#  INTRODUCTION

In the framework of the ExoplANETS-A project, archival data from ESA Space
Science archives (HST) combined with NASA Space Archives (Spitzer, Kepler)
will be exploited with novel data calibration and spectral extraction tools,
novel retrieval tools, to produce a homogeneous and reliable catalog of
exoplanet atmosphere properties. In parallel, a coherent and uniform database
of the relevant properties of host stars will be developed from ESA Space
Science archives (XMM, Gaia, Herschel), combined with data from international
space missions and ground-based telescopes. These exoplanet and host star
catalogues will be accompanied/interpreted with models to assess the
importance of star – planet interactions.

The project gathers the expertise of seven laboratories:

This document presents an initial version of the data management plan (DMP) of
the project, the deliverable number 3 of the management workpackage (WP1) due
6 months after the start of the project. It follows the template given in
Reference Document 1 (see below). It is a living document which will be
updated as the implementation of the project progresses.

#  APPLICABLE DOCUMENTS (AD)  
  
<table>  
<tr>  
<th>

AD-1

</th>  
<th>

ExoplANETS-A Grant Agreement

</th>  
<th>

N° 776403

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

AD-2

</td>  
<td>

ExoplANETS-A Consortium Agreement

</td>  
<td>

Version 3, 2017-12-22; DRF 0647_X30423

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

#  REFERENCE DOCUMENTS (RD)  
  
<table>  
<tr>  
<th>

RD-1

</th>  
<th>

_http://ec.europa.eu/research/participant
s/data/ref/h2020/gm/reporting/h2020tpl-oa-data-mgt-plan_en.docx_

</th>  
<th>

Version 1.0 ; 13 October 2016

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

#  DATA SUMMARY

**4.1 PURPOSE OF THE DATA COLLECTION/GENERATION AND ITS RELATION TO THE
OBJECTIVES OF THE PROJECT?**

The objectives of the project are :

  * To establish new knowledge on the atmosphere of exoplanets by exploiting archived space data (HST, Spitzer, Kepler) using novel data reduction methods, as well as improved techniques to retrieve atmospheric parameters from data. 

  

  * To establish new insight on the influence of the star on the planet atmosphere by exploiting archived space data (GAIA, XMM, Chandra, Herschel, IUE, HST) on the host stars, as well as complementary ground-based data. 

  

  * To disseminate new knowledge. 

  

Data are thus essential to the ExoplANETS-A project. Archival data are at the
start of the project and new data sets with added scientific value are
generated by the project and are made available to the community via a
knowledge server. The global concept is shown below.

Figure 1 : starting from archive data on exoplanets and their host star, the
project will develop novel data reduction and retrieval techniques to get an
homogeneous catalogues of a hundred of exoplanets; the data will be available
from a knowledge server.

**4.2 WHAT TYPES AND FORMATS OF DATA WILL THE PROJECT GENERATE/COLLECT?**

The science products of the project consist of spectra of exoplanet
atmospheres (see Figure 2), exoplanets and host-star parameters (such as
molecular content of exoplanet atmosphere see Figure 3), modelling and
retrieval algorithms, tools to analyze the data, ab-initio models of sources…

Figure 2. Example of transmission spectrum (left) and emission spectrum
(right) of an exoplanet (WASP43 b), as observed with the WFC3 instrument on
board of HST (white circle); the dark blue line show the best fit models from
retrieval analysis. The feature observed around 1.4 microns is a water
feature. The insert on the right image shows

the photometric points from observations with the Spitzer Space Telescope. (L.
Kreidberg et al. ApJL **793** , Issue 2, L27-32; arXiv:1410.2255)

Figure 3: A) Examples of Tau-REx results for three simulated super-Earths HD
219134 b, GJ 1132 b and Kepler 78 b

(planetary parameters from exoplanet.eu). Top: atmospheric spectra for varying
compositions at Hubble/WFC3 wavelengths. Expected error bars for observations
are also shown. Bottom: Tau-REx retrieved constraints of H2O abundance for the
spectra shown above. B) Posterior distributions of complex likelihood
functions encountered in spectral retrievals. Parameter spaces are often
highly dimensional (>20D) with non-linear inter-parameter

correlations. We will fully map these correlations and, using manifold
learning, identify model degeneracies. C)

Simulated observational data analysed by the Tau-REx framework. Multiple
atmospheric components are shown visually.

Our aim is to use, as much as possible, the Virtual Observatory standard or
one of the standard formats in the astronomical community, i.e. the fits
format.

**4.3 WHAT IS THE ORIGIN OF THE DATA?**

The data will be of various origins:

  * We will use archival data from observations of exoplanet atmospheres with space observatories, as well as ground-based observatories. 

  

  * Thanks to the development of novel data reduction, we will produce from the archival data, new calibrated data sets. 

  

  * From this new set of data and thanks to the development of new retrieval techniques, we will derive parameters of exoplanets atmospheres, such as its molecular contain. 

  

  * Data will also be generated from the modelling the atmosphere of exoplanets. 

  

  * We will use archival data from observations of exoplanet host stars with space observatories, as well as ground-based observatories. When needed, we will submit observing proposal to complete information on some of the host stars of our target list. 

  

  * From those data, we will derive, either directly or thanks to models, the star parameters: 

  

effective temperature, luminosity, gravity (also as an age estimate),
metallicity, rotational period, variability, proper motion, multiplicity,
magnetic field, topology of the field, wind …

  * From the parameters and thanks to star – planet interaction models, we will determine the importance of such interactions. 

    4. **WHAT IS THE EXPECTED SIZE OF THE DATA?**

  
  

  

To be determined precisely; but not big; in the gigaoctets range. One of the
end products will be a catalogue with the properties of the atmosphere of
about 100 targets.  
  
<table>  
<tr>  
<th>

Work Package number

</th>  
<th>

Deliverable type

</th>  
<th>

Data format

</th>  
<th>

Data size

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

WP1

</td>  
<td>

Documents

</td>  
<td>

PDF, excel

</td>  
<td>

At maximum in the

100 Moctets range

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

WP2

</td>  
<td>

Calibrated spectra of about 100 exoplanets

Data reduction Codes

Documents, Scientific papers

</td>  
<td>

Fits, PDF, excel

</td>  
<td>

At maximum in the 10 gigaoctets range

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

WP3

</td>  
<td>

Retrieved parameters for the atmosphere of about 100

exoplanets Retrieval codes

Documents, Scientific papers

</td>  
<td>

Fits, PDF, Excel

</td>  
<td>

At maximum in the 10 gigaoctets range

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

WP4

</td>  
<td>

Catalogues of host stars

Codes

Documents, Scientific papers

</td>  
<td>

TBD, FITS, PDF, Excel

</td>  
<td>

At maximum in the 10 gigaoctets range

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

WP5

</td>  
<td>

Models

Documents, Scientific papers

</td>  
<td>

TBD, FITS, PDF, Excel

</td>  
<td>

At maximum in the 10 gigaoctets range

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

WP6

</td>  
<td>

Science products

Web site

Videos

MOOC – SpoC

Documents

</td>  
<td>

TBD, FITS, PDF, Excel, Virtual Observatory standards, MP4, TXT,

PNG, JPEG, EPS

</td>  
<td>

In the few 100

gigaoctets range

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

  5. **TO WHOM MIGHT IT BE USEFUL ('DATA UTILITY')?**

  

  

The data will be useful in first place to the scientific community working on
exoplanets. It will also be of interest to students, as well as general
public.

#  MAKING DATA FINABLE ACCESSIBLE INTEROPERABLE AND RE-USABLE (FAIR)

The dissemination of knowledge is a key aspect of the project and a
WorkPackage, WP6, is dedicated to this aspect (see Figure 4).

Figure 4 : The data generated by the various WPs will be integrated to a
knowledge server.

The Knowledge Management WP aims at

  * Capturing knowledge produced by the other WPs (see Figure 4) within a **knowledge base** including all scientific products (data, models, tools and interpretation). 

  

  * Providing open access to them through a **knowledge displayer** with two interfaces, one for the scientific community with a direct access to the science products, the second for the general public with educational resources based on the science products. 

  

  * Getting feedback from the users 

  

Figure 5 describes the various aspects of knowledge management.

Figure 5: Overview of the knowledge management of the science products

  1. **MAKING DATA FINDABLE AND OPENLY ACCESSIBLE**

  

  

The Definition, design and production of a Knowledge Base is scheduled from
Month 6 to Month 36. The data produced in the framework of the project will be
made findable by means of a standard identification mechanism. The data will
be encapsulated with metadata. Metadata categories will be defined to follow
the different types of science products (ex: Observation, Code…) or
educational resources (ex: Video, Image…). For science products, we plan to
follow the naming convention used for the Virtual Observatory.

The architecture of the knowledge base (KB) that will capture, record and
format the science products for dissemination will be defined first. Once
defined collectively with the WorkPackages (WP) 2, 3, 4, 5, the KB will be
produced by deploying a server. The data will be stored under a relational
database. Indexes, Data Base (DB) engines and cache mechanism will be set up
to ensure the scalability and efficiency of the overall data access. Regarding
binary files, an indexed and versioning file system will be deployed to keep
track of modifications without losing content. The KB will be hosted on a
server (NodeJS or PHP according to the actual requirements identified during
the specification phase) providing

REST (REpresentational State Transfer) access to the data (i.e. a set of
standardised Uniform Resource Identifier (URI) allowing to obtain the
information in a JSON format). Specific routes will be defined to query the KB
according to several criteria: hierarchy, semantic, historic, syntactic...
This server will also provide a standardised export mechanism to export data
to a user-friendly product (e.g. PDF files or JPG images). We expect a hundred
of exoplanet atmospheres at the beginning for the Beta version; but the
project will be dimensioned to be able to handle thousands of entries. The
Definition and production of a Knowledge Displayer (KD) will start on Month
12. Once recorded in the knowledge base, the scientific community and general
public will be granted open access to our science products using two dedicated
interfaces. The Knowledge displayer will be in charge of displaying the KB to
the end-user by accessing the Knowledge Base Server REST API’s (Application
Programming Interfaces). All the data will be made openly available at the
latest at the end of the project.

  2. **MAKING DATA INTEROPERABLE**

  

  

The data produced in the project will be interoperable throughout virtual
Observatory standard for science products and Learning Tool Interoperability
(LTI) standard for education resources.

  3. **INCREASE DATA RE-USE**

  

  

The data will be made available for re-use through the knowledge server
following the deliverables plan of the project. There is no restriction in the
re-use of the data generated by the project.

The idea is to keep on feeding the data base after the end of the ExoplANETS-A
project, for example in the framework of ARIEL; Ariel is the ESA space mission
just selected for the M4 slot of the ESA cosmic vision 2015 – 2025 program and
whose adoption is scheduled end of 2020. In any case, we can guarantee that
the database will be designed to remain operational for at least 5 years after
the project end (for example putting the data on the CERN ZENODO data base).

#  ALLOCATION OF RESOURCES

The data management is one of the work packages of the project; its cost is
included in the project cost; it is covered partly by EC and partly by the
institutes participating in the project. 46 person.months have been attributed
to the workpackage. There is a lead from CEA and a co-lead from INTA.

#  DATA SECURITY

The data security (including data recovery as well as secure storage) will be
taken into account in the design of the data base and of the knowledge server.