check Do
Figure 7: Quality assurance principles
• Plan: is related to the objectives, processes, tools and resources needed to deliver the
results according to the work plan and the project requirements
• Do: is referring to the implementation of the planned work
• Check: is referring to monitoring and evaluating the project outcomes and services based
on the planned work and the requirements
• Act: is referring to the actions taken if necessary, to make correction and improve
outcomes and performance.
3 https://www.pmi.org/pmbok-guide-standards/foundational/pmbok
4
https://deming.org/explore/pdsa/#:~:text=The%20PDSA%20Cycle%20(Plan%2DDo,was%20first%20introduced%20to%2
0Dr
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research and innovation programme under grant agreement No 1010041886.2. Roles and Responsibilities
NOA, as the Coordinator of the project will ensure that he project’s personnel is aware of the
Quality Assurance Plan and of the way each partner contributes to the successful implementation
of the project and achievement of the project’s quality requirements. Moreover, NOA is
responsible for the control of the documented information of the project, which includes storage
& backup and versioning & control of changes.
The wiki which was chosen as the central repository for the project is supporting both
requirements and as such is ensuring that this information can be available at any time.
Each WP leader and UC leader are responsible for monitoring and controlling the implementation
phase of the project and ensuring conformity with the quality requirements. Technical testing and
user validation will be utilized throughout the implementation of the project measuring the
satisfaction of the quality requirements of the project.
6.3. Quality Criteria
All products of DeepCube either on the technical level (i.e., services, models etc.) or in written
form such as reports, deliverables, publications, have to be of high quality following certain quality
criteria. These criteria are based on the principles of completeness, correctness, and punctuality5 .
Regarding the content, completeness is seen as covering in depth the topic without missing any
important aspect or making redundancies. The accuracy is seen in the context of clear statement
of the results, sufficiently evidence supports of the research and outcomes, minimization of errors
and ambiguities. All the produced material has to follow the visual identity of the project and
follow the templates of DeepCube as well as conform to the specifications of the EC. Punctuality,
refers to the timely delivery of based on predefined deadlines.
6.4. Deliverable Quality Assurance Processes
A total of 51 deliverables will be submitted until the end of the project. The deliverables will all
follow the same template set up by NOA who will provide guidelines about their use, the time
plan, and the expected final result, to all partners.
The review of the deliverable will focus on consistency and clarity of the document, relevance and
coverage of the topic and language features. For each deliverable one partner is being assigned as
reviewer. The number of deliverables each partner will review, is calculated based on the assigned
effort in the project (Table 10).
Partner NOA MPG UVEG LC UoA GAEL TREA INFALIA MURM
Effort (PMs) 88.00 73.50 63.50 37.00 48.50 33.00 38.00 35.00 35.50
Effort (%) 19.50 16.30 14.00 8.20 10.70 7.30 8.40 7.70 7.90
5Bots, J.M., Heck, E. van, Swede, V.van, “Management information”, pub. CAP Gemini Publishing BV, Rijswijk, 1990, pp.
550-555
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research and innovation programme under grant agreement No 101004188Number of
Deliverables 10 8 7 4 5 4 4 4 4
for review
Table 10: Effort of each partner in the project
The reviewers for each Deliverable can be found in Table 11.
Part. Del.
WP Del. Deliverable name Reviewer
Name Month.
1 D1.1 Project Management & Quality plan NOA 2 UoA
1 D1.2 Interim Progress Report M6 NOA 6 INFALIA
1 D1.3 Data Management Plan v1 NOA 6 LC
1 D1.4 DWH data use for year 1 NOA 9 UoA
1 D1.5 DWH data use for year 2 NOA 9 MURM
1 D1.6 Status of Liaison activities v1 UoA 15 LC
1 D1.7 Interim Progress Report M18 NOA 18 GAEL
1 D1.8 DWH data use for year 2 NOA 21 UoA
1 D1.9 DWH data request for year 3 NOA 21 LC
1 D1.10 Data Management Plan v2 NOA 30 INFALIA
1 D1.11 DWH data use for year 3 NOA 30 MURM
1 D1.12 Interim Progress Report M30 NOA 30 INFALIA
1 D1.13 Status of Liaison activities v2 UoA 36 GAEL
DeepCube platform requirements, specs
2 D2.1 GAEL 4 NOA
and architecture-v1
2 D2.2 DeepCube technical components-v1 UoA 9 MPG
2 D2.3 DeepCube Platform – v1 GAEL 12 NOA
DeepCube platform requirements, specs
2 D2.4 GAEL 23 NOA
and architecture-v2
2 D2.5 DeepCube technical components-v2 LC 27 INFALIA
2 D2.6 DeepCube Platform – v2 GAEL 30 NOA
Creation of training datasets, datacubes &
3 D3.1 MPG 6 UVEG
ontologies
3 D3.2 EO and non-EO data ingestion report – v1 GAEL 15 NOA
Creation of training datasets, datacubes &
3 D3.3 UoA 24 MPG
ontologies -v2
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