#  Data Summary

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

For GoodBerry data collection and integration is absolutely necessary. It is
therefore of utmost importance that not only data is well generated but also
well annotated using open standards and metadata as it is laid out in the
following section. As GoodBerry aims at uncovering new principles across
diverse berry species and across diverse experiments, good documentation, data
keeping and integration are necessary.

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

We foresee that the following data will be collected and generated at the very
least: Firstly phenotypic data about berry plants, secondly environmental and
management data for plant growth, thirdly omics data including metabolomics
and transcriptomics data sets stemming from different disciplines such as next
generation sequencing (NGS) and/or qRT PCR based approaches. In addition
derived data from the original raw data sets will also be collected. This is
important, as different analytical pipelines might yield different results or
include ad-hoc data analysis parts. Therefore specific care needs to be taken
to document and archive these resources (including the analytic pipelines) as
well.

In addition, we will store correction values that arise from a ring exchange
experiment, to transparently correct for individual laboratory influences.

_**Will you re-use any existing data and how?** _

The project builds on existing data sets and relies on them. For instance
without a genomic reference it would not be possible to analyze NGS data sets.
However, it is of course also important to include existing data sets on the
expression and metabolic behavior of berry crops. Whilst genomic references
can simply be gathered from reference databases like the NCBI/EBI, in case of
expression data that is not available via the SRA/ENA one might have to resort
to gather it from supplemental tables. A current analysis however suggests
that most strawberry data at least is available in standardized format and
annotated with metadata either from the SRA and/or ENA. Unfortunately, this is
not the case for metabolite accumulation data which will thus be gathered from
publication resources. Similarly as experimental metadata descriptions for NGS
data are somewhat lax when plants are considered (due to the one size fits all
approach of minimal information), augmented metadata will be generated by
accessing the adjoining publications of submitted data sets.

_**What is the origin of the data?** _

Public data will be extracted as described in the previous paragraph. For
GoodBerry, specific data sets will be generated by the consortium partners. In
detail, we expect RNASeq and metabolite data as well as phenotypic and
environmental/management data.

_**What is the expected size of the data?** _

Metabolite data will comprise mostly evaluated data and therefore can be
stored in less than one GB of volume. In case of RNASeq data it is necessary
to keep read data at the current time. Given the project sample sizes and the
targeted read depth we expect up to 8 TB of data. In addition, data on the
environment and phenotype can usually be strongly compressed and will comprise
only several GB of data.

Within the project it will be determined, if image data shall be stored and
shared via EMPHASIS EU infrastructure for phenotyping facilities as well (🡪 to
be determined and further developed within next DMP versions).

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

The data will be useful to the GoodBerry community as it is immediately
necessary to analyze the data produced in GoodBerry to have data that can be
used for prediction, computation and biological interpretation. In addition,
at the very least the omics data will be interesting and useful for the whole
berry crop community. Given the large amount of RNASeq data, it will be
important even for an updated genome of the strawberry crops. This is because
the RNASeq date will be helpful in defining or even finding gene models in the
strawberry genome. Also some data sets particular about healthy metabolites
will be interesting for the general public as well.

#  FAIR data

##  Making data findable, including provisions for metadata

_**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)?** _

All data sets will receive persistent unique identifiers. In addition to make
the data sets useful, they will be annotated with at the very least metadata
to fulfill DOI and OpenAIRE standards. As in many cases more metadata is
necessary to allow true reusability, GoodBerry will provide more metadata than
stipulated by DOI/OpenAIRE. The use of DOIs is still under discussion (🡪 to
include in later DMP versions).

_**What naming conventions do you follow?** _

Data variables will use standard names. This is e.g. the case for genes and
metabolites. These will also be linked to free biomedical ontologies. In the
case of datasets, the dataset names will also encode the provenience.

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

Keywords about the experiment and the general consortium will be included as
well as an abstract about the data, where useful.

_**Do you provide clear version numbers?** _

To maintain data integrity and to be able to re-analyze data, data sets will
get version numbers.

_**What metadata will be created? In case metadata standards do not exist in
your discipline, please outline what type of metadata will be created and
how.** _

We foresee to use e.g. MinSEQe for sequencing data and MIAMET for metabolites
as well as MIAPE for phenotyping like data, but will also be relying on
specific SOPs established in the EUBerry project. The latter will thus allow
integrating data across projects and safeguards reusing established and tested
protocols.

##  Making data openly accessible

_**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 all data sets from GoodBerry will be shared and made openly
available. This is however usually after a gratuity period allowing partners
to importantly iterate through data and potentially clean up data in the
process and to allow partners to exert their publishing and patenting rights
prior to unlimited sharing.

_**Note that in multi-beneficiary projects it is also possible for specific
beneficiaries to keep their data closed if relevant provisions are made in the
consortium agreement and are in line with the reasons for opting out.** _

Does not apply.

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

Data will be made available via the project specific website (www.goodberry-
eu.eu) linked into the German plant primary database (plabipd.de). It will be
ensured that data which can be stored in international specialized
repositories (NCBI, EBI, SRA, ENA etc.), will be stored and processed there as
well.

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

No specialized software will be needed to access the data. Access will be
possible via web interfaces once publicly available. For data processing after
obtaining raw data, typical open source software can be used (in particular
R/BioConductor, Trimmomatic, Bowtie/BWA etc).

_**Is documentation about the software needed to access the data included?** _
As no software is needed, no documentation needs to be provided.

_**Is it possible to include the relevant software (e.g. in open source
code)?** _

As stated above, software is only needed AFTER data has been obtained by a
user in order to process and/or analyze the data. Here we use publicly
available open source certified software.

_**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.** _

As noted above specialized repositories like SRA/ENA are very likely the most
common ones.

_**Have you explored appropriate arrangements with the identified
repository?** _

The submission is for free and it is the goal (at least of ENA) to obtain as
much data as possible. Therefore, arrangements are neither necessary nor
useful.

_**If there are restrictions on use, how will access be provided?** _ There
are no restrictions.

_**Is there a need for a data access committee?** _

Consequently, there is no need for a committee.

_**Are there well described conditions for access (i.e. a machine readable
license)?** _ Yes where possible e.g. CC REL will be used.

_**How will the identity of the person accessing the data be ascertained?** _

In case data are only shared within the consortium due to data cleanup tasks
and/or publication preparations, a user specific log in is necessary.

In case data are publicly available, these data have to be available without
siphoning out personal information. I.e. access is anonymous, which is the
only way to real open data and which is in accordance with data protection
laws in Germany.

##  Making data interoperable

_**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)?** _

At all times, data will be stored in common and openly defined formats. By
default no proprietary formats will be used.

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

As mentioned above, we foresee to use e.g. MinSEQe for sequencing data and
MIAMET for metabolites as well as MIAPE for phenotyping like data, but will
also be relying on specific SOPs established in the EUBerry project. The
latter will thus allow integrating data across projects and safeguards reusing
established and tested protocols. Additionally, we will use ontology terms to
enrich the data sets relying on free and open ontologies.

_**Will you be using standard vocabularies for all data types present in your
data set, to allow interdisciplinary interoperability?** _

Indeed, open biomedical ontologies will be used where they are mature. In
certain cases like the environment, RWTH AACHEN is involved in extending open
ontologies for environmental characterisations so that these can be used as
well.

_**In case it is unavoidable that you use uncommon or generate project
specific ontologies or vocabularies, will you provide mappings to more
commonly used ontologies?** _ Common and OPEN ontologies will be used thus
this question does not apply.

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

_**How will the data be licensed to permit the widest re-use possible?** _
Open licenses will be used, such as CC.

_**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.** _

In general, due time will be given to the consortium partners to exploit the
data for publication and/or IP issues first. This is not least due to the fact
that data will often see an increase in quality by additional cleaning up for
publications. All consortium partners will be encouraged to make data
available prior to publication under pre-publication agreements such as those
started in Fort Lauderdale and set forth by the Toronto International Data
Release Workshop.

_**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.** _ There will be no restrictions once data is made
public.

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

Data will be made available for many years and potentially indefinitely after
the end of the project.

_**Are data quality assurance processes described?** _

Data will be analyzed using automatic procedures as well as by manual
curation. This will be based on tools developed by RWTH AACHEN for other
projects.

#  Allocation of resources

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

The costs comprise data curation, setup of databases and long term sustenance
and storage including electricity. The costs will amount to approximately
250.000€ of which most can be covered by eligible costs that will be claimed.

_**How will these be covered? Note that costs related to open access to
research data are eligible as part of the Horizon 2020 grant (if compliant
with the Grant Agreement conditions).** _

A large part of the cost is covered in WP5 – Data analysis and integration. As
this work package is underfunded, additional resources are taken from own core
funding.

_**Who will be responsible for data management in your project?** _

Partner 7 – RWTH AACHEN

_**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)?** _

The partner RWTH AACHEN decides on preservation. The partner has pledged long
term (potentially for decades) support based on own costs. RNA data will also
be available through ENA/SRA (so data will be available from redundant
resources). Here, EBI and NCBI will decide how to proceed in the future. At
the moment, data will be kept indefinitely, but of course one has to take
recent political developments especially in the EU into account.

#  Data security

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

Once data is transferred to the GoodBerry database, data security standards as
entailed by the German Plant Primary Data Database will be imposed. This
comprises with regard to secure storage the usage of encrypted connections to
the data (https and sftp) where passwords and usernames are generally
transferred via separate safe media. Also data blobs are tagged with access
rights and groups. Of course, necessary security updates will always be
employed in addition to in-house server hardening procedures. All
infrastructure will be based on open source so no backdoors introduced by
hostile third parties or industry espionage will be present.

In terms of data recovery, data is stored on a zfs based filesystem employing
RAID like redundancy (potential migration to btrfs in the future). In
addition, regular backups are made to LTO tape libraries. It will be
considered to include GoodBerry data into high available data served where all
data sets are mirrored and served from two redundant locations (30km apart)
with a high bandwidth connection (🡪 later DMP decision point).

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

Transcriptomics data will be also made available upon publication via the
standards ENA/SRA. In addition, the national resource will maintain
safekeeping of data also after the project end.

#  Ethical aspects

_**Are there any ethical or legal issues that can have an impact on data
sharing? These can also be discussed in the context of the ethics review. If
relevant, include references to ethics deliverables and ethics chapter in the
Description of the Action (DoA).** _

At the moment, we do not foresee ethical or legal issues with data sharing. In
terms of ethics, as this is plant data there is no need for an ethics
committee. The consortium makes its best efforts to ensure data sharing, at
the very latest after exploitation through papers and patents.

_**Is informed consent for data sharing and long term preservation included in
questionnaires dealing with personal data?** _

The only personal data that will potentially be stored is the submitter name
and affiliation in the metadata. As lengthy questionnaires tend to stifle
careful answering and deposition, this will be highlighted again to the
submitters and they can opt out in which case only their institution will be
mentioned. This is however a very unlikely case, as data evaluation will be
published in scientific journals anyway providing the names of the authors.

#  Other issues

_**Do you make use of other national/funder/sectorial/departmental procedures
for data management? If yes, which ones?** _

GoodBerry will make use of the German Plant Primary Data Database which is a
national resource. In addition, it needs to be evaluated whether EMPHASIS
infrastructure will be useful for phenotypic data.

#  Abbreviations

btrfs B-Tree File System

CC Creative Commons

CC REL Creative Commons Rights Expression Language

DMP Data Management Plan

DOI Digital Object Identifier

EBI European Bioinformatics Institute

EMPHASIS European Multi-Environment Plant Phenotyping And Simulation

Infrastructure

ENA European Nucleotide Archive https HyperText Transfer Protocol Secure

IP Intellectual Property

LTO Linear Tape Open

MIAMET Minimal Information about Metabolite experiment

MIAPE Minimal Information about Plant Phenotyping Experiment

MinSEQe Minimum Information about a high-throughput Sequencing

Experiment

NCBI National Center for Biotechnology Information

NGS Next Generation Sequencing

OpenAIRE Open Access Infrastructure for Research in Europe qRT PCR
quantitative Real time reverse transcriptase coupled PCR

RAID Redundant Array of Inexpensive Disks

RNASeq RNA Sequencing

sftp Secure File Transfer Protocol

SOP Standard Operating Procedures

SRA Short Read Archive WP Work Package

zfs Zettabyte File System