#  Introduction

The VALITEST project [ _https://www.valitest.eu/_ ] aims at improving the
diagnostics of plant pests by i) producing validation data on the performance
of the tests that are used in diagnostics,

ii) harmonising further processes and iii) enlarging/triggering enlargement of
the commercial offer for reliable detection and identification tests.

To achieve those objectives, a significant amount of data will be collected,
processed and generated. According to the European Commission (EC), “
__research data_ is information (particularly facts or numbers) collected to
be examined and considered, and to serve as a basis for reasoning, discussion,
or calculation _ ”. In general terms, VALITEST data will follow the “ __FAIR_
_ ” principles, meaning “ _Findable, Accessible, Interoperable and Re-usable_
”. The FAIR principles will ensure sound management of data, leading to
knowledge discovery and innovation, and to subsequent data and knowledge
integration and reuse. The data will be made findable and accessible within
the consortium, and to the broader research community, stakeholders and policy
makers. Also, data must be compliant with national and European ethic-legal
frameworks, such as the General Data Protection Regulation (GDPR, Regulation
(EU) 2016/679), which has been applicable since May 2018. This Data management
plan (DMP) describes the data management life cycle for all data to be
collected, processed and/or generated by the project. It includes information
on the handling of research data both during and after the end of the project,
the nature of the data, the methodology and standards applied, whether data
will be shared or made open access, and how the data will be curated and
preserved.

The DMP is intended to be a living document and can be further modified or
detailed during the project. The information can be made available on a finer
level of granularity through updates as the implementation of the project
progresses and when significant changes occur. These changes might include new
data or changes in consortium policies. At the very least, the DMP will be
updated in the context of the periodic evaluation/assessment of the project,
but the implementation of the DMP at project level will also be part of the
annual reporting.

The VALITEST DMP is structured according to the H2020 templates. It includes 6
components:

  * Data Summary 

  

  * FAIR data 

  

  * Allocation of resources 

  

  * Data security 

  

  * Ethical aspects 

  

  * Important topics requiring progress and/or update in future version of the DMP. 

  

#  Data Summary

##  What is the purpose of the data collection/generation and its relation to
the objectives of the project?

The VALITEST project [ _https://www.valitest.eu/_ ] aims at improving the
diagnostics of plant pests by i) producing validation data on the performance
of the tests that are used in diagnostics,

ii) harmonising further processes and iii) enlarging/triggering enlargement of
the commercial offer for reliable detection and identification tests.

The project will include two rounds of test performance studies (TPS) to
produce validation data, i.e. data concerning the performance of diagnostic
tests when used by several laboratories on a panel of representative samples.

To maximise the impact of the project, calls for interest will be organised to
include kits from suppliers outside the consortium in the validation programme
and to allow participation in the TPS from voluntary proficient laboratories.

For a better understanding of the demands for current and future testing
options, identified stakeholders will be contacted to collect their views.

Current harmonised procedures in Plant Health for validation and organisation
of TPS will be improved by including appropriate statistical approaches and by
adapting the process for new promising technologies.

The management of the project will also require the collection of data
concerning the partners.

##  What types and formats of data will the project generate/collect?

Several kinds of data are foreseen to be collected or generated during the
project:

  * Data concerning the partners; 

  

  * Data concerning the participants to the TPS outside of the consortium; 

  

  * Validation data; 

  

  * Data concerning the stakeholders and their views on the diagnostic market; 

  

  * Data concerning the interest of kit suppliers outside of the consortium;  Data concerning reference material;  Reports and publications. 

  

Data formats will be selected with the view to facilitate data storage and
transfer. Therefore, used data format will be machine-readable, but also
human-readable using common software. Additionally, the management team
recommends the use of non-proprietary formats, if possible.

##  Will you re-use any existing data and how?

The project will generate validation data but will also collect existing
validation data in order to enrich an existing database dedicated to
validation data. Existing data may be processed during the project in order to
adapt these data to the improvements of the database that will be made in the
course of the project.

##  What is the origin of the data?

Data will be either generated during the project or collected in the context
of surveys and of calls for interest. Validation data generated during the
project will correspond to results obtained by several laboratories performing
the same experiments. Data will be collected and analysed by the laboratory in
charge of the test performance study.

##  What is the expected size of the data?

The actions planned during the VALITEST project should not require the storage
and handling of big data sets. The exact data size will be evaluated by the
Consortium partners during the course of the project.

##  To whom might it be useful ('data utility')?

According to the domain of expertise, data generated within the VALITEST
project can be useful to:

  * Scientific community; 

  

  * Industries involved plant health diagnostics; 

  

  * Inspection services, National plant protection organisations; 

  

  * Policy and decision makers, governmental authorities; 

  

  * International and regional organisations involved in plant health, such as IPPC, APPPC, CAHFSA, EPPO…;  Farmers/growers, landowners, agricultural advisors, breeders, …;  Consumers and society. 

  

#  FAIR data

Through the life cycle of the project, the FAIR principles will be followed as
far as possible, while paying attention to the non-disclosure of data
susceptible to compromise the quality trademark of SMEs and ensuring
compliance with national and European ethic-legal framework.

##  Making data findable, including provisions for metadata

Data discoverability can be obtained by different means, which include:

  * Providing data visibility through a communication system (e.g. social media, website); 

  

  * Providing online links between research data and related publications or other related data; 

  

  * Providing open access (e.g. open data repository); 

  

  * Providing data documentation in a machine-readable format; 

  

  * Using metadata standards or metadata models; 

  

  * Providing access through applications; 

  

  * Providing online data visualisation/analysis tools for the data, to help researchers to explore data in order to determine its appropriateness for their purposes. 

  

2.1.1 Discoverability of data

Are the data produced and/or used in the project discoverable with metadata,
identifiable and locatable by means of a standard identification mechanism
(e.g. persistent and unique identifiers such as Digital Object Identifiers)?

The number of databases to store data and documents is increasing with the
expansion of the open access/open data approach. This means that access to
information is fragmented (different locations) which ironically has the
countereffect to hinder use and re-use of open data. In order to ensure
visibility and accessibility of data, the Digital Research Object Portal
(DROP) hosted within EPPO and maintained by Euphresco will be used to
reference the open data and documents produced by the VALITEST consortium. The
Portal constitutes a unique entry point to ease the retrieval of information,
it will direct users towards the various infrastructures where the actual data
(and documents) will be hosted. All the deliverables are also listed on the
VALITEST website ( _https://www.valitest.eu/index_ ). Links will be available
between the VALITEST website and the appropriate open repositories where
deliverables or datasets will be submitted. The chosen repository (Zenodo),
provides also social media links. Scientific publications will be advertised
using the website and social media, each will be identifiable and locatable by
means of a Digital Object Identifier (DOI).

According to the EC, _metadata_ provide a systematic method for describing
such resources and thereby improving access to them. In other words, it is
data about data. Metadata provides information that makes it possible to make
sense of data (e.g. documents, images, datasets), concepts (e.g.
classification schemes) and real-world entities (e.g. organisations, places).
Different types of metadata exist for different purposes, such as descriptive
metadata (i.e. describing a resource for purposes of discovery and
identification), structural metadata (i.e. providing data models and reference
data) and administrative metadata (i.e. providing information to help
management of a resource).

The Dublin Core Metadata Initiative is providing best practices in metadata.
All the data collected or generated during the project will be described using
the Dublin Core interoperable metadata standards. Furthermore, as stated in
the grant agreement of the project, the metadata will always include all of
the following:

  * the terms “European Union (EU)” and “Horizon 2020”; 

  

  * the name of the action, acronym and grant number; 

  

  * the publication date, and length of embargo period if applicable, and  a persistent identifier. 

  

Concerning validation data, specific metadata have been developed by the
European and Mediterranean Plant Protection Organisation Panel on Diagnostics
and Quality Assurance, these will systematically be used.

Finally, some criteria will be ascertained to ensure best practice in metadata
management:

  * Availability: metadata need to be stored where they can be accessed and indexed so they can be found; 

  

  * Quality: metadata need to be of consistent quality, so users know that it can be trusted; 

  

  * Persistence: metadata need to be kept over time; 

  

  * Open License: metadata should be available under a public domain license to enable their reuse. 

  

2.1.2 Naming conventions and clear versioning

Each deliverable can be identified by a unique number:
D.work_package_number.deliverable_number. Where applicable versioning is used.
Most documents made public will have a unique persistent identifier such as
Digital Object Identifier. DOI are automatically provided by the chosen
repository (Zenodo).

2.1.3 Will search keywords be provided that optimize possibilities for re-use?

To facilitate the queries by keywords, metadata of the digital objects
generated during the projects must include the term “VALITEST”. Other keywords
will belong to the harmonised vocabulary used in the domain  1  .

##  Making data openly accessible

According to the _H2020 online manual_ , open access refers to the practice of
providing online access to scientific information that is free of charge to
the end-user and reusable. In the context of research and innovation,
'scientific information' can mean: peer-reviewed scientific research articles
(published in scholarly journals), or research data (data underlying
publications, curated data and/or raw data). Open access to scientific
publications means free online access for any user. The costs of open access
publishing are eligible, as stated in the Grant Agreement. Open access to
research data refers to the right to access and reuse digital research data
under the terms and conditions set out in the Grant Agreement. Users should
normally be able to access, mine, exploit, reproduce and disseminate openly
accessible research data free of charge.

2.2.1 Which data produced and/or used in the project will be made openly
available as the default? If certain datasets cannot be shared (or need to be
shared under restrictions), explain why, clearly separating legal and
contractual reasons from voluntary restrictions.

By default, the data and metadata of VALITEST will be made openly available.
However, three types of restrictions will apply:

  * Open access is incompatible with rules on protecting personal data: protection of the personal right needs to be ascertained either by avoiding open access to sensitive and personal data, or by anonymising the data. Deliverable D9.1 (confidential) specifies the procedures implemented for personal data collection, storage, protection, retention and destruction. 

  

  * Open access is incompatible with the obligation to protect results that can reasonably be expected to be commercially or industrially exploited. The management board will identify project results with exploitation potential or commercial value or results that are costly and difficult to replicate. The management board will also assess the best options for exploiting these results (e.g. opt for disclosure and publication of results or protection through patent or other forms of IPR), consulting patent attorneys, IP specialist agents and officers at Knowledge Transfer Departments, if required. During every general assembly meeting, a special session on IP issues will be scheduled to enable open discussions and joint decisions on the best strategies for managing and exploiting the project results. Hence, protection of the interests of all the involved parties will be ensured. This will enable the exploitation strategy by all parties to be reviewed regularly and to make sure any relevant result is on route for exploitation (directly by the partners or indirectly by third parties) with appropriate terms and conditions for all project partners. Decisions relative to data management needed between meetings will be approved through electronic correspondence. 

  

  * Open access data may compromise the quality trademark of partners. The question of, whether or not the performance of the various tests evaluated will be disclosed, will be discussed on a case-by-case basis by WP1 leaders and kits providers. 

  

2.2.2 How will the data be made accessible (e.g. by deposition in a
repository)?

Within the consortium, the deliverables are accessible on a restricted access
platform. For the public and other stakeholders, the deliverables are listed
on the VALITEST website. The decision to make a deliverable freely available
will follow a two steps process. i) each WP leader seeks an agreement within
the WP, ii) the WP leader informs the SC of the proposal of the WP to publish
or not the deliverable and the SC takes the final decision. Deliverables made
freely available by the SC will be uploaded on the chosen repository (Zenodo),
a link will be provided from the VALITEST website and from the DROP portal.

Validation data will be hosted by the European and Mediterranean Plant
Protection Organisation on the Section 'validation data for diagnostic tests'
of the ‘EPPO Database on Diagnostic Expertise’ ( _https://dc.eppo.int/_ ).
This database already includes validation data for diagnostic tests for
regulated pests, generated by various laboratories in EPPO member countries.
The validation data are presented according to a common format developed by
the EPPO Panel on Diagnostics and Quality Assurance. Validation data can be
submitted by any laboratory registered in the EPPO database on diagnostic
expertise. At this point, this database does not comply with Open Data policy:
it is not referenced and the data can only be visualised and downloaded as a
PDF file. During the project, the database will be referenced and the data it
contains will be made findable, accessible, interoperable and reusable.

Other data-sets will be uploaded in machine-readable format on one single
OpenAire compliant repository (Zenodo) from which data can be found through a
web browser and downloaded by a potential interested user.

Regarding peer-reviewed publications, the ‘gold’ open access should be
preferred, in this case, the article is immediately provided in open access by
the publisher. At the minimum he VALITEST partners will provide a ‘green’ open
access and archive the publications on an online OpenAire compliant repository
and ensure open access within a maximum of six months.

2.2.3 What methods or software tools are needed to access the data?

Only standard software, e.g. web browsers, pdf-file readers, and text readers,
or open licence free software, e.g. ‘R’ will be needed.

2.2.4 Where will the data and associated metadata, documentation and code be
deposited? Preference should be given to certified repositories which support
open access where possible.

Validation data will be integrated to the ‘EPPO Database on Diagnostic
Expertise’ ( _https://dc.eppo.int/_ ). During the project, the database will
be referenced and the data it contains will be made findable, accessible,
interoperable and reusable.

Concerning other data sets and ‘green’ open access scientific articles, the
management board decided to rely on the online OpenAire compliant repository
Zenodo ( _https://zenodo.org/_ ).

2.2.5 Have you explored appropriate arrangements with the identified
repository?

Appropriate arrangements will be considered when the first data sets,
deliverables or publications will be made available.

2.2.6 If there are restrictions on use, how will access be provided?

As described in 2.2.1, three types of sensitive data (including personal data)
are identified. These will be managed following the GDPR requirements. Access
to these sensitive data will be granted by the management board. The data
processing will have to correspond to one of the uses announced to the subject
during the collection of the data. Data will be transferred by electronic mail
using a format including metadata. Deliverable D9.1 (confidential) provides
more details on the procedures implemented for personal data management.

2.2.7 Is there a need for a data access committee?

Data issues will systematically be discussed in the general assembly meetings.
The access to sensitive data will be granted by the management board of the
project.

##  Making data interoperable

2.3.1 Are the data produced in the project interoperable, that is allowing
data exchange and re-use between researchers, institutions, organisations,
countries, etc. (i.e. adhering to standards for formats, as much as possible
compliant with available (open) software applications, and in particular
facilitating re-combinations with different datasets from different origins)?

All the data collected or generated during the project and made public will be
available on machine readable format using common or open licence free
software, described using appropriate metadata.

2.3.2 What data and metadata vocabularies, standards or methodologies will you
follow to make your data interoperable?

For most of the data collected or generated during the project, used metadata
will follow the Dublin Core interoperable metadata standards. For the specific
validation data, used metadata will follow the standards developed by the
European and Mediterranean Plant Protection Organisation Panel on Diagnostics
and Quality Assurance.

2.3.3 Will you be using standard vocabularies for all data types present in
your data set, to allow inter-disciplinary interoperability?

To allow inter-disciplinary interoperability, all the documents and data
generated during the project will use the standard vocabulary developed by the
International Plant Protection Convention (IPPC) to provide a harmonised
internationally agreed vocabulary associated with phytosanitary measures  2  .

##  Increase data re-use (through clarifying licences)

2.4.1 How will the data be licensed to permit the widest re-use possible?

For public data, the reuse of the data will be possible through the open
repositories where they will be stored.

2.4.2 When will the data be made available for re-use? If an embargo is sought
to give time to publish or seek patents, specify why and how long this will
apply, bearing in mind that research data should be made available as soon as
possible.

The specific decision on an embargo for research data will be taken by the
management board. Scientific research articles should have an open access at
the latest on publication if in an Open Access journal, or within 6 months of
publication. For research data, open access should by default be provided when
the associated research paper is available in open access.

2.4.3 Are the data produced and/or used in the project useable by third
parties, in particular after the end of the project? If the re-use of some
data is restricted, explain why.

Most of the data collected or generated during the project will be available
from open repositories, and therefore reusable by third parties, even after
the end of the project. For ethical and legal reasons, personal or sensitive
data will not be made public (deliverable 9.1 - confidential). Data concerning
intellectual property will be discussed between relevant partners, and
decision will be taken according to the European and national rules.

2.4.4 How long is it intended that the data remains re-usable?

Regarding data stored on an OpenAIRE compliant public repository, all files
stored within the repository shall be stored after the project to meet the
requirements of good scientific practice.

For data stored otherwise, researchers, institutions, journals and data
repositories have a shared responsibility to ensure long-term data
preservation. Partners must commit to preserving their datasets, on their own
institutional servers, for at least five years after publication. If, during
that time, the repository to which the data were originally submitted
disappears or experiences data loss, the partners will be required to upload
the data to another repository and publish a correction or update to the
original persistent identifier, if required.

2.4.5 Are data quality assurance processes described?

For the VALITEST consortium, it is essential to provide good quality data.
This will be ensured through various methods. Firstly, partners have existing
data quality assurance processes, which are described in their quality manual.
Secondly, publications will be disseminated using peer-reviewed journals, and
similarly, research data will be deposited on repositories providing curation
system appropriate to the data.

#  Allocation of resources

##  What are the costs for making data FAIR in your project?

Costs directly associated to FAIR data management have been included within
the description of the different tasks of the project.

##  How will these be covered?

Costs related to open access to research data are eligible as part of the
Horizon 2020 grant (if compliant with the Grant Agreement conditions).

##  Who will be responsible for data management in your project?

The management team (ANSES, _valitest@anses.fr_ ) will ensure best practices
and FAIR principles in the data management of the project. Each partner will
be responsible for managing the data it uses, processes or generates in the
project. In relation with their data protection officer, each partner has
appointed a data manager ensuring the respect of DMP principles and involved
in personal data protection.

##  Are the resources for long term preservation discussed (costs and
potential value, who decides and how what data will be kept and for how long)?

As an intergovernmental organisation responsible for cooperation in plant
health within the Euro-Mediterranean region, EPPO will ensure the long term
preservation and availability of the validation data on its own resources.

#  Data security

##  What provisions are in place for data security (including data recovery as
well as secure storage and transfer of sensitive data)?

Partners will store and process personal data according to their internal
procedures. The security of data will be ensured by means of appropriate
technical and organisational measures.

##  Is the data safely stored in certified repositories for long term
preservation and curation?

Validation data will be stored on EPPO servers located in a French datacenter.
The databases are located on servers independent of the web platform. Access
to the servers is only possible from a secure network (dark fiber from EPPO HQ
or VPN). Access to raw data is only possible from the accounts of EPPO IT
Officers, through an authentication mechanism. The servers are monitored and
supervised 24/7 by a service provider. The provider may under no circumstances
access the data without the agreement of EPPO (the servers belong to EPPO).

Other public data will be stored on one single certified repository (Zenodo).

#  Ethical aspects

Deliverables D9.1 (personal data management) and D9.2 (ethical standards and
guidelines in non EU countries) specifically deal with ethical aspects of the
project. These documents include the management of personal and sensitive data
and the management of data imported to, or exported from EU. No difference of
data management will be made between data generated or provided by subjects
located in the EU or in third countries.

#  Important topics requiring progress and/or update in future versions of the
DMP

 Consider appropriate arrangements with the selected repository