<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

Raw format for more sophisticated final postprocessing.

Selected scanner logs.

</th>  
<th>

all active sites

TOTAL

350GB

</th>  
<th>

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

**QALY questionnaires**

</td>  
<td>

Questionnaire responses

</td>  
<td>

Structured text format

</td>  
<td>

<1MB

TOTAL

<1GB

</td>  
<td>

WP5 (150)

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

**CT scans for**

**hospital standard-of-care treatment**

</td>  
<td>

Human computed tomography images and coded radiology reports

</td>  
<td>

DICOM format for images. Structured text format for reports.

</td>  
<td>

250MB

TOTAL

150GB

</td>  
<td>

WP5 (150 x up to

4 scans per

patient)

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

**Clinical patient data**

</td>  
<td>

e.g. chemotherapy details extracted from clinical notes and coded before
storage to preserve anonymity

</td>  
<td>

XML format

</td>  
<td>

1MB

</td>  
<td>

WP5

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

**Prediction model**

</td>  
<td>

Optimised prediction model.

Outcomes of prediction model.

</td>  
<td>

Computer code

And

Spreadsheet of results e.g. XLSX format

</td>  
<td>

<1GB

</td>  
<td>

WP4 and WP5

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

**Health economic data**

</td>  
<td>

Predictions of the potential health economic impact of the new metabolic

MRI scans

</td>  
<td>

Health economic evaluation data – the CHEER standard

(Consolidated Health Economic Evaluation Reporting Standards)  1  which
provides guidelines

</td>  
<td>

<1GB

</td>  
<td>

After WP5

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

</td>  
<td>

</td>  
<td>

on reporting health economic evaluation data.

</td>  
<td>

</td>  
<td>

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

#  _Table 1: List of data types expected to be generated_

The NICI consortium strive to use recognised standards for data documentation
and meta-data production in order to promote re-use of our data outputs beyond
the consortium members. The final decision on which standards to use will be
made by the Management Board and will be included in revisions to this Data
Management Plan once test data sets are available. The following standards
might be applicable and are under consideration:

  * Imaging data – The DICOM standard (Digital Imaging and Communications in Medicine - http://medical.nema.org), which is the international standard for medical images and related information (ISO 12052). It defines the formats for medical images that can be exchanged with the data and quality necessary for clinical use. DICOM is one of the most widely deployed healthcare messaging standards in the world. We are developing a set of extensions to DICOM for the new metabolic imaging format which we intend to publish in due course (in WP4). 

  

  * Imaging data – Raw data formats defined by the vendors, Philips, Siemens, GE. This data will also be archived to ensure that any parameters missing from the DICOM files can be extracted for the final post-processing stages. This is a form of “insurance”. 

  

  * Imaging protocols – Exported in PDF and in binary format from each scanner during monthly QA will be archived to ensure protocol consistency. 

  

  * Clinical Study Design, Data collection and Analysis – CDISC standards (http://www.cdisc.org) provide a format to document the entire study in WP5. 

  

  * NMR data – the nmrML and nmrCV standards, which are a an open mark-up language for NMR data and an MSI-sanctioned NMR controlled vocabulary, to support the nmrML data standard for nuclear magnetic resonance data in metabolomics with meaningful raw data descriptors (http://nmrml.org). 

  

  * CT scans and radiographers reports will be obtained from the recruiting hospital systems, most probably in DICOM format and PDF or XML for the reports. 

  

We anticipate that the data from the organoid study and the human metabolic
imaging data will be of future interest to the community. They will provide a
thorough evaluation of the metabolic signatures seen in vitro, and in healthy
subjects (WP3) and in patients with colorectal cancer undergoing chemotherapy
(WP5). We anticipate that the prediction model and health economic predictions
will be of interest to the scientific community, and to experts in health
policy.

**4 FAIR data**

#  a. Making data findable, including provisions for metadata

Our dissemination strategy will make data generated during the NICI project
discoverable by stakeholders from academia, clinical medicine, MRI technology
companies and by patient groups.

##  _Table 2: Dissemination strategy_

We have created a Zenodo Community (  _https://zenodo.org/communities/nici/_ )
which will allow us to upload all of the smaller open access data sets, with
accompanying metadata to enable them to be found and searched. Zenodo provides
a DOI to all uploaded objects and has a robust system for tracking version
numbers and object metadata. In addition, we are also investigating the option
to use DataLad (  _https://www.datalad.org/_ )  which a Git-based open source
system for federated data sharing. Finally, we will reference the original
data sets where relevant in scientific publications arising from the NICI
project and on the NICI website. This will ensure that they can be located by
interested parties.

#  b. Making data openly accessible

To maximise data utilisation, the consortium will archive all data and provide
open access to all data that are not earmarked for exploitation.

Unless earmarked for exploitation (by the Management Board), data will be
shared. Data will be coded and anonymised in order to protect participants’
privacy. NICI will make use of an open access depository (e.g. the NICI Zendo
Community  , _https://zenodo.org/communities/nici/_ )  . Data will be
deposited as soon as possible.

Data from MR studies will be uploaded to UCAM for central archiving. We intend
to use the High Performance Hub for Informations (HPHI) at the Wolfson Brain
Imaging Centre for this purpose. Representatives from each imaging site are in
the process of being granted UCAM computer accounts. These permit access to
the HPHI cluster which has ample storage to host the project data sets. We
also have access to a PACS system and an XNAT instance for making the imaging
data discoverable and sharable in future with third parties.

##  c. Restricted-access data sets

Data from the organoid studies in WP1 that is the basis of the restricted
circulation deliverables D1.2 - D1.5 will not be publically released at this
time. This is to protect the prior IP Rights of the partners developing this
aspect of the project, and to make it possible for us to exploit IP that may
arise during the human study (WP2-5).

Discussions regarding the handling of identifiable clinical data for patients
recruited in WP5 are ongoing. We will seek approval for our proposed means of
handling identifiable clinical data from the recruiting hospitals in WP5 and
as part of our Ethics application for the clinical study.

##  5 Tools for Data Sharing

We intend to extend open-source tools from the imaging community during the
NICI project. For imaging, we intend to extend the NIH “Gadgetron” open source
reconstruction framework to support metabolic imaging. For spectroscopy, we
intend to extend the “Oxford Toolbox for Magnetic Resonance Spectroscopy”
(OXSA) for an integrated workflow with NICI metabolic datasets.

We are now piloting the use of the XNAT database, hosted on the HPHI cluster
at UCAM for data sharing across the consortium.

Imaging research and analysis is increasingly dependent on acquiring data from
large numbers of subjects, which in turn means searching across wide
geographical areas to find enough subjects that meet your study's criteria.
One way to manage this is to collaborate with a number of research
institutions to recruit and image subjects from.

While this is economically more feasible (and friendlier to your subjects), it
introduces a new host of challenges for study coordination: disparate scanning
technologies and devices; nonuniform process for image acquisition and data
handling; the challenge of aggregating all this data into a centralized
system, and then managing access to this data across a large number of
collaborators from outside your institution.

XNAT has evolved to solve this problem.

Because XNAT is a web-based application, it has the built-in capability to be
accessed from anywhere in the world. Necessarily, security and fine-grained
access controls are built into XNAT at the root level. XNAT administration has
been built to support the complexities of multi-center research projects,
including:

  * Highly configurable DICOM data importing, to unify data from multiple scan sources. 

  

  * Fully audited security. 

  

  * Siloed data access for each institution, with the capability of sharing data across all institutions. 

  

  * Customization of data queries that fit data into your study protocols. 

  

  * Protection from inadvertent PHI on data gathered from multiple sources. 

  

  * Reporting tools for study coordination. 

  

Currently, XNAT is supporting a number of high-profile multi-site research
studies, including the  _Human Connectome Project_ ,  the  _DIAN study_ of
inherited Alzheimer's Disease, the  _INTRUST_ _study_ of post-traumatic stress
disorders, and th  e _PREDICT-HD_ study of Huntington's Disease.

_**Description of XNAT from <https://www.xnat.org/case-studies/multi-center-
studies.php> ** _

For the novel metabolic imaging methods, we intend to extend the International
Societry for Magnetic Resonance in Medicine Raw Data Format (ISMRMRD) which is
a cross platform format for unprocessed  1  H imaging data to also support the
other nuclei in the NICI project (  31  P,  23  Na,  13  C and  19  F). Our
adaptations to the ISMRMRD format will be published. Our adaptations to the
software tools that convert from Siemens, Philips and GE proprietary data
formats into ISMRMRD will be published wherever possible. (It is possible that
in some instances these tools will only be available to sites owning a scanner
from the appropriate vendor, and who therefore have a research agreement with
that vendor covering the proprietary data formats used as input for these
conversion steps.)

We envisage at this point the following restrictions on re-use:

  1. Existing proprietary rights by the vendors (Siemens, Philips, GE) to raw data formats on their platforms. Our approach will be to convert data into a vendor-independent format (extended ISMRMRD format) for release so that only the acquiring sites need to use the vendors IP in these data formats. 

  

  2. The results of the NICI clinical study in terms of a predictive classifier for progression free survival will be evaluated for possible IP protection before they are published. This may be a valuable output from the project that should be exploited commercially by the consortium, or used in follow-on research. 

  

  3. Identifiable clinical data from patients enrolled in the validation study (WP5) will likely be subject to restrictions imposed by the Research Ethics Committees and by the recruiting hospitals in this study. 

  

  

Unless data are earmarked for exploitation, they will be shared. The NICI
consortium will make use of two data sharing modalities, dependent on whether
or not data will be made accessible with or without restrictions:

  * Open access data sharing: data will be deposited in an open access depository (e.g. at the NICI Zenodo Community https://www.zenodo.org/communities/nici/) 

  

  * Restricted open access data sharing: data will be deposited in a data depository that allows for restricted open access once access has been granted by the consortium. Interested parties can apply for access through a web portal made available by UMCU. 

  

The data, including associated metadata, needed to validate the results
presented in scientific publications will be deposited as soon as possible.

Future revisions of this Data Management Plan may specify which intermediate
datasets can be destroyed and at what point in time. We will aim to balance
the benefits of open access to data against the resources available to share
it.

The NICI consortium Management Board will scrutinise data access requests
during their regular teleconferences. Should the number of data access
requests exceed the capacity of this committee, we will appoint a data access
committee, chaired by the Data Manager (now being recruited to UCAM).

Data deposited at Zenodo will be provided with a machine readable license
where possible.

##  6 Making data interoperable

NICI will use recognised standards to promote re-use, reproduction and
interoperability (responsibility of Management Board). This includes DICOM and
raw data formats for imaging data, PDF for protocols, CDISC for clinical
study, raw data formats, nmrML and nmrCV for NMR data, CHEER standard for
health economic evaluation data, RECIST and DICOM standards for CT reports and
coded/anonymised data for personal data. These are listed in detail in Table 1
above.

To make our data interoperable, we have chosen to use:

  * Long lived file and storage formats. 

  

  * For all data of long-term value, up to a maximum of 50GB in size, we will use the open access depository, Zenodo, to publish them. Zenodo offers facilities for long term data preservation. 

  

  * During the project, internal copies of data will initially be stored at the acquiring time, and archived there for disaster recovery. Data will then be copied to the central data archive on the HPHI system at UCAM for sharing between the consortium. 

  

##  7 Increase data re-use

###  7.1 Data archival

We intend to make the data acquired in the NICI project re-usable for 10
years.

Funding the storage of the raw data (approx. 2TB) is beyond the means of the
NICI project.  2  It also exceeds the capacity of the Zenodo platform’s free
tier. However, we will nevertheless archive the raw data at each site
performing data acquisition on a “best effort” basis by the relevant
consortium member. In practice, this is likely to achieve the 10-year archival
goal.

By publishing our data formats in the scientific literature, by releasing open
source code to interoperate with the data, and by using open access
depositories (e.g., Zenodo), we intend to make all the _**processed** _ data
outlive the NICI project. This processed data will be archived for 10 years
(or longer) through the Zenodo platform, or through one of the long-term
institutional data repositories at the University of Cambridge.

###  7.2 Quality assurance

Quality assurance for the data archival system will be developed as the post-
processing and QA pipeline is constructed in WP4. Our aim is to provide open-
source software that can interpret the data acquired during the project and
publish this alongside our scientific findings.

###  7.3 Data licencing and access procedures

Ownership and access to key knowledge is defined in the NICI Consortium
Agreement, which was based on the comprehensive Model Consortium Agreement for
Horizon 2020 (DESCA 2020). In general:

  * Each partner remains sole owner of background information. Background information relevant for carrying out the work plan is available, royalty-free, to the consortium. 

  

  * Foreground information will reside with the partner(s) that generate the knowledge. 

  

  * The consortium is committed to the protection of IPR related to the project, in collaboration with expertise in legal, financing, business development and, if relevant, patenting procedures. Protection of IPR rights is overseen by the Management Board focusing on those strategies that converts IP into the highest possible value. 

  

  * Philips, GE and Siemens have prior agreements on IPR sharing; UMCU, Philips and MR Coils have prior agreements on IPR; UCAM and Siemens have a Master Research Agreement that covers IPR. 

  

In a later version of this Data Management Plan, we will confirm the licence
and procedures for accessing the restricted data in the NICI data repository.
Our initial working point is to adapt the procedures used successfully by the
UK Dementias network or by ConnectomDB. These are both projects that have
gathered substantial amounts of imaging data in human subjects, providing this
with open access, and involving partner organisations in the EU.

##  8 Allocation of resources

It is not currently clear what the full costs for making our data “FAIR” will
be. Costs for personnel time for the Data Manager will be met from the
personnel budget allocated to UCAM. The Data Manager (recruitment in progress)
will pay special attention to:

  1. Data collection and storage 

  

  2. Data standards 

  

  3. Data sharing, accessibility and exploitation 

  

  4. Data preservation and curation 

  

  

The Data Manager will report regularly to the project Management Board and the
Coordinator.

We intend wherever possible to use centrally-funded repositories, avoiding
adding specific costs in the NICI project budget. The acquiring sites will
need to run the conversion tools to convert their proprietary raw data into
the NICI project’s standardised extended ISMRMRD format and Metabolic Imaging
DICOM format, then upload the data to the UCAM HPHI repository. This will
require personnel time and computing hardware which is available through their
NICI project staff and existing infrastructure at the sites.

The NICI consortium intend to nominate a Data Manager to take overall
responsibility for the execution of this Data Management Plan. This role is
planned to be allocated to a Research Associate now being recruited to the
University of Cambridge (UCAM).

The NICI Management Board will review this Data Management Plan by month 24.
An important aspect of this review will be to assess what data are likely to
be of long-standing value to the community so that these can be properly
archived before the end of the NICI project.

##  9 Data security

Data generated during the NICI validation study will be securely transferred
to the High Performance Hub for Clinical Informatics (HPHI) at the University
of Cambridge for processing and archiving. To access the imaging data users
from each NICI site will make a written application for an account on the HPHI
system. Once granted, this account will give researchers at all the
participating sites appropriate access to the data.

The HPHI was funded by part of the MRC Clinical Research Infrastructure Award
which refreshed the imaging hardware of the Wolfson Brain Imaging Centre, and
has been developed and is managed in close collaboration with the University
High Performance Computing Group. It forms part of the BioCloud initiative for
the integration of medical and biological datasets with high performance
computing facilities. The HPHI imaging nodes are hosted in the West Cambridge
Data Centre with storage and offsite backup provided. We benefit from the
Information Security expertise of the University of Cambridge central IT team.

##  10 Ethical aspects

The requirements for sharing human data, acquired in WP3 and WP5 have not yet
been established. We intend to include our proposals for access to this data
in the ethics applications for the technical development steps in WP3 and WP4,
and for the clinical study in WP5. We will incorporate the ethically approved
procedure in a later revision of this Data Management Plan.

##  11 Other issues

The great potential for exploitation of NICI results requires a sound strategy
for knowledge management, protection and data management to achieve targeted
exploitation that returns the highest value.  
  
<table>  
<tr>  
<th>

**Result**

</th>  
<th>

**Further research**

</th>  
<th>

**Exploitation of products and processes**

</th>  
<th>

**Exploitatio n**

**of services**

</th>  
<th>

**Standardisatio**

**n**

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

Predictive biomarker signature for patient stratification using 7T MR imaging
technology

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

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

Metabolic MR imaging processing pipeline/software

</td>  
<td>

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

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

Biomarker identification and validation processes

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

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

Validation of MR accessories for deep tissue (phosphorus) imaging

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

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

Sales of 7T upgrades

</td>  
<td>

</td>  
<td>

X

</td>  
<td>

</td>  
<td>

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

Understanding of treatment biology

</td>  
<td>

X

</td>  
<td>

</td>  
<td>

X

</td>  
<td>

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

Validated prototype Bodycoil

</td>  
<td>

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

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

Metabolic acquisition

Hardware

</td>  
<td>

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

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

##  d. General strategy for knowledge management and protection

All IP generated before the start of the project will continue to belong to
the partner that brings in this IP. In addition, NICI creates new knowledge in
the form of results, procedures and possibly patents. The important aspects in
the process of valorising this knowledge are:

  * The participants of the NICI consortium are committed to the protection of intellectual property rights (IPR) related to the project results. Continuous assessment of potential for commercialization of results obtained in the course of the research will be undertaken in collaboration with expertise in legal issues, financing, business development and, if relevant, patenting procedures. The Technology Transfer Office (TTO) affiliated with UMCU (called 

  

Holding UU) and the corresponding unit at partners Philips, Siemens, GE,
MRCoils and Tesla

will provide specific support to the coordinator for the management of IP
assets. Philips, GE and Siemens already have agreements on sharing IP, and
also UMCU, Philips, Tesla and MR Coils have consortium agreements on IP.
Protection of IP rights within the consortium will be assessed separately each
time knowledge is generated, and is overseen by the Management Board. Measures
for exploitation will be oriented on market needs to ensure that a strategy is
chosen that converts IP into the highest possible value.

  * Our general strategy is to provide open access according to the ‘gold’ open access model to all data and results which may be utilized for further research activities by third parties (scientists and developers outside the consortium). Publication of sensitive data, e.g. in the context of commercial exploitation prospects for consortium members will be managed according to the ‘green’ open access route, unless there is a specific benefit of prompt dissemination, or unless disclosure is shown to be impossible during the ethical review. The coordinator will ensure that all those associated with the research, whether staff, students, fellows or visitors, are aware of, and accept, these exploitation requirements. 

  

  * The NICI Consortium Agreement, based on DESCA 2020, and approved by all partners provides the specific rules regulating intellectual property in the NICI project.