#  EXECUTIVE SUMMARY

Data or Big Data rapidly has become a new resource or asset in the current
economy, also in the agricultural sector. This development leads to several
issues that have to be addressed such as data availability, quality, access,
security, responsibility, liability, ownership, privacy, costs and business
models. In Europe several initiatives have already been established to work on
this in a general context (e.g. the EU open data policy, General Data
Protection Regulation) and more specifically in agriculture (e.g. GODAN, COPA-
COGECA).

IoF2020 has to address these issues by developing a Data Management Plan
(DMP). This document (D1.4) provides a first version of a DMP containing a
general overview of relevant developments and a first inventory of needs and
issues that play a role in the use cases. This has resulted in a number of
general guidelines for data management in IoF2020:

  * Research papers derived from the project should be published under open access policy 

  

  * Research data should be stored in a central repository according to the FAIR principles: findable, accessible, interoperable and reliable 

  

  * The use cases in IoF2020 should be clearly aligned to the European Data Economy policy and more specifically in line with the principles and guidelines provided by the stakeholder community _i.e._ COPA-COGECA. 

  

  * All IoF2020 use cases should be GDPR-compliant. 

  

A number of follow-up actions were identified to establish open, transparent
data management in IoF2020:

  * Investigate what research data is involved in the use cases and other project activities and define how they should be treated according to the open data policy. 

  

  * Participate actively in the debate and developments of the European Data Economy and data sharing in agriculture. 

  

  * Analyze and explore the use cases in a deeper way in order to identify which data management issues potentially play a role and define plans how to deal with them. 

  

  * Concerning collaboration with other projects, a more systematic and structural approach should be explored in order to maximize the benefits and impact of the mutual activities on data management. 

  

  * A feasible, but sufficient, plan has to be developed to make the use cases GDPR-compliant. 

  

As a result the DMP will be continuously adapted and updated during the
project’s period.

##  1 INTRODUCTION

###  1.1 CONTEXT AND BACKGROUND

Big Data is becoming a new resource, a new asset, also in the agricultural
sector. Big Data in the agricultural sector includes enterprise data from
operational systems, farm field sensor data (e.g. temperature, rainfall,
sunlight), farm equipment sensor data (from tractors, harvesters, milking
robots, feeding robots), data from wearable animal sensors (neck tag, leg
tag), harvested goods and livestock delivery vehicles sensor data (from farms
to processing facilities) etc. Increasingly, Big Data applications, Big Data
initiatives and Big Data projects are implemented and carried out, aiming for
improving the farm and chain performance (e.g., profitability and
sustainability) and support associated farm management decision making.

In the IoF2020 different use cases are taking place in which data also plays a
key role involving farm companies that share their (big) farm data with
enterprises and organisations who strive to add value to that data. This
implies that the data from one party is combined with data from other parties
in the chain, and then analysed and translated into advices, knowledge, or
information for farmers. In this way Big Data becomes an asset in supporting
farmers to further improve their business performance (e.g., higher yield,
better quality, higher efficiency). These data-driven developments often
involve collaborations between agri-IT companies, farmers’ cooperatives and
other companies in the food supply chain. These business-to-business
initiatives and interactions are increasingly conducted through inter-
organisational coordination hubs, in which standardised IT-based platforms
provide data and business process interoperability for interactions among the
organisations.

In Figure 1 an example of such a network of collaborating organisations is
provided. It involves four stakeholders around the farmer (i) sperm supplier,
(ii) milk processor, (iii) feed supplier and (iv) milking robot supplier.
Multiple farms can be involved, and for each stakeholder relation data are
collected from the farm and collected in a data platform. All stakeholders can
then receive multiple datasets back from this platform, depending on
authorization settings. This has to be arranged and governed by some form of
network administrative organization. It can be expected that the IoF2020 use
cases can be modelled into a similar picture.

**C**

**loud DATA platform**

Farmer

Supplier C

Supplier A

Supplier B

Customer X

feed

sperm

milk

milking

robot

**data**

**data**

**data**

**data**

**data**

**data**

**data**

**data**

**data**

**data**

**data**

**data**

**data**

Network

Administrative

Organization

_Figure 1 Example of a data sharing network in dairy farming in the agri-food
sector_

There are multiple ways of expanding and intensifying such a Big Data network.
For instance, at the end of the market chain, consumers may play a role in
future. They could be interested in Big Data as a quality check for products,
and they could in principle provide consumer based data to the platform.
Moreover, the role of government can be relevant. While governments seem to be
interested in open data possibilities, this may cause uncomfortable privacy
concerns for suppliers in the network, who prefer to keep business models away
from the public.

The management of Big Data initiatives comprises challenges of privacy and
security, which impact discouragements and distrust among famers. Trust is
considered to be a starting point in increasing Big Data applications. Many
involved companies are refraining from sharing data because of the fear of
issues such as data security, privacy and liability. Corresponding to the
different stages of the data value chain (data capture, data storage, data
transfer, data transformation, data analytics and data marketing), several key
issues of Big Data applications have been identified and can be summarised as
data availability, data quality, access to data, security, responsibility,
liability, data ownership, privacy, costs, and business models. Ownership and
value of data appear to be important issues in discussions on the governance
of Big Data driven inter-organisational applications. A growing number of Big
Data initiatives address privacy and security concerns. Also Big Data
applications raise power-related issues that sometimes can lead to potential
abuses of data. And many companies expect to develop new business models with
data but are in a kind of deadlock and afraid of taking the first step. So the
challenge is how companies could or should deal with these inter-
organisational governance issues as they are considered as (the most)
hindering factors for fulfilling the promises and opportunities of Big Data in
agri-food sector.

Against this background, the network structure raises multiple questions in
the scope of data management. For instance, how is the communication among the
different actors? On what conditions do farmers take part, and how easy is it
to enter or leave the network? What is the role of a network administrative
organization and how openly do they perform together with partners of the
network?

###  1.2 OBJECTIVE OF THIS DOCUMENT

Task 1.4 ‘Development Data Management Plan’ is meant to address these
questions by developing a plan and specific guidelines for the use cases and
the project as a whole concerning data management.

The Data Management Plan (DMP) will be developed, outlining:

  * how (research) data will be collected, processed or generated within the project; 

  

  * what methodology and standards will be adopted; 

  

  * whether and how this data will be shared and/or made open; 

  

  * and how this data will be curated and preserved during and after the project. 

  

The DMP aims to ensure that IoF2020 activities are compliant with the H2020
Open Access policy and the recommendations of the Open Research Data pilot.
The DMP will furthermore explain how the project will be connected with other
past and on-going initiatives such as EIP-Agri, agINFRA and global channels,
such as OpenAIRE, CIARD and GODAN.

Under this task an Open Access Support Pack will be developed translating the
generic H2020 requirements and recommendations into specific guidelines and
advice that can be applied in the project.

This document (D1.4) will provide first version of Data Management Plan
containing a general overview of relevant developments and a first inventory
of needs and issues that play a role in the use cases. The application of the
DMP by all IoF2020 partners will be continuously monitored under this task and
an updated version of the DMP including more detailed specific support packs
for the use cases (D1.5) will be delivered in Month 36.

###  1.3 OUTLINE

The remainder of this document is organized as follows. Chapter 2 will
describe the approach how the results were found. This results in an overview
of general developments on data management, described in Chapter 3. Chapter 4
will provide a short overview of relevant past and on-going initiatives and
projects on data management in agriculture. Then a first inventory of the
needs and potential issues of the use cases will be described. Based on the
findings in these Chapters, a concrete Data Management Plan will be provided
in Chapter 6, followed by some general conclusions in Chapter 7.

##  2 APPROACH

The approach that was followed to generate this report consists of the
following steps (see Figure 2):

  1. Identification and description of relevant external developments in the field of data management in general and more specific for agriculture (see Chapter 3). 

  

  2. Identification and description of relevant initiatives and/or projects (in the recent past of ongoing) in the field of (agricultural) data management (see Chapter 4). 

  

  3. Based on the results of the previous two steps a quick scan of the needs and issues in the IoF2020 use cases is made (see Chapter 5). 

  

  4. Based on the results of all previous steps a preliminary version of the IoF2020 Data 

  

  

Management is defined that provides general guidelines for the use cases (see
Chapter 6).

_Figure 2 Steps that were taken to achieve a first version of the Data
Management Plan for IoF2020_

After these steps the data management plan will be further refined and
tailored into a specific support pack for the use cases, but this is outside
the scope of this deliverable. The results of this deliverable will be
iteratively updated and refined as a living document but consolidated in a
next deliverable in Month 36 (D1.5).

##  3 EXTERNAL DEVELOPMENTS

In this chapter we will highlight and briefly describe some data management
developments that are most relevant to a H2020 innovation action project such
as IoF2020 in Section 3.1. In Section 3.2 we will then describe the possible
consequences for IoF2020 of these developments.

###  3.1 DATA MANAGEMENT DEVELOPMENTS

####  **3.1.1 H2020 Open Access Policy** 1

Open access (OA) can be defined as the practice of providing on-line access to
scientific information that is free of charge to the user and that is re-
usable. In the context of R&D, open access to 'scientific information' refers
to two main categories:

  * Peer-reviewed scientific publications (primarily research articles published in academic journals) 

  

  * Scientific research data: data underlying publications and/or other data (such as curated but unpublished datasets or raw data) 

  

It is now widely recognised that making research results more accessible to
all societal actors contributes to better and more efficient science, and to
innovation in the public and private sectors The Commission therefore supports
open access at the European level (in its framework programmes), at the Member
States level and internationally.

####  Peer-reviewed scientific publications

All projects receiving Horizon 2020 funding are **required** to make sure that
any peer-reviewed journal article they publish is openly accessible, free of
charge (article 29.2. Model Grant Agreement).

####  Research data

The Commission is running a **pilot on open access** to research data in
Horizon 2020: the Open Research Data (ORD) pilot. This pilot takes into
account the need to balance openness with the protection of scientific
information, commercialisation and Intellectual Property Rights (IPR), privacy
concerns, and security, as well as questions of data management and
preservation. The pilot applies to research data underlying publications but
beneficiaries can also voluntarily make other datasets open. Participating
projects are required to develop a Data Management Plan, in which they will
specify what data will be open. In previous work programmes, the ORD Pilot was
limited to some specific areas of Horizon 2020. Starting with the 2017 work
programme, however, the ORD pilot was extended to cover **all thematic areas**
of Horizon 2020, thus realising the Commission's ambition of "open research
data per default" (but allowing for opt-outs).

####  More information

For details of how open access applies to beneficiaries in projects funded
under Horizon 2020, please see the  **_Guidelines_ ** **_on_ ** **_Open_ **
**_Access_ ** **_to_ ** **_Scientific_ ** **_Publications_ ** **_and_ **
**_Research_ ** **_Data_ ** and/or the  **_Guidelines_ ** **_on_ ** **_data_
** **_management_ ** .

Also:

  * _Participants_ _Portal_

  

  * _OpenAIRE_

  

  * _Open_ _access_ _in_ _FP7_

  

#####  3.1.2 Open Research Data pilot  2

_What is the open research data pilot?_

Open data is data that is free to access, reuse, repurpose, and redistribute.
The Open Research Data Pilot aims to make the research data generated by
selected Horizon 2020 projects accessible with as few restrictions as
possible, while at the same time protecting sensitive data from inappropriate
access.

If your Horizon 2020 project is part of the pilot, and your data meets certain
conditions, you must deposit your data in a research data repository where
they will be findable and accessible for others. Don’t panic - you are not
expected to share sensitive data or breach any IPR agreements with industrial
partners. You do not need to deposit all the data you generate during the
project either – only that which underpins published research findings and/or
has longer-term value. In addition to supporting your research’s integrity,
openness has many other benefits. Improved visibility means your research will
reach more people and have a greater impact – for science, society and your
own career. Recent studies have shown that citations increase when data is
made available alongside the publication; these papers also have a longer
shelf-life.

_Which H2020 strands are required to participate?_

Projects starting from January 2017 are by default part of the Open Data
Pilot. If your project started before earlier and stems from one of these
Horizon 2020 areas, you are automatically part of the pilot as well::

  * Future and Emerging Technologies 

  

  * Research infrastructures (including e-Infrastructures) 

  

  * Leadership in enabling and industrial technologies – Information and Communication Technologies 

  

  * Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and 

  

Biotechnology: ‘nanosafety’ and ‘modelling’ topics

  * Societal Challenge: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy - selected topics in the calls H2020-SFS2016/2017, H2020-BG-2016/2017, H2020-RUR-2016/2017 and H2020-BB-2016/2017, as specified in the work programme 

  

  * Societal Challenge: Climate Action, Environment, Resource Efficiency and Raw materials – except raw materials 

  

  * Societal Challenge: Europe in a changing world – inclusive, innovative and reflective Societies 

  

  * Science with and for Society 

  

  * Cross-cutting activities - focus areas – part Smart and Sustainable Cities. 

  

Maybe data sharing is not appropriate for your project; the  _EC’s Guide on
Open Access_ _Scientific_ _Publications and Research Data_ lists conditions
that would allow or require you to opt out of the pilot.

In that case please consider if a partial opt-out is possible.

_What is a data management plan (DMP)?_

To help you optimise the potential for future sharing and reuse, a Data
Management Plan (DMP) can help you to consider any problems or challenges that
may be encountered and helps you to identify ways to overcome these. A DMP
should be thought of as a “living” document outlining how the research data
collected or generated will be handled during and after a research project.
Remember, the plan should be realistic and based around the resources
available to you and your project partners. There is no point in writing a
gold plated plan if it cannot be implemented!

It should describe:

  * The data set: What kind of data will the project collect or generate, and to whom might they be useful later on? The pilot applies to (1) the data and metadata needed to validate results in scientific publications and (2) other curated and/or raw data and metadata that may be required for validation purposes or with reuse value. 

  

  * Standards and metadata: What disciplinary norms will you adopt in the project? What is the data about? Who created it and why? In what forms it is available? Metadata answers such questions to enable data to be found and understood, ideally according to the particular standards of your scientific discipline. Metadata, documentation and standards help to make your data Findable, Accessible, Interoperable and Re-usable or FAIR for short. 

  

  * Data sharing: By default as much of the resulting data as possible should be archived as Open Access. Therefore legitimate reasons for not sharing resulting data should be explained in the DMP. Remember, no one expects you to compromise data protection or breach any IPR agreements. Data sharing should be done responsibly. The DMP Guidelines therefore ask you to describe any ethical or legal issues that can have an impact on data sharing. Furthermore, 

  

  * Archiving and preservation: Funding bodies are keen to ensure that publicly funded research outputs can have a positive impact on future research, for policy development, and for societal change. They recognise that impact can take quite a long time to be realised and, accordingly, expect the data to be available for a suitable period beyond the life of the project. Remember, it is not simply enough to ensure that the bits are stored in a research data repository, but also consider the usability of your data. In this respect, you should consider preserving software or any code produced to perform specific analyses or to render the data as well as being clear about any proprietary or open source tools that will be needed to validate and use the preserved data. 

  

The DMP is not a fixed document. The first version of the DMP is expected to
be delivered within the first 6 months of your project, but you don’t have to
provide detailed answers to all the questions yet. The DMP needs to be updated
over the course of the project whenever significant changes arise, such as new
data or changes in the consortium policies or consortium composition. The DMP
should be updated at least in time with the periodic evaluation or assessment
of the project as well as in time for the final review. Consider reviewing
your DMP at regular intervals in the project and consider how you might make
use of scheduled WP and/or project staff meetings to facilitate this review.

_What practical steps should you take?_

  1. When your project is part of the pilot, you should  _create a Data Management Plan_ .  Your institution may offer Research Data Management support to help you planning. 

  

  2. Also, you should  _select a data repository_ that will preserve your data, metadata and possibly tools in the long term. It is advisable to contact the repository of your choice when writing the first version of your DMP. Repositories may offer guidelines for sustainable data formats and metadata standards, as well as support for dealing with sensitive data and licensing. 

  

  3. As noted earlier, you do not need to keep everything. Curating data requires time and effort so you want to make sure that you are putting your effort into the outputs that really matter. Select what data you’ll need to retain to support validation of your finding but also consider any data outputs that may have longer term value as well – for you and for others. 

  

  

####  Links

  * EC’s Guide on Open Access to Scientific Publications and Research Data in Horizon 2020 

  

(updated August 25,

2016)
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/_
_h2020-hi-oa-pilot-guide_en.pdf_

  * EC’s Guidelines on Data Management in Horizon 2020 (updated July 26, 

  

2016):
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/_
_h2020-hi-oa-data-mgt_en.pdf_

  * EC’s Agenda on Open Science:  _https://ec.europa.eu/digital-agenda/en/open-science_

  

  * DMPonline tool:  _https://dmponline.dcc.ac.uk/_

  

  * DCC How to Write a DMP guide:  _http://www.dcc.ac.uk/resources/how-guides/develop-dataplan_

  

  * DCC How to Select What Data to Keep guide:  _http://www.dcc.ac.uk/resources/howguides/five-steps-decide-what-data-keep_

  

  * DCC How to Licence Research Data guide:  _http://www.dcc.ac.uk/resources/howguides/license-research-data_

  

  * RDNL video The what, why and how of data management planning:  _http://datasupport.researchdata.nl/en/start-de-cursus/iiplanfase/datamanagementplanning/_

  

  * Software Sustainability Institute’s Software Management 

  

Plan:
_https://www.software.ac.uk/sites/default/files/images/content/SMP_Checklist_2016__
_v0.1.pdf_

#####  3.1.3 European Data Economy

Building a European data economy is part of the Digital Single Market
strategy. The initiative aims at fostering the best possible use of the
potential of digital data to benefit the economy and society. It addresses the
barriers that impede the free flow of data to achieve a European single
market.

####  General need for action

_Digital data_ is an essential resource for economic growth, competitiveness,
innovation, job creation and societal progress in general. The EU needs to
ensure that data flows across borders and sectors. This data should be
accessible and reusable by most stakeholders in an optimal way. A coordinated

European approach is essential for the development of the data economy, as
part of the  _Digital Single_

_Market strategy_ .  The European Commission adopted a  _Communication_ on
"Building a European Data Economy", accompanied by a  _Staff Working Document_
on January 2017, where it:

  * looks at the rules and regulations impeding the free flow of data and present options to remove unjustified or disproportionate data location restrictions, and 

  

  * outlines legal issues regarding access to and transfer of data, data portability and liability of non-personal, machine-generated digital data. 

  

The European Commission has launched a  _public consultation_ and  _dialogue
with stakeholders_ on these topics to gather further evidence. This process
will help identify future policy or legislative measures that will unleash
Europe's data economy. The development of the European Data economy is one of
the three emerging challenges identified in the  _mid-term review_ .  The
actions to be implemented are:

  * to prepare a legislative proposal on the EU free flow of data cooperation framework (autumn 2017) 

  

  * to prepare an initiative on accessibility and re-use of public and publicly funded (spring 2018) 

  

In addition, the Commission will continue its work on liability and other
emerging data issues. For more details, read the  _Communication_ .

####  Facing the challenge - removing data localisation restrictions: the free
flow of data

Free flow of data means the freedom to process and store data in electronic
format anywhere within the EU. It is necessary for the development and use of
innovative data technologies and services. In order to achieve the free flow
of data, the European Commission will collect more evidence on data location
restrictions and assess their impacts on businesses, especially SMEs and
start-ups, and public sector organisations. The Commission will also discuss
the justifications for and proportionality of those data location restrictions
with Member States and other stakeholders. It will then take justified and
appropriate follow-up actions, in line with  _better regulation principles_ ,
to address the issue.

####  Exploring the emerging issues relating to the data economy

The European Commission is currently defining, scoping and articulating the
following issues in order to trigger and frame a dialogue with stakeholders:

  * Non-personal machine-generated data need to be tradable to allow innovative business models to flourish, new market entrants to propose new ideas and start-ups to have a fair chance to compete. 

  

  * Data-driven technologies are transforming our economy and society, resulting in the production of ever-increasing amounts of data. This phenomenon leads to innovative ways of collecting, acquiring, processing and using data which can pose a challenge to the current legal framework. 

  

  * Access to and transfer of non-personal data, data liability, as well as portability of nonpersonal data, interoperability and standards are complex legal issues. 

  

This  _consultation_ process will contribute to the policy choices taken by
the European Commission in the future.

####  Useful links

  * Have a look at the  _workshops_ organised on how to build a European data economy. 

  

  * _Press release and MEMO_ _-_ _Q &A _

  

  * _Communication_ on Building a European Data Economy 

  

  * _Staff Working Document_ on Building a European Data Economy 

  

  * _Factsheet_ on Building a European Data Economy 

  

  * Study on  _Measuring the economic impact of cloud computing in Europe_

  

  * Study on  _Facilitating cross border data flow in the DSM_

  

  * Intermediary study on  _Cross-border data flow in the Digital Single Market: data location_ _restrictions_

  

  * _Speech from Commissioner Oettinger_ at the Conference "Building European Data Economy" (17 October 2016). 

  

  * _Speech from Vice-President Ansip_ at the Digital Assembly 2016, "Europe should not be afraid of data" (29 September 2016). 

  

#####  3.1.4 Agricultural Data developments

In view of the technical changes brought forth by Big Data and Smart Farming,
we seek to understand the consequences for the stakeholder network and
governance structure around the farm in this section.

The literature suggests major shifts in roles of and power relations among
different players in existing agri-food chains. We observed the changing roles
of old and new software suppliers in relation to Big Data and farming and
emerging landscape of data-driven initiatives with prominent role of big tech
and data companies like Google and IBM. In Figure 3, the current landscape of
data-driven initiatives is visualized.

The stakeholder networks exhibits a high degree of dynamics with new players
taking over the roles played by other players and the incumbents assuming new
roles in relation to agricultural Big Data. As opportunities for Big Data have
surfaced in the agribusiness sector, big agriculture companies such as
Monsanto and John Deere have spent hundreds of millions of dollars on
technologies that use detailed data on soil type, seed variety, and weather to
help farmers cut costs and increase yields. Other players include various
accelerators, incubators, venture capital firms, and corporate venture funds
(Monsanto, DuPont, Syngenta, Bayer, DOW etc.).

_Figure 3 The landscape of the Big Data network with business players._

Monsanto has been pushing big-data analytics across all its business lines,
from climate prediction to genetic engineering. It is trying to persuade more
farmers to adopt its cloud services. Monsanto says farmers benefit most when
they allow the company to analyse their data - along with that of other
farmers - to help them find the best solutions for each patch of land.

While corporates are very much engaged with Big Data and agriculture, start-
ups are at the heart of action, providing solutions across the value chain,
from infrastructure and sensors all the way down to software that manages the
many streams of data from across the farm. As the ag-tech space heats up, an
increasing number of small tech start-ups are launching products giving their
bigger counterparts a run for their money. In the USA, start-ups like
FarmLogs, FarmLink and 640 Labs challenge agribusiness giants like Monsanto,
Deere, DuPont Pioneer. One observes a swarm of dataservice start-ups such as
FarmBot (an integrated open-source precision agriculture system) and Climate
Corporation. Their products are powered by many of the same data sources,
particularly those that are freely available such as from weather services and
Google Maps. They can also access data gathered by farm machines and
transferred wirelessly to the cloud. Traditional agri-IT firms such as NEC and
Dacom are active with a precision farming trial in Romania using environmental
sensors and Big Data analytics software to maximize yields.

Venture capital firms are now keen on investing in agriculture technology
companies such as Blue River Technology, a business focusing on the use of
computer vision and robotics in agriculture. The new players to Smart Farming
are tech companies that were traditionally not active in agriculture. For
example, Japanese technology firms such as Fujitsu are helping farmers with
their cloud based farming systems. Fujitsu collects data (rainfall, humidity,
soil temperatures) from a network of cameras and sensors across the country to
help farmers in Japan better manage its crops and expenses. Data processing
specialists are likely to become partners of producers as Big Data delivers on
its promise to fundamentally change the competitiveness of producers.

Beside business players such as corporates and start-ups, there are many
public institutions (e.g., universities, USDA, the American Farm Bureau
Federation, GODAN) that are actively influencing Big Data applications in
farming through their advocacy on open data and data-driven innovation or
their emphasis on governance issues concerning data ownership and privacy
issues. Well-known examples are the Big Data Coalition, Open Agriculture Data
Alliance (OADA) and AgGateway. Public institutions like the USDA, for example,
want to harness the power of agricultural data points created by connected
farming equipment, drones, and even satellites to enable precision agriculture
for policy objectives like food security and sustainability. Precision farming
is considered to be the “holy grail” because it is the means by which the food
supply and demand imbalance will be solved. To achieve that precision, farmers
need a lot of data to inform their planting strategies. That is why USDA is
investing in big, open data projects. It is expected that open data and Big
Data will be combined together to provide farmers and consumers just the right
kind of information to make the best decisions.

Data ownership is an important issue in discussions on the governance of
agricultural Big Data generated by smart machinery such as tractors from John
Deere. In particular, value and ownership of precision agricultural data have
received much attention in business media. It has become a common practice to
sign Big Data agreements on ownership and control data between farmers and
agriculture technology providers. Such agreements address questions such as:
How can farmers make use of Big Data? Where does the data come from? How much
data can we collect? Where is it stored? How do we make use of it? Who owns
this data? Which companies are involved in data processing?

There is also a growing number of initiatives to address or ease privacy and
security concerns. For example, the Big Data Coalition, a coalition of major
farm organizations and agricultural technology providers in the USA, has set
principles on data ownership, data collection, notice, third-party access and
use, transparency and consistency, choice, portability, data availability,
market speculation, liability and security safeguards  3  . And AgGateway, a
non-profit organization with more than 200 member companies in the USA, have
drawn a white paper that presents ways to incorporate data privacy and
standards  4  . It provides users of farm data and their customers with issues
to consider when establishing policies, procedures, and agreements on using
that data instead of setting principles and privacy norms. The European
farmers and agri-cooperatives association COPA-

COGECA has recently also published their ‘Main principles underpinning the
collection, use and

exchange of agricultural data’  5  . The principles concern:

  * _Ownership of farm data_ – data produced on the farm or during farming operations should be owned by the farmers themselves. If this data is used, also indirectly through combined services, the farmer should be somehow compensated for this. 

  

  * _Ownership of the underlying rights to derived data_ \- In any case, it should be clear when farm data is used and for what purpose; the farmer should be in full control of his/her data. When farm data is used by third parties anonymization and security are of utmost importance. 

  

  * _Duration, suspension and termination of supply_ – farmers must be provided with the possibility to opt out of a contract on data use with the right to delete all historical data. 

  

  * _Guarantee of compliance with laws and regulation_ – data collection should not be in conflict with general laws (e.g. on privacy) and data should not be used for unlawful purposes 

  

  * _Liability_ – in contracts on data use intellectual property rights of farmers and agri-cooperatives must be protected and liabilities must be clearly described. It is not always possible to go into all possible details so there should be a good balance between what is written in the contract and trust between partners. 

  

Based on these type of principles several codes of conduct/practice with
concrete models for contracts have been developed such as the New Zealand Farm
Data Code of Practice  6  , the Dutch BO-Akkerbouw Code of Conduct  7  , and
Ag Data Transparency has established a certifying procedure  8  . More similar
codes and model contracts will pop-up in the near future.

The ‘Ownership Principle’ of the Big Data Coalition states that “We believe
farmers own information generated on their farming operations. However, it is
the responsibility of the farmer to agree upon data use and sharing with the
other stakeholders (...).” While having concerns about data ownership, farmers
also see how much companies are investing in Big Data. In 2013, Monsanto paid
nearly 1 billion US dollars to acquire The Climate Corporation, and more
industry consolidation is expected. Farmers want to make sure they reap the
profits from Big Data, too. Such change of thinking may lead to new business
models that allow shared harvesting of value from data.

In conclusion, Big data applications in Smart Farming will potentially raise
many power-related issues. There might be companies emerging that gain much
power because they get all the data. In the agrifood chain these could be
input suppliers or commodity traders, leading to a further power shift in
market positions. This power shift can also lead to potential abuses of data
e.g. by the GMO lobby or agricultural commodity markets or manipulation of
companies. Initially, these threats might not be obvious because for many
applications small start-up companies with hardly any power are involved.
However, it is a common business practice that these are acquired by bigger
companies if they are successful and in this way the data still gets
concentrated in the hands of one big player. It can be concluded that Big Data
is both a huge opportunity as a potential threat for farmers.

#####  3.1.5 General Data Protection Regulation  9

Security and privacy of personal data increasingly have become a public
concern which has led to the General Data Protection Regulation (GDPR) imposed
by the EU in 2018. The aim of GDPR is that:

  * It allows European Union citizens to better control their personal data. It also modernises and unifies rules allowing businesses to reduce red tape and to benefit from greater consumer trust. 

  

  * The GDPR is part of the  _EU data protection reform package_ ,  along with the  _data protection_ _directive for police and criminal justice authorities_ . 

  

_Key points:_

####  **Citizens’ rights**

The GDPR strengthens existing rights, provides for new rights and gives
citizens more control over their personal data. These include:

  * **easier access to their data** — including providing more information on how that data is processed and ensuring that that information is available in a clear and understandable way; 

  

  * **a newright to data portability** — making it easier to transmit personal data between service providers; 

  

  * a clearer **right to erasure (‘right to be forgotten’)** — when an individual no longer wants their data processed and there is no legitimate reason to keep it, the data will be deleted; 

  

  * **right to know when their personal data has been hacked** — companies and organisations will have to inform individuals promptly of serious data breaches. They will also have to notify the relevant data protection supervisory authority. 

  

####  **Rules for businesses**

The GDPR is designed to create business opportunities and stimulate innovation
through a number of steps including:

  * **a single set of EU-wide rules** — a single EU-wide law for data protection is estimated to make savings of €2.3 billion per year; 

  

  * a **data protection officer,** responsible for data protection, will be designated by public authorities and by businesses which process data on a large scale; 

  

  * **one-stop-shop** — businesses only have to deal with one single supervisory authority (in the 

  

EU country in which they are mainly based);

  * **EU rules for non-EU companies** — companies based outside the EU must apply the same rules when offering services or goods, or monitoring behaviour of individuals within the EU; 

  

  * **innovation-friendly rules** — a guarantee that data protection safeguards are built into products and services from the earliest stage of development (data protection by design and by default); 

  

  * **privacy-friendly techniques** such as **pseudonymisation** (when identifying fields within a data record are replaced by one or more artificial identifiers) and **encryption** (when data is coded in such a way that only authorised parties can read it); 

  

  * **removal of notifications** — the new data protection rules will scrap most notification obligations and the costs associated with these. One of the aims of the data protection regulation is to remove obstacles to free flow of personal data within the EU. This will make it easier for businesses to expand; 

  

  * **impact assessments** — businesses will have to carry out impact assessments when data processing may result in a high risk for the rights and freedoms of individuals; 

  

  * **record-keeping** — SMEs are not required to keep records of processing activities, unless the processing is regular or likely to result in a risk to the rights and freedoms of the person whose data is being processed. 

  

The  _European Commission_ must submit a report on the evaluation and review
of the regulation by 25 May 2020. The GDPR will apply as of 25 May 2018\.

For more information, see:

  * _Press release_ ( _European Commission_ ) 

  

  * _‘2018 reform of EU data protection rules’_ ( _European Commission_ ). 

  

###  3.2 CONSEQUENCES FOR IOF2020

The following consequences for IoF2020 could be derived from the developments
that were described in the previous section:

  1. Any peer-reviewed journal article should be published openly accessible 

  

  2. Research data should be stored in a central repository where it is findable and accessible by others 

    1. Since IoF2020 is an Innovation Action type of project, the first question is whether _research_ data is actually involved; this has to be identified first 

  

    2. It should be further explored whether data sharing is appropriate in IoF2020 and if a partial opt-out for the Open Research Data pilot is appropriate 

  
  

  

  3. A data management plan needs to be written for IoF2020 according to the guidelines provided by the ORD Pilot 

  

  4. It should be identified if and to what extent the data-driven technologies in the IoF2020 use cases are aligned with the European Data Economy policy. 

    1. Since this policy is also under debate, IoF2020 should actively participate in this debate and development of this Data Economy. 

  
  

  

  5. It should be explored which issues around agricultural data sharing (e.g. data ownership/control, rights to use data, etc.) are (potentially) playing a role and if principles and guidelines, especially provided by IoF2020 partner COPA-COGECA, are applicable. And if yes, what type of contracts should be set-up? 

  

  6. The IoF2020 use cases should be analysed for GDPR compliance and take measures on this where necessary. 

  

  

##  4 RELEVANT INITIATIVES AND PROJECTS

In this chapter several projects and initiatives are described that are
dealing with (Agricultural) Data and are potentially relevant for IoF2020.

###  4.1 PROJECTS AND INITIATIVES

####  **4.1.1 GODAN** 10

GODAN supports the proactive sharing of open data to make information about
agriculture and nutrition available, accessible and usable to deal with the
urgent challenge of ensuring world food security. It is a rapidly growing
group, currently with over 584 partners from national governments, non-
governmental, international and private sector organisations that have
committed to a joint  _Statement of Purpose._

The initiative focuses on building high-level support among governments,
policymakers, international organizations and business. GODAN promotes
collaboration to harness the growing volume of data generated by new
technologies to solve long-standing problems and to benefit farmers and the
health of consumers. GODAN encourages collaboration and cooperation between
stakeholders in the sector.

The GODAN initiative was launched At the 2012 G-8 Summit, G-8 leaders
committed to the New Alliance for Food Security and Nutrition, the next phase
of a shared commitment to achieving global food security.

As part of this commitment, they agreed to “share relevant agricultural data
available from G-8 countries with African partners and convene an
international conference on Open Data for Agriculture, to develop options for
the establishment of a global platform to make reliable agricultural and
related information available to African farmers, researchers and
policymakers, taking into account existing agricultural data systems.”

In April 2013, the commitment to convene an international conference on Open
Data for Agriculture was fulfilled when the G8 International Conference on
Open Data for Agriculture took place.

This conference worked to ‘obtain commitment and action from nations and
relevant stakeholders to promote policies and invest in projects that open
access to publicly funded global agriculturally relevant data streams, making
such data readily accessible to users in Africa and world-wide, and ultimately
supporting a sustainable increase in food security in developed and developing
countries.

The GODAN initiative was a by-product of this conference and was announced at
the Open Government Partnership Conference in October 2013\.

Any organization that supports open access to agriculture and nutrition data
can become member of GODAN. Partners include government, donors, international
and not-for-profit organizations and businesses. GODAN partners support the
shared principles based on GODAN’s Statement of Purpose:

  * Agricultural and nutritional data to be available, accessible, usable and unrestricted 

  

  * Partners aim to build high level policy and private sector support for open data 

  

  * Encourage collaboration and cooperation across existing agriculture, nutrition and open data activities and stakeholders to solve long-standing global problems 

  

GODAN partners commit to:

  * Host regular conversations with our peer multilateral and local organisations to identify and share best practices and determine how to more effectively share data and provide useable analysis for local application 

  

  * Recruit new partners to GODAN 

  

GODAN activities and its Secretariat are financially supported by the US
Government, the UK

Department for International Development (DFID), the Government of the
Netherlands, FAO, Technical Centre for Agricultural and Rural Cooperation
(CTA), GFAR, The Open Data Institute (ODI), the CGIAR and CABI.

_More information:_

  * _Statement of Purpose_

  

  * _Theory of Change_

  

  * _Ownership of Open Data: Governance Options for Agriculture and Nutrition_

  

  * _A Global Data Ecosystem for Agriculture and Food_

  

####  **4.1.2 EIP-Agri** 11

The agricultural European Innovation Partnership (EIP-AGRI) works to foster
competitive and sustainable farming and forestry that 'achieves more and
better from less'. It contributes to ensuring a steady supply of food, feed
and biomaterials, developing its work in harmony with the essential natural
resources on which farming depends.

The European Innovation Partnership for Agricultural productivity and
Sustainability (EIP-AGRI) has been launched in 2012 to contribute to the
European Union's strategy 'Europe 2020' for smart, sustainable and inclusive
growth. This strategy sets the strengthening of research and innovation as one
of its five main objectives and supports a new interactive approach to
innovation: _European Innovation Partnerships._

The EIP-AGRI pools funding streams to boost interactive innovation. Having an
idea is one thing, turning it into an innovation action is another. Different
types of available funding sources can help get an agricultural innovation
project started, such as the **European Rural Development policy** or the EU's
research and innovation programme **Horizon 2020** . The EIP-AGRI contributes
to integrating different funding streams so that they contribute together to a
same goal and duplicate results.

Rural Development will in particular support **Operational Groups** and
**Innovation Support**

**Services** within a country or region. **Horizon 2020** will fund multi-
actor projects and thematic networks involving partners from at least three EU
countries. Other policies may offer additional opportunities.

The EIP-AGRI brings together innovation actors (farmers, advisers,
researchers, businesses, NGOs and others) at EU level and within the rural
development programmes (RDPs). Together they form an EU-wide EIP network. EIP
Operational Groups can be funded under the RDPs, are project-based and tackle
a certain (practical) problem or opportunity which may lead to an innovation.
The Operational Group approach makes the best use of different types of
knowledge (practical, scientific, technical, organisational, etc.) in an
interactive way. An Operational Group is composed of those key actors that are
in the best position to realise the project's goals, to share implementation
experiences and to disseminate the outcomes broadly. The first Operational
Groups are currently being set up in several EU countries and regions.

The Rural Networks' Assembly, which was launched in January 2015, coordinates
two networks - the

EIP-AGRI Network and the European Network for Rural Development (ENRD). The
Assembly

includes several subgroups, one of them being the permanent Subgroup on
Innovation for agricultural productivity and sustainability. This Subgroup on
Innovation will support the EIP-AGRI Network.

The EIP-AGRI website has exciting and interactive features. All visitors can
voice their research needs, discover funding opportunities for innovation
projects and look for partners to connect with. Through the website's
interactive functions, users can share  **innovative project ideas** and
practices, information about  **research and innovation projects** ,
including projects' results, by filling in the available easy-to-use e-forms.
Various EIP-AGRI-related  **publications** are available for download on the
website, providing visitors with information on a wide range of interesting
topics. Future functionalities will be developed for **Operational Groups**
and European funds managing authorities once the programmes start. Through
this collaborative effort, the EIP-AGRI website will become a one-stop-shop
for agricultural innovation in Europe.

The **EIP-AGRI network** is run by the European Commission (DG Agriculture and
Rural Development) with the help of the  **EIP-AGRI Service Point** .  The
Service Point offers a wide range of tools and services which can help you
further your ideas and projects. It also facilitates networking activities;
enhancing communication, knowledge sharing and exchange through conferences,
**Focus Groups** ,  workshops, seminars and publications.

A recent series of workshops on Agricultural Data is particularly relevant:

  * ‘Data revolution: emerging new data-driven business models in the agri-food sector’, Sophia, 

  

Bulgaria, 22-23 June 2016.
_https://ec.europa.eu/eip/agriculture/en/event/eip-agri-seminar%E2%80%98data-
revolution-emerging-new_

  * ‘Data Sharing: ensuring fair sharing of digitisation benefits in agriculture’ Tuesday 4-5 April 2017, Bratislava, Slovakia.  _https://ec.europa.eu/eip/agriculture/en/event/eip-agri-workshopdata-sharing_

  

  * ‘Digital Innovation Hubs: mainstreaming digital agriculture’, 1-2 June 2017, Kilkenny, Ireland. 

  

_https://ec.europa.eu/eip/agriculture/en/event/eip-agri-seminar-digital-
innovation-hubs_

_More about EIP-AGRI:_

  * **_Brochure: EIP-AGRI network_ **

  

  * **_Brochure: EIP-AGRI Service Point_ **

  

####  **4.1.3 AgInfra+** 12

**AGINFRA+** aims to exploit core e-infrastructures such as  _EGI.eu_ ,
_OpenAIRE_ ,  _EUDAT_ and  _D4Science_ ,  towards the evolution of the AGINFRA
data infrastructure, so as to provide a sustainable channel addressing
adjacent but not fully connected user communities around Agriculture and Food.

To this end, the project will develop and provide the necessary specifications
and components for allowing the rapid and intuitive development of variegating
data analysis workflows, where the functionalities for data storage and
indexing, algorithm execution, results visualization and deployment are
provided by specialized services utilizing cloud based infrastructure(s).

Furthermore, **AGINFRA+** aspires to establish a framework facilitating the
transparent documentation and exploitation and publication of research assets
(datasets, mathematical models, software components results and publications)
within AGINFRA, in order to enable their reuse and repurposing from the wider
research community.

**AGINFRA** is the European research hub and thematic aggregator that
catalogues and makes discoverable publications, data sets and software
services developed by Horizon 2020 research projects on topics related to
agriculture, food and the environment.

It is part of the broader vision of the European research e-infrastructure
“European Open Science Cloud”, a synergy between  _OpenAIRE_ ,  _EUDAT_ ,
_GEANT_ ,  _EGI_ ,  _LIBER_ .

With the integration of big data processing components from projects like the
Horizon 2020 BigDataEurope and the FP7  _SemaGrow_ ,  _AGINFRA_ evolves into a
big data analytics capable einfrastructure for agri-food, to respond to the
needs of three (3) adjacent yet not fully connected user communities:

  * _**H2020 SC1** _ Health 

  

  * _**H2020 SC2** _ Food security and sustainable agriculture 

  

  * _**H2020 SC5** _ Climate action and environment 

  

**AGINFRA+** addresses the challenge of supporting user-driven design and
prototyping of innovative einfrastructure services and applications. It
particularly tries to meet the needs of the scientific and technological
communities that work on the multi-disciplinary and multi-domain problems
related to agriculture and food. It will use, adapt and evolve existing open
e-infrastructure resources and services, in order to demonstrate how fast
prototyping and development of innovative data- and computingintensive
applications can take place.

This project builds upon the extensive experience and work of its partners,
who are key stakeholders in the e-infrastructures ecosystem. It also
implements part of a strategic vision shared between **Agroknow** , the
National Agronomic Research Institute of France ( **INRA** ), the Alterra
Institute of the Wageningen University & Research Center ( **ALTERRA** ), the
National Institute for Risk Assessment of Germany ( **BfR** ), and the Food
and Agriculture Organization ( **FAO** ) of the United Nations - the latter
one, not participating as a funded beneficiary, but supporting the project and
its activities. These stakeholders are part of a core group of internationally
recognised players (including the Chinese Academy of Agricultural Sciences)
aiming to put in place a free global data infrastructure for research and
innovation in agriculture, food and environmental science. This data
infrastructure will become an incubator of the large infrastructure
investments that global donors (including the European Commission) make in the
field of agricultural research around the world.

**AGINFRA+** will evolve and develop further the resources and services of the
AGINFRA data infrastructure, which has been developed in the context of the
FP7  _agINFRA_ p  roject. The new project will build upon core components of
AGINFRA, such as:

  * the federated data and software registry of  _CIARD RING_ , 

  

  * the  _AGINFRA API_ gateway for indexing and hosting executable software components for advanced data processing & analysis, 

  

  * the open source software stack for data analysis, indexing, publication and querying developed by projects such as FP7  _SemaGrow_ and H2020  _Big Data Europe_ , 

  

  * the semantic backbone of the Global Agricultural Concept Scheme (GACS1) that has been based upon the alignment of FAO’s AGROVOC with the USDA’s National Agricultural Library 

  

Thesaurus and CABI’s Thesaurus,

  * the advanced research data set processing & indexing demonstrators developed within FP7 SemaGrow for specific scientific communities such as  _Trees4Futures_ and  _gMIP_ . 

  

The envisaged pilots will focus on three societal challenges that are of
primary importance for our planet and for humanity:

  * Food safety risk assessment and risk monitoring, addressing H2020 SC1 Health, demographic change and well-being. 

  

  * Plant phenotyping for food security, addressing H2020 SC2 Food security, sustainable agriculture and forestry, marine and maritime and inland water research, and the Bioeconomy. 

  

  * Agro-climatic and Economic Modelling, addressing H2020 SC5 Climate action, environment, resource efficiency and raw materials. 

  

In order to realize its vision, AGINFRA+ will achieve the following
objectives:

  * identify the requirements of the specific scientific and technical communities working in the targeted areas, abstracting (wherever possible) to new AGINFRA services that can serve all users; 

  

  * design and implement components that serve such requirements, by exploiting, adapting and extending existing open e-infrastructures (namely, OpenAIRE, EUDAT, EGI, and D4Science), where required; 

  

  * define or extend standards facilitating interoperability, reuse, and repurposing of components in the wider context of AGINFRA; 

  

  * establish mechanisms for documenting and sharing data, mathematical models, methods and components for the selected application areas, in ways that allow their discovery and reuse within and across AGINFRA and served software applications; 

  

  * increase the number of stakeholders, innovators and SMEs aware of AGINFRA services through domain specific demonstration and dissemination activities. 

  

The development of fully defined demonstrator applications in each of the
three application areas will allow to showcase and evaluate the AGINFRA
components in the context of specific end-user requirements from different
scientific areas.

####  **4.1.4 BigDataEurope** 13

Big Data Europe will undertake the foundational work for enabling European
companies to build innovative multilingual products and services based on
semantically interoperable, large-scale, multilingual data assets and
knowledge, available under a variety of licenses and business models.

Big Data Europe aims to:

  * Collect requirements for the ICT infrastructure needed by data-intensive science practitioners tackling a wide range of societal challenges; covering all aspects of publishing and consuming semantically interoperable, large-scale, multi-lingual data assets and knowledge. 

  

  * Design and implement an architecture for an infrastructure that meets requirements, minimizes the disruption to current workflows, and maximizes the opportunities to take advantage of the latest European RTD developments, including multilingual data harvesting, data analytics, and data visualization. 

  

Societal challenges and their Big Data focus areas are:

  * Health - heterogeneous data linking and integration, biomedical semantic indexing 

  

  * Food & Agriculture - large-scale distributed data integration 

  

  * Energy - real-time monitoring, stream processing, data analytics, decision support 

  

  * Transport - streaming sensor network and geospatial data integration 

  

  * Climate - real-time monitoring, stream processing and data analytics 

  

  * Social Sciences - statistical and research data linking and integration 

  

  * Security - real-time monitoring, stream processing and data analytics, image data analysis 

  

The **Food and Agriculture pilot** (SC2) within BDE is focusing on
viticulture. The problem of discovery and linking of information is present in
every major area of agricultural research and agriculture in general. This is
especially true in viticulture where different research methodologies produce
a great amount of heterogeneous data from diverse sources; scientists need to
be able to find all this information so as to analyse and correlate it to
provide integrated solutions to the emerging problems in the European and
global vineyard. These problems arise largely because of the impact of climate
change and therefore the exploitation of the appropriate grapevine varieties
is very important. Factors to bear in mind include the intensity of diseases,
the intensification of the cultivation, the proper implementation of precision
viticulture systems that affect the quality of viticultural products and their
role in human health.

The overall goal of the SC2 Pilot is to demonstrate the ability of Big Data
technologies to complement existing community-driven systems (e.g.  _VITIS_
for the Viticulture Research Community) with efficient large-scale back-end
processing workflows.The pilot deployment is organised in three Cycles with
different targeted objectives:

  * **Pilot Cycle 1 (SC2 Pilot Pitch-Deck)** \- The goal of this Pilot Cycle is to showcase a largescale processing workflow that automatically annotates scientific publications relevant to Viticulture. The focus of the first demonstrator cycle is on the Big Data aspects of such a workflow (i.e. storage, messaging and failure management) and not on the specificities of the NLP modules/tools used in this demonstrator. 

  

  * **Pilot Cycle 2 (SC2 Pilot Maturity / Functionality Expansion)** \- The goal of this Pilot Cycle is to showcase the ability of scalable processing workflows to handle a variety of data types (beyond bibliographic data) relevant to Viticulture. 

  

  * **Pilot Cycle 3 (Lowering SC2 Community Boundaries)** \- The goal of this Pilot Cycle is to provide an engaging, intuitive graphical web interface addressing key data-oriented questions relevant to the Viticulture Research Community, and if possible, intuitive interfaces for endusers for sharing and linking their on-the-field generated data. 

  

In SC2 Pilot Cycle 1, content mainly refers to open scientific publications
relevant to Viticulture, available at FAO/AGRIS and NCBI/PubMed in PDF format
(about 26K and 7K publications respectively). In Cycle 2, the content pool has
been extended to include:

  * Weather Data, available via publicly available APIs (e.g. OpenWeatherMap, Weather Underground, AccuWeather etc.) 

  

  * User-generated data, e.g. geotagged photos from leaves, young shoots and grape clusters, ampelographic data, SSR-marker data etc. 

  

Additional data sources include:

  * Sensor Data, measuring temperature, humidity and luminosity retrieved from sensors installed in selected experimental vineyards, 

  

  * ESA Copernicus Sentinel 2 Data, for selected experimental vineyards. 

  

The goal of the inclusion of these data is to complement the existing SC2
Pilot Demonstrator Knowledge Base so as to support complex real-life research
questions, based on the correlation of environmental conditions with real
observations on crop production and quality.

####  **4.1.5 DATABIO** 14

he Data-Driven Bioeconomy project (DataBio) focuses on the production of best
possible raw materials from agriculture, forestry and fishery for the
bioeconomy industry to produce food, energy and biomaterials taking into
account responsibility and sustainability

In order to meet the above objectives, DataBio is controlling and putting to
use the innovative ICTs and information flows centered mostly around the use
of proximal and remote sensors, in order to provide a streamlined Big Data
Infrastructure for data discovery, retrieval, processing and visualizing, in
support to decisions in bioeconomy business operations.

The main goal of the **DataBio project** is to show the benefits of Big Data
technologies in the raw material production from **agriculture** ,
**forestry** and **fishery/aquaculture** for the bioeconomy industry **to
produce food** , **energy** and **biomaterials** responsibly and sustainably.

**DataBio** proposes to deploy a state of the art, big data platform on top of
the existing partners’ infrastructure and solutions – the **Big DATABIO
Platform** .

The work will be continuous cooperation of experts from end user and
technology provider companies, from bioeconomy and technology research
institutes, and of other partners.

DataBio is working on pilots in three areas: Agriculture, Fishery and
Forestry. The Agicultural pilots are divided into:

  * **Precision Horticulture including vine and olives** o A1. Precision agriculture in olives, fruits, grapes and vegetables o A2. Big Data management in greenhouse eco-systems 

  

  * **B. Arable Precision Farming** o B1. Cereals and biomass crops o B2. Machinery management and environmental issue 

  

  * **C. Subsidies and insurance** o C1. Insurance o C2. CAP support 

  

In the pilots also associated partners and other stakeholders will be actively
involved.

The selected pilots and concepts will be transformed into pilot
implementations using co-innovative approaches and tools where the bioeconomy
sector end users, experts and other stakeholders will give input to the user
and sector domain understanding for the requirement specifications for ICT,
Big Data and Earth Observation experts and for other solution providers in the
consortium.

Based on the preparation and requirement specifications work, the pilots are
implemented using and selecting the best suitable market ready or almost
market ready Big Data and Earth Observation methods, technologies, tools and
services to be integrated to the common **Big DATABIO Platform** .

During the pilots the close cooperation continues and feedback from the
bioeconomy sector user companies will be harnessed in the technical and
methodological upgrades for pilot implementations.

Based on the pilot results and the new solutions also new business
opportunities are expected.

In addition during the pilots the end users are participating in trainings to
learn how to use the solutions and developers also outside the consortium will
be active in the Hackathons to design and develop new tools, services and
application for the platform.

**Databio’s expected achievements include but are not limited to:**

  * Demonstrate increase of productivity in bioeconomy 

  

  * Increase of market share of Big Data technology providers in the bioeconomy sector 

  

  * More than double the use of Big Data technology in bioeconomy  Leveraging additional target sector investments by a factor of >5 

  

  * More than 100 organizations in demonstrations 

  

  * Liaison with other Big Data actions 

  

  * Closely working with BDVA 

  

###  4.2 RELATIONSHIP WITH IOF2020

Through overlapping partners between the mentioned initiatives and projects
and IoF2020, there is already quite a natural ground for collaboration and
knowledge exchange on data management.

Especially IoF2020’s coordinator Wageningen University & Research is involved
in most relevant initiatives and projects. However since we are dealing with
relatively large initiatives, projects and organizations, knowledge exchange
and collaboration it is not automatically guaranteed. Specific attention needs
to be paid to this. Work on very similar specific pilot areas (e.g.
viticulture, agricultural machinery data, etc.) would be a good starting point
for this.

Since the beginning of the IoF2020 project there were already several
occasions (conferences, workshops, etc.) where explicit connections were made
with these projects and initiatives. It was decided to setup an explicit
collaboration with the DataBio project.

##  5 INVENTORY OF USE CASES

Based on the issues that are identified and described in the previous
chapters, a first scan of the IoF2020 use cases was made.

A quick scan for the constraints to data privacy and security was conducted by
a questionnaire at use case level. The results are listed in **Table 1** .

**Table 1** Potential constraints to data privacy and security for each use
case.  
  
<table>  
<tr>  
<th>

**Trial and use cases**

</th>  
<th>

**Data privacy & security constraints **

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

**Arable Trial**

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

UC 1.1. Within-field management zoning

</td>  
<td>

Farmer’s data can only be used outside his company only if he agrees. Other
partners provide proprietary software tools and information.

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

UC 1.2 Precision Crop Management

</td>  
<td>

The data collected by the Bosch systems are intended to remain confidential
and will be the property of the farmer who has acquired a system. The Bosch
cloud must be able to guarantee this confidentiality and the security of its
data.

The API-AGRO interface, which is an API management platform operated by a
consortium represented in the Use Case by Arvalis, allows to add a layer of
management of the confidentiality and possible opening to other actors of
those data. While remaining under the control of the owner of the systems, it
will be possible to communicate the acquired data according to conditions
which still have to be defined while guaranteeing a control by the owner of
the data of the destination that he wants to give to his data.

The Arvalis models and agro-climatic references that will be used for DSS are
the property of Arvalis.

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

UC1.3 Soya Protein Management

</td>  
<td>

No constraints so far about data privacy and security in UC 1.3.

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

UC1.4 Farm Machine Interoperability

</td>  
<td>

The constraints will be dependent on the agreements achieved with the partners
and farmers involved in the UCs that we collaborate with, i.e. UC 1.1 and UC
1.3. UC 1.4 members will have to sign agreements with the farmers for the
access to their data, but also NDAs for specific software elements and
equipment components with the other use case participants.

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

**Dairy Trial**

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

UC2.1 Grazing Cow Monitor

</th>  
<th>

No specific data protection issues arise

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

UC2.2 Happy Cow

</td>  
<td>

The platform is secured to prevent access to cow/farm data by default. Login
opens the data associated to the user. Communication is encrypted.

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

UC2.3 Silent Herdsman

</td>  
<td>

An agreement on the use of the data acquired on each trial site has to be
established. The principle is that the data will be owned by the farmer and
will be made available for the purposes of the project. Any public reporting
of the key findings regarding the trial farms must be anonymised and cleared
by the data owners.

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

UC2.4 Remote Milk Quality

</td>  
<td>

Up to our present knowledge, there are no constraints. There will be no data
exchanged. Data of users will be monitored and handled but not used. Only
alerts and checks are exchanged

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

**Fruit Trial**

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

UC3.1 Fresh table grapes chain

</td>  
<td>

No issues mentioned.

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

UC3.2 Big wine optimization

</td>  
<td>

None till the edition of this document. However, the following considerations
will be taken into account during the implementation phase, others could come
according to the discover needs:

 All the communications are expected to be carried out over DTLS (Datagram
Transport Layer Security) using DTLS 1.2 version, it means ECC asymmetric
cryptography for the key negotiation and authentication, in conjunction with
AES symmetric cryptography for the efficient and optimal data exchange. 
Since the protocol, expected to be used, is OMA LwM2M, it includes all the
security from this protocol for defining an access control for the list of
servers that can interact with the devices and also the definition of a
bootstrap server and commissioning service to guarantee the secure
provisioning of all the credentials and configuration details.

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

UC3.3 Automated olive chain

</th>  
<th>

Farmers involved in the action would like to remain privacy their personal
data.

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

UC3.4 Intelligent fruit logistics

</td>  
<td>

Collection of data on customer sites using our IoT-RTI:

  * Who owns the data collected on premise by the IoT-enabled RTI? 

  

  * Who can do what with the data? 

  

  * Which data can be shown to whom (datadriven business models!)? 

  

  * Encryption of data 

  

Questions regarding security of IoT-Technology used in the pilot:

  * Is there a danger of hacking, data capturing by others, interference with communication? 

  

  * Would it be possible to manipulate collected data? 

  

  * Can the central database be accessed by external parties? 

  

  * To which security level is it possible to encrypt the data? 

  

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

**Vegetable Trial**

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

UC4.1 City farming leafy vegetables

</td>  
<td>

The data generated in a city farm, in general, are related to plant growth and
the operation of a city farm. Data related to persons are out of scope. The
data are owned by the owner of the city farm. The data collected in a city
farm represent a value from an economic point of view. Access to the data in
general only takes place with the consent of the owner. Data access should be
protected. Also, any data link should be secured.

Measures need to be in place against unauthorized or unlawful access or
processing of data as well as against accidental loss or damage of data. Data
access rights management should be in place.

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

UC4.2 Chain-integrated greenhouse production

</td>  
<td>

Data should be owned by farmers and agri-business, and privacy issues should
be respected according to European regulation. As for Intellectual Property, a

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

</th>  
<th>

consortium Agreement (CA) will be negotiated between all partners, settling
among other things the internal organization of the consortium, reflecting
what has been described about the project management structure. With respect
to this Use Case 4.3, Background IP remains with the partner(s) who created it
and Foreground IP (if any) goes to the partner(s) that developed it.

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

UC4.3 Added value weeding data

</td>  
<td>

Yes, farmers need to provide access to their machine data and crop data.

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

UC4.4 Enhanced quality certification system

</td>  
<td>

No issues mentioned

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

**Meat Trial**

</td>  
<td>

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

UC5.1 Pig farm management

</td>  
<td>

</td>  
<td>

Raw pig farm data is confidential and owned by the farmer – and must only be
accessed in aggregated fashion. Aggregated feed data must be kept in the farm
and only accessed by Cloud Service component when actually needed. All
accesses should be logged so that farmer can be informed about what is being
read by whom. The same is true for slaughterhouse data.

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

UC5.2 Poultry chain management

</td>  
<td>

</td>  
<td>

Privacy:

  * Delivering data to platforms (who owns data…) 

  

  * The data remain the ownership of the provider – since SADA is an integrated company, all raw data is property of SADA 

  

  * Support on legal and ethical issues on workers manipulation model Security: 

  

  * Authentication of the data delivered to the platform 

  

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

UC5.3 Meat Transparency and

Traceability

</td>  
<td>

Data privacy and security must be handled within this project. Farms may
provide information which can result in detailed insights on their internal
processes. Data access restriction is provided by the access layer. This layer
implements a set of static and dynamic access rules for EPCIS events,
supporting different

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

</td>  
<td>

roles and actors. Data providers (farmer) stay owner of their data and decide
which supply chain partner can access what kind of information.

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

From this table it can be generally concluded that there is quite some
difference between the several use cases in thinking about data management
issues. Some use cases have thought about this in much detail and are very
aware of the various issues that might arise. Other use cases say that they
don’t see any issue or only have thought about in a general way.

The results from this scan will be shared and discussed between the use cases
and it is expected that they can learn from each other. This will result in a
more generic list of data management issues that in the end again can be made
specific for each use case.

For all trials and use cases together an inventory was made on what type of
data is expected as is presented in Table 2.

_Table 2 Data that is expected to be generated by all use case and trials_  
  
<table>  
<tr>  
<th>

**Type**

</th>  
<th>

**Origin**

</th>  
<th>

**Format**

</th>  
<th>

**Estimated size**

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

Dissemination material:

  * press releases, 

  

  * leaflets, 

  

  * audio-visual material, 

  

  * posters, 

  

  * images/photos 

  

</td>  
<td>

IoF2020 consortia generated

</td>  
<td>

Adobe Photoshop

(.psd)

MS PowerPoint (.ppt, pptx)

JPEG (.jpg, .jpeg)

MS Word (.doc, .docx)

Adobe Acrobat Reader

(.pdf)

Adobe Illustrator (.ai)

Hard copy

</td>  
<td>

0.5 Gb

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

Demographic and personal data of third parties interviewed

</td>  
<td>

Interviews, questionnaires, cooperation agreements,

invitation letters to participate in the pilots, application forms, informed
consent forms

</td>  
<td>

Google forms

MS Excel (.xls, .xlsx)

MS Word (.doc, .docx)

Adobe Acrobat Reader

(.pdf)

Comma Separated

Values (.csv)

</td>  
<td>

0.5 Gb

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

Demographic and personal data of partners within Use Cases

</td>  
<td>

Survey, questionnaires

</td>  
<td>

Google forms

MS Excel (.xls, .xlsx)

MS Word (.doc, .docx)

</td>  
<td>

0.5 Gb

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

D1.4 Data Management Plan  
  
<table>  
<tr>  
<th>

**Type**

</th>  
<th>

**Origin**

</th>  
<th>

**Format**

</th>  
<th>

**Estimated size**

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

</td>  
<td>

</td>  
<td>

Adobe Acrobat Reader

(.pdf)

Comma Separated

Values (.csv)

</td>  
<td>

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

Project reports/deliverables with internal reviewing process

</td>  
<td>

WP2 team generated

</td>  
<td>

MS Excel (.xls, .xlsx)

MS Word (.doc, .docx)

</td>  
<td>

0.5 Gb

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

Project reports/deliverables with external reviewing process

</td>  
<td>

WP2 team generated

</td>  
<td>

MS Excel (.xls, .xlsx)

MS Word (.doc, .docx)

</td>  
<td>

0.5 Gb

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

Contact details of project partners and advisory/scientific board(s)

(Name, Email, Phone, Skype ID…)

</td>  
<td>

Survey, questionnaires

</td>  
<td>

MS Excel (.xls, .xlsx)

MS Word (.doc, .docx)

Adobe Acrobat Reader

(.pdf)

</td>  
<td>

0.5 Gb

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

Guidelines for consortium members

</td>  
<td>

WP2 team generated

</td>  
<td>

MS Word (.doc, .docx)

Adobe Acrobat Reader

(.pdf)

MS PowerPoint (.ppt, pptx)

</td>  
<td>

0.5 Gb

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

Outputs generated at project events

</td>  
<td>

Agendas and meeting minutes,

Attendance sheets

</td>  
<td>

MS Word (.doc, .docx)

Adobe Acrobat Reader

(.pdf)

MS PowerPoint (.ppt, pptx)

</td>  
<td>

0.2 Gb

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

Dataset produced by aggregating data during project implementation

</td>  
<td>

Interview

</td>  
<td>

MS Word (.doc, .docx)

</td>  
<td>

1 Gb

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

**Type**

</td>  
<td>

</td>  
<td>

**Origin**

</td>  
<td>

**Format**

</td>  
<td>

**Estimated size**

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

</td>  
<td>

</td>  
<td>

Focus group discussion

Observation

Survey

</td>  
<td>

Adobe Acrobat Reader

(.pdf)

MS PowerPoint (.ppt, pptx)

</td>  
<td>

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

A dataset documenting and providing evidence for either a report or a
publication produced in the context of project activities

</td>  
<td>

Peerreviewed

scientific publications

</td>  
<td>

Interview

Focus group discussion

Observation

Survey

Desk research etc.

</td>  
<td>

MS Word (.doc, .docx)

Adobe Acrobat Reader

(.pdf)

</td>  
<td>

0.5 Gb

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

Scientific publications with internal reviewing process

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

##  6 DATA MANAGEMENT PLAN

###  6.1 GENERAL GUIDELINES FOR DATA MANAGEMENT IN IOF2020

From the previous Chapters in this document the following general guidelines
for data management in IoF2020 can be derived:

  * Research papers that are derived from the project should be published according to the open access policy 

  

  * Research data should be stored in a central repository according to the FAIR principles: findable, accessible, interoperable and reliable 

  

  * The use cases in IoF2020 should be clearly aligned to the European Data Economy policy and more specifically in line with the principles and guidelines provided by the stakeholder community _i.e._ COPA-COGECA. 

  

  * All IoF2020 use cases should be GDPR-compliant. 

  

###  6.2 FURTHER ACTIONS AND RECOMMENDATIONS

The following steps should be taken to further develop data management in
IoF2020:

  * Investigate what research data is involved in the use cases and other project activities and define how they should be treated according to the open data policy. 

  

  * Participate actively in the debate and developments of the European Data Economy and data sharing in agriculture. 

  

  * Analyze and explore the use cases in a deeper way in order to identify which data management issues potentially play a role and define plans how to deal with them. 

  

  * Although many collaborative actions with other relevant projects and initiatives are already taking place and there are many natural connections through the project partners, a more systematic and structural approach should be explored in order to maximize the benefits and impact of the mutual activities on data management. 

  

  * Collect information concerning GDPR from all IoF2020 use cases based on questionnaires that can be provided by several partners that are already GDPR-compliant. 

  

  * Prepare for each use case a Data Protection Policy, Privacy Policy Statement, Consent Forms and a Data Breach Notification Procedure 

  

For the latter two points, an IoF2020 GDPR package has already been developed
containing several tools and templates (see a separate file  ‘ _IoF2020_ _GDPR
package.zip_ ’)  . However, a full implementation of this package is expected
to be a too heavy load for all IoF2020 use cases. Through an iterative
approach with the use cases and advise from internal and external experts we
will search for an appropriate approach to have a light, but sufficient
implementation.

This Data Management Plan and its future updates will be actively communicated
with all partners in IoF2020 and the use cases in particular. It is planned to
have a workshop on this issue, possibly combined with other similar issues
such as ‘Ethics’ during the annual project meeting in Spring 2018 organized by
WP4.

##  7 CONCLUSIONS

Data or Big Data rapidly has become a new resource or asset in the current
economy, also in the agricultural sector. This development leads to several
issues that have to be addressed such as data availability, quality, access,
security, responsibility, liability, ownership, privacy, costs and business
models. In Europe several initiatives and projects have already been
established to work on this in a general context (e.g. the EU open data
policy) and more specifically in agriculture (e.g. GODAN,

COPA-COGECA).

The consequences for IoF2020 are partly mandatory (open access publishing,
open research data and GDPR) and for the other part guiding principles that
also have to be further explored. In this document we have presented a first
version of a Data Management Plan with concrete actions to be taken in order
to establish open, transparent data management in IoF2020.