#  Executive summary

This deliverable is based on the  _Guidelines on FAIR Data Management in
Horizon 2020_ including the Horizon 2020 FAIR Data Management Plan (DMP)
template. Version: 26 July 2016. This Data Management Plan is a key
deliverable in this program, and documents the approach taken by the REPROGRAM
consortium to render research data findable, accessible, interoperable and re-
usable, where possible, within the context of the project.

#  Introduction

##  Purpose of the document

This Data Management Plan (DMP) describes the data management life cycle for
the data sets to be collected and processed by the REPROGRAM project. The DMP
outlines the handling of research data during the project, and how and what
parts of the data sets will be made available after the project has been
completed. This includes an assessment of when and how data can be shared
without disclosing directly or indirectly identifiable information from, for
instance, study participants.

This initial DMP needs to be updated over the course of the REPROGRAM project
whenever significant changes arise, such as (but not limited to):

  * new data; 

  

  * changes in consortium policies (e.g. new innovation potential, decision to file for a patent); 

  

  * changes in consortium composition and external factors (e.g. new consortium members joining or former members leaving). 

  

The DMP will be updated as a minimum in time with the periodic and final
evaluation of the project. This DMP will have a clear version number and
include a timetable for updates (see page 2).

The DMP specifies in combination with the Compulsory Deliverables related to
Clinical Studies performed during the REPROGRAM project the availability of
research data, describes measures to ensure data are properly anonymized to
ensure the privacy of study participants, and to ensure the open data strategy
reflects the consortium agreement and remains consistent with the continuously
evolving exploitation roadmap.

With regard to open access to scientific publications and conforming to the
recent ‘open access to publications obligations In Horizon 2020’ letter sent
by Robert-Jan Smits on March 27, 2017, the REPROGRAM consortium aims to
publish in open access journals (gold open access), and to establish links to
publications behind pay-walls available as final peerreviewed manuscripts in
an online repository after publication (green open access). To ensure gold
open access, the REPROGRAM consortium places priority on relevant journals
choosing gold open access journals. With regard to the latter, following the
recommendations of the DMP ensures we will only submit our work to journals
with an easy access to third parties. This is expected to contribute to the
current state-of-play of compliance with the Horizon 2020 open access
obligation. Currently, 68% of publications produced with Horizon 2020 funding
are subject to open access, the majority through the green route.

##  Intended readership

This DMP deliverable is intended for use internally in the REPROGRAM project
only and provides guidance on data management to the consortium and involved
staff at the premises of each beneficiary responsible for data management
activities. It is particularly relevant for partners responsible for data
collection and processing. It is a snapshot of the DMP at the current stage;
however, the DMP will evolve throughout the project as new procedures etc. are
added or existing ones are changed.

##  Structure of the document

The structure of this deliverable is based on the Horizon 2020 FAIR Data
Management Plan (DMP) template. Version: 26 July 2016:

  * Section 2: Data summary 

  

  * Section 3: FAIR data 

  

  * Section 4: Allocation of resources 

  

  * Section 5: Data security and ethical aspects 

  

  * Section 6: Other issues 

  

##  Relationship with other deliverables

This DMP complements the:

  * Consortium Agreement 

  

  * Quality Assurance File (D1.1) 

  

  * Roadmap for exploitation (D6.1) 

  

  * Exploitation report (D6.3) 

  

  * Dissemination report (D6.4) 

  

#  Data summary

The REPROGRAM project aims to uncover the novel revealed memory feature of
innate immunity (trained immunity) as a common mechanism perpetuating
inflammation in cardiovascular disease as well as its pathophysiological
relevance in other chronic inflammatory diseases, in particular rheumatoid
arthritis with a comparable societal impact. The disease-aetiology focus of
the REPOGRAM project will contribute to support innovation in the development
of evidence-based treatments in modulating highly relevant trained immunity
pathways. Hence, the REPROGRAM project bears direct clinical relevance for a
large number of individuals: subjects with cardiovascular disease risk
factors, patients with established cardiovascular disease, as well as patients
with chronic inflammatory diseases states. The integrated approach combining
molecule-to-man-to-mass studies is critical to succeed in understanding the
regulation, relevance and therapeutic modulation of trained immunity as a
common mechanism of disease, by which this project aims to deliver new safe
and effective treatment strategies attenuating the inflammatory state in
atherosclerosis as well as other chronic inflammatory disease.

The REPROGRAM project will generate both preclinical and clinical data
according to methodological GLP and GCP standards and standard operating
procedures defined by locally certified staff members. During the REPROGRAM
project all members will actively share data through operational standardized
databases that are developed, curated and preserved by qualified data managers
working in their institution.

**Grant agreement** . Research data which is created in the project is owned
by the (joint) partner(s) who generate(s) it (Grant Agreement Art. 26). Each
partner must disseminate its results as soon as possible unless there is
legitimate interest to protect the results. A partner that intends to
disseminate its results must give advance notice to the other partners (at
least

45 days) together with sufficient information on the results it will
disseminate (Grant Agreement Art. 29.1). In accordance with Grant Agreement
Art. 25, data must be made available to partners upon request, including in
the context of checks, reviews, audits or investigations. Data will be made
accessible and available for re-use and secondary analysis.

Types of data generated during the REPROGRAM project:

  * Observational data (captured in situ, can’t be recaptured, recreated or replaced) 

  

  * Experimental data (data collected under controlled conditions, in situ, in vitro, in vivo and ex vivo, should be reproducible) 

  

  * Derived or compiled data (should be reproducible) 

  

  * Reference data (static collection [peer-reviewed] datasets, most probably published and/or curated) 

  

Research data comes in many varied formats: text, numeric, multimedia, models,
software languages, discipline specific, and instrument specific.

The list of data formats generated in the REPROGRAM project is extensive, and
includes (but is not limited to):

  * delimited text of given character set (.txt) 

  

  * widely-used proprietary formats, e.g. MS Word (.doc/.docx) - Rich Text Format (.rtf) 

  

  * SPSS portable format (.por) Comma-separated values (CSV) files (.csv) 

  

  * MS Excel files (.xls/.xlsx) 

  

  * IBM Statistics package (SPSS) 

  

  * MS Access (.mdb/.accdb) 

  

  * OpenDocument Spreadsheet (.ods) 

  

  * structured text or mark-up file containing metadata information, e.g. DDI XML file 

  

  * JPEG (.jpeg, .jpg) 

  

  * TIFF (other versions; .tif, .tiff) 

  

  * Adobe Portable Document Format (.PDF) 

  

  * Gene Transfer Format [.GTF] 

  

  * MPEG-4 High Profile (.mp4) 

  

  * PET image format (DICOM) 

  

Within the REPROGRAM project approximately 57 separate datasets will be
created (see list in table below). They are listed under each of the work
package deliverables taken from the GA Annex 1 – Description of Action. The
datasets will have the same structure, in accordance with the guide of Horizon
2020 for the Data Management Plan.

_Table 1. Potential datasets_  
  
<table>  
<tr>  
<th>

**Set no.**

</th>  
<th>

**WP**

</th>  
<th>

**Data type**

</th>  
<th>

**Format**

</th>  
<th>

**IPR owner**

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

1

</td>  
<td>

2

</td>  
<td>

In vivo data on atherosclerosis-induced epigenetic changes in myeloid
(precursor) cells by feeding atherosclerotic LDLR-/- mice a high fat diet.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

2

</td>  
<td>

2

</td>  
<td>

In vivo data on myocardial infarction-induced epigenetic changes in myeloid
(precursor) cells.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

3

</td>  
<td>

2

</td>  
<td>

In vivo data on long-lasting changes in innate immune system activation by
competitive adoptive transfer of bone marrow harvested from the induced
atherosclerosis model.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

4

</td>  
<td>

2

</td>  
<td>

In vivo data on long-lasting changes in training of hematopoietic stem cells
by competitive adoptive transfer of bone marrow harvested from the induced
atherosclerosis model.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

5

</td>  
<td>

2

</td>  
<td>

In vivo data on long-lasting changes in innate immune system activation by
competitive adoptive transfer of bone marrow harvested from the induced
myocardial infarction model.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

6

</td>  
<td>

2

</td>  
<td>

In vivo data on long-lasting changes in training of hematopoietic stem cells
by competitive adoptive transfer of bone marrow harvested from the induced
myocardial infarction model.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

7

</td>  
<td>

2

</td>  
<td>

In vivo data on the impact of atherosclerosis induced histone modifications on
atherosclerotic burden (hematopoietic stem cells, plaque size and stage) in
REVERSA mice.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

8

</td>  
<td>

2

</td>  
<td>

In vivo data on the impact of myocardial infarction induced histone
modifications on atherosclerotic burden (hematopoietic stem cells, plaque size
and stage) in REVERSA mice.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

9

</td>  
<td>

2

</td>  
<td>

In vitro screening data of epigenetic modulators for their capacity to
reprogram histones and prevent atherogenic risk factor/myocardial infarction
-induced histone modifications in immune cells.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

LMU

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

10

</th>  
<th>

2

</th>  
<th>

In vivo data on in vitro identified epigenetic modulators ith respect to their
effect on immune cell function and phenotype as well as histone modification
marks, in relation to their impact on atherosclerotic lesion burden and stage.

</th>  
<th>

.xlsx,

PDF, jpeg

</th>  
<th>

AMC

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

11

</td>  
<td>

3

</td>  
<td>

In vitro data on phenotype characterization of risk factor induced pro-
atherogenesis in human innate immune cells and its progenitors from healthy
control subjects.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

12

</td>  
<td>

3

</td>  
<td>

In vitro data on the major activating histone modifications upon risk factor
induced pro-atherogenesis in human innate immune cells and its progenitors
from healthy control subjects using chromatin immunoprecipitation (ChIP)
sequencing assays.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

13

</td>  
<td>

3

</td>  
<td>

In vitro data on the transcriptome of human innate immune cells and its
progenitors from healthy control subjects upon risk factor induced pro-
atherogenesis using RNA sequencing.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

14

</td>  
<td>

3

</td>  
<td>

In vitro metabolome analysis of human innate immune cells and its progenitors
from healthy control subjects upon risk factor induced pro-atherogenesis using
mass spectrometry.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

15

</td>  
<td>

3

</td>  
<td>

In vitro data on selective compounds targeting epigenetics or cellular
metabolism are able to prevent the proatherogenic switch in the healthy donor
monocytes.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

16

</td>  
<td>

3

</td>  
<td>

In vitro data on bone marrow precursor (hematopoietic stem cells) activation
from healthy control subjects assessing lineage differentiation, inflammatory
markers and proliferative capacity after exposure to pro-atherogenic
substances.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC/AMC

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

17

</td>  
<td>

3

</td>  
<td>

In vitro data on trained circulating monocytes isolated from patients with
familial hypercholesterolemia using flow cytometry, stimulation assays with
TLR ligands, transendothelial migration, and analysis of the epigenome,
transcriptome and metabolome

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

AMC

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

18

</td>  
<td>

3

</td>  
<td>

In vitro data on trained circulating monocytes isolated from patients with
isolated elevated levels of lp(a) using flow cytometry, stimulation assays
with TLR ligands, transendothelial migration, and analysis of the epigenome,
transcriptome and metabolome

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC/AMC

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

19

</td>  
<td>

3

</td>  
<td>

In vitro data on trained circulating monocytes isolated from patients with
isolated low HDL cholesterol levels using flow cytometry, stimulation assays
with TLR ligands, transendothelial migration, and analysis of the epigenome,
transcriptome

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

AMC

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

20

</td>  
<td>

3

</td>  
<td>

In vitro data on trained circulating monocytes isolated from patients with
excessive smoking behaviour using flow cytometry, stimulation assays with TLR
ligands, transendothelial migration, and analysis of the epigenome,
transcriptome and metabolome*

_*This dataset has been removed and explained so in Deliverable 4.1._

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

21

</td>  
<td>

3

</td>  
<td>

In vitro data on trained circulating monocytes isolated from patients with
premature atherosclerosis using flow cytometry, stimulation assays with TLR
ligands,

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

22

</th>  
<th>

3

</th>  
<th>

In vitro data on trained circulating monocytes isolated from patients after an
acute cardiovascular event is using flow cytometry,

</th>  
<th>

.xlsx,

PDF

</th>  
<th>

AMC

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

23

</td>  
<td>

3

</td>  
<td>

In vitro data on trained monocytes isolated from healthy subjects using flow
cytometry, stimulation assays with TLR ligands, trans-endothelial migration,
and analysis of the epigenome, transcriptome and metabolome

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

24

</td>  
<td>

3

</td>  
<td>

In vitro data on healthy donor monocytes that are exposed to pooled serum of
the selected patient groups in presence / absence of specific inhibitors.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

25

</td>  
<td>

3

</td>  
<td>

In vitro data on the lineage differentiation, inflammatory markers and
proliferative capacity of hematopoietic stem cells from patients with
atherogenic risk factors or postmyocardial infarction.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC/AMC

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

26

</td>  
<td>

3

</td>  
<td>

In vitro data on the lineage differentiation, inflammatory markers and
proliferative capacity of hematopoietic stem cells from healthy controls.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

RadboudUMC

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

27

</td>  
<td>

3

</td>  
<td>

FDG-PET data for inflammatory activity of the arterial wall as well as bone
marrow and splenic activity in patients within 1 week and >3months after acute
coronary syndrome.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

28

</td>  
<td>

3

</td>  
<td>

FDG-PET data for inflammatory activity of the arterial wall as well as bone
marrow and splenic activity in patients with high LDL cholesterol levels.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

29

</td>  
<td>

3

</td>  
<td>

FDG-PET data for inflammatory activity of the arterial wall as well as bone
marrow and splenic activity in patients with elevated levels of lp(a).

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

RadboudUMC/AMC

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

30

</td>  
<td>

3

</td>  
<td>

FDG-PET data for inflammatory activity of the arterial wall as well as bone
marrow and splenic activity in patients with low HDL cholesterol levels.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

31

</td>  
<td>

3

</td>  
<td>

FDG-PET data for inflammatory activity of the arterial wall as well as bone
marrow and splenic activity in patients with excessive smoking behavior.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

retracted

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

32

</td>  
<td>

3

</td>  
<td>

FDG-PET data for inflammatory activity of the arterial wall as well as bone
marrow and splenic activity in healthy controls.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

RadboudUMC/AMC

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

33

</td>  
<td>

3

</td>  
<td>

Integrated data set following a systems medicine approach to assess the
regulation of systemic and local immune cell production/activity in patients
within 1 week and >3months after acute coronary syndrome.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

34

</td>  
<td>

3

</td>  
<td>

Integrated data set following a systems medicine approach to assess the
regulation of systemic and local immune cell production/activity in patients
with high LDL cholesterol levels.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

35

</td>  
<td>

3

</td>  
<td>

Integrated data set following a systems medicine approach to assess the
regulation of systemic and local immune cell production/activity in patients
with elevated levels of lp(a).

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

36

</th>  
<th>

3

</th>  
<th>

Integrated data set following a systems medicine approach to assess the
regulation of systemic and local immune cell production/activity in patients
with low HDL cholesterol levels.

</th>  
<th>

xlsx, PDF, jpeg,

DICOM

</th>  
<th>

AMC

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

37

</td>  
<td>

3

</td>  
<td>

Integrated data set following a systems medicine approach to assess the
regulation of systemic and local immune cell production/activity in patients
with excessive smoking behavior.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

retracted

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

38

</td>  
<td>

3

</td>  
<td>

Integrated data set following a systems medicine approach to assess the
regulation of systemic and local immune cell production/activity in healthy
controls.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

RadboudUMC

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

39

</td>  
<td>

4

</td>  
<td>

Gene score of prevalent SNPs in enzymes contributing to epigenetic modulation
in humans.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

RadboudUMC

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

40

</td>  
<td>

4

</td>  
<td>

Clinical assessment of predictive value of critical enzymes of epigenetic
reprogramming on cardiovascular risk in the general population.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

REGIONH

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

41

</td>  
<td>

4

</td>  
<td>

Clinical data on the reversibility of epigenetic remodeling by lowering LDL-c
in relation to inflammatory activation.

</td>  
<td>

xlsx,

PDF

</td>  
<td>

AMC

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

42

</td>  
<td>

4

</td>  
<td>

Ex vivo data on monocyte phenotyping combined with epigenome/transcriptome
analysis in patients with genetically elevated LDL cholesterol who underwent
statininduced LDL cholesterol lowering treatment.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

AMC

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

43

</td>  
<td>

4

</td>  
<td>

Multi-level PET/CT clinical data (spleen/bonemarrow/arterial wall) on
inflammatory activation in patients with genetically elevated LDL cholesterol
who received oral dosing of the short-chain fatty acid butyrate.

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

44

</td>  
<td>

4

</td>  
<td>

Ex vivo data on monocyte phenotyping combined with epigenome/transcriptome
analysis in patients with genetically elevated LDL cholesterol who received
oral dosing of the short-chain fatty acid butyrate.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

AMC

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

45

</td>  
<td>

4

</td>  
<td>

Clinical data from a proof-of-concept study in patients at increased
cardiovascular risk to evaluate whether epigenetic marks and inflammatory
activation can be reversed, using multi-level PET/CT
(spleen/bonemarrow/arterial wall).

</td>  
<td>

xlsx, PDF, jpeg,

DICOM

</td>  
<td>

AMC

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

46

</td>  
<td>

4

</td>  
<td>

Ex vivo data on monocyte phenotyping combined with epigenome/transcriptome
analysis from a proof-of-concept

study in patients at increased cardiovascular risk to evaluate whether
epigenetic marks and inflammatory activation can be reversed.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

AMC

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

47

</td>  
<td>

4

</td>  
<td>

Clinical data on nanoparticle delivery of promising treatment candidates by
liposomal packaging.

</td>  
<td>

xlsx, PDF,

GTF,

DICOM

</td>  
<td>

AMC

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

48

</td>  
<td>

5

</td>  
<td>

In vitro data on the relation between chronic inflammatory disease-associated
DAMPs and histone modification in human innate immune cells.

</td>  
<td>

.xlsx,

PDF

</td>  
<td>

UZH

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

49

</td>  
<td>

5

</td>  
<td>

In vitro data on genome-wide methylome analysis (DNA methylation and
hydroxymethylation) for the assessment of the epigenetic landscape at the
level of major activating histone modifications.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

UZH

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

50

</td>  
<td>

5

</td>  
<td>

In vitro data on chromatin immunoprecipitation (ChIP)sequencing assays for the
assessment of the epigenetic landscape at the level of major activating
histone modifications.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

UZH

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

51

</td>  
<td>

5

</td>  
<td>

Clinical data on phenotype, function and epigenome of monocytes harvested from
rheumatoid arthritis patients, with active disease. healthy matched control
subjects

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

UZH

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

52

</td>  
<td>

5

</td>  
<td>

Clinical data on phenotype, function and epigenome of monocytes harvested from
rheumatoid arthritis patients in stable remission.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

UZH

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

53

</td>  
<td>

5

</td>  
<td>

Clinical data on phenotype, function and epigenome of monocytes harvested from
healthy matched control subjects.

</td>  
<td>

xlsx, PDF,

GTF

</td>  
<td>

UZH

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

54

</td>  
<td>

5

</td>  
<td>

Clinical FDG-PET data for the assessment of inflammatory activity of the
arterial wall and bone marrow to assess the

correlation between circulating monocytes, DNA hydroxymethylation, histone
modification marks and inflammatory activity of arterial wall in patients with
active rheumatoid arthritis.

</td>  
<td>

xlsx, PDF,

GTF,

DICOM

</td>  
<td>

UZH

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

55

</td>  
<td>

5

</td>  
<td>

Clinical FDG-PET data for the assessment of inflammatory activity of the
arterial wall and bone marrow to assess the

correlation between circulating monocytes, DNA hydroxymethylation, histone
modification marks and inflammatory activity of arterial wall in patients with
remissive rheumatoid arthritis.

</td>  
<td>

xlsx, PDF,

GTF,

DICOM

</td>  
<td>

UZH

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

56

</td>  
<td>

5

</td>  
<td>

Clinical FDG-PET data for the assessment of inflammatory activity of the
arterial wall and bone marrow to assess the

correlation between circulating monocytes, DNA hydroxymethylation, histone
modification marks and inflammatory activity of arterial wall in healthy
matched control subjects.

</td>  
<td>

xlsx, PDF,

GTF,

DICOM

</td>  
<td>

UZH

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

57

</td>  
<td>

5

</td>  
<td>

Integrated imaging data on bone marrow/spleen/arterial wall and data on
circulating cells as well as epigenetic data to gain insight in the regulation
of systemic and local immune cell production/activity in rheumatoid arthritis.

</td>  
<td>

xlsx, PDF,

GTF,

DICOM

</td>  
<td>

UZH

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

_Table 2. Work task leaders in accordance with the GA Annex 1 – Description of
Action_  
  
<table>  
<tr>  
<th>

**Task**

</th>  
<th>

**Task leader**

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

2.1

</td>  
<td>

Prof. Lutgens (LMU)

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

2.2

</td>  
<td>

Prof. Lutgens (LMU)

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

2.3

</td>  
<td>

Prof. Lutgens (LMU)

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

2.4

</td>  
<td>

Prof. Stroes (AMC)

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

3.1

</td>  
<td>

Prof. Riksen (RadboudUMC)

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

3.2

</td>  
<td>

Prof. Riksen (RadboudUMC)

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

3.3

</td>  
<td>

Prof. Riksen (RadboudUMC)

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

4.1

</td>  
<td>

Prof. Riksen (RadboudUMC)

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

4.2

</td>  
<td>

Prof. Nordestgaard (REGIONH)

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

4.3

</td>  
<td>

Prof. Stroes (AMC)

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

4.4

</td>  
<td>

Prof. Stroes (AMC)

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

4.5

</td>  
<td>

Prof. Stroes (AMC)

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

5.1

</td>  
<td>

Prof. Neidhart (UZH)

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

5.2

</td>  
<td>

Prof. Neidhart (UZH)

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

5.3

</td>  
<td>

Prof. Neidhart (UZH)

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

The expected size of the data collected within the REPROGRAM project is
calculated as follows:

  * Each in situ and/or in vitro experiment log will be approximately 5 megabytes. 

  

  * Each in vivo animal experiment log will be approximately 5 megabytes. 

  

  * Each imaging experiment will be approximately 50-150 megabytes. 

  

  * Each patient record will be approximately 1 megabyte. 

  

  * RNA and chromatin immunoprecipitation sequencing assays records will be approximately 1 gigabyte per subject (in total 150 gigabytes for WP3 and 100 gigabytes for WP4) 

  

In total, the REPROGRAM project will generate between 250-275 gigabytes of
data.

During the REPROGRAM project existing data is being re-used in task 4.1 in
which functionality of identified SNPs in major enzymes involved in epigenetic
modulation will be assessed using published data as well as prediction
programs. Furthermore, the correlation between the gene score and the trained
immunity responses on the one hand, or epigenetic histone marks on the other
hand, will be assessed in the available data from the Human Functional
Genomics cohorts (  _www.humanfunctionalgenomics.org)_ .

Secondary use of the data generated within the REPROGRAM project is
foreseeable as follows:

  * Further use by original researchers; 

  

  * Combinations with other data; 

  

  * Re-analysis using novel methods; 

  

  * Meta-analysis; • General reference. 

  

Data generated within the REPROGRAM project will be useful to researchers
within and outside of the REPROGRAM consortium. External parties such as
pharmaceutical companies, and health (policy) agencies.

#  FAIR data

Wilkinson et al (2016) published on The FAIR Guiding Principles for scientific
data management and stewardship. This source defines good data management
which is not a goal in itself, but rather is the key conduit leading to
knowledge discovery and innovation, and to subsequent data and knowledge
integration and reuse by the community after the data publication process.
Unfortunately, the existing digital ecosystem surrounding data publication
prevents us from extracting maximum benefit from our research investments.
Partially in response to this, science funders, publishers and governmental
agencies are beginning to require data management and stewardship plans for
data generated in publicly funded experiments. Beyond proper collection,
annotation, and archival, data stewardship includes the notion of ‘long-term
care’ of valuable digital assets, with the goal that they should be discovered
and re-used for downstream investigations, either alone, or in combination
with newly generated data. The outcomes from good data management and
stewardship, therefore, are high quality digital publications that facilitate
and simplify this ongoing process of discovery, evaluation, and reuse in
downstream studies. What constitutes ‘good data management’ is, however,
largely undefined, and is generally left as a decision for the data or
repository owner. Therefore, bringing some clarity around the goals of good
data management and stewardship, and defining simple guideposts to inform
those who publish and/or preserve scholarly data, would be of great utility.
This article described four foundational principles— **Findability,
Accessibility, Interoperability, and Reusability** — that serve to guide data
producers and publishers as they navigate around these obstacles, thereby
helping to maximize the added-value gained by contemporary, formal digital
publishing. Importantly, the principles apply not only to ‘data’ in the
conventional sense, but also to the algorithms, tools, and workflows that led
to that data.

##  Making data findable, including provisions for metadata

Data generated in the REPROGRAM project will be documented and be made
discoverable and accessible through a dedicated webpage on the project’s
website:  _http://reprogramhorizon2020.eu_ . Upon scientific publication, a
DOI will be assigned to datasets for effective and persistent citation when it
is uploaded to a repository (e.g. NCBI GEO database). This DOI can be used in
any relevant publications to direct readers to the underlying dataset.

Each dataset generated during the project will be allocated a dataset
identifier. This dataset identifier will be, in combination with dataset
information, included in a metadata file at the beginning of the
documentation, and updated with each version.

The REPROGRAM dataset identifier will comprise of the following:

  1. A unique chronological number of the datasets in the project will be added to the metadata file. 

  

  2. A prefix "REP" indicating a REPROGRAM dataset. 

  

  3. A unique identification number linking with the dataset work package and task. 

  

  4. For each new version of a dataset it will be allocated with a version number. 

  

  5. For example: 01_REP_WP2_T2.1_v0.1.xlsx 

  

  

Search key words will be provided in the metadata file when the dataset is
deposited which will optimise possibilities for re-use. The specific metadata
contents, formats and volume are given in the table below and will be further
defined in future versions of the DMP.

_Table 3. Datasets fields (example)_  
  
<table>  
<tr>  
<th>

Dataset Identifier

</th>  
<th>

01_REP_WP2_T2.1_v0.1.xlsx

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

Title of Dataset

</td>  
<td>

In vivo data on atherosclerosis-induced epigenetic changes in myeloid
(precursor) cells by feeding atherosclerotic LDLR-/- mice a high fat diet.

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

Lead Partners

</td>  
<td>

LMU

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

Work Package

</td>  
<td>

2

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

Dataset Description

</td>  
<td>

Experimental results on epigenetic changes …

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

Dissemination goals

</td>  
<td>

Peer reviewed journal

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

Data Format

</td>  
<td>

.xlsx / .PDF

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

Expected Size

</td>  
<td>

5 megabytes

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

Expected Repository

</td>  
<td>

NCBI GEO database

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

DOI (if known)

</td>  
<td>

To be inserted once the dataset is reposited

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

Date of Submission

</td>  
<td>

31-12-2018 or before if used for publication

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

Key words

</td>  
<td>

Atherosclerosis, LDL receptor knock-out mice, epigenetics …

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

Version Number

</td>  
<td>

v0.1

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

Link to metadata file

</td>  
<td>

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

##  Making data openly accessible

**Consortium Agreement:** Any party whose tasks under the Work Packages
include the collection of data shall procure that it has obtained or will
obtain data in accordance with all applicable local laws, regulations and
codes of practice, and in accordance with a current ethics committee approval,
and from participants that have given their informed consent for their Data to
be used and stored for research purposes. The database (REPROGRAM data
repository) shall be considered as a jointly owned results owned by all the
parties without prejudice of the rights of any party owning or generating any
data contained in the Database. The database shall be made available to the
other parties for the purpose of the project and the exercise of Access
Rights. No party may make any use of such provided data for any purposes
outside of the implementation of the project (including publication) or other
than for the exercise of Access Rights without first securing agreement from
the project coordinator. Each party shall ensure that, to the best of its
knowledge, it can grant Access Rights with regard to its data contained in the
database and fulfil its obligations under this Consortium Agreement
notwithstanding any rights of its employees, or persons it engages to perform
collection of the data. Each party ensures that all data that is transferred
under this Consortium Agreement or held in the database will be de-identified
and will contain no identifiable health information.

**REPROGRAM repository** . Not all project partners have access to an
institutional repository. Therefore, data generated in the REPROGRAM project
will be documented and be made discoverable and accessible through a dedicated
webpage (REPROGRAM data repository) on the project’s website:
_http://reprogram-horizon2020.eu_ . The use of the REPROGRAM data repository
ensures that data management procedures are unified across the project. A
webpage functionality will be setup for easy upload of project datasets and
inclusion in the metadata file. Details of how to access the data will be
available on this webpage. A ‘data request form’ will be created to facilitate
this process.

A data access committee consisting of Prof. Stroes, Prof. Lutgens, and Prof.
Riksen will gather monthly to process all data requests. If a request is
granted, a Data Access Agreement form will be executed, after which transfer
of the data can be arranged.

Data objects will be deposited in the REPROGRAM’s repository under:

  * Open access to data files and metadata and data files provided over standard protocols. 

  

  * Use and reuse of data permitted. 

  

  * Privacy of its users protected. 

  

Since the data is being deposited in a central repository, a dataset registry
record should also be created in local host institutions repositories e.g.
PURE for UST. The registry record should include relevant metadata explaining
what data exists, and a DOI linking to where the data is available in the
external repository. Any data which is deposited externally in a closed state,

i.e. it is not accessible, should also be deposited in a local institutional
repository, so that the partner is still able to access the data.

During embargo periods, information about the restricted data will be
published in the REPROGRAM data repository, and details of when the data will
become available will be included in the metadata. Where a restriction on open
access to data is necessary, attempts will be made to make data available
under controlled conditions to other individual researchers. All the public
data of the project will be made openly accessible in the repository. Non-
public data will be archived at the repository using the “closed access”
option.

For appropriate intellectual property management, there are several restricted
datasets. These will be shown in the REPROGRAM data repository metadata file.
These datasets are proprietary to the relevant partners and may only be used
in the restricted application of developing compounds to support the work of
this project. As these activities are enabling aspects of the project allowing
the development of new therapies, it is not felt that restrictions will impact
on eventual dissemination of the project outputs for the enhanced
understanding of common mechanisms of chronic inflammatory diseases and their
relevance in comorbidities.

##  Making data interoperable

The REPROGRAM project aims to collect and document the data in a standardised
way to ensure that, the datasets can be understood, interpreted and shared in
isolation alongside accompanying metadata and documentation.

Generated data will be preserved in the REPROGRAM data repository and on
institutional intranet platforms until the end of the project. A metadata file
will be created and linked within each dataset. It will include the following
information:

**General Information**

  * Title of the dataset 

  

  * Dataset Identifier 

  

  * Responsible Partner 

  

  * Author Information 

  

  * Date of data collection 

  

  * Geographic location of data collection 

  

  * The title of project and funding sources that supported the collection of the data 

  

**Sharing/Access Information**

  * Licenses/access restrictions placed on the data 

  

  * Link to data Repository 

  

  * Links to other publicly accessible locations of the data - Links to publications that cite or use the data - Was data derived from another source? 

  

**Dataset/File Overview**

  * This dataset contains X sub-dataset as listed below 

  

  * What is the status of the documented data? – “complete”, “in progress”, or “planned” - Are there plans to update the data? 

  

**Methodological Information**

  * Used materials 

  

  * Description of methods used for experimental design and data collection: <Include links or references to publications or other documentation containing experimental design or protocols used in data collection>

  

  * Methods for processing the data: <describe how the submitted data were generated from the raw or collected data>

  

  * Instruments and software used in data collection and processing-specific information needed to interpret the data 

  

  * Standards and calibration information, if appropriate 

  

  * Environmental/experimental conditions 

  

  * Describe any quality-assurance procedures performed on the data 

  

  * Dataset benefits 

  

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

The datasets will be made available for re-use through data requests to the
REPROGRAM data repository and uploads to public repositories upon scientific
peer-reviewed publication. In principle, the data will be stored in the
REPROGRAM data repository after the conclusion of the project without
additional cost. All the research data will be of the highest quality, have
long-term validity and will be well documented in such a way that other
researchers are able to gain access and understand them after several years.

If datasets are updated, the partner that possesses the data has the
responsibility to manage the different versions and to make sure that the
latest version is available in the case of publically available data. Quality
control of the data is the responsibility of the relevant responsible partner
generating the data.

#  Allocation of resources

There are minor immediate costs anticipated to make the datasets produced
FAIR. First the dedicated webpage needs to be developed, including its defined
functionalities (e.g. data request form, metadata file, etc.). The datasets
will be deposited in the REPROGRAM repository for at least 5 years after the
conclusion of the project. These costs will be covered by the local
institution of the Project Coordinator (AMC).

Prof. Stroes and qualified data managers based at the Academic Medical Center
(AMC) are responsible for data management within the REPROGRAM project,
specifically for D6.5 creation of initial data management plan and updating
the data management plan and ensuring the datasets are recorded. The PI of
each partner will have overall responsibility for implementing the data
management plan.

Each REPROGRAM consortium partner should respect the policies set out in this
data management plan. Datasets have to be created, managed and stored
appropriately and in line with European Commission, national and local
legislation. Dataset validation and registration of metadata and backing up
data for sharing through repositories is the responsibility of the partner
that generates the data in the particular WP.

The datasets in the REPROGRAM repository will be preserved in line with the
European Commission Data Deposit Policy. The data will be preserved
indefinitely (minimum of 5 years) and the costs anticipated for archiving data
in this repository will be covered by AMC.

Costs related to open access to research data in Horizon 2020 are eligible for
reimbursement

during the duration of the project under the conditions defined in the H2020
Grant Agreement, Art 6, but also other articles relevant for the cost category
chosen.

#  Data security and ethical aspects

For the duration of the project, datasets will be stored on the responsible
partner’s centrally provided storage, detailed in the table below.  
  
<table>  
<tr>  
<th>

AMC

</th>  
<th>

All data is stored at the same time on internal servers of AMC (G-drive). The
AMC Medical Library’s centralised research data management system, maintained
by qualified data managers, offers the secure storage of research data. This
central data storage system provides a good place to archive processed
datasets, and to archive qualitative data, such as recordings of interviews.
Data would also be fully copied in cloud-based repositories once provided by
the central ICT system of AMC including Surfdrive. Selected data is also
stored in cloud-based repositories (Dropbox, Google drive) for sharing easily.

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

RadboudUMC

</td>  
<td>

RadboudUMC uses file folders on the university’s network drive that enables
research to grant colleagues reading and/or writing rights in the ICT
infrastructure. They can use an e-number (guest account) to grant access to
external colleagues. Furthermore, Surfdrive is used as the legally secure
alternative to the USA grounded Dropbox service. Surfdrive is a personal cloud
storage service for Dutch higher education and research, hosted in the
Netherlands.

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

LMU

</td>  
<td>

tbd

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

REGIONH

</td>  
<td>

All code and resultant data are stored in data repositories that are fully
copied across numerous computers both on site at REGIONH and off site.
Selected data is also stored in cloud-based repositories (Dropbox, Google
drive, etc.).

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

UMF

</td>  
<td>

Data security is provided by access controls defined at a user level. The data
will be stored on network drives. Sensitive data can be encrypted and then
stored in the “Home directory” of UMF. Non-sensitive data is stored in the
same directory. Data is securely stored and backed up daily, so a deleted file
can be restored within 24 hours.

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

UNIMI

</td>  
<td>

tbd

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

UZH

</td>  
<td>

In cooperation with unit Service and Support for Science IT (S3IT) and the
Zentralbibliothek the Main Library is pursuing the goal of developing
consultation opportunities in the fields of data management, long-term
archiving and data publishing, based on the accumulated experiences in the
field of Open Access. The ScienceCloud is a multipurpose compute and storage
infrastructure of the University of Zurich serving most computational and
storage needs of research. It is an Infrastructure-as-a-Service (IaaS)
solution specifically targeted to address large scale computational research;
it is based on the

OpenStack cloud management software and on Ceph for the underneath storage
infrastructure. UZH also uses Zenodo, an open access data repository at CERN
in cooperation with OpenAIRE2020.

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

MGH

</td>  
<td>

Project data is stored on the internal intranet servers. Dedicated security
software is used to back up files on a daily basis. This is a cloud-based
solution and data is backed up to a data center in Boston. Data on this server
is also covered with a data recovery procedure

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

</td>  
<td>

which is replicated real time to the same data center to enable remote access.

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

Sensilab

</td>  
<td>

Sensilab has an internal archive where all proprietary data are stored.
Moreover, all data are stored in secondary secure archives that are backed up
every night.

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

Servier

</td>  
<td>

The company has an IT group who have responsibility for IT infrastructure and
data security. Electronic data is stored locally on network drives and/or data
base systems. Data is backed up daily.

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

Descin

</td>  
<td>

Descin has access to a dedicated cloud-based data archive where all
proprietary data are securely stored.

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

In the REPROGRAM project, (genetic or other sensitive) data and selected
preclinical data will be included to study common pathophysiological
mechanisms of chronic inflammatory diseases. For maximum safety of data a
number of safety procedures will be implemented:

  * Rule of data austerity: all databases will host only phenotype and biosample data which are absolutely essential for clear definition of phenotypes and biosamples. “Reserve” data will rejected and not hosted. 

  

  * Data transfer into the database will be performed only using highly secured transfer protocols (128bit encryption). 

  

  * Access to the database will be granted only to few selected and appropriately educated personnel. These persons will be provided with a personalized user name and password. 

  

  * Only pseudonymized patient data will be integrated in the database. 

  

  * All genetic and / or molecular data imply a potential risk for depseudonymization if they can be connected to phenotype data. To avoid any potential risk of depseudonymization phenotype data and molecular / genetic data will be physically separated. 

  

  * The process of depseudonymization will be impossible at the central database. 

  

For REPROGRAM partners involved in use and storage of biological human
samples, they will explicitly follow Directive 2001/20/EEC of the European
Parliament and of the Council of 4th April 2001 on the approximation of the
laws, regulations and administrative provisions of the Member States relating
to the implementation of good clinical practice in the conduct of clinical
trials on medicinal products for human use, as well as the Guidelines as
suggested by the European Science foundation, in European science foundation
policy briefing May 2001, on Controlled clinical trials.

From the intellectual property management point of view, all REPROGRAM
partners will adhere to Directive 98/44/EC of the European Parliament and of
the Council of 6 July 1998 on the legal protection of biotechnological
inventions.

#  Ethical aspects

The REPROGRAM project is an international research and innovation project that
proposes that trained immunity is an important final common pathway
contributing to the maintenance of an activated state of innate immune cells;
hence, modulation of the molecular mechanisms mediating trained immunity
provides a promising strategy to safely and effectively reverse the chronic
inflammatory state in both atherosclerosis as well as other chronic
inflammatory diseases states. It is executed by a highly trans-disciplinary
and intersectoral consortium of leading experts in atherosclerosis, immunology
and epigenetics. Within the REPROGRAM project several animal studies will be
performed as well as work with clinical samples (blood-based) from cohorts and
five clinical trials in WP4. In specific, the REPROGRAM project plans to
perform the following research activities:

  * Animal experiments in mice. 

  

  * Use and long-term storage of biological samples from human individuals (e.g., blood). 

  

  * Collection and use of clinical data, including genetic data. 

  

  * Research in patients with (risk factors for) atherosclerosis or active and remissive RA as well as research with healthy human subjects from population-based cohorts. 

    * **Clinical study 1** : The inflammatory state in patients at risk for atherosclerosis (WP3). Multicentre (Netherlands and Italy) observational study in human subjects. Subjects will visit the centre twice: 1 visit to harvest monocytes and HSCs, and 1 visit to perform FDG-PET imaging. o **Clinical study 2** : The effect of modulating risk factors on trained immunity (WP4). Double-blind, placebo-controlled, randomized clinical trial (Netherlands) to assess the effects of lowering LDL-c levels, to decrease the training effects of circulating LDL-c. Subjects will undergo PET imaging <7 days before start of intervention. After 3 months (+/- 3 days) of study medication subjects will undergo a repeat scan. 

  

    * **Clinical study 3** : Proof-of-concept study in patients at increased cardiovascular risk to evaluate whether epigenetic marks and inflammatory activation can be reversed (WP4). Double-blind, placebo-controlled, randomized intervention trial (Netherlands) to assess the effects of epigenetic modulators, to decrease the training effects. Subjects will undergo PET imaging <7 days before start of intervention. After 3 months (+/- 3 days) of study medication subjects will undergo a repeat scan. 

  

    * **Clinical study 4** : Effect of short-chain fatty acid Butyrate to prevent trained immunity (WP4). Double-blind, placebo-controlled, randomized intervention trial (Netherlands) to assess the effects of epigenetic modulators, to decrease the training effects. Subjects will undergo PET imaging <7 days before start of Butyrate. After 3 months (+/- 3 days) of study medication subjects will undergo a repeat scan. o **Clinical study 5** : Inflammatory activity in patients with active and remissive RA (WP5). This is an observational study in human subjects. Subjects will visit the centre twice: 1 visit to harvest monocytes, and 1 visit to perform FDG-PET imaging. 

  

  

More detailed, the REPROGRAM project plans to conduct the following type of
studies:

  * Preclinical studies in existing and/or de novo in vitro, ex vivo and in vivo models. 

  

  * Prospective clinical studies involving patients with cardiovascular risk factors and atherosclerosis at different age stages as well as appropriate control individuals. 

  

  * Retrospective observational studies involving biological samples from human individuals. 

  

All REPROGRAM consortium members state that the proposed research activities
do not involve:

  * Human cloning for reproductive purposes. 

  

  * Research intended to modify the genetic heritage of human beings, which could make such changes heritable. 

  

  * Activities intended to create human embryos solely for the purpose of research or for the purpose of stem cell procurement, including by means of somatic cell nuclear transfer. 

  

  * Research involving use of human embryos or embryonic stem cells with the exception of blanked or isolated human embryonic stem cells in culture. 

  

All REPROGRAM partners comply with the ethical principles as set out in
Article 34 of the Grant Agreement, which states that all activities must be
carried out in compliance with:

  1. ethical principles (including the highest standards of research integrity — as set out, for instance, in the European Code of Conduct for Research Integrity (European Science Foundation, 2011) — and including, in particular, avoiding fabrication, falsification, plagiarism or other research misconduct) and 

  

  2. applicable international, EU and national law. 

  

  

#  Confidentiality

The REPROGRAM partners must retain any data, documents or other material as
confidential during the implementation for the project. Further details on
confidentiality can be found in Art 36 of the Grant Agreement along with the
obligation to protect results in Art 27 and relevant articles defined in the
fully executed Consortium Agreement.

#  Other issues

In addition to the European Commission policies on open data management, The
REPROGRAM consortium partners must also adhere to their national, local and/or
institutional policies and procedures for data management.

**AMC:** _https://www.amc.nl/web/AMC-website/Research-Code/1-Introduction.htm_

**RadboudUMC** :  _https://www.radboudumc.nl/en/research/principles-of-
research/scientificintegrity/the-backbone-of-research_

**LMU** :  _https://www.helmholtz-muenchen.de/fileadmin/HZM-Corporate-_

_Website/Bilder/HZM/Forschung/pdf/Rules_for_Safeguarding_Good_Scientific_Practice_at_H_

_MGU_c-l-s_eng__06.10.2015.pdf_

_http://www.hfsp.org/sites/www.hfsp.org/files/webfm/Communications/empfehlung_wiss__
_praxis_0198.pdf_

**REGIONH** :  _http://www.science.ku.dk/english/research/good-scientific-
practice/_

**UMF** : tbd

**UNIMI** : tbd

**UZH** :
_https://rechtssammlung.sp.ethz.ch/_layouts/15/start.aspx#/default.aspx_

**MGH** :  _https://ori.hhs.gov_

_https://www.nsf.gov/od/oise/intl-research-integrity.jsp_

**Sensilab** : Sensilab has its own set of internal policies and procedures on
data management.

**Servier** : Data management and information technology policies for the
company are set out in written policies which are subject to periodic review.

**Descin** : Descin has its own set of internal policies and procedures on
data management.