#  1\. Data management and responsibility

The FLORA project is engaged in the Open Research Data (ORD) pilot which aims
to improve and maximise access to and re-use of research data generated by
Horizon 2020 projects and takes into account the need to balance openness and
protection of scientific information, commercialisation and Intellectual
Property Rights (IPR), privacy concerns, security as well as data management
and preservation questions.

The management of the project data/results requires decisions about the
sharing of the data, the format/standard, the maintenance, the preservation,
etc.

Thus the Data Management Plan (DMP) is a key element of good data management
and is established to describe how the project will collect, share and protect
the data produced during the project. As a living document, the DMP will be
up-dated over the lifetime of the project whenever necessary.

In this frame the following policy for data management and responsibility has
been agreed for the FLORA project:

  * **The FLORA Project Management Committee (ECL and CERFACS) and the topic manager** analyse the results of the FLORA project and will decide the criteria to select the Data for which make the OPT-IN. They individuate for all the dataset a responsible (Data Management Project Responsible (DMPR)) that will ensure dataset integrity and compatibility for its internal and external use during the programme lifetime, etc. They also decide where to upload the data, when upload, when how often update, etc. 

  

  * **The Data Management Project Responsible (DMPR)** is in charge of the integrity of all the dataset, their compatibility, the criteria for the data storage and preservation, the long-term access policy, the maintenance policy, quality control, etc. Of course he will discuss and validate these points with the Project Management Committee (ECL and CERFACS) and the topic manager. 

  

  
<table>  
<tr>  
<th>

**Data management Project Responsible (DMPR)**

</th>  
<th>

**Pierre DUQUESNE**

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

DMPR Affiliation

</td>  
<td>

Ecole Centrale de Lyon

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

DMPR mail

</td>  
<td>

pierre.duquesne@ec-lyon.fr

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

DMPR telephone number

</td>  
<td>

**+33 (0)4 72 18 61 94**

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

  * **The Data Set Responsibles (DSR)** are in charge of their single Dataset and should be the partner producing the data: validation and registration of datasets and metadata, updates and management of the different versions, etc. The contact details of each DSR will be provided in each data set document presented in the annex I of the DMP. 

  

In the next section “2. Data summary”, the FLORA Project Management Committee
(ECL and CERFACS) and the topic manager (SHE) have listed the project’s
data/results that will be generated by the project and have identified which
data will be open.

Data needed to validate the results presented in scientific publications can
be made accessible to third parties. 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.

#  2\. Data summary

The next tables present the different dataset generated by the FLORA project.
For each dataset that will be open to public, a dedicated dataset document
will be completed in Annex I once the data are generated.

##  2.1 General data overview

In table 1 the different databases are presented with focus on authorship and
ownership. "WP generation" and "WP using" corresponding respectively to the
work package in which the database is generated and in which data are reused
in the FLORA project. The "Data producer" corresponds to the partner who
generates the data, "Data user" corresponds to the partners who can use data
for internal research (in addition to the data owner) and "Data owner" is the
final owner of the database. The confidentiality level includes restriction on
both external and internal data exchange. Some data associated with results
may have potential for commercial or industrial protection and thus will not
be made accessible to a third party (confidential confidentiality level) ;
other data needed for the verification of results published in scientific
journals can be made accessible to third parties (public confidentiality
level).

**Table 1. Dataset generation.**  
  
<table>  
<tr>  
<th>

**Dataset**

</th>  
<th>

**WP**

**generation**

</th>  
<th>

**WP using**

</th>  
<th>

**Data producer**

</th>  
<th>

**Data user**

</th>  
<th>

**Data owner**

</th>  
<th>

**Confidentiality level**

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

**1\. Required data**

</td>  
<td>

NA

</td>  
<td>

WP 2,3

</td>  
<td>

SHE

</td>  
<td>

ECL/CERFACS

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**2\. Test bench data**

</td>  
<td>

WP 2

</td>  
<td>

WP 2

</td>  
<td>

ECL

</td>  
<td>

ECL

</td>  
<td>

ECL

</td>  
<td>

Confidential

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

**3\. Experimental raw data**

</td>  
<td>

WP 2

</td>  
<td>

WP 2

</td>  
<td>

ECL

</td>  
<td>

ECL

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**4\. Validated experimental data**

</td>  
<td>

WP 2

</td>  
<td>

WP 2,4

</td>  
<td>

ECL

</td>  
<td>

ECL/CERFACS*

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**5\. Data experimental guide**

</td>  
<td>

WP 2

</td>  
<td>

WP 2,4

</td>  
<td>

ECL

</td>  
<td>

ECL/CERFACS*

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**6\. Published experimental data**

</td>  
<td>

WP 4

</td>  
<td>

WP 4

</td>  
<td>

ECL

</td>  
<td>

ECL/CERFACS*

</td>  
<td>

SHE

</td>  
<td>

Public

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

**7\. (U)RANS results data**

</td>  
<td>

WP 3

</td>  
<td>

WP 3,4

</td>  
<td>

ECL

</td>  
<td>

ECL/CERFACS*

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**8\. (U)RANS data guide**

</td>  
<td>

WP 3

</td>  
<td>

WP 3,4

</td>  
<td>

ECL

</td>  
<td>

ECL/CERFACS*

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**9\. LES results data**

</td>  
<td>

WP 3

</td>  
<td>

WP 3,4

</td>  
<td>

CERFACS

</td>  
<td>

CERFACS/ECL

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**10\. LES data guide**

</td>  
<td>

WP 3

</td>  
<td>

WP 3,4

</td>  
<td>

CERFACS

</td>  
<td>

CERFACS/ECL

</td>  
<td>

SHE

</td>  
<td>

Confidential

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

**11\. Published numerical data**

</td>  
<td>

WP 4

</td>  
<td>

WP 4

</td>  
<td>

ECL/CERFACS

</td>  
<td>

ECL/CERFACS

</td>  
<td>

SHE

</td>  
<td>

Public

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

* Only for the nominal speed (100 Nn) or with a special authorisation from SHE. 

##  2.2 Data purposes and objectives

Table 2 presents the type of data, the content and the objective of each
database. The last column qualifies if the database will have a long-term
value for both internal and external research.

**Table 2. Objectives of datasets.**  
  
<table>  
<tr>  
<th>

**Dataset**

</th>  
<th>

**Type**

</th>  
<th>

**Purposes/objectives**

</th>  
<th>

**Long term used**

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

**1\. Required data**

</td>  
<td>

CAD/Plan

</td>  
<td>

\- Contains plans and CAD of the compressor module. - Provides necessary
information for test bench implementation and numerical simulation.

</td>  
<td>

No

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

**2\. Test bench data**

</td>  
<td>

Metrology

</td>  
<td>

\- Contains sensors calibration and position, testbench qualification tests,
tests log ... - Provides necessary information on the measurements and test
bench setup.

</td>  
<td>

No

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

**3\. Experimental raw data**

</td>  
<td>

Experimental measurements

</td>  
<td>

  * Contains all measurements in measured primary units (generally volt). Including steady and unsteady pressure and LDA measurements. 

  

  * Provides measurement ready to be converted in the physical units. 

  

</td>  
<td>

No

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

**4\. Validated experimental data**

</td>  
<td>

Experimental measurements

</td>  
<td>

  * Contains only validated measurements in physical units. 

  

  * Provides measurements for the analysis step. 

  

</td>  
<td>

Yes

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

**5\. Data experimental guide**

</td>  
<td>

Documentation

</td>  
<td>

  * Contains measurement descriptions and the operating conditions from the validated experimental database. 

  

  * Provides necessary information to perform analysis of the validated experimental database. 

  

</td>  
<td>

Yes

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

**6\. Published experimental data**

</td>  
<td>

Experimental measurements

</td>  
<td>

  * Contains experimental data used for publication purposes. 

  

  * Provides an experimental open-access database for the research community. 

  

</td>  
<td>

Yes

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

**7\. (U)RANS results data**

</td>  
<td>

Numerical simulation

</td>  
<td>

  * Contains numerical results of the (U)RANS simulations. 

  

  * Provides (U)RANS numerical results for the analysis step. 

  

</td>  
<td>

Yes

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

**8\. (U)RANS data guide**

</td>  
<td>

Documentation

</td>  
<td>

\- Contains the (U)RANS numerical strategy setup (excluding the mesh or all
geometrical aspects). - Provides the necessary setup to initialise numerical
simulations with elsA software.

</td>  
<td>

Yes

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

**9\. LES results data**

</td>  
<td>

Numerical simulation

</td>  
<td>

  * Contains numerical results of the LES simulation. 

  

  * Provides LES numerical results for the analysis step. 

  

</td>  
<td>

Yes

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

**10\. LES data guide**

</td>  
<td>

Documentation.

</td>  
<td>

  * Contains the LES numerical strategy setup (excluding the mesh or all geometrical aspects). 

  

  * Provides the necessary setup to initialise numerical simulations withTurbo-AVBP software. 

  

</td>  
<td>

Yes

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

**11\. Published numerical data**

</td>  
<td>

Numerical simulation.

</td>  
<td>

  * Contains numerical data used for publication purposes. 

  

  * Provides a numerical open-access database for the research community. 

  

</td>  
<td>

Yes

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

##  2.3 Data technical information

Table 3 presents the different formats used in each database, including the
data volume order of magnitude, where the data are stored and the transfer
protocol used between partners.

**Table 3. Database technical information.**  
  
<table>  
<tr>  
<th>

**Dataset**

</th>  
<th>

**Format**

</th>  
<th>

**Volume (OOM)**

</th>  
<th>

**Long-term storage**

</th>  
<th>

**Transfer protocol**

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

**1\. Required data**

</td>  
<td>

.pdf

.step

</td>  
<td>

1 GB

</td>  
<td>

SHE storage server

</td>  
<td>

CD

Internet

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

**2\. Test bench data**

</td>  
<td>

.txt

.bin

</td>  
<td>

1 GB

</td>  
<td>

ECL storage server

</td>  
<td>

No transfer

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

**3\. Experimental raw data**

</td>  
<td>

.txt

.bin

</td>  
<td>

1 TB

</td>  
<td>

ECL storage server

</td>  
<td>

No transfer

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

**4\. Validated experimental**

**data**

</td>  
<td>

.txt

.bin

</td>  
<td>

1 TB

</td>  
<td>

ECL and SHE storage servers

</td>  
<td>

Hard disk

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

**5\. Data experimental guide**

</td>  
<td>

.docx+.pdf

</td>  
<td>

10 MB

</td>  
<td>

ECL and SHE storage servers

</td>  
<td>

Internet

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

**6\. Published experimental**

**data**

</td>  
<td>

.txt

</td>  
<td>

100 MB

</td>  
<td>

ECL and SHE storage servers

ZENODO internet server

</td>  
<td>

Internet

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

**7\. (U)RANS results data**

</td>  
<td>

.CGNS

</td>  
<td>

TB

</td>  
<td>

ECL and SHE storage servers

</td>  
<td>

Hard disk

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

**8\. (U)RANS data guide**

</td>  
<td>

.docx+.pdf

</td>  
<td>

10 MB

</td>  
<td>

ECL and SHE storage servers

</td>  
<td>

Internet

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

**9\. LES results data**

</td>  
<td>

.CGNS

</td>  
<td>

TBs

</td>  
<td>

CERFACS and SHE storage servers

</td>  
<td>

Hard disk

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

**10\. LES data guide**

</td>  
<td>

.docx+.pdf

</td>  
<td>

10 MB

</td>  
<td>

CERFACS and SHE storage servers

</td>  
<td>

Internet

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

#  3\. FAIR Data

**3.1 Making data findable**

##  Public database (data sets 6 and 11)

###  Publication repository

The technical, professional and scientific publications that will be produced
in the FLORA project will be open accessed in order to be compliant with the
general principle of the Horizon 2020 funding programmes.

Expected journals to be used (but not limited to):

  * International Journal of Turbomachinery Propulsion and Power 

  

  * Journal of Turbomachinery 

  

  * Experiments In Fluids 

  

_Online depositories_

  * **ZENODO** , a repository for open-access repository for publication and data by OpenAIRE and CERN. 

  

###  Publication and data identification

Articles and the attached data will be findable via their DOI, unique and
persistent identifier. A DOI is usually issued to every published record on
each publisher review and on other repositories as ZENODO and ResearchGate. A
homepage of FLORA project will be created on ResearchGate with a link to
Zenodo to make data findable.

Also, any dissemination of results from FLORA project must acknowledge the
financial support by the EU and thus the following acknowledgement will be
added to each publication and dataset description: “This project has received
funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 820099”

##  Confidential database

###  Database repository

Confidential databases are composed of both the methods (databases 5, 8 and
10) and the results (databases 3, 4, 7 and 9). SHE is the owner of all
results. Partners are owners of methods used to generate results. Each owner
is responsible for its database repository.

A partner, not owner of the result, can use results produced by himself for
internal research ECL and CERFACS are authorised to exchange all necessary
data about the nominal speed (100 Nn). For other speed, SHE needs to validate
the data exchange.

###  Data identification

Each measurement raw data (database 2) are identified by a unique identifier.
Each measurement is recorded in the test log using this identifier and the
measurement information (database 3). A validated measurement data (database
4) uses the same identification as the corresponding raw data. Main
information on measurement is reported in the data experimental guide
(database 5).

Each numerical run (databases 7 and 9) corresponds to a unique identifier
recorded in the corresponding data guide (databases 8 and 10).

####  3.2 Making data openly accessible

Some data associated with results may have potential for commercial or
industrial protection and thus will not be made accessible to a third party
(confidential confidentiality level) other data are needed for the
verification of results published in scientific journals can be made
accessible to third parties (public confidentiality level).

##  Public database (data set 6 and 11)

###  Access procedures

Databases declared public will be available on online depositories (ZENODO) to
a third party. All data set contains conditions to use public data in the file
header. These conditions are an obligation to refer to the original papers,
the project name and a reference to Clean Sky 2 Joint Undertaking under the
European Union’s Horizon 2020.

###  Tools to read or reuse data

Public data are produced in common electronic document/data/image formats
(.docx, .pdf, .txt, .jpg, etc.) that do not require specific software.

##  Confidential database

###  Access procedures

ECL and CERFACS are authorised to exchange all necessary data about the
nominal speed (100 Nn). For other speed, SHE needs to validate the data
exchange. At the end of the work package, ECL or CERFACS needs to provide the
databases (4,7 and 9) to SHE on a hard disk.

At long term the data generated by ECL and CERFACS can be used for internal
research.

####  3.3 Making data interoperable

Classical vocabulary in turbomachinery domain is used (based on the experience
of all partners in turbomachinery publications).

Even if this project is dedicated to a radial compressor, FLORA results can
help to improve other types of turbomachines. Understanding and control of the
boundary layer separation is also a challenge in modern fluid mechanics, the
FLORA case can contribute to increase the understanding of this complex flow
phenomena.

##  Public database (databases 6 and 11)

For both goals, the confidentiality and the generalisation, all public values
are dimensionless. Reference values (length, frequency, velocity ...) and
formulas are clearly identified in the paper or in the published database
files. Reference values are confidential, but are selected to permit
comparison with other cases and make physical interpretation possible.

##  Confidential database

Validated databases are used for analysis (4, 7 and 9). These databases are
directly expressed in physical units (using SI unit system). Necessary
information about results are recorded in the different data guides (5, 8 and
10).

**3.4 Increase data re-use**

_Data licence_

Data from public databases are open access and used a common creative licence
(CC-BY).

###  Data quality assurance processes

The project will be run in the frame of the quality plan developed at LMFA,
since 2005, in the context of the measurement campaigns carried out with the
high-speed compressors of LMFA. This quality plan is based on an ISO 9001
version 2000 approach and the use of an intranet tool (MySQL database coupled
with a dynamic php web site) to store, to classify and to share the data
between the partners, such as measurement data, documents including a
reference system.

_After the end of the project_

##  Public database (databases 6 and 11)

With the impulsion of FLORA project, the open access databases can be used by
other laboratories and industrials to made comparison with other machines.

Methods developed and physical analysis become references to other test cases
and improve the knowledge of the community.

##  Confidential database

The experimental setup and the huge quantity of experimental and numerical
data cannot be completely exploited in the FLORA project. The project is the
starting point to a long collaboration. At the end of the project, the re-use
of data and test bench can be:

  * Analysis of data generated in FLORA project: 

    * Subsequent projects for consortium members and SHE. 

  

    * Additional academic partners to work on not exploited data. 

  

  

  * Supplementary experimental measurements: o Using the already installed compressor module on new operating conditions o Measurements of supplementary field with FLORA project results. 

    * Investigates new concept of flow control. 

  

  

  * Investigation of numerical prediction performances: o Calibrate low fidelity numerical method using higher fidelity methods. 

    * High fidelity simulation of other speed. 

  

  

For all these next projects the agreement with SHE is necessary.

#  4\. Allocation of resources

##  _Costs related to the open access and data strategy_

 Data storage in partner data repositories: Included in partners structural
operating cost.  Data archiving with ZENODO data repositories: Free of
charge.

##  _Data manager responsible during the project_

The Project Coordinator (ECL) is responsible for the establishment, the
updates during the lifetime of the project and the respect of the Data
Management Plan. The relevant experimental data and the generated data from
numerical simulations during the FLORA project will be made available to the
Consortium members within the frame of the IPR protection principles and the
present Data Management Plan.

##  _Responsibilities of partners_

SHE is the owner of all generated data. Methods and analysis keep the
ownership of the partner which generates it. Every partner is responsible for
the data it produces, and must contribute actively to the data management as
set in the DMP.

#  5\. Data security

##  Public database (databases 6 and 11)

_Long-term preservation_ : Using ZENODO repositories.

_Data Transfer_ : Using ZENODO web platforms

Intellectual property: All data set contains are attached to a common creative
licence.

##  Confidential Data

_Long-term preservation:_ ensured by partner institutions’ data repositories.

_Data Transfer:_ depending on the data volume:

  * Small and medium size files are transferred by partners securitised data exchange platform (Renater FileSender,OpenTrust MFT ...) 

  

  * Huge size files are transferred by an external hard disk during face to face meeting. This type of transfer is infrequent and only concerns transfer of final databases from ECL and CERFACS to SHE. _Intellectual property:_ Data are confidential and need to strictly respect the definition of data producer, user and owner. 

  

  6. **Ethical aspects**

  

  

No ethical issue has been identified.

  7. **Other**

  

  

No other procedure for data management.