#  Executive Summary

A 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”_ .

GAIN DMP was structured in accordance with the Guidelines on FAIR Data

Management in Horizon 2020 (European Commission, 2016): the plan is presented
in

(O’Donncha F. & Pastres R., 2018) which was submitted at an early stage of the
project, Month 6, as required by GAIN Description of Action.

At the end of the first reporting period, the present document reviews GAIN
DMP, in order to identify gaps in its early version and amend the plan, if
required. The document includes:

  1. a summary of data has been collected, generated and re‐used during the first reporting period; 

  

  2. an assessment of the strategy implemented for making data findable, accessible, interoperable and re‐usable (FAIR) 

  

  3. an assessment of the data security measures adopted, including 

  

  

The main conclusions are:

  1. the data collected during the first reporting period fell into the data typologies identified in D7.3 and the strategy and protocols for archiving them proved effective: therefore, there is no need to change them; 

  

  2. data processing and dissemination of data/results will take place mainly during Month 18‐ Month 42: based on the limited experience pertaining the first reporting period, the strategy for making data findable, accessible, interoperable and re‐usable (FAIR) seems adequate; 

  

  3. thus far, no leaking of personal data and sensitive data has been detected, thus confirming that the security and data protection measures adopted in GAIN proved to be effective. 

  

  

Deliverable 7.4

#  Summary of data collected during the first reporting period

As foreseen in (O’Donncha F. & Pastres R., 2018), during the first reporting
period GAIN collected, generated and re‐used data of different types and from
different sources. In this section we summarise the purpose of the data
collection/generation and provide an overview of the data collected thus far,
in order to identify deviations from the data typeand formats and archiving
protocols planned at an early stage of the project and summarized in
(O’Donncha F. & Pastres R., 2018).

##  Sensor data collected at pilot sites

GAIN collected large volumes of sensor data at the pilot farm sites. The data
collection is ongoing and will end at M36 (April 2021). The 10 GAIN Pilot
sites are described in detail in (Service, M. et al., 2019): their main
features are summarized in Table 1.

Table 1 ‐ Summary of Pilot Sites  
  
<table>  
<tr>  
<th>

**Pilot Site**

</th>  
<th>

**Country**

</th>  
<th>

**Species**

</th>  
<th>

**Type of Aquaculture**

</th>  
<th>

**Partner**

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

1) Dundrum Bay

</td>  
<td>

Northern

Ireland ‐ UK

</td>  
<td>

Oysters ( _Magellana_

_gigas_ )

</td>  
<td>

Shellfish Aquaculture

</td>  
<td>

AFBI

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

2) Belfast Lough

</td>  
<td>

Northern

Ireland ‐ UK

</td>  
<td>

Mussels ( _Mytilus_

_edulis_ )

</td>  
<td>

Shellfish Aquaculture

</td>  
<td>

AFBI

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

3) Sagres

</td>  
<td>

Portugal

</td>  
<td>

Mussels ( _Mytilus_

_galloprovincialis_ )

</td>  
<td>

Shellfish Aquaculture

</td>  
<td>

SGM

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

4) Rossøya Nord

</td>  
<td>

Norway

</td>  
<td>

Salmon ( _Salmo salar_ )

</td>  
<td>

Sea cages

</td>  
<td>

GIFAS

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

5) Carness Bay

</td>  
<td>

Scotland

</td>  
<td>

Salmon ( _Salmo salar_ )

</td>  
<td>

Sea cages

</td>  
<td>

UoS

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

6) McNutt’s Island,

Shelburne

</td>  
<td>

Canada

</td>  
<td>

Salmon ( _Salmo salar_ )

</td>  
<td>

Sea cages

</td>  
<td>

DAL

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

7) El Gorguel, Cartagena

</td>  
<td>

Spain

</td>  
<td>

Seabass ( _Dicentrarchus_

_labrax_ )

</td>  
<td>

Sea cages

</td>  
<td>

LEBCH

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

8) Preore, Troticoltura

Leonardi

</td>  
<td>

Italy

</td>  
<td>

Rainbow trout

( _Oncorhyncus mykiss_ )

</td>  
<td>

Land‐based raceways

</td>  
<td>

UNIVE

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

9) FES NOWE Czarnowo

</td>  
<td>

Poland

</td>  
<td>

Carp ( _Cyprinius carp_ )

</td>  
<td>

Land‐based RAS/pond

</td>  
<td>

ZUT

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

10) Fenzhou Village

</td>  
<td>

China

</td>  
<td>

Shrimp ( _Litopenaeus_

_vannmei_ , _Macrobrachium_

_roserbergii_ )

</td>  
<td>

Land‐based pond

</td>  
<td>

SCSFRI

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

The variables collected at each site are summarized in Table 2 and 3: data
collection frequencies, f  d  , are summarized as follows:

  * H : f  d   1/hour 

  

  * D : 1/day  f  d  < 1/hour 

  

  * W: 1/week  f  d  < 1/day 

  

  * F: 1/2 weeks  f  d  < 1/week 

  

  * M: 1/month  f  d  < 1/week 

  

  * S: fd  1/month and/or as a response to observed anomalies. 

  

In accordance with the theoretical framework of Precision Fish Farming, these
variables are classified as environmental and animal variables.

Table 2. Summary of environmental variables monitored on GAIN pilot sites  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Site 1**

</th>  
<th>

**Site 2**

</th>  
<th>

**Site 3**

</th>  
<th>

**Site 4**

</th>  
<th>

**Site 5**

</th>  
<th>

**Site 6**

</th>  
<th>

**Site 7**

</th>  
<th>

**Site 8**

</th>  
<th>

**Site 9**

</th>  
<th>

**Site 10**

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

Water

Temperature

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H/D

</td>  
<td>

D

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

Salinity

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

D

</td>  
<td>

</td>  
<td>

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

D

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

Dissolved Oxygen

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H/D

</td>  
<td>

D

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

pH

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

D

</td>  
<td>

H/D

</td>  
<td>

D

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

ORP

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

D

</td>  
<td>

</td>  
<td>

W

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

Turbidity

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

</td>  
<td>

D

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Chlorophyll a

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

</td>  
<td>

W

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

Tryptophan

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

N‐NH4

</td>  
<td>

</td>  
<td>

F

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

D

</td>  
<td>

W

</td>  
<td>

D

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

N‐NO3

</td>  
<td>

</td>  
<td>

F

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

D

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

P‐SRP

</td>  
<td>

</td>  
<td>

F

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Si‐SiO2

</td>  
<td>

</td>  
<td>

F

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

POM

</td>  
<td>

</td>  
<td>

F

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

W

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

POC

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

W

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

TSS

</td>  
<td>

</td>  
<td>

F

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

W

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

Table 3. Summary of animal variables monitored on GAIN pilot sites  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Site 1**

</th>  
<th>

**Site 2**

</th>  
<th>

**Site 3**

</th>  
<th>

**Site 4**

</th>  
<th>

**Site 5**

</th>  
<th>

**Site 6**

</th>  
<th>

**Site 7**

</th>  
<th>

**Site 8**

</th>  
<th>

**Site 9**

</th>  
<th>

**Site 10**

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

**Size**

**(weight/length) distribution**

</td>  
<td>

F

</td>  
<td>

F

</td>  
<td>

M

</td>  
<td>

H

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

H

</td>  
<td>

W/M

</td>  
<td>

W

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

**Biomass**

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

H

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

W

</td>  
<td>

W/M

</td>  
<td>

S

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

**Relative biomass distribution in cages**

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

**Feeding activity**

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

H

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

**Fish speed and location**

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

H

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

**Parasites**

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

M

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

</td>  
<td>

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

**Welfare indicators**

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

M

</td>  
<td>

M

</td>  
<td>

M

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

**Mortality**

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

S

</td>  
<td>

D

</td>  
<td>

W

</td>  
<td>

W

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

S

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

The data are fed to the Information Management System, which is being
developed in Task 1.4 “Development of a real‐time Information Management
System” As described in detail in (O’Donncha F., et al., 2019), a protocol for
standardizing the data flow from pilot sites to the platform was set and
implemented, in order to ensure proper data archiving and interoperability.

The total size of data generated is in accordance with the estimate provided
in

much smaller data but in a more unstructured format, Examples of these type of
measurements coming from Rossøya Nord are:

  * 'Welfare‐Score_Condition', 

  

  * 'Welfare‐Score_Deformity',  'Welfare‐Score_EyeHealth', 

  

  * 'Welfare‐Score_FinDamage', 

  

  * 'Lice‐Average_chalimus', 

  

  * 'Lice‐Average_matureFemale', 

  

  * 'Lice‐Average_pre‐adult', 

  

  * 'Mortality', 

  

##  Data related to feed design

During the first reporting period, the collection of data concerning feed
design, manufacturing with innovative ingredients and testing was planned in
detail.

Task 1.1 concerns the identification of microalgae strains which could
bioaccumulate Zinc (Zn) and Selenium (Se). These trace elements are very
important for fish growth and welfare: small amounts of Zn and Se enriched
microalgae in feed could therefore enhance feed performances. Therefore, in
Task 1.1 the following data are being collected.

  * Variables affecting microalgae growth, e.g. light intensity, water temperature, concentration of macronutrients in the culture medium. 

  

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

  

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

  

The first feed trials are being carried out and preliminary results about feed
Key Performance Indicators, see Table 4, are available, as described in GAIN
Deliverable D1.4. 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.

No marked deviation from the GAIN DoA and D7.3 can be expected at this stage.

Table 4. Key Performance Indicators for assessing novel feeds.  
  
<table>  
<tr>  
<th>

**Category**

</th>  
<th>

**Key performance**

**Indicators**

</th>  
<th>

**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  1

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

**Category**

</th>  
<th>

**Key performance**

**Indicators**

</th>  
<th>

**Tissues/samples**

</th>  
<th>

**Analysis**

</th>  
<th>

**Species**

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

</td>  
<td>

Mucosal function

</td>  
<td>

Skin, gills

</td>  
<td>

Histology  2

</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  3

</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

The enhancement of circular economy in the aquaculture sector is one of the
pillar of GAIN approach to ecological intensification. Data collected from
finfish products included:

  * 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.), as detailed in (Malcorps W, et al., 2019). 

  

  * Data concerning the yield and composition of marine peptones, protein hydrolysates, oils, minerals, collagen and gelatines obtained from Enzymatic Hydrolysis of byproducts, as detailed in (Vazquez J.A., et al., 2019). 

  

  * Data concerning yield and safety of by‐products from the innovative processes for mortality disposal and RAS wastewater treatment. 

  

These data are being collected from laboratory analysis.

Data collected from shellfish by‐products included:

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

  

Data will be used to assess the viability of bivalve shell in several
applications, in land‐based aquaculture systems and as substitute in
construction industry. 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 has already been generated during the first reporting
period. 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 is using of several geospatial datasets during the project, such as

  * Meteorological 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 is using 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 ‐ Production and Environment.

#  FAIR data

The steps for making data _**Findable, Accessible, Interoperable, and
Reusable** _ (FAIR) are described in detail in (O’Donncha F. & Pastres R.,
2018). In summary:

**Findable**

GAIN plans to make project data findable by associating open data sets with
scientific publications. 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 (the facility of being able to
associate DOI to data is one of the reasons we favoured Zenodo). We have
posted preprint of scientific publications in our public repository to ensure
availability to the scientific community.

**Accessible**

Collected data which are non‐personal and non‐confidential are, at present,
made available to project partners through the project hub. Data which is
uploaded to this repository are available in an interoperable format,
depending on the type and volume of data as well as on domain conventions and
standards. Example data formats are: 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.

We created a Zenodo community for the GAIN project curated by IBM

(https://zenodo.org/communities/gain_h2020/). To protect the integrity of the
repository, we enforced a number of conditions of use, namely:

Only GAIN partners are allowed to upload new data to the community.

Institutions uploading data for this community are responsible to ensure that:

  1. they do not upload any sensitive personal data 

  

  2. they do not upload regulated data, i.e. medical data, defence/judicial data, export regulated data or any export sensitive data 

  

  

The Zenodo repository 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 5: 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; however, we will
explore making open partial sets of the data

</td>  
<td>

Results of trials will be published and data concerning KPI listed in Table

4 will be made available.

</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>

Results concerning optimal enzymatic hydrolysis conditions, and main
parameters leading to cost effective drying of RAS wastewater and mortalities
will be published in peer reviewed papers.

</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

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

**Dataset**

</td>  
<td>

**Perspective on open‐access**

</td>  
<td>

**Plan to open**

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

</td>  
<td>

</td>  
<td>

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

As foreseen in (O’Donncha F. & Pastres R., 2018), interoperability of GAIN
data has been accomplished through standardised data and appropriate
meta‐description 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). We have made public the
code to upload and download data to our cloud service to ensure that the
approaches we use are transparent and visible to others (note that for
security reasons, user credentials are required to actually upload or download
data).

##  Data re‐use

Re‐use of data has been 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

##  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 has been taken to facilitate data
recovery, provide adequate data storage, through the project hub, and enable
transfer of sensitive data. Partners, however are responsible for transferring
the data to the project hub and Information Management System for real‐time
management of aquaculture data. The data recovery strategy for this cloud
service platform relies on the recovery infrastructure of the cloud
environment where it is hosted: see (O’Donncha F., et al., 2019) for more
details. 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. Thus far, the operational
protocols adopted in GAIN has ensured that no data leaking has occurred.

##  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 (Pastres R., & Licata C., 2018).

##  Conclusions

This deliverable reviews the data management plan for the GAIN project
presented in (O’Donncha F. & Pastres R., 2018), in order to identify order to
identify gaps in its early version and amend the plan, if required.

The main conclusions are:

  1. the data collected during the first reporting period fell into the data typologies identified in (O’Donncha F. & Pastres R., 2018) and the strategy and protocols for archiving them proved effective: therefore, there is no need to change them; 

  

  2. data processing and dissemination of data/results will take place mainly during Month 18‐ Month 42: based on the limited experience pertaining the first reporting period, the strategy for making data findable, accessible, interoperable and re‐usable (FAIR) seems adequate; 

  

  3. thus far, no leaking of personal data and sensitive data has been detected, thus confirming that the security and data protection measures adopted in GAIN proved to be effective.