#  Data Summary

##  Purpose of the Data Collection / Relation to the Project Objectives

The data collected by UPWARDS is technical data of new wind turbines required
to parameterize integrated simulation model as well as social data.  
  
<table>  
<tr>  
<th>

**Major objective**

**1**

</th>  
<th>

The UPWARDS project will establish a high-fidelity multi-physics, mechatronic
and multi-scale simulation framework for wind turbines that enables integrated
modelling of wind flow, mechanical movements, structural/control dynamics and
stresses with a level of detail that today only is achievable in a sequential
fashion meaning a comprehensive holistic vision is not possible. The collected
technical data will be required to parametrize the model.

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

**Major objective**

**2**

</td>  
<td>

UPWARDS will define a virtual prototype of a 15MW horizontal axis wind turbine
including descriptions of aerodynamic design, structural design, transmission
and generator, and control system. The purpose of the virtual prototype is to
serve as a study case to ensure that the developed simulation tools performs
as required, and enable generation of realistic and relevant simulation result
for knowledge extraction and further exploitation.

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

**Major objective**

**3**

</td>  
<td>

Using the innovative resources that will be developed as well as feedback from
the public and stakeholder opinions and needs that will be gathered, the
UPWARDS project will perform high-fidelity simulations of important wind
turbine related phenomena to exploit and increase the understanding of their
physical behavior and interaction. State of the art data mining methods will
be used to extract and structure relevant information from the data that will
inform new wind turbine designs.

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

To fulfill the above-mentioned objectives, a clear data management strategy
and methodology, also enabling the ability to easily share data between
partners are both necessary. The large volume of the data that will be
generated during this project poses significant challenges for the
implementation of such a methodology.

In order to determine the needs and preferences, a questionnaire based on the
"Guidelines on FAIR Data Management in Horizon 2020" of the European
Commission was prepared and distributed to all partners (cf. Annex C).

To ensure easy availability of key data sets to key stakeholders and
audiences, UPWARDS data will be classified into three specific categories as
follows.  
  
<table>  
<tr>  
<th>

**GOLD**

Analysed data that has clear scientific significance, provides interesting
results that have resulted in new understanding in the field, possibly
supporting high impact journal publications.

Such data will be made available online using a reliable, indexable repository
that is compatible with the European Commission’s OpenAIRE platform i.e.

Zenodo.

</th>  
<th>

**GREEN**

Analysed data that is relevant for the UPWARDS partners and can be used to
develop new models within the project work plans.

Such data will be shared in a searchable repository accessible by all
partners, the UPWARDS website intranet.

</th>  
<th>

**WHITE**

Raw data or data that has been analysed but is not thought at this stage to be
significant to either the project or wider community.

Such data may still have unseen value and will be stored in a local,
searchable archive

</th> </tr> </table>

All data generated during the project will be tracked and recorded in a
project database. The stored information will describe the category of the
data, the type of data, a brief description of the data and where it can be
located.

##  Types, Formats, and Utility of the collected Data

The type of data and formats of data are described and agreed in the Grant
Agreement pp 37-38. The update is given in the table below:  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**WP**

</th>  
<th>

**Standards**

</th>  
<th>

**Accessibility**

</th>  
<th>

**Curation/preservation**

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

Progress, interim and final reports

</td>  
<td>

1

</td>  
<td>

EC templates

</td>  
<td>

Restricted to the project partners and the EC

</td>  
<td>

Website’s intranet

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

Raw model output data

</td>  
<td>

2

</td>  
<td>

Model dependent

</td>  
<td>

Restricted to the project partners

</td>  
<td>

Depending on data status (c.f. section 1.3)

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

Processed model output

data (adapted to interface with the following models in the chain)

</td>  
<td>

2

</td>  
<td>

Model dependent

</td>  
<td>

Restricted to the project partners

</td>  
<td>

Depending on data status (c.f. section 1.3)

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

Flow results around wind turbine, strains, stresses, velocities,
accelerations, local forces & moments in mechanisms, sensors and actuators
data

</td>  
<td>

3

</td>  
<td>

Model dependent, curves, 3D graphical views and realistic animations

</td>  
<td>

Restricted to the project partners

</td>  
<td>

Depending on data status (c.f. section 1.3)

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

Wind turbine CFD database

</td>  
<td>

4

</td>  
<td>

Model dependent

</td>  
<td>

Restricted to the project partners

</td>  
<td>

Depending on data status (c.f. section 1.3)

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

Wind turbine near-field noise database

</td>  
<td>

4

</td>  
<td>

Model dependent

</td>  
<td>

Restricted to the project partners

</td>  
<td>

Depending on data status (c.f. section 1.3)

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

Report and data on the effect of fatigue loading history on damage development

</td>  
<td>

5

</td>  
<td>

Templates Established by partners

</td>  
<td>

Public domain

</td>  
<td>

website’s intranet

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

Simulation model and

data of blade substructure

</td>  
<td>

5

</td>  
<td>

Model dependent

</td>  
<td>

Public domain

</td>  
<td>

website’s intranet

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

Experimental fatigue material

</td>  
<td>

5

</td>  
<td>

Templates Established by partners

</td>  
<td>

Restricted to the project partners and the EC

</td>  
<td>

website’s intranet

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

Integrated system simulation

</td>  
<td>

6

</td>  
<td>

As in WP 2-5

</td>  
<td>

As in WP 2-5

</td>  
<td>

Raw data: local server

Results: Depending on

data status (c.f. section 1.3)

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

Business cases/market studies

</td>  
<td>

7, 8

</td>  
<td>

Templates established by partners

</td>  
<td>

Restricted to the project partners and the EC. All subject of publications and
related to the open

</td>  
<td>

Dedicated project data management system

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

pilot strategy will be made available

</td>  
<td>

Open data for publications and open pilot strategies will be followed.

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

Dissemination data

</td>  
<td>

7, 8

</td>  
<td>

Defined in dissemination plan

</td>  
<td>

Public

</td>  
<td>

Public domain (website and intranet)

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

In WP6 an integrated system simulation model is generated. The data is
depending on the models of WP2-WP5 and the data accessibility is defined in
the corresponding WPs. Raw data and intermediate results of the simulations
will be stored at partners’ premises.

The impact of the UPWARDS project outcomes within and beyond its lifespan will
be maximized through a systematic set of communication, dissemination, market
analyses and business planning actions, and transmit the project results to
the relevant stakeholders including policy makers, industry, and society.

##  UPWARDS data management methodology

The roadmap for data management in UPWARDS is shown in Figure 1.1. There are
three key aspects that will be considered:

  1. Architecture. The structure and function of the data archiving system. The physical locations of the data storage repositories and the tools required to manage and recall data. 

  

  2. Process. The process for adding to the UPWARDS data and ensuring that data is in a standardized format and easily searchable. 

  

  3. Reporting. The process of monitoring the data archives, and the ability to extract Key Performance Indicators that can inform decision making for further improvements in the Data Management plan. 

  

  

###  Architecture

Due to the high volume of data that is expected to be generated during the
project, all of the data will be required to be stored locally at each
partner’s site, but results of significance will be shared internally using a
common UPWARDS repository and externally using standard online repositories,
as shown in Figure 1.1.

The database with the data available will be stored in the UPWARDS intranet
and kept updated.

A contribution of Upwards is to provide the integration system with all
components as Open Accessible Framework. Hence, we specified to package each
simulation step as software container in Docker  1  . Further, we defined a
concrete format each container has to follow in order to be used in Upwards
and integrated within the workflow.

  * The container can be created be executing a provided build script (i.e., Dockerfile) 

  

  * The container executes the encapsulated software by passing a single command line script (here, a Bash script named runAll.sh). This script takes input data from files and writes simulation results into files. These file are stored at the host system and mounted into the container runtime. 

  

  * Licenses of proprietary or commercial software programs (e.g., StarCCM+, Samcef) and databases (e.g., ERA5) are injected via configurable license server paths by the use of environment variables. 

  

  * Commercial software binaries are injected by placing the files in dedicated directories, which are the applied and integrated by the build script. 

  

  * All resources and documentation is managed in a web-based software managing and version control system GITLAB  2  , which is hosted and provided by Fraunhofer ITWM. It is accessible to each consortium partner  3  . 

  

Furthermore, for large data sets with green status (e.g. simulation results
with significant relevance) Fraunhofer ITWM provides access to a large-scale
file sharing server.

###  Process

The management of UPWARDS data requires standardized and timely reporting of
data which can be shared within the project and with external stakeholders.

The preliminary standard process for handling new data generated during the
project is defined in Figure 1.2 and will be the backbone of the definitive
data management plan. This process will ensure that all data generated is
captured and can be made available to both internal and external stakeholders
if required. UPWARDS’ partners will also be able to see that data has been
recorded in advance of processing and analysis and can prepare activities in
advance of the publication of results.

**Figure 1.2 Process**

###  Reporting

Database monitoring tools will be used for extracting key KPIs from the
extensive data sets in the archive.

#  FAIR Data

UPWARDS will use the Zenodo depository (zenodo.org) to store all data which
are released for free access. Zenodo is a free large capacity platform for the
exchange and curation of research data managed by CERN established as a result
of the OpenAIRE project. Zenodo has built in functionality to meet most FAIR
criteria and to generate searchable metadata (see section 7.2).

In Zenodo data is stored in records with associated metadata. All data must be
associated with a community. For that purpose, a "UPWARDS H2020 Project"
community has been established that all data from UPWARDS will be associated
to. In addition, data will be associated to other communities as the "Wind
Energy" community.

##  Making data findable, including provisions for metadata

To make the open access data findable they will be assigned unique Digital
Object Identifiers, descriptive names keywords and metadata. Relevant search
keywords will be assigned to all data sets and included in the metadata.

###  Naming conventions

Data will be named using the following naming conventions:

Deliverables: [DT] UPWARDS_[DN]_[UDN].[VN]

Publications: [DT] UPWARDS_[PN]_[UDN].[VN]

[DT] Descriptive Text

[DN] Deliverable Number

[PN] Publication Number

[UDN] Unique Data Number

[VN] Version Number

###  Digital Object Identifiers (DOI)

DOIs for all datasets will be reserved and assigned with the DOI functionality
provided by Zenodo. DOI versioning will be used to assign unique identifiers
to updated versions of the data records.

###  Metadata

Metadata associated to each published dataset will by default be

  * Digital Object Identifiers and version numbers 

  

  * Bibliographic information 

  

  * Keywords 

  

  * Abstract/description 

  

  * Associated project and communities 

  

  * Associated publications and reports 

  

  * Grant information 

  

  * Access and licensing info 

  

  * Language 

  

##  Making data openly accessible

As described above, the open data is exchanged via the Zenodo depository.
Metadata, including licenses for individual records and data collections, can
be harvested using the OAI-PMH protocol by the record identifier and the name
of the collection. Metadata can also be retrieved via the public REST API. The
data can be called up on the Internet at www.zenodo.org and is therefore
accessible via any web browser application. The data is freely searchable and
the identity of the persons accessing the data is not determined.

##  Making data interoperable

All files use standard scientific notations as S.I. units and vocabulary as in
ISO test standards.

The depository Zenodo, where shared data will be stored, uses JSON schemes as
internal representation of metadata and offers export to other popular formats
such as Dublin Core, MARCXML, BibTeX, CSL, DataCite and export to Mendeley.
The data record metadata will utilize the vocabularies applied by Zenodo. For
certain terms these refer to open, external vocabularies, e.g.: license (Open
Definition), funders (FundRef) and grants (OpenAIRE). Reference to any
external metadata is done with a resolvable URL.

##  Increase data re-use

The data will be licensed under different levels of creative commons licenses

(https://creativecommons.org/licenses/). As default, the CC-BY-SA license will
be applied for open UPWARDS data. This license lets others remix, tweak, and
build upon the data even for commercial purposes, as long as they credit
UPWARDS and license their new creations under the identical terms. This
license is often compared to “copyleft” free and open source software
licenses. All new works based on CC-BY-SA licensed data will carry the same
license, so any derivatives will also allow commercial use. This does not
preclude use of less restrictive licenses as CC-BY or more restrictive
licenses as CC-BY-NC not allowing commercial usage. This will be assessed in
each case.

For data published in scientific journals, the data are made available at the
same time as Open Access is granted for the paper or preprint. The data will
accompany the paper. For data associated with public deliverables data will be
shared after approval of the deliverable by the EC.

Open data will be reusable as defined by their licenses. Data defined as
confidential will not be reusable as default due to commercial exploitation.
See table in section 1.2.

The data re-usability is only limited by the lifetime of the Zenodo
depository. This is currently the lifetime of the host laboratory CERN, which
currently has an experimental program defined for the next 20 years at least.
In cases where Zenodo expires, its policy is to transfer data and metadata to
other appropriate depositories.

#  Allocation of Resources

The cost is only the PM cost required to organize and upload the data and will
be covered by the project grants. Fraunhofer ITWM will be responsible for the
data management with Dr. Andreas Wirsen as technical manager.

Wavestone will be in charge of updating the project related databases i.e.
Zenodo, monitoring database in UPWARDS intranet and the continuous reporting
feature in the EC’s participant portal.

Gold Data stored on Zenodo depository: Since an externally freely usable,
already financed depository is used, there are no costs for long-term
archiving for the project. The longevity of the data curation is only limited
by the lifetime of the Zenodo depository, which currently has an experimental
program defined for the next 20 years at least

Self-archiving or so-called 'green' Open Access will be also applied through
the developed Zenodo repository. As also required, open access to the
publication will be ensured in a maximal delay of 6 months. The difference
between gold and green open access (including related fees) can be found in
the table below.  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Gold open access**

</th>  
<th>

**Green open access**

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

**Definition**

</td>  
<td>

Open access publishing (also called 'Gold' open access) means that an article
is immediately provided in open access mode by the scientific publisher. The
associated costs are shifted away from readers, and instead to (for example)
the university or research institute to which the researcher is affiliated, or
to the funding agency supporting the research.

</td>  
<td>

Self-archiving (also called 'Green' open access) means that the published
article or the final peer-reviewed manuscript is archived by the researcher –
or a representative - in an online repository before, after or alongside its
publication. Access to the article is often – but not necessarily - delayed
(‘embargo period’) as some scientific publishers may wish to recoup their
investment by selling subscriptions and charging payper-download:view fees
during an exclusivity period.

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

</td>  
<td>

• Publish in an open access journal

</td>  
<td>

• Link to the article

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

**Options**

</td>  
<td>

• Or in a journal which supports open access

</td>  
<td>

  * Select a journal that features an open archive 

  

  * Self-archive a version of the article 

  

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

**Access**

</td>  
<td>

  * Public access is to the final published article 

  

  * Access is immediate 

  

</td>  
<td>

• Free access to a version of the article • Time delay may apply (embargo
period)

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

**Fees**

</td>  
<td>

  * Open access fee is paid by the author 

  

  * Fees range between $500 and $5,000 USD depending on the journal 

  

</td>  
<td>

• No fee is payable by the author as publishing costs are covered by library
subscriptions

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

**Use**

</td>  
<td>

• Authors can choose between a commercial and noncommercial user license

</td>  
<td>

  * Accepted manuscripts should attach a Creative Common 

  

Licence

  * Authors retain the right to reuse their articles for a wide range of purposes 

  

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

#  Data Security

The data security is as specified by the Zenodo depository (see section 8.4):

  1. **Versions:** Data files are versioned. Records are not versioned. The uploaded data is archived as Submission Information Package. Derivatives of data files are generated, but original content is never modified. Records can be retracted from public view; however, the data files and record are preserved. 

  

  2. **Replicas:** All data files are stored in CERN Data Centres, primarily Geneva, with replicas in Budapest. Data files are kept in multiple replicas in a distributed file system, which is backed up to tape on a nightly basis. 

  

  3. **Retention period:** Items will be retained for the lifetime of the repository. This is currently the lifetime of the host laboratory CERN, which currently has an experimental program defined for the next 20 years at least. 

  

  4. **Functional preservation:** Zenodo makes no promises of usability and understandability of deposited objects over time. 

  

  5. **File preservation:** Data files and metadata are backed up nightly and replicated into multiple copies in the online system. 

  

  6. **Fixity and authenticity:** All data files are stored along with a MD5 checksum of the file content. Files are regularly checked against their checksums to assure that file content remains constant. 

  

  7. **Succession plans:** In case of closure of the repository, best efforts will be made to integrate all content into suitable alternative institutional and/or subject based repositories. 

  

  

If the file-sharing server for large-scale data with green status expires,
ITWM will inform all partners at an early stage in order to secure the data
with significant relevance.

#  Ethical Aspects

No sensitive personal data will be collected (see Grant Agreement, Annex 1
(part B), pp. 97-98).

#  Other Procedures

No other national, funder, sectorial, or departmental procedures for data
management are planned.