#  1 Publishable Summary

The LITMUS Data Management Plan (DMP) is an important deliverable and will
evolve throughout the lifespan of the project. The initial version of the DMP
will describe the organisational and technical details about data collection,
storage, retention, destruction, privacy and confidentiality. The Plan also
includes details of the procedures for the transfer of data and confirmation
of compliance with national and EU law on the protection of individuals with
regard to the processing of personal data. The DMP is a living document and
will be updated throughout the project.

#  2 Introduction

A data management plan is a key aspect of any modern life sciences project
where the work plan proposes generating large scale, high throughput data.
“LITMUS Investigation: Testing Marker Utility in Steatohepatitis”, is just
such a project, with multiple parallel work packages, proposing the generation
of imaging, genetic, epigenetic, transcriptomic, metabolomic, lipidomic,
proteomic and metagenomic data as well as a range of targeted biomarkers,
epidemiologic, nutritional and clinical data. Section 0 looks at the data
management structure within LITMUS and the integration and sharing of data
between collaborators and work packages. Section 2.2 provides confirmation of
compliance said collaborators, with national and EU law on the protection of
individuals with regard to the processing of personal data.

In total, it is predicted that the LITMUS project will generate a vast amount
of data. With the exception of the raw imaging data that will be stored in a
specialised facility, all data generated in LITMUS will be archived in the
dedicated and purpose built UNEW Research Data Warehouse, which has a capacity
of 6PB. The majority of this data comes from standardised techniques and is
generated in standardised format. This, along with the anonymisation of
samples, will allow much of the data generated by LITMUS to ultimately be made
available for sharing in public repositories of research data.

The DMP focuses on the security and robustness of local data storage and
backup strategies, and on a plan for this repository-based sharing, where and
when appropriate. The DMP will describe the organisational and technical
details about data collection, storage, retention, destruction, privacy and
confidentiality. Based on the guidelines provided by the EU and the Digital
Curation Centre, a template DMP document that captures these key aspects has
been developed. Section 2.3 describes the development of this template and a
copy of the template itself is available in Appendix A.

This template document has then been adapted for each work package (and, for
specific tasks within work packages) to ensure that relevant information is
captured for all aspects of the project where significant data sets will be
generated. Section 2.4 herein describes this process. The individual plans are
described in detail from Section 3 of this document onwards.

Thus, the template, and the individually adapted work package plans are
compiled into this single document, the “LITMUS Data Management Plan”. The DMP
is a working document and will evolve throughout the life of the project. The
DMP is a living document and will be updated throughout the project.

##  2.1 Overview of Data Management in LITMUS

LITMUS is a multi-national, multi-disciplinary research programme that
involves over 50 collaborators, divided into eight work-packages:

  * WP1 Project Coordination & Oversight 

  

  * WP2 Analysis, Evaluation & Evidence Synthesis 

  

  * WP3 Patient Cohorts & Bioresources 

  

  * WP4 Central Laboratory 

  

  * WP5 Imaging 

  

  * WP6 Reverse-translation & Pre-clinical models 

  

  * WP7 ‘QED’ Qualification, Exploitation & Dissemination 

  

  * WP8 Ethics requirements 

  

All work-packages are tightly integrated so that data, samples and technical
expertise are shared throughout the programme, however, to reduce the risk of
bias and concerns about applicability in biomarker test performance,
individuals working in WP4-5 (some of whom may be using proprietary
technologies) will **_not_ ** have access to the clinical data, against which
the fidelity of each biomarker technology will be measured, until the analyses
are completed. The relationship between the workpackages, flow of data, and
this “firewall” is shown in Figure 1, below.

_Figure 1: Overview of LITMUS Data Management_

The project will generate the following types of data:

  * Participant information (WP3 & WP5): anonymised participant data, biochemical/histological indices, etc. – text. 

  

  * Imaging data (WP5): raw image data and processed image data. 

  

  * Research data (WP4 & WP6): large ’omics datasets, results of individual experiments, etc. 

  

  * Evaluation data (WP3): e.g. results of biomarker utility testing – text, spreadsheets. 

  

In addition to the clinical data that will be collected in WP3, the various
LITMUS experimental work packages (particularly, but not limited to, WP4, WP5
& WP6) will produce a large amount of diverse data. Analyses conducted in WP2
by AMC, UNEW, ORU and other partners will also generate a large body of
derived data from these raw inputs. With the exception of raw image data,
which will be handled within WP5 by specialists from PERS and ANT and stored
in a separate facility that is optimised for imaging data, all data generated
in the LITMUS project will be robustly archived to geographically distinct
physical servers in the dedicated and purpose built UNEW Research Data
Warehouse, which has a capacity of 6PB. All data will be stored in accepted,
community-wide standard formats (e.g. FASTQ/BAM for sequencing data). Where
possible, data processing will proceed via command line or programmatic
procedures, and scripts will be maintained alongside the data – as well as in
a widely-used, online code repository, using version control (e.g. GitHub).
Furthermore, all data will be annotated with standards-compliant metadata,
such that downstream submission to a suitable community repository will be
simplified once it becomes necessary (i.e. on publication). A lightweight
front-end, web-based system for access to raw LITMUS datasets will be produced
as an extension to the system already under development for the EPoS project.
The addition of access-control features, allowing specific data to be shared
with specific partners when appropriate, will be implemented to ensure the
robust processes for data handling and role-specific access within LITMUS are
adhered to.

Throughout the programme of work, LITMUS will endeavour to fulfil the FAIR
principles: that research data are Findable, Accessible, Interoperable and
Reusable. Supporting data used in academic peer reviewed publications will be
made available, after publication via a recognised suitable datasharing
repository (e.g. zenodo or national repository if available). This policy will
be followed unless a partner can show that disseminating this data will
compromise IP or other commercial advantage. Processes for addressing this are
defined within the LITMUS Consortium Agreement. The project will use the
metadata standards and requirements of the repository used for sharing the
data.

##  2.2 Compliance with Data Protection Law

By signing the LITMUS Consortium Agreement, all member organisations have
agreed to comply with key principles and applicable laws and regulations when
collecting, storing, using or transferring and Personal Data and/or Human
Samples for activities conducted within LITMUS, in accordance with _Appendix
2: Actions involving Personal Data and/or Human Samples, Consortium Agreement
for LITMUS_ . In particular, all members of the LITMUS consortium have agreed
to comply with all laws, rules, regulations and guidelines applicable to the
collection, use, handling, disposal and further Processing of the Personal
Data and the Human Samples (including but not limited to, as far as Personal
Data are concerned, the EU Data Protection Directive 95/46/EC or succeeding
regulations as the General Data Protection Regulation 2016/679 and
implementing national data protection laws), all as updated from time to time
and applicable laws and regulations relating to the privacy of patient health
information.

##  2.3 Development of the Template Plan

The template for the LITMUS data management plan was developed during the EPoS
project using guidance provided by the EU [1] and the Digital Curation Centre
[2], specifically the Checklist for a Data Management Plan, v4.0 [3]. The
template consists of nine _subparts_ – two introductory subparts outlining the
principles of the plan, and seven subparts describing the data itself, the
methods of collection, the plan for management and curation, data security,
sharing and the responsibilities of actors with respect to the data and the
DMP.

Each subpart of the DMP that requires individual contributions is accompanied
by guidance notes to assist Work Package leaders and data management
maintainers in filling out the plan for the work package under consideration
(see Appendix A).

All subparts of the plan are mandatory for all work packages, but some (e.g.
regulation of users, formal accreditation, etc.) may not be directly
applicable to all – this should be stated clearly for the relevant package and
subpart, the subpart heading should not be deleted.

##  2.4 Work Package Adaptation of the Template Plan

As referenced in Section 0, each work package (or WP task) within LITMUS
likely to generate significant data is required to complete the subparts 3 – 9
of the DMP template (see Appendix A for description). For many tasks, the core
information contained within these parts is likely to be similar, and so
adaptation of standard _boiler plate_ text is an accepted route for
streamlining this element of the deliverable, as long as the specific details
relevant to the work package or task are accurate.

Particular attention should be paid to the subparts describing the scale and
scope of the data, on the plan for data security and backup, and on the plan
for data sharing (where appropriate), bearing in mind the stated EU goal of
the Pilot Action on Open Access to Research Data “to improve and maximise
access to and re-use of research data generated by projects”.

It should also be noted that it is possible to exclude data from the open
access scheme where it involves identifiable information or where results can
reasonably be expected to be commercially or industrially exploited. Such
exceptions should be clearly noted in the relevant part of the DMP.

##  2.5 Conclusions

A robust Data Management Plan improves the understanding of the data to be
generated within a project, and of the requirements of securing and archiving
that data. It also highlights the data publication potential of a project –
data sets which can be released are identified early and appropriate steps can
be taken to ensure that sharing happens in a timely and efficient manner.

This document describes the development of just such a DMP for the LITMUS
project. This plan is a key element in maximising the impact of LITMUS, and
the plan itself is included in the appendices.

The data management plan is not a static document, but will continue to
undergo review as LITMUS progresses, with formal revisions due in Period 2 and
Period 4. New datasets will have their own DMP, and existing plans will be
checked as data is generated. Importantly the plan must also be implemented,
and progress checked against this implementation.