#  Preamble

The iPSpine data management plan (DMP) provides an initial overview of the
data and information collected within and throughout the iPSpine project. The
DMP shows the interrelation of the data collecting activities within and
between work packages. Furthermore, the DMP also links these activities to the
iPSpine project partners and describes their responsibilities with respect to
data handling.

The DMP is intended to be a ‘living document’, which will be updated over the
course of the project when appropriate and at least at every reporting period
of the project.

This is the first version of the DMP which is part of work package 9 ‘Project
Management’.

This document made use of the HORIZON 2020 FAIR DATA MANAGEMENT PLAN TEMPLATE
and was written with reference to the Guidelines to FAIR data management in
Horizon 2020 [1] and the GDPR (Regulation (EU) 2016/679).

#  1\. Data Summary

##  1.1 Purpose of data collection/generation

iPSpine will generate data in a broad range of R&D activities in order to
achieve its objectives within the project. Research data will be generated by
the project partners. These include a large amount of data, Standard Operating
Procedures (SOPs) and guidelines. Table 1 summarizes the type of data and data
sets that are being generated in the project. Data will be made available
through publications and via two interlinked platforms: the iPSpine ‘Open-
access knowledge platform’ (WP3) and ‘smart digital ATMP management platform’
(WP4) (depending on the type of data). The table will be updated throughout
later versions of this data management plan.  
  
<table>  
<tr>  
<th>

</th>  
<th>

**Types of data generated in iPSpine**

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

</td>  
<td>

Data description

</td>  
<td>

Main Partners

</td>  
<td>

WP

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

Without personal data

</td>  
<td>

Including personal data

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

**1**

</td>  
<td>

Research data from i _n vitro_ and _ex vivo_ experiments

</td>  
<td>

All partners involved in these

WPs

</td>  
<td>

1-5

</td>  
<td>

\-

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

**2**

</td>  
<td>

Clinical research data from _in vivo_ experiments

</td>  
<td>

UU, UN, UdM

</td>  
<td>

6

</td>  
<td>

6  #  
</td> </tr>  
<tr>  
<td>

**3**

</td>  
<td>

Interviews with professionals, patients and other users

</td>  
<td>

UMCU

</td>  
<td>

\-

</td>  
<td>

7

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

**4**

</td>  
<td>

Interviews with iPSpine researchers on ATMP

development

</td>  
<td>

TU/e

</td>  
<td>

\-

</td>  
<td>

4

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

#  Applicable only to Partners UU and UN. It refers to personal data of the
clients participating in the clinical studies described in WP6.

**Specify if existing data is being re-used (if any), and to whom this might
be useful**

Applicable to the following partners:

**ARI** : In addition, ARI will overview and re-use existing data from
previous spine-related research projects (i.e.TargetCaRe, projects funded by
AO Foundation and AOSpine), as well as data available in data archives and
digital repositories. These previous data could be combined with iPSpine new
data to have comparison parameters.

##  1.2 Types and formats of data that will be generated throughout the
project

This is an early stage identification of standards; the consortium will define
at a later stage which formats of the raw data and the final data are most
appropriate for sharing through the aforementioned paths:  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

**Text based documents**

**(e.g. reports, manuscripts, deliverables, interviews, informed consents)**

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

Project Partners

</td>  
<td>

All

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

Format

</td>  
<td>

.doc, .docx, and .txt file formats

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

Size of data

(approximately)

</td>  
<td>

Gigabytes

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

Data Storage

</td>  
<td>

UU: university network drives (U- drive/ O-drive) and local storage when
employing a laptop. U-drive is employed by the personnel for the
tasks/activities of the specific person. O-drive is employed for documents
that are commonly shared between the member of the UU team.

Data from local storage will be on regular basis updated; based on discussions
with the Data management team of the UU it will be decided which storage modus
is the best to use ( U-/O-drive or on One Drive (cloud); Yoda).

Research data (including finalized protocols and raw research data from in
vitro & in vivo data) is stored at the E-lab.

TU/e: Local drive, central server, mirrored backup NAS, cloud-based disk
(collective univ.-based)

UMCU: Storage on local UMC Utrecht G drive.

NUIG: OneDrive for Business NUIG and M:drive

ARI: the data will be directly collected on computers and stored in project
folders in the local "I" drive at AO Foundation Davos.

PharmaLex: Storage on Server

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

Comments

</td>  
<td>

UU: To minimize data size the UU iPSpine group will be working with OneNote
for minutes of the groups meetings and share files via Sharepoint.

UCMU: Considers both primary and secondary data (transcript of interviews as
well as interpretation after use of N-Vivo program)

ARI: ARI: each person has a user name and a password to enter in the local
drive.

TU/e: All encrypted

PharmaLex: Considers only secondary data of in vitro and in-vivo study reports
(such as for review or for interpretation for regulatory purposes)

NUIG: Local drive at Genomic and Screening Core Facility NCBES, Biomedical

Science Building, M:drive and OneDrive for Business

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

**Type of data**

</th>  
<th>

**Microsoft office for research data raw data based on continues and binary
data**

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

Project Partners

</td>  
<td>

All, except for Catalyze and ReumaNL

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

Format

</td>  
<td>

.xls, .xlsx, .csv

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

Size of data

(approximately)

</td>  
<td>

Idem as text based documents

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

Data Storage

</td>  
<td>

Idem as text based documents

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

Comments

</td>  
<td>

Idem as text based documents

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

**Type of data**

</th>  
<th>

**Presentations**

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

Project Partners

</td>  
<td>

All

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

Format

</td>  
<td>

.ppt, .pptx.

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

Size of data

(approximately)

</td>  
<td>

Idem as text based documents

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

Data Storage

</td>  
<td>

Idem as text based documents

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

Comments

</td>  
<td>

Idem as text based documents

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

**Type of data**

</th>  
<th>

**Illustrations and graphic design**

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

Project Partners

</td>  
<td>

All

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

Format

</td>  
<td>

Microsoft Visio (Format: .vsd), Graphpad Prism (Format: .pzf, .pzfx),
Photoshop

(Format: different types possible, mostly .png), and will be made available as
.jpg, .psd, .tiff, .png and/or .ai files. PDFs, PIDs and layouts will
preferentially use inkscape.org, an open source software for vector graphics.
(Format: .svg), and will be made available as .png, .jpg and .pdf files.

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

Size of data

(approximately)

</td>  
<td>

Idem as text based documents

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

Data Storage

</td>  
<td>

Idem as text based documents

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

Comments

</td>  
<td>

Idem as text based documents

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

**Type of data**

</th>  
<th>

**Audio files**

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

Project Partners

</td>  
<td>

TU/e, UMCU

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

Format

</td>  
<td>

MP3 or WAV

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

Size of data

(approximately)

</td>  
<td>

GBs

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

Data Storage

</td>  
<td>

UMCU: Storage on local UMC Utrecht G drive.

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

Comments

</td>  
<td>

UMCU: Concerns primary data

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

**Type of data**

</th>  
<th>

**Magnetic Resonance Imaging**

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

Project Partners

</td>  
<td>

UU

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

Format

</td>  
<td>

DICOM files

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

Size of data

(approximately)

</td>  
<td>

0.5-2MB (~20-150kB per MR slide and size of the object ; ~500 kB per CT slide)

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

Data Storage

</td>  
<td>

Stored online on a server (Xero platform) which can be accessed by a UU app to
visualize and analyse data.

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

Comments

</td>  
<td>

If and when files will be shared with Partners they will be anonymized and
zipped to minimize size of data transfer. It is anticipated that this data
will be at least shared with UUlm and SpineServe

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

**Type of data**

</th>  
<th>

**Video files**

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

Project Partners

</td>  
<td>

UN, TU/e, UBern

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

Format

</td>  
<td>

Quicktime Movie or Windows Media Video

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

Size of data

(approximately)

</td>  
<td>

TBs

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

Data Storage

</td>  
<td>

Idem as text based documents

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

**Type of data**

</th>  
<th>

**Mass-spectometry (LC-MS/MS)**

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

Project Partners

</td>  
<td>

NUIG

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

Format

</td>  
<td>

.RAW, .csv

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

Size of data

(approximately)

</td>  
<td>

20GB

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

Data Storage

</td>  
<td>

Local data server at Conway Core Facility University College Dublin, M:drive
and OneDrive for Business at NUIG

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

**Type of data**

</th>  
<th>

**Mass-spectometry (UPLC)**

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

Project Partners

</td>  
<td>

NUIG

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

Format

</td>  
<td>

.DAT, .EXP, .CKS, .csv, .pdf

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

Size of data

(approximately)

</td>  
<td>

20GB

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

Data Storage

</td>  
<td>

Local drive at NIBRT, Dublin, M:drive and OneDrive for Business at NUIG

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

**Type of data**

</th>  
<th>

**Confocal imaging**

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

Project Partners

</td>  
<td>

NUIG

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

Format

</td>  
<td>

.OIF, .OIB, .tif, .avi

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

Size of data

(approximately)

</td>  
<td>

50GB

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

Data Storage

</td>  
<td>

Local shared M:drive and OneDrive for Business at NUIG

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

**Type of data**

</th>  
<th>

**qPCR**

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

Project Partners

</td>  
<td>

NUIG, ARI, UU

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

Format

</td>  
<td>

.eds, .xls, .csv

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

Size of data

(approximately)

</td>  
<td>

50MB

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

Data Storage

</td>  
<td>

Idem as text based documents

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

**Type of data**

</th>  
<th>

**Flow-cytometry**

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

Project Partners

</td>  
<td>

NUIG

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

Format

</td>  
<td>

.fcs

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

Size of data

(approximately)

</td>  
<td>

50MB

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

Data Storage

</td>  
<td>

Local drive at Flow Cytometry Core Facility NCBES, Biomedical Science
Building, M:drive and OneDrive for Business at NUIG

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

**Type of data**

</th>  
<th>

**ELISA**

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

Project Partners

</td>  
<td>

NMI-RI, NMI-RI

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

Format

</td>  
<td>

.exp, .csv

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

Size of data

(approximately)

</td>  
<td>

50MB

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

Data Storage

</td>  
<td>

Local drive at Genomic and Screening Core Facility NCBES, Biomedical Science
Building, M:drive and OneDrive for Business at NUIG

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

**Type of data**

</th>  
<th>

**Genomic data**

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

Project Partners

</td>  
<td>

UU

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

Format

</td>  
<td>

Read data: general(CRAM, BAM, Fastq) Assembled and annotated sequence data:
flat file format (FASTA, XML), Multiple Sequence Alignment (MSA) formats

Quanitative tabular data with minimal metadata:.csv; .tab; .xls; .xlsx, .txt;
.mdb; .accdb; .dbf; .ods

Quantittatve tabular data with extensive metadata: .por; SPSS, .sav; .dta

Qualitative data: .xml; .rtf; .txt; .html; .doc; .docx;

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

Size of data

(approximately)

</td>  
<td>

Sequencing data ±10GB/sample, Other files: MBs

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

Data Storage

</td>  
<td>

Idem as text documents

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

Comments

</td>  
<td>

UU: e.g. Sequencing (DNA, RNA,), annotation of features, protein structural
information, gene expression profiles, alignment data, chromosomal mapping,
phylogenetic trees, Single Nucleotide Polymorphisms (SNPs), functional
genomics, Proteomics,

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

These file formats have been chosen because they are accepted standards and in
widespread use. Files will be converted to open file formats where possible
for long-term storage.

#  2\. FAIR data

##  2\. 1. Making data findable, including provisions for metadata

For sharing of the output of finalized research data during the Project (e.g.
deliverables, publications, other dissemination activities) the consortium
uses Microsoft Sharepoint Teamsite which is hosted from Utrecht University and
is fully compliant with regulations for data security and privacy.

All data files used in the Sharepoint Teamsite are related to project
management activities, include the term “iPSpine”, followed by file name which
briefly describes its content, followed by a version number (or the term
“FINAL”), followed by the short name of the organisation which prepared the
document (if relevant). An example of the Teamsite is provided below.

UU: A folder structure is created that is guided by the work plan description
of the iPSpine Action. The folder structure is as follows: work packages,
within the work packages the different tasks in which the UU is involved,
within the tasks separate folders entail the different experiments conducted.
Raw and analyzed data will be separately stored per task as defined within the
iPSpine. Raw data will be stored in a separate file that will be marked with
“read only”; those will be stored in E-lab. Master copies are maintained at
one location, for this purpose the team stores data on E-lab. Back up will be
organized with Yoda.

**Outline the discoverability of data (metadata provision)** This will be
updated later in the project.

**Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?**

UU: Data that is stored at YODA will receive a DOI and become open access upon
publication of the respective manuscript. Specifically RNAseq and Chip-seq
data will be submitted to ArrayExpress or GEO. Regardless of the conditions
all data will comply with the MISEQ standards.

**Outline naming conventions used**

All Partners should be using the same approach for naming conventions used.
This is currently being discussed at the ESC level and will communicated to
the consortium.

**Outline the approach for clear versioning**

All Partners should be using the same approach for versioning. This is
currently being discussed at the ESC level and will communicated to the
consortium.

**Specify standards for metadata creation (if any). If there are no standards
in your discipline describe what metadata will be created**

At the consortium level the following will be organized for proper data
management:

At least the first and the corresponding author of any publication should have
complete access to the data they are reporting on, and in case that someone
wants a replication package they should only need to contact the corresponding
author of the publication.

That is upon data creation every institution is responsible for storing and
eventually archiving that data. If data need to be put together to create a
publication and this involves various institutions there will inevitably be
one corresponding author or (last author ) who is the lead in that particular
publication. For this to happen data needs to be transferred and congregated
in one institution ( or as many of the institutions that work together on that
publication) to properly interpret and analyze the data and to write the
article. The corresponding author’s institution will be responsible for this
data and archive all the data that was required to create the publication
accordingly within their own institution so that if someone contacts them to
gain access he should be able to grant access easily.

All Partners should be using the same approach for metadata. This is currently
being discussed at the ESC level and will communicated to the consortium.

###  2.2. Making data openly accessible

Data will be made "as open as possible, as closed as necessary". In this
respect, the consortium aims to make research data publicly available where
possible and make sure that data is closed where necessary for protection of
the results of the project, as described in article 23 of the grant agreement.

Research data which is created within the project is owned by the partner who
generates it (Art. 26 of the grant agreement). Each partner must disseminate
its results as soon as possible unless there is a legitimate reason to protect
the results.

Where possible all data will be licenced using CC BY 4.0 or later (Creative
Commons Corporation). Where mandated by the publisher, the individual journal
licence where the data is published, will be utilised. More restrictive,
custom licenses will only be used for commercially sensitive data. The
researchers will decide at a later stage for which data an embargo period will
apply as well as for the duration of such embargo, especially after the
project has finished.

_**Figure 1.** Open access to scientific publications and research data in the
wider context of dissemination and exploitation. From Guidelines to the Rules
on Open Access to Scientific publications and Open Access to Research Data in
Horizon 2020) _

Each beneficiary must ensure open access (free of charge online access for any
user) to all peer-reviewed scientific publications (Article 29 of the Grant
Agreement). Research data needed to validate the results in the scientific
publications coming from the project must be deposited in a publicly
accessible data repository, as depicted in figure 1.

Data must be made available to project partners upon request, including in the
context of checks, reviews, audits or investigations, following the
regulations described in Art. 25 of the Grant Agreement. In the case of
personal data, data will be anonymized before it is made available to others.
Data will be made accessible and available for re-use and secondary analysis.

The smart digital ATMP management platform will be based on the latest
information technology developments from the fields of business process
management and adaptive case management. The platforms will be open to the
public only after patent filing, publication and/or completion of the project.

###  2.3. Making data interoperable

The iPSpine project aims to collect and document the data in a standardised
way to ensure that, the datasets can be understood, interpreted and shared
alongside accompanying metadata and documentation. Generated data will be
preserved on institutional intranet platforms until the end of the Project.

In addition to these datasets that are not intended for interoperability and
belong to and are hosted with individual partners, the consortium uses two
platforms to collaborate on specific datasets.

  1. **Open-access knowledge-sharing platform for high-quality data on the epigenetic, genetic, phenotypic, transcriptional and proteomic profiles**

  

  

An open-access knowledge-sharing platform will be generated within WP3, to
collect and share highquality data on the epigenetic, genetic, phenotypic,
transcriptional and proteomic profiles of cells cultured with or without the
biomaterials. Firstly a prototype platform will be developed by partner NMI-RI
and its linked third party, the bioinformatics group core facility of the
University of Tuebingen (QBIC). This platform will be accessible to the
iPSpine partners for depositing and accessing data, protocols and tools. The
iPSpine partners will as such perform the usability and functionality testing
of the platform, to further optimize the platform towards a first-viable
product with open-access at project end. In this platform data will be
formatted and stored in such a way that it allows integrative bioinformatics
analysis of big data towards pattern identification, pathway and network
analysis.

The suggested data management setup follows the FAIR guidelines. iPSpine data
from qPortal is disseminated to public repositories using an automated
interface. Furthermore, to make sure that the data that results from raw data
processing pipelines is as “findable” as the actual data, DOIs (digital object
identifiers) will be utilised. Adoption of open data standards is crucial for
the data interoperability. To improve data ‘Interoperability’, where
applicable, we will adopt open standard data formats (mzML, mzIdentML, mzTab,
etc) for iPSpine datasets. qPortal already uses a variety of public metadata
ontologies such as the vocabulary taken from the NCBI taxonomy database. For
mass spectrometry, metadata following the Proteomics Standards Initiative
(PSI) vocabularies is automatically extracted. In addition, qPortal supports
open standards for data sharing like ISA-Tab. We are working on export
functionality to other format standards like GEO to disseminate data and
metadata to public repositories. The Quantitative Biology Center (QBiC) will
enhance the qPortal infrastructure should important metadata vocabularies for
standard data types, as they are used in the project, be missing. If it is
necessary to produce uncommon data or metadata, mappings to more commonly used
ontologies will be provided. We will also promote that the software produced
supports these standards. We follow FAIR guidelines and provide tested and
versioned software by utilising proven tools like Maven, Travis and GitHub.

The reproducibility of data analysis will be guaranteed by developing state-
of-the-art processing pipelines for ‘omics’ data. Most of the data analysis
procedures are not performed by using monolithic software, but by deploying
complex pipelines. Tuebingen University recently joined the nf-core community
(https://github.com/nf-core) that aims at collecting high-quality scientific
workflow that base on Nextflow as a workflow engine. As part of the open
software practices, the most frequently used metabolomics workflows will be
ported to Nextflow and will be made available through nf-core. Furthermore,
data will be formatted and stored in a manner allowing integrative
bioinformatics analysis of big data towards pattern identification, pathway
and network analysis. In applications where reference data is needed for
analysis, such as genetic or proteome analyses, standard reference genomes
from EnsEMBL, UCSC and/or NCBI can be used. All parameters used in runs of
different pipelines are stored as metadata with the results, facilitating
reproducibility.

  2. **Open digital platform to guide the design of in vitro/ex vivo Proof-of-Concept demonstration for advanced therapies**

  

  

Furthermore, within iPSpine an open digital platform will be developed to
guide design of in vitro/ex vivo Proof-of-Concept demonstration for advanced
therapies, complying with the 3Rs principles. Based on guideline requirements
(Task 4.1) and regulatory requirements (Task 7.6-8) a smart digital platform
will be designed and developed for more efficiently managing the innovative
preclinical translation process of ATMPs and biomaterials. In-depth interviews
with experts in ATMPs/biomaterials (consortium partners and advisors) will be
performed to extract knowledge on the general structure and bottlenecks of the
translation process, including the testing procedures and the resulting
decisions that the translation requires for a specific ATMP/biomaterial. Based
on these interviews, both a template ATMP/biomaterial translation process with
decision points as well as different template testing procedures for
ATMPs/biomaterials will be designed. The template process and procedures will
be best practices that can be easily adjusted to meet the needs for a specific
ATMP/biomaterials translation process. The platform will use the templates to
provide advanced automated support for the flexible design and execution of
the complete translation process. Innovative information technology from the
fields of business process management and adaptive case management will serve
as foundation for the platform to streamline the translation process and
remove inefficiencies such as rework and other development bottlenecks. In
addition, the platform will also keep track of different regulatory
requirements to significantly improve the quality and efficiency of the
translation process. The platform will support the smart instantiation and
execution of translation processes for new ATMPs/biomaterial. This includes
the instantiation and execution of related testing procedures and their
follow-up decisions. Decision points within translational process and the data
sources upon which decisions are based are also registered in the platform,
which will help to significantly speed up and make more efficient the
translation processes. The platform will also check, register and report on
the compliance of executed translation processes and the performed testing
procedures and decisions, with pre-identified regulatory requirements.
Development of the platform and data for it will be generated from the
consortium (WP1-7) using the iPS-NLC:biomaterials ATMP as a show case. To
validate the platform and demonstrate its potential, the platform will be used
retrospectively near the end of the program to determine how the process could
have been done more efficiently. Although the platform will initially be
specific to the ATMP/biomaterials developed in this program, its architecture
and processes may be reused and translated thereafter to include other
ATMPs/biomaterials and targets. Thus, the smart ATMP/biomaterial translation
process management platform will become an innovative solution that enables a
speed-up in the effective development of new ATMPs/biomaterials in line with
the 3Rs philosophy.

The platform collects data generated by iPSpine researchers in experiments. We
aim to store this data using ontologies, e.g. OSCI (
_http://www.ontobee.org/ontology/OSCI_ )  . However, stem cell ontologies
appear to be under development; there is not yet a well-accepted standard. The
choice for an ontology will be made in consultation with the researchers.
**2.4. Increase data re-use (through clarifying licences)**

Currently the topic of data re-use through clarifying licenses does not apply
to the iPSpine project. If this becomes applicable later in the project, the
DMP will be updated.

#  3\. Allocation of resources

Data management of the iPSpine project will be done as part of WP9, and UU as
project coordinator, will lead the data management efforts in the project. UU,
as well as all other partners have allocated a part of the overall budget
(including person months) to WP9 in order to cover for these activities. Costs
related to open access of scientific publications are eligible for funding as
part of the H2020 grant, and are covered by the budget of the individual
partners. Within WP3 and WP4 two platforms will be developed by designated
Partners and the costs for the data storage in these platforms is being
covered by the budget of the individual Partners (i.e. NMI-IT and TU/e). NMI-
RI has a linked third party involved for this specific task in the iPSpine
project: QBiC has had start-up funding of the German Research Foundation to
build up a bioinformatics and data management support infrastructure.

#  4\. Data security

For the duration of the project, all research data will be stored at the
individual partner’s storage system. Each partner is responsible to ensure
that the data is stored safely and securely and in full compliance with the EU
data protection legislature. For data that is transferred to a data repository
during or after the project, all responsibilities concerning data security and
recovery will be shifted to the repository chosen for storing the dataset.
Periodic risk evaluation of privacy risks related to data processing
activities of the Project will be conducted and reported corresponding with
the reporting periods of the Project.  
  
<table>  
<tr>  
<th>

**Partner**

</th>  
<th>

**Provisions in place for data security**

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

**UU**

</td>  
<td>

Data stored on OneDrive/Surfdrive by the individual scientists and final
versions of the documents and the raw data are place in YODA as repository.

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

**UN**

</td>  
<td>

UN-cloud University of Nantes. Personal hard copy securely stored at
University of Nantes

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

**TU/e**

</td>  
<td>

Data will be stored in a datalab environment such as DataVerse or iRODS; data
archive is available via 4TU.ResearchData. To safeguard the privacy of
patients while being able to trace the patients in case needed, we request
that research organizations that provide experimental data for the smart
digital ATMP platform pseudonymise their data, i.e., replace each patient ID
by a unique number that TU/e cannot link to the patient, and anonymise all
other private data of the patient.

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

**UMCU**

</td>  
<td>

G drive of the UMC Utrecht Julius Center (which is the secured drive where all
research is stored)

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

**NUIG**

</td>  
<td>

The raw data are stored on an institutional core facility data server or hard
drive. These data are backed up and securely stored together with all analysed
data and research files on NUIG network such as M:drive and OneDrive for
Business for long term storage. M:drive is used to store and collaboratively
share the research data among iPSpine researchers in NUIG.

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

**UULM**

</td>  
<td>

All data are stored on the institute server with daily backups to the
university backup system

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

**UBERN**

</td>  
<td>

Secured institutional storage. Further information: Informatikdienste Bern.

_helpdesk@id.unibe.ch_

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

**INSERM**

</td>  
<td>

INSERM data are under the supervision of the General Data Protection
Regulation (GDPR) previously cited above

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

**NMI-RI**

**QBiC**

</td>  
<td>

Project related data and metadata at QBiC are stored on a password-secured,
geographically redundant storage system which is backed-up continuously. Data
integrity is guaranteed by a RAID system. Access via the web interface of
qPortal is safeguarded in two layers. User credentials allow the use of the
general portal functionality. Data of a project is stored in one or more
workspaces of our data management system openBIS. Multiple users can be
assigned to a workspace. Users

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

</th>  
<th>

can only create, access or download project data and metadata if they are
assigned to the respective workspace. Single sign-on (SSO) access control
achieved via the Lightweight Directory Access Protocol (LDAP) is used to
connect the two layers. The same credentials can be used to download data via
the command line, if a user has access to the respective workspace.

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

**ARI**

</td>  
<td>

All project related research data will be stored in the password-secured
storage drive of the AO-IT (Information Technology Group in the Support Units
Department of the AO Center). The access, handling, storage and backups of the
data will be planned and controlled by the AO-IT department according to
internal guidelines, which include daily backups and mirroring the server to
an offsite location.

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

**SHU**

</td>  
<td>

[Provisions in place] All laboratory procedures will be recorded in laboratory
notebooks recording methodology and results, all data files containing results
from experimental analysis will be cross referenced to enable exact
experimental procedures to be linked to results. All data files will contain
clear descriptions of variables under investigation. All lab books will be
checked and countersigned by the line managers to confirm the recordings are
correct and that all documentation is clear during regular briefings.

All primary data generated by the group will be stored locally and backed up
immediately onto the University research storage facility (Q:drive). A shared
folder will be provided for the project. Access to the folder is restricted to
researchers working on the project. The primary copy of the data is stored on
a storage array located in one of the university's data centres. As data is
written it is replicated over a secure private network to a storage array
located in the other data centre. This provides an up to date second copy of
the data providing excellent disaster recovery capabilities. Access to the
Q:drive over the network is secured by a number of methods. Users are required
to enter a valid username and password before access is permitted. The service
is protected from malicious attack by firewalls and anti-virus software.
Systems are patched on a regular basis to protect against known
vulnerabilities. All data transfers over the internet are encrypted.

_http://research.shu.ac.uk/rdm/research-store.html_

All data from the study at completion will be archived in the SHU Research
Data Archive (SHURDA, http://shurda.shu.ac.uk). The University retention
schedule stipulates data will be stored for 10 years since the last time any
third party has requested access to it.

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

**UCBM**

</td>  
<td>

All data will be stored in a dedicated folder on the UCBM server and saved
regularly (back-up every night). The access to the folder will be given only
to our staff and closed to third parties.

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

**NTrans**

</td>  
<td>

Printed data is securely stored within the NTrans facility. Furthermore,
printed data is scanned and stored in digital format in a password protected
Cloud-based database. This Cloud-based storage ensures instant storage and
back-up of all data.

A back-up of the digital database is locked in a safe to ensure long term
preservation.

Storage and sharing of personal data of NTrans personnel (date of birth,
social security number, employment contracts) is done within the OwnCloud
database. With respect to laws on private information, NTrans personnel has
signed a consent to allow

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

</td>  
<td>

keeping personal records in its administration and to allow sharing of this
data with regulatory bodies like accountants and grant administration offices.

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

**UdM**

</td>  
<td>

Experimental data are stored on institutional secure storage backup and on
secure Cloud Octopus Backup (rented to and hosted by Computer Services )

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

**MU**

</td>  
<td>

Under UM policy data is securely stored, managed and accessed using BOX, a
highly encrypted, secure (password protected links using a double-
authentication system, folders have the ability to restrict permissions and
set expi-ration dates), online (cloud-based) environment.

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

**SpineServ**

</td>  
<td>

All data are stored on our local server with daily backups and yearly backup
in different places

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

**HKU**

</td>  
<td>

All project related information will be stored on a password-secured storage
system which is backed-up regularly within the investigator’s lab. HKU also
have a data repository facility for the storage of data with restricted assess
and data sharing.

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

**PharmaLex**

</td>  
<td>

All project related information is stored on a password-secured storage system
which is backed-up continuously. PharmaLex does not create or store any
research data.

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

**Catalyze**

</td>  
<td>

All project related information is stored on a password-secured storage system
which is backed-up continuously. Catalyze does not create or store any
research data.

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

**ReumaNL**

</td>  
<td>

ReumaNL does not create or store any research data.

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

#  5\. Ethical aspects

This section deals with ethical and legal compliance. Data protection and good
research ethics are major topics for the iPSpine consortium.

iPSpine partners have to comply with the ethical principles set out in Article
34 of the Grant Agreement. This article states that all activities must be
carried out in compliance with:

  * ethical principles (including the highest standards of research integrity) 

  

  * applicable international, EU and national law. 

  

There will be regular ethics checks for all ethical aspects concerning human
participants, human cells/tissues, animals for all EU and non-EU partners
involved in the iPSpine project. To enable structured ethics checks an
overview table will be generated and uploaded on the team site. Herein it is
the responsibility of each Partner to upload the ethics related documents in
the designated file and inform the coordinator on ethical approval.

##  5.1 Informed Consent

Informed consent forms will be provided to any individual participating in
iPSpine interviews, workshops or other research activities which may lead to
the collection of data that will ultimately be used in the project. An example
of an Informed Consent Form is provided in the Annex of this document. Signed
informed consent forms are collected by the Partner leading the activity and
stored appropriately to meet the GDPR.  
  
<table>  
<tr>  
<th>

**Partner**

</th>  
<th>

**WP**

</th>  
<th>

**Collecting informed consent forms?**

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

**UU**

</td>  
<td>

6

</td>  
<td>

Yes, client owned dogs that will participate in the clinical trial described
in WP6 will be informed and provided with an informed consent form. This is
due in year 4 of the Project

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

**UN**

</td>  
<td>

6

</td>  
<td>

No

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

**UMCU**

</td>  
<td>

1, 7

</td>  
<td>

Yes, informed consent will be obtained in the interviews

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

**UBern**

</td>  
<td>

1

</td>  
<td>

Yes

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

**SHU**

</td>  
<td>

1, 3

</td>  
<td>

Yes, informed consent is obtained from patients / relatives for disc tissue
collection. Furthermore informed consent is obtained from patient users who
join the local patient user groups.

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

##  5.2 Confidentiality

iPSpine partners must retain any data, documents or other material as
confidential during the implementation of the project. Article 36 of the Grant
Agreement describes further details on confidentiality, along with Article 27,
which describes the obligation to protect results. Awareness of
confidentiality will be guaranteed by putting this as a regular agenda point
during each Project Steering Committee meetings, and meetings of the
Scientific advisory board and the Patient advisory board. The members of the
Scientific advisory board and the Patient advisory board will be asked to sign
a confidentiality agreement prior to committing to this task.

##  5.3 Involvement of non-EU countries

iPSpine non-EU partners (UBERN, UM, HKU) have confirmed that the ethical
standards and guidelines of Horizon 2020 will be applied, regardless of the
country where the research activities are carried out. Activities carried out
outside the EU will be executed in compliance with the legal obligations in
the country where they are carried out, with an extra condition that the
activities must also be allowed in at least one EU Member State. Each Party
has agreed that personal data will not be transferred from the EU to a non-EU
country. However, in the case this becomes necessary, these transfers will be
made in accordance with Chapter V of the General Data Protection Regulation
2016/679.

#  6\. Outlook towards the next version of the data management plan

The next version of the data management plan will be prepared latest after
month 18 since an update of this plan will be part of the periodic reporting
of reporting period 1. As emphasized in the introduction of this document, the
DMP is a living document, which will be updated over the course of the
project. Hence, the next version of the DMP will update the issues raised
above.