#  1\. INTRODUCTION

##  1.1 OPERA MOTIVATION

The OPERA project participates in the Pilot on Open Research Data launched by
the European

Commission (EC) along with the H2020 programme. This pilot is part of the Open
Access to Scientific Publications and Research Data programme in H2020. The
goal of the programme is to foster access to research data generated in H2020
projects. The use of a Data Management Plan (DMP) is required for all projects
participating in the Open Research Data Pilot.

Open access is defined as the practice of providing on-line access to
scientific information that is free of charge to the reader and that is
reusable. In the context of research and innovation, scientific information
can refer to peer-reviewed scientific research articles or research data.

Research data refers to information, in particular facts or numbers collected
to be examined and considered, and as a basis for reasoning, discussion, or
calculation. In a research context, examples of data include statistics,
results of experiments, measurements, observations resulting from fieldwork,
survey results, interview recordings and images. The focus is on research data
that is available in digital form.

The Consortium strongly believes in the concepts of open science, and in the
benefits that the European innovation ecosystem and economy can draw from
allowing the reuse of data at a larger scale.

Furthermore, there is a need to gather experience in open sea operating
conditions, structural and power performance and operating data in wave
energy. In fact, there has been very limited open sea experience in wave
energy, which is essential in order to fully understand the challenges in
device performance, survivability and reliability. The limited operating data
and experience that currently exists are rarely shared, as testing is partly
private-sponsored.

This project proposes to remove this roadblock by delivering for the first
time, open access, high-quality open sea operating data to the wave energy
development community.

Nevertheless, data sharing in the open domain can be restricted as a
legitimate reason to protect results that can reasonably be expected to be
commercially or industrially exploited [1] . Strategies to limit such
restrictions will include anonymising or aggregating data, agreeing on a
limited embargo period or publishing selected datasets.

##  1.2 PURPOSE OF THE DATA MANAGEMENT PLAN

The purpose of the DMP is to provide an analysis of the main elements of the
data management policy that will be used by the Consortium with regard to the
project research data.

The DMP covers the complete research data life cycle. It describes the types
of research data that will be generated or collected during the project, the
standards that will be used, how the research data will be preserved and what
parts of the datasets will be shared for verification or reuse. It also
reflects the current state of the Consortium agreements on data management and
must be consistent with exploitation and IPR requirements.

The DMP is not a fixed document, but will evolve during the lifespan of the
project, particularly whenever significant changes arise such as dataset
updates or changes in Consortium policies.

This document is an update of the DMP which was delivered in Month 6 of the
project (D8.5). It included an overview of the datasets to be produced by the
project, and the specific conditions that are attached to them. The current
version of the DMP gets into more detail and describes the practical data
management procedures implemented by the OPERA project with reference with the
IT tools developed in WP1. The final version of the DMP will be delivered in
Month 30 (D8.7).

This document has been produced following the EC guidelines for project
participating in this pilot and additional consideration described in ANNEX I:
KEY PRINCIPLES FOR OPEN ACCESS TO RESEARCH DATA.

##  1.3 RESEARCH DATA TYPES IN OPERA

The data types that will be produced during the project are focused on the
Description of the Action (DoA) and their results.

According to such consideration, Table 1.1 reports a list of categories of
research data that OPERA will produce. These research data types have been
mainly defined in WP1, including data structures, sampling and processing
requirements, as well as relevant standards. This list may be adapted with the
addition or removal of datasets in the next versions of the DMP to take into
consideration the project developments. A detailed description of each dataset
is given in the following sections of this document.

###  TABLE 1.1: OPERA TYPES OF DATA  
  
<table>  
<tr>  
<th>

**#**

</th>  
<th>

**Dataset category**

</th>  
<th>

**Lead partner**

</th>  
<th>

**Related WP(s)**

</th> </tr>  
<tr>  
<td>

**1**

</td>  
<td>

Environmental monitoring

</td>  
<td>

TECNALIA

</td>  
<td>

WP1

</td> </tr>  
<tr>  
<td>

**2**

</td>  
<td>

Mooring performance

</td>  
<td>

UNEXE

</td>  
<td>

WP1, WP2, WP5

</td> </tr>  
<tr>  
<td>

**3**

</td>  
<td>

Bi-radial performance

</td>  
<td>

IST

</td>  
<td>

WP1, WP3

</td> </tr>  
<tr>  
<td>

**4**

</td>  
<td>

Power output

</td>  
<td>

OCEANTEC

</td>  
<td>

WP1, WP4, WP5

</td> </tr>  
<tr>  
<td>

**5**

</td>  
<td>

Power quality

</td>  
<td>

UCC

</td>  
<td>

WP1, WP5

</td> </tr>  
<tr>  
<td>

**6**

</td>  
<td>

Offshore operations

</td>  
<td>

TECNALIA

</td>  
<td>

WP6

</td> </tr> </table>

Specific datasets may be associated to scientific publications (i.e.
underlying data), public project reports and other raw data or curated data
not directly attributable to a publication. Datasets can be both collected,
unprocessed data as well as analysed, generated data. The policy for open
access are summarised in the following picture.

Research data linked to exploitable results will not be put into the open
domain if they compromise its commercialisation prospects or have inadequate
protection, which is a H2020 obligation. The rest of research data will be
deposited in an open access repository.

When the research data is linked to a scientific publication, the provisions
described in ANNEX II: SCIENTIFIC PUBLICATIONS will be followed. Research data
needed to validate the results presented in the publication should be
deposited at the same time for “Gold” Open Access  1  or before the end of the
embargo period for “Green” Open Access  2  . Underlying research data will
consist of selected parts of the general datasets generated, and for which the
decision of making that part public has been made.

Other datasets will be related to any public report or be useful for the
research community. They will be selected parts of the general datasets
generated or full datasets (e.g. up to 2 years of key operating data) and be
published as soon as they become available.

##  1.4 ROLES AND RESPONSIBILITIES

Each OPERA partner has to respect the policies set out in this DMP. Datasets
have to be created, managed and stored appropriately and in line with
applicable legislation.

The Project Coordinator has a particular responsibility to ensure that data
shared through the OPERA website are easily available, but also that backups
are performed and that proprietary data are secured.

OCEANTEC, as WP1 leader, will ensure dataset integrity and compatibility for
its use during the project lifetime by different partners.

Validation and registration of datasets and metadata is the responsibility of
the partner that generates the data in the WP. Metadata constitutes an
underlying definition or description of the datasets, and facilitate finding
and working with particular instances of data.

Backing up data for sharing through open access repositories is the
responsibility of the partner possessing the data.

Quality control of these data is the responsibility of the relevant WP leader,
supported by the Project Coordinator.

If datasets are updated, the partner that possesses the data has the
responsibility to manage the different versions and to make sure that the
latest version is available in the case of publicly available data. WP1 will
provide naming and version conventions.

Last but not least, all partners must consult the concerned partner(s) before
publishing data in the open domain that can be associated to an exploitable
result.

#  2\. DATA COLLECTION, STORAGE AND BACK-UP

The OPERA project will generate data resulting from instrumentation recordings
during the lab testing and open-sea testing. In addition to the raw,
uncorrected sensor data, converted and corrected data, as well as several
other forms of derived data will be produced.

Instrumentation, data acquisition and logging systems are thoroughly described
in D1.1 [2] . A database management system will be used in the project to
create, read, update and delete data from a database. The software platform
being used is MySQL 5.7.9. A SCADA system will allows partners to access
monitoring information locally and remotely.

The following sections describe the different datasets that will be produced
in the course of the project.

##  2.1 ENVIRONMENTAL MONITORING DATA

Environmental monitoring data will be collected at two locations, namely the
Mutriku shoreline plant and the open sea test site BiMEP. These numeric
datasets will be directly obtained through observations, and derived using
statistical parameters and models.

In general, environmental monitoring datasets will be useful for further
research activities beyond the scope of OPERA objectives. Metocean
observations are common practice for different uses. Dataset could be
integrated and reused, particularly for the characterisation of wave resource
and the estimation of device performance. They will be also valuable for
technology developers who plan to test their devices at either Mutriku or
BiMEP.

Although the raw datasets are useful by themselves, it is the objective of the
OPERA project to use the data as a basis for at least one scientific
publication.

A short description of the environmental monitoring datasets is given next.

###  TABLE 2.1: WAVE RESOURCE AT MUTRIKU  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Wave_Mutriku

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Wave resource data 200 m off the shoreline plant.  Main data are the
pressure fluctuations over time.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 RBR & Isurki Pressure Gauges

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

  * MySQL database for real-time data 

  

  * TXT for instrument recordings and derived data 

  

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Scilab program to transform pressure into wave height and period.  Spectral
analysis software.

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 2 GB (6 months @ 2 Hz sampling frequency)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 Internal USB memory stick, on-site database server, and real-time
replication onto cloud-hosted database server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

 Daily back-ups on both local and cloud-hosted servers. 15-day retention
period for incremental backups in the latter.

</td> </tr> </table>

###  TABLE 2.2: WAVE RESOURCE AT BIMEP  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Wave_BiMEP

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

  * Wave resource at 300 m up-wave of the WEC. 

  

  * Datasets mainly consist of wave parameters such as wave H  s  , T  p  , direction and spreading. 

  

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 TRIAXYS surface following buoy

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

  * MySQL database for real-time data 

  

  * TXT for instrument recordings 

  

  * MS Excel for derived and filtered data 

  

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Spectral analysis software

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

  * 150 MB of statistical data (20 min x 2 years) 

  

  * 1 GB of real-time data (20 min x 2 Hz sampling frequency when real-time communications activated) 

  

  * 8 GB (2 years @ 2 Hz sampling frequency) 

  

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 Internal USB memory stick, on-site database server and real-time replication
onto cloud-hosted database server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

  * Daily back-ups on both local and cloud-hosted servers. 

  

  * 15-day retention period for incremental backups in the latter. 

  

</td> </tr> </table>

##  2.2 MOORING PERFORMANCE DATA

Experimental data will be collected at the DMAC facility in UNEXE [4] .
Besides, field tests will be conducted at the open sea test site at BiMEP.
Datasets consists of mooring performance data will be both experimental and
observational, raw and derived (using statistical parameters and models).

The mooring performance dataset will be useful to inform technology
compliance, survivability and reliability as well as economical improvements.
They will be also valuable for the certification processes of other technology
developers. These data will be the basis for at least one scientific
publication.

A short description of the mooring performance datasets is given below.
**TABLE 2.3: TETHER LOADS AT DMAC**  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Tethers_Lab

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Characterisation of design load behaviour, fatigue and durability of several
elastomeric tether specimens.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 DMAC facility

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Experimental

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

 CSV (processed data)

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Labview, Optitrack Motive and Matlab

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 8.7 GB (50 Hz sampling frequency)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 Network storage

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

 Network drive is backed up daily with two-disk fault tolerance (i.e.

backups are safe even if two disks fail). Backups are stored in a different
building and protected by a dedicated UPS.

</td> </tr> </table>

###  TABLE 2.4: MOORING LOADS AT BIMEP  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Mooring_BiMEP

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

  * Extreme loads and motion response to different sea states will be monitored. 

  

  * The loading data will be combined with the environmental monitoring dataset to derive the final mooring performance dataset. 

  

  * Comparison between the polyester lines and the elastomeric mooring tethers. 

  

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

  * MARMOK-A-5 prototype. 

  

  * A mooring condition monitoring has been implemented for the project consisting of 4 load shackles deployed in two mooring nodes of the prototype. 

  

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

  * MySQL database for real-time data 

  

  * TXT for raw instrument recordings 

  

  * MS Excel for derived and filtered data 

  

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Statistical and spectral analysis software

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 ≤ 400 GB (2.5 years recording x 16 measurements @ 20 Hz)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 On-site database server and real-time replication onto cloudhosted database
server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

  * Daily back-ups on both local and cloud-hosted servers. 

  

  * 15-day retention period for incremental backups in the latter. 

  

</td> </tr> </table>

##  2.3 BIRADIAL TURBINE PERFORMANCE DATA

Experimental data will be collected at existing IST Turbomachinery Laboratory
(Dry Lab) for tests in varying unidirectional flow. Also, field tests will be
conducted both at Mutriku shoreline plant and the BiMEP open sea test site.
Bi-radial turbine performance data will be both experimental and
observational, raw and derived (using statistical parameters and models).

The bi-radial turbine performance dataset will be useful to assess turbine
efficiency and reliability. The loading data will be combined with the
environmental monitoring dataset to derive the final bi-radial turbine
performance dataset.

This dataset will be the basis for at least one scientific publication.

A short description of the bi-radial turbine performance datasets is given
below.

###  TABLE 2.5: BI-RADIAL TURBINE PERFORMANCE AT DRY LAB FACILITY  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Biradial_Turbine_Lab

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Assess turbine performance though unidirectional steady-state and
alternating flow.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

  * IST Turbomachinery Laboratory. 

  

  * Sensor data acquired at a frequency of 1kHz for turbine pressure head, plenum temperature and humidity, turbine rotational speed, turbine flow rate and the instantaneous position of the flow control valve. 

  

  * The voltage and the current of the three AC phases at the input and output of the power electronics were acquired at a frequency of 62.5kHz. 

  

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Experimental

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

 Matlab “mat” files and comma separated value “csv” text files.

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

  * Matlab (Experimental data acquisition) 

  

  * A special purpose parallelized C++ software (Data filtering), 

  

  * A software package written in the Julia language (Computation of the instantaneous and time-averaged turbine shaft power, electrical power and available pneumatic power) 

  

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 320 GB

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 Local PC storage

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

 Static data stored at three computers

</td> </tr> </table>

**TABLE 2.6: BI-RADIAL TURBINE PERFORMANCE AT MUTRIKU**  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Biradial_Turbine_Mutriku

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Assess turbine performance and collect extensive data on drivers of
components fatigue such as high rpm and accelerations; electrical, temperature
and pressure load cycles; humidity in the cabinet (which exacerbates
electrical stress damage); rate of salt accumulation and corrosion.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

  * Mutriku Wave Power Plant. 

  

  * Bi-radial turbine-generator set and chamber #9 have been instrumented for the project. 

  

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

  * MySQL database for real-time data 

  

  * MS Excel for derived and filtered data 

  

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Statistical and spectral analysis software

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 ≤ 50 GB (6-month recording x 150 measurements @ 4 Hz)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 On-site database server and real-time replication onto cloudhosted database
server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

  * Daily back-ups on both local and cloud-hosted servers. 

  

  * 15-day retention period for incremental backups in the latter. 

  

</td> </tr> </table>

**TABLE 2.7: BI-RADIAL TURBINE PERFORMANCE AT BIMEP**  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Biradial_Turbine_BiMEP

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Internal water level, chamber pressure/temperature/humidity, rotation speed
and torque to assess turbine efficiency in response to different sea states to
compare turbine performance drivers of components fatigue

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

  * MARMOK-A-5 prototype. 

  

  * Bi-radial turbine-generator set and hull structure have been instrumented for the project. 

  

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

 MySQL database for real-time dataMS Excel for derived and filtered data

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Statistical and spectral analysis software

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 ≤ 100 GB (12-month recording x 150 measurements at 4 Hz)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 On-site database server and real-time replication onto cloudhosted database
server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

  * Daily back-ups on both local and cloud-hosted servers. 

  

  * 15-day retention period for incremental backups in the latter. 

  

</td> </tr> </table>

**2.4**

**POWER OUTPUT DATA**

Experimental data will be collected at electrical test rigs of UCC [5] and
TECNALIA [6] . Besides, field tests data will be collected at Mutriku
shoreline plant and at the BiMEP open sea test site. Numerical models will be
also used to extend the dataset beyond sea-trials data. In the latter,
specialist software may be needed for further processing the data. Selected
parts of the generated datasets generated will be made public. Power output
data will be both experimental and observational, raw and derived such as
mean, standard deviation, minimum and maximum values.

Power output data will be useful to identify sources of uncertainty in power
performance prediction and for the certification processes of other technology
developers.

This dataset will be the basis for at least one scientific publication. A
short description of the power output datasets is given below.

###  TABLE 2.8: POWER OUTPUT AT ELECTRICAL TEST RIG  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Power_Output_Lab

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Generator speed, voltage, frequency and electric power.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 Electrical test rigs of UCC and TECNALIA

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Experimental and Simulation

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

 MS Excel

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 MATLAB numerical model of the Mutriku Wave Power Plant

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 20 GB (7 CLs x approx. 300 MB)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 Network storage

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

 Daily back-ups

</td> </tr> </table>

###  TABLE 2.9: POWER OUTPUT AT MUTRIKU  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Power_Output_Mutriku

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Generator speed, voltage, frequency and electric power, including phase
voltages & currents.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

  * Mutriku Wave Power Plant. 

  

  * Bi-radial turbine-generator set and chamber #9 have been instrumented for the project. 

  

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

  * MySQL database for real-time data 

  

  * MS Excel for derived and filtered data 

  

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Statistical and spectral analysis software

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 ≤ 50 GB (6-month recording x 150 measurements @ 4 Hz)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 On-site database server and real-time replication onto cloudhosted database
server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

  * Daily back-ups on both local and cloud-hosted servers. 

  

  * 15-day retention period for incremental backups in the latter. 

  

</td> </tr> </table>

###  TABLE 2.10: POWER OUTPUT AT BIMEP  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Power_Output_BiMEP

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Generator speed, voltage, frequency and electric power, including phase
voltages & currents.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 MARMOK-A-5 prototype. Bi-radial turbine-generator set and hull structure
have been instrumented for the project.

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

  * MySQL database for real-time data 

  

  * MS Excel for derived and filtered data 

  

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 Statistical and spectral analysis software

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 ≤ 100 GB (12-month recording x 150 measurements at 4 Hz)

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 On-site database server and real-time replication onto cloudhosted database
server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

  * Daily back-ups on both local and cloud-hosted servers. 

  

  * 15-day retention period for incremental backups in the latter. 

  

</td> </tr> </table>

##  2.5 POWER QUALITY DATA

Experimental data will be collected at electrical test rig of UCC [10]. Also,
field tests data will be collected at the Mutriku shoreline plant. Simulated
models may be used to assess the power quality for other operating conditions,
such as varying control algorithms, resource conditions, grid strengths, and
control using a dry-lab to create a wider profile for the WEC. Power quality
data will be both experimental and observational, raw and derived (using
statistical parameters and models). Selected parts of the experimental
datasets generated will be made public.

Power quality data will be useful to identify sources of uncertainty in
assessing the impact of the wave energy converter on the performance of the
grid. They will be also valuable for the certification processes of other
technology developers.

This dataset will be the basis for at least one scientific publication.

A short description of the power quality datasets is given next.

**TABLE 2.11: POWER QUALITY AT ELECTRICAL TEST RIG**  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Power_Quality_Lab

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Current, voltage, power quality characteristic parameters (such as voltage
fluctuations, harmonics, inter-harmonics, active/reactive power, and flicker).

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 Electrical test rig at UCC

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Experimental and Simulation

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

 MS Excel

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 MATLAB Simulink numerical model of the Mutriku Wave Power Plant

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

  * Maximum 1.2 GB per 10-minute test (at 20 kHz sampling frequency). 

  

  * 4 signals at 20 kHz for 10 minutes per test 

  

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 Network storage

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

 Daily back-ups

</td> </tr> </table>

**TABLE 2.12: POWER QUALITY AT MUTRIKU**  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Power_Quality_Mutriku

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Data will be collected from both a single turbine and the plant as a whole,
obtaining valuable conclusions about how aggregation of multiple turbines
affects the power quality.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 Mutriku Wave Power Plant.

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational and derived

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

  * MySQL database for real-time data 

  

  * MS Excel for derived and filtered data 

  

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

  * LabView 

  

  * Statistical and spectral analysis software 

  

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

  * > 200 GB (12-month recording x 12 measurements @ 20 kHz). 

  

  * Given the large data storage requirements, the measurements will be triggered, and not carried out continuously. After sufficient power quality analysis has been carried out at 20 kHz, the sampling rate will then be reduced (to approximately 12 kHz, and 10 kHz). 

  

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 On-site database server and real-time replication onto cloudhosted database
server

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

  * Daily back-ups on both local and cloud-hosted servers. 

  

  * 15-day retention period for incremental backups in the latter. 

  

</td> </tr> </table>

##  2.6 OFFSHORE OPERATIONS DATA

Field tests will be conducted at the BiMEP open sea test site. The offshore
operations data will be combined with the environmental monitoring dataset to
derive the final dataset. Collected datasets will be made public. Offshore
operations data will be observational and derived.

Offshore operations data will be useful to reduce the uncertainty on the
determination of risk and cost of offshore operations, and to optimise these
activities. The offshore logistics experience can be extrapolated to different
scenarios of larger deployment with a view to more accurately assess the
economies of scale and identify logistics bottlenecks when deployed in large
arrays.

Although the raw datasets are useful by themselves, it is the objective of the
OPERA project to use the dataset as a basis for at least one scientific
publication.

A short description of the offshore operations datasets is given below.
**TABLE 2.13: OFFSHORE OPERATIONS**  
  
<table>  
<tr>  
<th>

**Reference/Name**

</th>  
<th>

 DS_Offshore_Operations

</th> </tr>  
<tr>  
<td>

**Description**

</td>  
<td>

 Failures, type of maintenance, offshore resources (such as vessels,
equipment, personnel, parts and consumables), health & safety, and activity
log.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

 Unlike the previous datasets, these are not based on process instrumentation
and therefore will not be stored in the WP1 database.

</td> </tr>  
<tr>  
<td>

**Type**

</td>  
<td>

 Observational

</td> </tr>  
<tr>  
<td>

**Format**

</td>  
<td>

 MS Excel

</td> </tr>  
<tr>  
<td>

**Software**

</td>  
<td>

 n/a

</td> </tr>  
<tr>  
<td>

**Estimated size**

</td>  
<td>

 10 MB

</td> </tr>  
<tr>  
<td>

**Storage**

</td>  
<td>

 Network storage

</td> </tr>  
<tr>  
<td>

**Back-up**

</td>  
<td>

 Daily back-ups on a separate server

</td> </tr> </table>

#  3\. DATA STANDARDS AND METADATA

The following standards should be used for data documentation:

  * Ocean Data Standards Project [7] : it contains an extensive number of references on Oceanographic Data Management and Exchange Standards. It includes references on Metadata, Date and Time, Lat/Lon/Alt, Country names, Platform instances, Platform types, Science Words, Instruments, Units, Projects, Institutions, Parameters, Quality Assurance and Quality Control. 

  

  * ISO 19156:2011 [8] : it defines a conceptual schema for observations, and for features involved in sampling when making observations. These provide models for the exchange of information describing observation acts and their results, both within and between different scientific and technical communities. 

  

  * IEC TS 62600-101 [9] : technical specification for wave energy resource assessment and characterisation. 

  

  * DNVGL-OS-E301 [10] : it contains criteria, technical requirements and guidelines on design and construction of position mooring systems. The objective of this standard is to give a uniform level of safety for mooring systems, consisting of chain, steel wire ropes and fibre rope. 

  

  * IEC TS 62600-10 [11] : technical specification for assessment of mooring system for Marine Energy Converters (MECs). 

  

  * IEC TS 62600-100 [12] technical specification on power performance assessment of electricity producing wave energy converters 

  

  * IEC TS 62600-102 [13] technical specification on wave energy converter power performance assessment at a second location using measured assessment data 

  

  * IEC TS 62600-30 [14] technical specification on electrical power quality requirements for wave, tidal and other water current energy converters 

  

  * IEC 61000-4-7:2002 [15] further instructions on processing harmonic current components are given in for power supply systems and equipment connected thereto. 🞂 FRACAS [16] Failure the Reporting, Analysis and Corrective Action System 

  

  * ISO 14224:2006 [17] collection and exchange of reliability and maintenance data for equipment. 

  

Metadata records will accompany the data files in order to describe,
contextualise and facilitate external users to understand and reuse the data.

OPERA will adopt the DataCite Metadata Schema [18] , a domain agnostic
metadata schema, as the basis for harvesting and importing metadata about
datasets from data archives. The core mission of DataCite is to build and
maintain a sustainable framework that makes it possible to cite data through
the use of persistent identifiers.

The following metadata should be created to identify datasets:

  * Identifier: A unique string that identifies the dataset 

  

  * Author/Creator: The main researchers involved in producing the data in priority order 

  

  * Title: A name or title by which a data is known 

  

  * Publisher: The name of the entity that holds, archives, publishes prints, distributes, releases, issues, or produces the data. 

  

  * Publication Year: The year when the data was or will be made publicly available 🞂 Subject: Subject, keyword, classification code, or key phrase describing the resource. 

  

  * Contributor: Name of the funding entity (i.e. "European Union" & "Horizon 2020") 

  

  * Size: Unstructured size information about the dataset (in GBs) 

  

  * Format: Technical format of the dataset (e.g. cvs, txt, xml, ...) 

  

  * Version: The version number of the dataset 

  

  * Access rights: Provide a rights management statement for the dataset. Include embargo information if applicable 

  

  * Geo-location: Spatial region or named place where the data was gathered 

  

#  4\. DATA SHARING AND REUSE

During the life cycle of the OPERA project datasets will be stored and
systematically organised in a database tailored to comply with the
requirements of WP1 (for more details on the database architecture, please see
D1.1 Process instrumentation definition [2] ). An online data query tool was
operational in Month 12, and available for open dissemination by Month 18. The
database schema and the queryable fields, will be also publicly available to
the database users as a way to better understand the database itself.

In addition to the project database, relevant datasets will be also stored in
ZENODO [19] [10] , which is the open access repository of the Open Access
Infrastructure for Research in Europe, OpenAIRE [20]

All collected datasets will be disseminated without an embargo period unless
linked to a green open access publication. Data objects will be deposited in
ZENODO under:

  * Open access to data files and metadata and data files provided over standard protocols such as HTTP and OAI-PMH. 

  

  * Use and reuse of data permitted. 🞂 Privacy of its users protected. 

  

Data access policy is summarised in the following table.

##  TABLE 4.1: DATA ACCESS POLICY  
  
<table>  
<tr>  
<th>

**Dataset**

</th>  
<th>

**Data access policy**

</th> </tr>  
<tr>  
<td>

DS_Wave_Mutriku

</td>  
<td>

  * Unrestricted since no confidentiality or IPR issues are expected regarding the environmental monitoring datasets 

  

  * Licence: CC-BY 

  

</td> </tr>  
<tr>  
<td>

DS_Wave_BiMEP

</td> </tr>  
<tr>  
<td>

DS_Tethers_Lab

</td>  
<td>

  * Restricted to WP2 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER3). 

  

  * Samples of aggregated data (e.g. load averages or extreme load ranges) will be shared in the open domain for the most relevant sea states.  Licence: CC-BY-ND 

  

</td> </tr>  
<tr>  
<td>

DS_Mooring_BiMEP

</td> </tr>  
<tr>  
<td>

DS_Biradial_Turbine_Lab

</td>  
<td>

  * Restricted to WP3 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER2). 

  

  * Samples of aggregated data (e.g. chamber pressure, air flow, mechanical power) will be shared in the open domain. 

  

  * Licence: CC-BY-ND 

  

</td> </tr>  
<tr>  
<td>

DS_Biradial_Turbine_Mutriku

</td> </tr>  
<tr>  
<td>

DS_Biradial_Turbine_BiMEP

</td> </tr>  
<tr>  
<td>

DS_Power_Output_Lab

</td>  
<td>

  * Restricted to WP4 and WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1, KER4 and KER6). 

  

  * Samples of aggregated data (e.g. electric power for the different control laws) will be shared in the open domain. 

  

  * Licence: CC-BY-ND 

  

</td> </tr>  
<tr>  
<td>

DS_Power_Output_Mutriku

</td> </tr>  
<tr>  
<td>

DS_Power_Output_BiMEP

</td> </tr>  
<tr>  
<td>

DS_Power_Quality_Lab

</td>  
<td>

  * Restricted to WP5 participants, in order to protect the commercial and industrial prospects of exploitable results (KER1, KER4 and KER6). 

  

  * Samples of aggregated data (e.g. active, reactive power and power factor) will be shared in the open domain. 

  

  * Licence: CC-BY-ND 

  

</td> </tr>  
<tr>  
<td>

DS_Power_Quality_Mutriku

</td> </tr>  
<tr>  
<td>

DS_Offshore_Operations

</td>  
<td>

  * The aggregated dataset (e.g. operation time, forecast vs recorded wave conditions) will just be shared in the open domain in order to protect the commercial and industrial prospects of exploitable results (KER1 and KER8). 

  

  * Licence: CC-NC-BY-ND 

  

</td> </tr> </table>

#  5\. DATA ARCHIVING AND PRESERVATION

The OPERA project database will be designed to remain operational for 5 years
after project end. By the end of the project, the final dataset will be
transferred to the ZENODO repository, which ensures sustainable archiving of
the final research data.

Items deposited in ZENODO will be retained for the lifetime of the repository,
which is currently the lifetime of the host laboratory CERN and has an
experimental programme defined for the at least next 20 years. Data files and
metadata are backed up on a nightly basis, as well as replicated in multiple
copies in the online system. All data files are stored along with a MD5
checksum of the file content. Regular checks of files against their checksums
are made.