#  1 Summary

This document outlines the data management strategies that will be implemented
throughout the CHIC research data lifecycle. In particular, it describes (i)
the type, format and volume of the generated data, (ii) the metadata and
documentation provided to make it findable, interoperable and reusable, (iii)
the long-term preservation plan, (iv) how data will be shared and licensed for
re-use, (v) the resources that need to be allocated to data management, (vi)
data storage and back up policies during the active phase of the project, and
(vii) the handling of personal data.

As stipulated in the Guidelines on FAIR Data Management in Horizon 2020, this
DMP will be updated when important changes to the project occur and, at least,
as part of the periodic reviews and at the end of the project.

The data management plans of the CHIC project and of the NEWCOTIANA project
(grant agreement 760331) have been generated in close collaboration between
these two projects. Both projects will generate datasets on technical
performance, safety assessment, socio-economic and stakeholder interactions
related to the use of NPBT for the development of multipurpose crops for
molecular farming. Aligning and standardising the data management between
these projects will facilitate data reuse and data interoperability. In
addition, no reporting and metadata standards are currently available for
NPBTs. The CHIC and the NEWCOTIANA projects will together contribute to the
development of reporting requirements for datasets related to NPBTs.

#  2 CHIC Data Summary

CHIC aims to develop new chicory varieties with improved dietary fiber
characteristics and improved terpene composition. Additionally, we will
address the self-incompatibility which hampers the breeding efforts for this
crop. This goal will be achieved by new plant breeding techniques NPBTs. More
precisely in CHIC we will develop and apply gene editing approaches all based
on the CRISP/Cas technology. We will use stable agrobacteriummediated gene
editing, transient gene editing techniques and the application of
ribonucleoproteins to edit the genome DNA in the chicory protoplasts.

In the CHIC project data to assess the technological performance of these
different methods will be collected. Additionally, the data related to the
risk assessment of different NPBT techniques used, such as the off-target
effects, will be generated. In improved chicory lines data about the dietary
fiber and terpene composition and bioactivity will be evaluated. Economic
feasibility and socio-economic impact of the newly produced chicory varieties
will be evaluated. The data generated will contribute to evidence-based
informed decisions on the legal status and regulation of NPBT crops.

To accomplish this a series of datasets will be generated:

  * Improved genome assembly of _C. Intybus_

  

  * RNAseq data on _C. intybus_

  

  * gRNA inventory and gene editing efficiencies 

  

  * genetic part and construct designs 

  

  * Dietary fiber characterisation 

  

  * Terpene characterisation 

  

  * Data on regulatory networks for secondary metabolite biosynthesis in _C. intybus_ \- Bioactivity data for dietary fiber / terpenes 

  

  * Phenotypic and agricultural parameters of newly developed _C. intybus_ varieties 

  

  * Safety assessment, including untargeted effects, of different NPBT applications 

  

  * Socio-economic impacts 

  

  * Broader societal impacts - Stakeholder views 

  

Table 1 provides a list of research data categories that will be produced in
CHIC and the expected data volume for each of them.

CHIC will re-use the constructs, RNAseq data and available genome data as well
as established protocols for tissue-culture cultivation, protoplast
transformation and regeneration of chicory that is available at different
partners to maximize the use of resources.

Stakeholder views on commercial cultivation and use of GE chicory will be
collected in order to clarify possible hurdles and facilitating factors for
chicory innovation using GE techniques. Stakeholder views will be collected in
the course of document reviews, interviews, questionnaires, workshops and
focus groups. Data will be gathered as audio recordings, transcripts,
interviews, and workshop notes. Only data gathered in the course of the CHIC
project will be used

These data will not only serve to meet the objectives of the current project,
but will also be useful for stakeholders including the scientific community,
plant breeders, farmers, industry, legislators and regulators, and the general
public.

Thus, the scientific community will benefit from the development of the NPBT
techniques for chicory. The improved gene editing and knowledge on the off-
target effects can be applied broader for gene editing of (the asteraceous)
crops. The project will create added value for chicory farmers, by providing
improved dietary fibre yield and quality and terpene yields. Additionally, the
work on chicory incompatibility and the development of NPBTs will benefit
chicory breeders. Finally, the generated data on the utility, efficiency and
safety of NPBTS as well as the generated communication materials will help EU
and National legislators and regulators and the general public make informed
decisions on the regulation and public acceptance of NPBTs.

To enhance the usability of the data, open or otherwise widely-used file
formats will be the preferred option for data collection (see Table 1).
Formats that are open and/or in widespread use stand the best change to be
readable in the future; on the contrary, proprietary formats used only by a
particular software are prone to becoming obsolete. In those cases in which
the laboratory instrument used to perform the measurement outputs the data in
an instrument specific proprietary format, a converted version of the output
file to an open data format will be shared together with the original file
thus fostering data interoperability.

Table 1. CHIC foreseen data types, size and selected file formats.  
  
<table>  
<tr>  
<th>

Research data

</th>  
<th>

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

Data Type

</td>  
<td>

File Format

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

Genome sequence data (raw and

processed data)

</td>  
<td>

bam, fastq

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

DNA parts and constructs

</td>  
<td>

genbank, fasta plain text, ASCII (.txt), Ab1 (.ab1),

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

qPCR data

</td>  
<td>

Raw data, comma-separated values (.csv), text (tab delimited) (*.txt)

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

RNA-Seq data (raw and processed data)

</td>  
<td>

.ffn

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

Metabolomics data (raw and processed data)

</td>  
<td>

mzML, netCDF (.cdf), comma-separated values (.csv), text (tab delimited)
(*.txt)

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

Images (e.g. microscopy, immunoblots)

</td>  
<td>

TIFF (.tiff), png (.png), jpeg (.jpg)

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

Tabular data (e.g. ELISA tests, metabolite yield, purity and functionality)

</td>  
<td>

comma-separated values (.csv), text (tab delimited) (*.txt), MS excel (.xlsx)

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

Plant phenotypic data (contained and field conditions)

</td>  
<td>

text (tab delimited) (*.txt), comma-separated values (.csv), MS excel (.xlsx)

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

Plant genotypic descriptions

</td>  
<td>

text (tab delimited) (*.txt), comma-separated values (.csv), MS excel (.xlsx)

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

Stakeholder views : audio recordings, transcripts, interview and workshop
notes, questionnaires

</td>  
<td>

audio recordings (mp3), MS Word (.docx), MS excel (.xlsx), comma-separated
values (.csv).

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

Standard operating procedures, protocols

</td>  
<td>

pdf (.pdf), MS word (.docx)

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

Scientific publications

</td>  
<td>

pdf (.pdf), MS word (.docx)

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

Project reports

</td>  
<td>

pdf (.pdf), MS word (.docx)

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

#  2\. FAIR Data

##  2.1. Making data findable, including provisions for metadata

The provision of adequate metadata (a description of the key attributes and
properties of each dataset) is fundamental to enable the finding,
understanding and reusability of the data, as well as the validation of
research results. Descriptive metadata in particular, aims to provide
searchable information that makes data discovery and identification possible.
CHIC will adopt the DataCite Metadata Schema, one of the broadest crossdomain
standards available, as the basis for dataset description. The minimum set of
descriptors established for a CHIC dataset include:

  * Type: a description of the resource. 

  

Recommended best practice: use of a controlled vocabulary such as the DCMI
Type Vocabulary).

  * Identifier: a unique string that identifies a resource. Provided by repository where the dataset is stored. 

  

Preferred option: digital object identifier (DOI); also accepted URL, URN,
Handle, PURL, ARK.

  * Publication date: date when the data was or will be made publicly available. 

  

Format: YYYY-MM-DD

  * Title: a name by which a resource is known (free text). 

  

  * Authors: the main researcher(s) involved in producing the data, or the authors of the publication, in priority order and affiliation. Recommended inclusion of a name identifier (e.g. ORCID) 

  

Personal name format: family, given. Affiliation format: free text

  * Description: additional information that does not fit in any of the other categories. Example: publication abstract. 

  

Format: open.

  * Version: the version number of the resource. 

  

Format: track major_version.minor_version. Examples: 1.0, 2.1

  * Language: primary language of the resource 

  

  * Rights: information about rights held in and over the resource 

  

Values: openAccess, embargoedAccess, restrictedAccess, closedAccess.

  * Licence: information about the type of licence applying to the dataset 

  

  * Contributors: institution or person responsible for collecting, managing, distributing, or otherwise contributing to the development of the resource. 

  

This property must also be used to allow unique and persistent identification
of the funder. Values: European Commission (EU), H2020, Research and
Innovation action, CHIC, Grant Agreement Number 760891 .

  * Subject: subject, keywords, classification code, or key phrase describing the resource (free text). 

  

Additionally, metadata elements and documentation providing specific
information about the data collection processes, methodology, data analysis
procedures, variable definitions, or relationships between the different files
of a dataset will be compiled to ensure data interpretability and reusability.
These metadata elements will be covered in section 2.3. The relevant metadata
categories mentioned above will also be applied for data related to
stakeholder interactions.

##  2.2. Making data openly accessible

CHIC project results will be made openly accessible provided that open
publication does not interfere with the obligation to protect and exploit the
results or the protection of personal data.

Regarding protection of results, to ensure that dissemination of the CHIC
research outputs does not jeopardize their exploitation potential, project
results will be subject to evaluation prior to any dissemination activity.
CHIC IPR management and dissemination strategies are described in document
D7.1 – PEDR. Results approved for dissemination will be made accessible
through a variety of channels including project webpage (www.chicproject.com)
social-media, scientific conferences, scientific publications in peer-reviewed
journals, and data repositories, among others.

Regarding the protection of personal data, stakeholder views will be either
audio recorded or documented in writing as interview or workshop notes. A
restricted access policy will be implemented for stakeholder consultation data
in order to insure confidentiality of personal data. These raw data will be
only be handled and analysed by the teams conducting the respective research
tasks. Summaries of stakeholder views will be presented in project reports
which will be made publicly available on the project website and in open-
access repositories. In these reports stakeholder views will be presented in a
pseudonymised way. No reference will be made to individual stakeholder
representatives or individual stakeholder organisations.

Being part of the Open Research Data Pilot (ORDP), the CHIC consortium is
committed to provide Open Access (free-of-charge access) to all scientific
publications and associated research data. The Open Access policy
implementation is described in D7.1 – PEDR.

Open Access (OA) to CHIC peer reviewed scientific publications will be mostly
granted through "Gold" OA, although "Green" OA will be also be considered if
"Gold" OA is not provided by the selected journal. Final versions of articles
accepted for publication and their associated metadata (see section 2.1 and
below) will be deposited in Zenodo, an interdisciplinary open data repository
service created through the European Commission’s OpenAIRE project and hosted
at CERN, and will be made openly accessible at the time of publication ("Gold"
OA) or with a maximum of 6 months embargo (for "Green" OA). Zenodo is
compliant with the FAIR principles: it assigns a DOI to each deposited object,
supports DOI versioning, is compliant with the DataCite Metadata Schema, is
searchable, provides clear and flexible licensing, and provides secure back-up
(see section 4).

In addition to the scientific publication, OA will also be provided to the
research data required to validate the published results. Although Zenodo
allows the deposit of data as well as publications, the use of
disciplinespecific repositories is often a more convenient option since (i)
they have been developed to cover the subject specific needs and (ii) being
widely used by the community, facilitate integration with other datasets. At
present time, several discipline-specific repositories are under consideration
for the deposit of CHIC datasets. These include:

  * Metabolights: a metabolomics cross-platform and cross-species repository maintained by the European Bioinformatics Institute (EMBL-EBI). Metabolights supports the Core Information for Metabolomics Reporting (CIMR) metadata standard and submission of datasets follows the ISA-Tab format, a general purpose framework with which to collect and communicate complex metadata used by a growing number a repositories and publishers. 

  

  * Gene Expression Omnibus (GEO), Sequence Read Archive (SRA): two public data repositories at the US National Center for Biotechnology Information (NCBI) suitable for the deposit of RNA-Seq data (GEO) and highthroughput sequencing data (SRA) which are compliant with the Minimum Information about a highthroughput SEQuencing Experiment (MINSEQE) standard. 

  

All data deposited in a discipline-specific repository will also have a record
in Zenodo for the associated publication with a link to the externally
deposited data files. Additionally, Zenodo will be the repository of choice
for those data types for which a disciplinary repository is not available. The
deposited dataset will include all the information needed to interpret and re-
use the data following reporting standards when available (see section 2.3).
These will include: publication file, raw and processed data files (in open or
widely used formats), detailed protocols with information on instruments and
settings used, a codebook for the variables used, and a readme file describing
the files that compose the dataset and the relation between them.

As already mentioned, open or widely used file formats that can be accessed
with open software (or software that is in widespread use) will be the
preferred option for data collection. When the use of proprietary formats is
necessary, the name and version of the software used to generate the file will
be indicated in a readme.txt file included in the dataset.

All data deposited in a repository will be made openly accessible under no
access restrictions other than the embargo period for "Green" OA publications
mentioned above.

##  2.3. Making data interoperable

Promoting data exchange and integration to its full potential requires the use
of standardised data formats, metadata elements, and ontologies that ensure
the reusability of the underlying data. As discussed in section 1, open or
otherwise widely used file formats will be used to collect and share the data
derived from CHIC research activities, thus facilitating data retrieval and
analysis by other users.

With regard to metadata, likewise discipline-specific repositories, discipline
specific metadata schemes broadly accepted by the scientific community should
be the preferred alternative since they have been developed to cover subject
specific needs. Accordingly, disciplinary repositories often show compliance
with such specific metadata standards in combination with (recommended)
controlled vocabularies. Metadata standards and ontologies that will be used
to document datasets generated within the CHIC project include:

  * Core Information for Metabolomics Reporting (CIMR) (metabolomics data) 

  

  * Minimum Information about a high-throughput SEQuencing Experiment (MINSEQE) (RNA-Seq and genome sequence data) 

  

  * Minimum Information about a Plant Phenotyping Experiment (MIAPPE) (plant phenotypic data) 

  

  * Minimum Information about a Proteomics Experiment (MIAPE) (protein mass spectrometry data) 

  

  * Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) (qPCR data) • Data Documentation Initiative (DDI) (survey data) 

  

  * Plant Ontology 

  

  * Gene ontology 

  

  * OBI ontology 

  

  * NCBI taxonomy 

  

There is currently no available reporting standard for CRISPR experiment
metadata. To cover this need, the NIST Genome Editing Consortium works on the
development of suggested minimal information reporting for public studies and
the generation of a common lexicon for genome editing. CHIC will follow the
progress of the Genome Editing Consortium on the development of a standard
CRISPR metadata.

At the same time, NEWCOTIANA and CHIC partners have initiated a common
dialogue to define the metadata elements that should be collected for each
genome editing experiment in order to facilitate sharing, validation, and
interpretability of the results. A first draft metadata checklist (see Annex
1) covering the whole genome editing workflow has been assembled as a result
of this work. This draft will continue to be refined in future working
discussions involving both projects.

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

Re-use is one of the pillars of FAIR data. Data re-use increases the impact
and visibility of research, maximises transparency and accountability,
promotes the improvement and validation of research methods, stimulates
innovation through new data uses, and saves resources avoiding unnecessary
replications. For data to be reusable it should be in an open or widely-used
file format, well described with rich metadata that meet domainrelevant
community standards, and released under a clear data usage licence. The way
CHIC will approach the first two points has already been discussed in section
1 (file formats) and sections 2.1 and 2.3 (metadata). Regarding licensing, as
a default standard CHIC will share scientific publications and the associated
research data under a Creative Commons Attribution Licence CC-BY whenever
possible. CC-BY does not impose any restriction on access and reuse of the
data; it allows users to copy, distribute, transmit, adapt and make commercial
use of the data with the sole condition that the creator is appropriately
credited. Most data repositories as well as most open access and hybrid
publishers support the use of CC-BY licence.

Data quality check is the responsibility of the partners involved in the
generation of the dataset and will be supported by a peer-review process at
publication. Should errors be detected in already published data, these will
be corrected and adequately documented in a new version of the dataset.

#  4\. Allocation of resources

Adequate data management is an integral part of good research practice and as
such it concerns every person involved in the research process. All CHIC
partners have agreed to the general guidelines set up in this DMP and it is
the responsibility of the group leaders to ensure that they are known and
implemented by all members of their research group. For each dataset, the
partner that generates the data is accountable for registering and storing all
data and metadata according to the guidelines of this DMP, applying adequate
back up policies, and sharing all public data through the selected open access
repository. The project coordinator is in addition responsible for the
maintenance of the project website and the Sharepoint hosting service (see
Section 4) for the sharing and storing of CHIC main documents during the
active phase of the project.

As indicated in section 2.2, "Gold" OA publication will be chosen as the
preferred publication option. Article processing charges for OA publishing
were budgeted at the proposal stage and will be covered by the main partner of
the publication out of their allocated funds. The estimated costs of applying
open access publication is 2500€. It is not possible at this stage to
determine the number of publications that will be produced. Resources for data
storage and back up during the active phase of the project will be provided by
the respective partner’s institutions (costs included in standard indirect
costs). No direct costs for data sharing and long term preservation are
anticipated given that all the considered data repositories are free of
charge.

#  5\. Data security

All CHIC partners have adequate storage capability and back up policies at
their respective institutions that guarantee the safe storage of the generated
research data during the active phase of the project. Additionally, a variety
of platforms are being used for internal data sharing, which also serve to the
purpose of backup storage. All project documents (grant and consortium
agreements, deliverables, meeting minutes, project reports and presentations,
scientific manuscripts) are stored in a shared folder in Sharepoint, an
Wageningen University and Research hosted sharing platform administered by WR
that supports control access back-up and file version control.

Sustainable long-term preservation of the data beyond project completion is
guaranteed by the use of trustworthy repositories such as Zenodo. Zenodo
accessibility principles guarantee that deposited data and metadata will be
retained for the lifetime of the repository, which is currently the lifetime
of the host laboratory CERN, with an experimental programme defined for the
next 20 years at least. Data files and metadata are backed up nightly and
replicated into multiple copies in the online system ensuring file
preservation. Finally, to preserve data authenticity and integrity, all files
are stored along with a MD5 checksum of the file content and are regularly
checked against their checksums to assure that file content remains constant.

Audio recordings and written notes of stakeholder views, as well as internal
reports will be stored on passwordprotected servers only accessible for the
partner conducting the research tasks.

#  6\. Ethical aspects

In order to comply with the EU General Data Protection Regulation (GDPR)
stakeholders participating in the project will be informed about the purpose,
method, storage, processing, and publication of personal stakeholder data and
data containing stakeholder views and asked for their permission. Stakeholder
data collected in the course of the CHIC project will not include any
sensitive personal data in the meaning of the GDPR. In publicly available
reports stakeholder views will be presented in a anonymized way.

There are no other ethical or legal issues relevant to data sharing of
stakeholder data nor on the ethics Deliverables.