#  Introduction

This document fulfils deliverable 7.3 by providing information on the data
management policy for GAIN project. This data management plan (DMP) is
required for projects participating in the Horizon 2020 data pilot. The
objectives of the DMP is to detail _“what data the project will generate,
whether and how it will be exploited or made accessible for verification and
re-use, and how it will be curated and preserved”_ .

Guidance from the H2020 Programme indicates that a data management plan should
be submitted early in the project and subsequently revised as the project
matures and more information becomes available. Thus, it is not expected that
a first version of this plan will provide complete detail on all aspects of
data management. Accordingly, updates to this document are expected in line
with interim project reviews.

The remainder of the document is structured following the Guidelines on FAIR
Data Management in Horizon 2020 (European Commission, 2016). Section 1
provides a summary of data collected, generated and re-used on the project.
Section 2 discusses making data findable, accessible, interoperable and re-
usable (FAIR). Section 3 describes the allocation of resources for making data
FAIR. Section 4 considers data security. Section 5 notes ethical aspects
related to data management. Section 6 concludes the report.

#  Data summary

The GAIN Consortium includes the 20 partners listed in Table 1 and an
International Partner, namely NOAA, National Ocean and Atmospheric
Administration (US), which cooperates: as Third Party of the Coordinator,
UNIVE: non-EU partners are marked in bold.

##  Table 1. GAIN Consortium  
  
<table>  
<tr>  
<th>

Participant Nº (leadership role)

</th>  
<th>

Participant legal name

</th>  
<th>

Country

</th>  
<th>

Type

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

1 (Coordinator; WP5; WP7)

</td>  
<td>

Universita Ca' Foscari Venezia (UNIVE)

</td>  
<td>

Italy

</td>  
<td>

RTD

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

2 (WP3)

</td>  
<td>

The University of Stirling (UoS)

</td>  
<td>

UK

</td>  
<td>

RTD

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

3 (WP1)

</td>  
<td>

Alfred-Wegener-Institut Helmholtz- Zentrum für Polar- und Meeresforschung
(AWI)

</td>  
<td>

Germany

</td>  
<td>

RTD

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

4

</td>  
<td>

IBM Ireland Limited (IBM)

</td>  
<td>

Ireland

</td>  
<td>

CORP  1

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

5 (WP2)

</td>  
<td>

Agencia Estatal Consejo Superior de Investigaciones Cientificas (CSIC)

</td>  
<td>

Spain

</td>  
<td>

RTD

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

6 (WP4)

</td>  
<td>

Longline Environment Limited (LLE)

</td>  
<td>

Ireland

</td>  
<td>

SME

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

7 (WP6)

</td>  
<td>

Sparos Lda (SPAROS)

</td>  
<td>

Portugal

</td>  
<td>

SME

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

**8**

</td>  
<td>

Salten Havbrukspark (SHP)

</td>  
<td>

Norway

</td>  
<td>

SME

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

9

</td>  
<td>

Wageningen University (WU)

</td>  
<td>

Netherlands

</td>  
<td>

RTD

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

10

</td>  
<td>

Johann Heinrich von Thuenen-Institut, Bundesforschungsinstitut Fuer Laendliche
Raeume, Wald Und Fischerei (TI)

</td>  
<td>

Germany

</td>  
<td>

RTD

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

11

</td>  
<td>

Agrifood and Biosciences Institute (AFBI)

</td>  
<td>

UK

</td>  
<td>

RTD

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

12

</td>  
<td>

Zachodniopomorski Uniwersytet Technologiczny W Szczecinie (ZUT)

</td>  
<td>

Poland

</td>  
<td>

RTD

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

13

</td>  
<td>

Asociacion Nacional de Fabricantes de Conservas de Pescados y Mariscos-

Centro Tecnico Nacional de Conservacion de Productos de la Pesca (ANFACO)

</td>  
<td>

Spain

</td>  
<td>

NPO  2

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

**14**

</td>  
<td>

Multivector AS (MV)

</td>  
<td>

Norway

</td>  
<td>

SME

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

**15**

</td>  
<td>

Gildeskal Forskningsstasjon AS (GIFAS)

</td>  
<td>

Norway

</td>  
<td>

SME

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

16

</td>  
<td>

Lebeche (LEBCH)

</td>  
<td>

Spain

</td>  
<td>

CORP  1

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

17

</td>  
<td>

Sagremarisco-Viveiros de Marisco Lda (SGM)

</td>  
<td>

Portugal

</td>  
<td>

SME

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

18

</td>  
<td>

Fondazione Edmund Mach (FEM)

</td>  
<td>

Italy

</td>  
<td>

NPO  2

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

**19**

</td>  
<td>

Dalhousie University (DAL)

</td>  
<td>

Canada

</td>  
<td>

RTD

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

**20**

</td>  
<td>

South China Sea Fisheries Research Institute (SCSFRI)

</td>  
<td>

China

</td>  
<td>

RTD

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

GAIN is structured in 7 Work Packages, plus an Ethics Work Package, which was
added by the EC during the negotiation (see Fig. 1). WP leaders are indicated
in Table 1. The main objects of each WP are listed below.

WP1 - Production and Environment: will develop novel sustainable feeds and
tools for enhancing aquaculture sustainable management of aquafarm based on
Big Data analytics. WP2 - Secondary products: will develop new co-products, in
order to enhance circularity, sustainability and profitability of aquaculture
supply chains.

WP3 - Policy and markets. will analyse the state-of-the-art of EU and national
legislations with respect to the valorisation and marketing of innovative GAIN
products and co-products and provide suggestions to policy makers.

WP4 - Eco-intensification: will develop new approaches and tools for assessing
the level of eco-intensification of GAIN innovative solutions, in comparison
with standard practices. WP5 - Professional development: will deliver both on-
line and in presence courses, in order to facilitate the adoption of GAIN
innovative solutions by aquafarm operators.

WP6 - Dissemination, Exploitation, Communication: will maximize GAIN impact,
by careful matching communication&dissemination tools to targeted audiences
and developing platforms for exploiting GAIN results beyond its life time.

WP7 - Coordination: will ensure the timely delivery of all GAIN contractual
items.

WP6

WP1

Production &

environment

WP4

Eco-intensification

WP2

Secondary

outputs

WP3

Policy &

markets

Dissemination,

Exploitation,

Communication

WP5

Professional

development

Coordination

WP7

**Fig. 1. GAIN structure**

GAIN collects, generates and re-uses data of different types and from
different sources to accomplish the project objectives. In this section we
summarise: the purpose of the data collection/generation, data type-and
formats, origin of the data and expected volumes.

##  Sensor data collected at pilot sites

GAIN will collect large volumes of sensor data at the pilot farm sites. These
sites include:

  * a salmon farm at GIFAS production site of Rossøya, Norway; 

  

  * a salmon farm belonging to Cooke Aquaculture, which joined the GAIN Consortium as committed end-user. 

  

  * a salmon farm in Nova Scotia, Canada 

  

  * LEBCH seabass and seabream farms located near Murcia, Spain 

  

  * a rainbow trout farms, i.e. Troticoltura Leonardi, located in Trentino Alto-Adige, Northern Italy, which joined the GAIN Consortium as committed end-user. 

  

  * SGM mussel farm, located in the Algarve, Portugal. 

  

  * a set of mussel farms located in Northern Ireland, UK, which are currently been monitored by AFBI. 

  

Data collected will include:

  * time series of environmental variables, e.g. water temperature, dissolved oxygen, chlorophyll a concentration, salinity, current, etc. 

  

  * Acoustic monitoring data providing information on biomass, movement patterns, etc. 

  

  * Imagery data from in-situ camera and drone imagery. 

  

The collection of this data is necessary to enable ‘precision aquaculture’
type analytics and better inform on farm condition, environmental status and
time evolution. This data originates at the pilot farm sites participating in
the project and is not a re-use of existing data. The data will be critical to
the Information Management System developed during the project and of
significant scientific value to the academic partners in the project. The
total size of data generated is expected to be in the range of Petabytes and
varies according to the type. For example, the acoustic monitoring system
returns very large datasets generating 2D spatial maps at five second
frequency, while the time series datasets return hourly data at five pilot
sites producing about 43,000 data points per year for each variable collected.

##  Operational data collected at pilot sites

We will collect a range of operational datasets at the pilot sites. Data
collected will include:

  * Production data concerning GAIN pilot sites. (e.g. total biomass, yield, feed conversion ratio). 

  

  * Data concerning husbandry practices, (e.g. feeding time, feed rations, feed compositions, parasite presence). 

  

  * Data concerning fish welfare (e.g. behaviour, sea louse counts, mortalities, fish growth and condition, fish health parameters). 

  

The collection of this data is necessary to enable ‘precision aquaculture’
type analytics and understand how external factors influence farm productivity
and effects. This data originates at the pilot farm sites participating in the
project and is not a re-use of existing data. The data will be critical to
developing data-driven modelling related to feature extraction, predictive
analytics and decision support. Further, the data is of significant scientific
value to the academic partners in the project. Data type will encompass both
unstructured data (e.g. reporting) and structured data (e.g. biomass data) and
expected volumes will be in the Gigabytes per farm.

##  Data related to feed design

We will collect a range of data to guide feed design also re-using data
collected at pilot farm sites (described above). Data collected will include:

  * Microalgae species and strains, based on literature and specific experiments carried out in GAIN. 

  

  * Environmental variables affecting microalgae growth. 

  

  * Concentrations of Zn and Selenium in microalgae and culture medium. 

  

  * Economic data on microalgae production at pilot scale and industrial scale. 

  

The collection of this data is necessary to explore the viability of using
algae as feed components. Further data will be collected related to the
selected feed formulations including:

  * Data concerning feed pellet features e.g. mixing homogeneity, pellet durability index, pellet hardness, physical water stability, sinking velocity, fat absorption/leaking, starch gelatinization and nutrient leaching. 

  

  * Data related to feed trials testing the performance and FCR of the selected formulation. 

  

These datasets are critical to developing novel feed formulation in WP1.

Data concerning feed pellets will be determined during specific tests at its
premises by SPAROS, a GAIN partner with strong expertise on feed formulation
and manufacturing. Data related to feed originates from a set of feed trials
on four selected species, namely Atlantic salmon, rainbow trout, seabream, and
turbot as a niche species, in at least 8 feeding trials performed by SPAROS
(seabream), AWI (turbot), FEM (trout) and salmon (GIFAS). Key Performance
Indicators determined in these trial are summarized in Table 2.

The expected volume of data related to feed design is in the range of 100,000
data points.

**Table 2. Key Performance Indicators determined in GAIN feed trials.**  
  
<table>  
<tr>  
<th>

_Category Key performance Indicators Tissues/samples_

</th>  
<th>

_Analysis_

</th>  
<th>

_Species_

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

_**Performance** _

</td>  
<td>

_**Feed intake** _

</td>  
<td>

_**Whole body** _

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Weight gain** _

</td>  
<td>

_**Whole body** _

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Condition factor** _

</td>  
<td>

_**Whole body** _

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Biomarkers** _ 3  
</td>  
<td>

_**Liver** _

</td>  
<td>

_**PCR array** _

</td>  
<td>

_**Seabream, salmon** _

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

_**Resource efficiency** _

</td>  
<td>

_**Feed conversion (FCR)** _

</td>  
<td>

_**Whole body** _

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Digestibility** _

</td>  
<td>

_**Feed, Faeces** _

</td>  
<td>

_**Macro and micronutrients** _

</td>  
<td>

_**All** _

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

_**Retention efficiency** _

</td>  
<td>

_**Whole body** _

</td>  
<td>

_**Macro and micronutrients** _

</td>  
<td>

_**All** _

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

_**Health & welfare ** _

</td>  
<td>

_**Mortality** _

</td>  
<td>

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Enteritis** _

</td>  
<td>

_**Intestine** _

</td>  
<td>

_**Histology** _

</td>  
<td>

_**All** _

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

_**Parasitic infestation** _

</td>  
<td>

_**Skin** _

_**Intestine** _

</td>  
<td>

**Lepeophtheirus salmonis Enteromixum leei**

</td>  
<td>

_**Salmon Seabream** _

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

_**Mucosal function** _

</td>  
<td>

_**Skin, gills** _

</td>  
<td>

_**Histology** _ 4  
</td>  
<td>

_**All** _

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

_**Plasma lysosyme** _

</td>  
<td>

_**Plasma** _

</td>  
<td>

_**Enzymatic activity** _

</td>  
<td>

_**All** _

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

_**Bactericidal activity** _

</td>  
<td>

_**Plasma** _

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Biomarkers** _ 5  
</td>  
<td>

_**Head kidney** _

</td>  
<td>

_**PCR array** _

</td>  
<td>

_**Seabream, salmon** _

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

_**Quality** _

</td>  
<td>

_**Dressout loss** _

</td>  
<td>

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Fillet yield** _

</td>  
<td>

_**Fillet** _

</td>  
<td>

</td>  
<td>

_**All** _

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

_**Pigment** _

</td>  
<td>

_**Fillet** _

</td>  
<td>

_**Visual and/or chemical** _

</td>  
<td>

_**Salmon, trout** _

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

_**Texture** _

</td>  
<td>

_**Fillet** _

</td>  
<td>

_**Shear force** _

</td>  
<td>

_**All** _

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

_**Taste** _

</td>  
<td>

_**Fillet** _

</td>  
<td>

_**Organoleptic (chefs)** _

</td>  
<td>

_**All** _

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

##  Data related to aquaculture by-products

We will collect a range of data related to aquaculture by-products. Data
collected from finfish products will include:

  * Data concerning potential by-products composition, in terms of specific flesh yields, fatty acid and different fractions (e.g. fillet, head, trimming, viscera, skin, etc.), 

  

  * Data concerning the yield and composition of marine peptones, protein hydrolysates, oils, minerals, collagen and gelatines obtained from Enzymatic Hydrolysis of byproducts. 

  

Data collected will be critical to allow an analysis of potential
valorisation, and an assessment of increase of the edible proportion and waste
reduction. It will be used to determine environmental and economic benefits of
redirecting by-product fractions to innovative ecointensification. Data will
be collected from laboratory analysis during the project and is not a re-use
of existing data.

Data collected from shellfish by-products will include

  * Data concerning the efficiency of shell-based materials for water purification in RAS, biofilters in Aquaponics and Phosphorus removal from land-based fish farm effluents. 

  

  * Data about the efficiency of fillers for the cement industry based on shells. 

  

Data collected will be required to achieve project objectives related to the
valorisation of shellfish by-product. It will be used to assess the viability
of bivalve shell in several applications, namely as a biofilter in land-based
aquaculture systems and as substitute in construction industry. Data will be
collected from laboratory and prototype testing during the

project and is not a re-use of existing data. We expect to generate tens of
thousands of data points assessing each valorisation application.

##  Production and consumption data

To achieve project objectives related to production and consumption of seafood
and implications for policy, the GAIN project will collect data related to the
amount of seafood consumed for different product/species in different
countries. Data will be collected based on information from large retailers,
interviews with key operators, questionnaires. The data will be collated from
a variety of sources and will hence be partly a re-use of data but will
generate a much more complete representation of production and consumption
data. The data is necessary to inform policy recommendations ensuring the most
comprehensive data. We expect the data volume to be relatively small resulting
in a few hundred data points per Country.

##  GAIN model data

A range of model data will be generated during the GAIN project. These include
mechanistic models related to fish growth and data-driven models for 1)
prediction, 2) feature extraction and 3) decision support platform. These data
will be generated during the project and will not be a re-use of existing
data. The data will be critical to the precision aquaculture ambition of the
project that aims to leverage analytics to better inform farm operations. We
expect the data volumes to be in the range of 100s of Gigabytes for individual
farms

##  Existing Geospatial data

GAIN intends to make use of several geospatial datasets during the project.
Specifically, we will use the following:

  * Weather data (e.g. wind speeds, air temperature) 

  

  * Ocean model data (e.g. ocean currents, wave heights) 

  

  * Satellite data (e.g. sea-surface temperature, Chlorphylla) 

  

GAIN will make use of these datasets to inform on environmental condition at
pilot farm sites. The project intends to re-use these datasets rather than
generate and a number of potential sources have been identified (e.g. MODIS
satellite data, ECMWF ocean model data. The data will be used as part of the
precision aquaculture component in WP1.

#  FAIR data

This section details how GAIN will make data _**Findable, Accessible,
Interoperable, and Reusable** _ (FAIR). Each topic is addressed in turn.

##  Making data findable, including provision for metadata

Following H2020 guidelines, two types of datasets are considered here:

  1. **The ‘underlying data’ –** data and metadata related to scientific publication generated during the project 

  

  2. **Any other data –** for instance curated data not directly attributable to a publication, or raw data 

  

  

GAIN plans to make project data findable by associating open data sets with
scientific publications. Publications may include traditional research papers
as well as “data only” articles which present and summarize data, but do not
offer in-depth analysis or draw conclusions. A scientific paper about a
dataset is an ideal forum to provide metadata, discuss naming conventions,
point to standards, and list relevant keywords. Publication venues which
utilize Digital Object Identifiers will be favoured for GAIN and all attempts
will be made to associate unique DOI with research data also.

##  Making data openly accessible

Data will be made openly available by uploading to an appropriate repository.
Data which is uploaded to a repository will use an interoperable format with
the exact format dependent on the type and volume of data together with domain
conventions and standards. Example data formats will be text files of comma
separated values and CF convention NetCDF files. We will use the _Zenodo_
repository (zenodo.org) provided by CERN for open datasets generated during
the project. Datasets made publicly available will not require restricted
access.

The close connection between _Zenodo_ and Horizon 2020 projects made it a
natural and easy fit. We created a _Zenodo_ community for the GAIN project
curated by IBM. This links directly to the _OpenAire_ page for the GAIN
project, provides a concise summary of publications emanating from the project
and all GAIN datasets will readily accessible by searching for the ‘GAIN
community in _Zenodo_ . Further, any dataset uploaded to _Zenodo_ is assigned
a unique DOI, which makes referencing (e.g. in publications) very convenient.

Table summarises the datasets being generated in GAIN and open-access status.

###  Table 3: Perspectives on open-access for GAIN datasets  
  
<table>  
<tr>  
<th>

**Dataset**

</th>  
<th>

**Perspective on open-access**

</th>  
<th>

**Plan to open**

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

Sensor data collected at

pilot sites

</td>  
<td>

Data related to environmental data collected at farm sites is scientifically
useful but often commercially sensitive

</td>  
<td>

Ongoing communication with farm owners to separate commercially sensitive data
from data that can be

released

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

Data on farm operations

</td>  
<td>

Data related to farm operations are often commercially sensitive

</td>  
<td>

NO

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

Data related to feed

design

</td>  
<td>

Data related to feed design may be commercially exploitable;

</td>  
<td>

?

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

**Dataset**

</td>  
<td>

**Perspective on open-access**

</td>  
<td>

**Plan to open**

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

</td>  
<td>

however we will explore making open partial sets of the data

</td>  
<td>

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

Data related to

aquaculture by-products

</td>  
<td>

Data may be commercially exploitable but we will explore making open partial
sets of the data

</td>  
<td>

?

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

Production and

consumption data

</td>  
<td>

Some production and consumption data may be commercially sensitive (retailer
data), but we will explore making open, possibly with suitable anonymisation
and

aggregation

</td>  
<td>

As stated in GAIN Deliverable

8.1, confidential information on production and consumption will be collected
only upon informed consent of the data owners. Informed consent forms will
clearly state that the data will be used only after anonymisation and

aggregation

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

GAIN model data

</td>  
<td>

Data related to environmental and operational insights on farm production are
often

commercially sensitive

</td>  
<td>

Ongoing communication with farm owners to separate commercially sensitive data
from data that can be released.

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

Existing geospatial data

</td>  
<td>

These are re-used data which are either already openly available or which the
project does not have the right to publish.

</td>  
<td>

NO

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

##  Interoperability of data

Interoperability of GAIN data will be accomplished through standardised data
formats (and we aim to promote standardisation of data formats through GAIN)
and appropriate metadescription and documentation. We plan to accompany open
data with a scientific article or technical white paper to promote reuse. GAIN
aims to promote semantic mapping and relevant ontologies for aquaculture farms
to promote interoperability and increase access to individuals from outside
domains (particularly from data science fields where the focus is much more on
the data itself rather than domain characteristics).

##  Data re-use

Re-use of data will be encouraged through clear licensing, prompt
dissemination, archiving, and quality assurance. At this writing, license for
GAIN data has not yet been selected. We note the preference expressed in the
OpenAIRE Licensing Study (Dietrich et. al. 2014) for version 4.0 of the
Creative Commons Licenses. Dissemination of GAIN data will promptly follow
publication of related scientific papers. We plan to disseminate data once
papers have been accepted for publication. Data published from GAIN will be
disseminated through Zenodo facilitating re-use of the data by third parties
for an indefinite period after the project is over. Data published from the
project will undergo a quality assurance process managed by the individuals
responsible for the scientific publication

#  Allocation of resources

The project budget allocates resources to make GAIN data FAIR. Tasks in the
project which are related to open data include

  * Task 1.2 related to developing novel feed components 

  

  * Task 1.3 Instrumentation of commercial aquaculture facilities 

  

  * Task 1.4 Development of a real-time Information Management System 

  

  * Task 3.2 Assessment of production and consumption data and implications for policy Once data has been made FAIR during the course of the project and placed in a repository, further costs are not anticipated for the project team. Benefits of long term preservation include historical benchmarking and comparison activities. 

  

#  Data Security

The security of data collected and generated during GAIN is important to the
success of the project and essential to providing subsequent open access to
selected project data. In this regard, care will be taken to facilitate data
recovery, provide adequate data storage and enable transfer of sensitive data
as required by the project.

Responsibility for data recovery falls to individual software and hardware
components of the GAIN system. Much of the data considered for open access
will move through the cloud service platform for real-time management of
aquaculture data. The data recovery strategy for cloud service platform will
rely on the recovery infrastructure of the cloud environment where it is
hosted. Box provides a secure storage environment for data in the cloud. Where
it is necessary to transfer sensitive data, care will be taken to use
encrypted connections. Taking these measures in relevant activities
contributes to data security on the GAIN project.

#  Ethical aspects

The ethics review for the GAIN project raised the issues of:

  * informed consent for the collection, storage, and protection of personal data. 

  

  * In case personal data being transferred from/to a non-EU country or international organisation, confirmation that this complies with national and EU legislation, together with the necessary authorisations. 

  

These issues are treated in project Deliverables 8.1 and 8.2.

#  Conclusions

This deliverable describes the data management plan for the GAIN project and
our participation in the Open Data pilot. The range of data being collected,
generated and used as part of the project are described; data typology and
volume are presented together with the utility both towards the GAIN project
and for the wider scientific community. A detailed description of open-access
procedures are provided together with an assessment of the datasets that can
be made open either partially or in totality. A final decision on providing
any particular dataset as open data will be documented in a future revision of
this report.