#  Introduction

In a research project it is common that several partners work together and
produce a lot of data related to the project. Therefore, it is important to
specify in an early stage of the project what data will be generated, how it
will be shared between the project partners and if it will be publicly
available. A data management plan (DMP) is a tool which should assist in
managing the data created during the project.

In general, the DMP of the PrECISE project will specify what data is already
available and what data will be generated, collected, and processed during the
project. It should also provide information whether and how data will be
exploited and open for public and re-use. The DMP includes information on what
standards and methodologies will be used and how the data will be handled
during and after the research project (how the data will be curated and
preserved).

The DMP is not a fixed document. It will evolve and gain more precision and
substance during the lifespan of the PrECISE project. The first version of the
DMP, including information from the first six months of the project, includes
the following:

  * Methodology (Chapter 2): Data production, storage, dissemination and anaylsis 

  

  * Data Generation (Chapter 3): Data set description, research data identification 

  

  * Processing and explanation of generated data (Chapter 4): Information about the entity, which is responsible for the data, how the data is collected, an identification of the end-users of the data, and research data identification. 

  

  * Accessibility – Data sharing, archiving and preserveration (Chapter 5) 

  

#  Methodology

The DMP describes how data are managed and handled within the PrECISE project
among consortium members. In short, clinical and genetic data are produced by
UZH, whereas proteomic data are provided by ETH Zurich.

Every data point is anonymized, using a two step process: First, a pseudonym
is given by the lab information system PathPro at UZH, where only UZH has
access. Second, another independent ID is given to each data point on the
PrECISE server by AstridBio. Two PrECISE servers will be set up by AstridBio:
one for clinical data collection and storage and another one for experimental
(genomics, proteomics) data collection and storage. and the key to this second
pseudonym is only known by AstridBio. As a result, distribution of data to the
other consortium members can be regarded anonymized. Other consortium members,
other than UZH, only have access to coded (ETHZ, AstridBio) or anonymized data
(all remaining groups). The relevant contributions and data flows are
summarizes in the figure below: Data will be provided by UZH and ETHZ. Data
storage and dissemination will be done by AstridBio. The consortium members on
the right side of the figure will provide algorithm outlines, code and
analysis results as stated in the grant proposal. During the project, data and
algorithms will shared within members of the project. Upon publication,
anonymized data and algorithms/ code will be made publicly available.

In Figure 1, data production, storage, dissemination and analysis is
summarized for the PrECISE project.

Figure 1: PrECISE data production, storage, dissemination and analysis

#  Data Generation

The data generation, which is illustrated in Table 1, is described below.

**_Data set reference and name:_ **

Identifier for the data set to be produced.

**_Data set description:_ **

Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration of reuse.

**_Research Data Identification_ **

The boxes (D, A, AI, U and I) symbolize a set of questions that should be
clarified for all datasets produced in this project.

**Discoverable:**

Are the data and associated software produced and/or used in the project
discoverable (and readily located), identifiable by means of a standard
identification mechanism (e.g. Digital Object Identifier).

**Accessible:**

Are the data and associated software produced and/or used in the project
accessible and in what modalities, scope, licenses (e.g. licencing framework
for research and education, embargo periods, commercial exploitation, etc.)
**Assessable and intelligible:**

Are the data and associated software produced and/or used in the project
assessable for and intelligible to third parties in contexts such as
scientific scrutiny and peer review (e.g. are the minimal datasets handled
together with scientific papers for the purpose of peer review, are data
provided in a way that judgements can be made about reliability and the
competence of those who created them)?

**Useable beyond the original purpose for which it was collected:**

Are the data and associated software produced and/or used in the project
usable by third parties even long time after the collection of the data (e.g.
is the data safely stored in certified repositories for long term preservation
and curation; is it stored together with the minimum software, metadata and
documentation to make it useful; is the data useful for the wider public needs
and usable for the likely purposes of non-specialists)?

**Interoperable to specific quality standards:**

Are the data and associated software produced and/or used in the project
interoperable allowing data exchange between researchers, institutions,
organisations, countries, etc. (e.g. adhering to standards for data
annotation, data exchange, compliant with available software applications, and
allowing re-combinations with different datasets from different origins?)

It is recommended to make an “x” to each applicable box and explain it
literally in more detail afterwards.  
  
<table>  
<tr>  
<th>

**Data Nr.**

</th>  
<th>

**Respon-**

**sible Bene-**

**ficiary**

</th>  
<th>

**Data set reference and name**

</th>  
<th>

**Data set description**

</th>  
<th>

</th>  
<th>

**Research data identification**

</th>  
<th>

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

**End user (e.g.**

**university, research**

**organization,**

**SME’s, scientific publication)**

</th>  
<th>

**Existence of similar data**

**(link,**

**information)**

</th>  
<th>

**Possibility for integration and**

**reuse (Y/N) + information**

</th>  
<th>

**D** 1  
</th>  
<th>

**A** 2  
</th>  
<th>

**AI 3  **

</th>  
<th>

**U** 3  
</th>  
<th>

**I** 4  
</th> </tr>  
<tr>  
<td>

1

</td>  
<td>

UZH

</td>  
<td>

PPM39_r naSeq

</td>  
<td>

Members of PhosphoNetPPM

project in the framework of SystemsX.ch

initiative;

members of the PrECISE project

</td>  
<td>

Similar data was produced in the framework of The Cancer Genome Atlas project

(http://cancerge nome.nih.gov/ca ncersselected/pr ostatecancer)

</td>  
<td>

Yes. Data can be integrated

with information on DNA and proteins from the same samples and compared to

/integrated with data from TCGA

</td>  
<td>

Digital Object

Indentifiers

(DOIs) will be provided to ensure

that data and

algorithms are

discoverable.

</td>  
<td>

Yes, see

Chapter

5

</td>  
<td>

Data is being presently

analysed and

assembled

for

publication in a high-

impact peerreviewed journal.

</td>  
<td>

Yes, see Chapter 5

</td>  
<td>

Data formats adhere to

commonly used

scientific

standards (e.g.

BAM format,

VCF files etc.)

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

2

</td>  
<td>

UZH

</td>  
<td>

PPM39_e xomeSeq

</td>  
<td>

Members of PhosphoNetPPM

project in the framework of SystemsX.ch

initiative;

members of the PrECISE project

</td>  
<td>

Similar data was produced in the framework of The Cancer Genome Atlas project

(http://cancerge nome.nih.gov/ca ncersselected/pr ostatecancer)

</td>  
<td>

Yes. Data can be integrated with information on RNA and proteins from the same
samples and compared to

/integrated with data from TCGA

</td>  
<td>

Digital Object

Indentifiers

(DOIs) will be provided to ensure

that data and

algorithms are

discoverable.

</td>  
<td>

Yes, see

Chapter

5

</td>  
<td>

Data is being presently

analysed and

assembled

for

publication in a high-

impact peerreviewed journal.

</td>  
<td>

Yes, see Chapter 5

</td>  
<td>

Data formats adhere to

commonly used

scientific

standards (e.g.

BAM format)

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

3

</td>  
<td>

UZH/E THZ

</td>  
<td>

PPM39_s wathMS

</td>  
<td>

Members of

PhosphoNetPPM

</td>  
<td>

\-

</td>  
<td>

Yes. Data can be integrated

</td>  
<td>

Digital Object Indentifiers

</td>  
<td>

Yes, see

</td>  
<td>

Data is being presently

</td>  
<td>

Yes, see Chapter 5

</td>  
<td>

Data formats will be

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

**Data Nr.**

</th>  
<th>

**Respon-**

**sible Bene-**

**ficiary**

</th>  
<th>

**Data set reference and name**

</th>  
<th>

**Data set description**

</th>  
<th>

**Research data identification**

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

**End user (e.g.**

**university, research**

**organization,**

**SME’s, scientific publication)**

</th>  
<th>

**Existence of similar data**

**(link,**

**information)**

</th>  
<th>

**Possibility for integration and**

**reuse (Y/N) + information**

</th>  
<th>

**D 1  **

</th>  
<th>

**A 2  **

</th>  
<th>

**AI 3  **

</th>  
<th>

**U 4  **

</th>  
<th>

**I 5  **

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

project in the framework of SystemsX.ch

initiative;

members of the PrECISE project

</td>  
<td>

</td>  
<td>

with information on DNA and RNA from the same samples and compared to
/integrated

with data from

TCGA

</td>  
<td>

(DOIs) will be provided to ensure

that data and

algorithms are

discoverable.

</td>  
<td>

Chapter 5

</td>  
<td>

analysed and

assembled

for

publication in a high-

impact peerreviewed journal.

</td>  
<td>

</td>  
<td>

developed as part of this study.

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

4

</td>  
<td>

UZH

</td>  
<td>

PPM39_cl

inicopathologic

</td>  
<td>

Members of PhosphoNetPPM

project in the framework of SystemsX.ch

initiative;

members of the PrECISE project

</td>  
<td>

\-

</td>  
<td>

Yes. Data can be integrated

with information on DNA, RNA and protein from the same samples.

</td>  
<td>

Digital Object

Indentifiers

(DOIs) will be provided to ensure

that data and

algorithms are

discoverable.

</td>  
<td>

Yes, see

Chapter

5

</td>  
<td>

Data is being presently

analysed and

assembled

for

publication in a high-

impact peerreviewed journal.

</td>  
<td>

Yes, see Chapter 5

</td>  
<td>

**I** Data formats adhere to

commonly used

scientific

standards

(WHO/ISUP201 6, GCP)

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

5

</td>  
<td>

UZH

</td>  
<td>

PPP500_ swathMS

</td>  
<td>

Members of the PrECISE project

</td>  
<td>

\-

</td>  
<td>

Yes. Data can be integrated with clinicopathological information from the same
samples.

</td>  
<td>

Digital Object

Indentifiers

(DOIs) will be provided to ensure

that data and

algorithms are

discoverable.

</td>  
<td>

Yes, see

Chapter

5

</td>  
<td>

Data is being presently

analysed and

assembled

for

publication in a high-

impact peerreviewed journal.

</td>  
<td>

Yes, see Chapter 5

</td>  
<td>

Data formats will be

developed as part of this study.

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

6

</td>  
<td>

UZH

</td>  
<td>

PPP500_

</td>  
<td>

Members of the

</td>  
<td>

\-

</td>  
<td>

Yes. Data can

</td>  
<td>

Digital Object

</td>  
<td>

Yes,

</td>  
<td>

Data is being

</td>  
<td>

Yes, see

</td>  
<td>

Data formats

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

**Data Nr.**

</td>  
<td>

**Respon-**

**sible Bene-**

**ficiary**

</td>  
<td>

**Data set reference and name**

</td>  
<td>

**Data set description**

</td>  
<td>

**Research data identification**

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

**End user (e.g.**

**university, research**

**organization,**

**SME’s, scientific publication)**

</td>  
<td>

**Existence of similar data**

**(link,**

**information)**

</td>  
<td>

**Possibility for integration and**

**reuse (Y/N) + information**

</td>  
<td>

**D 1  **

</td>  
<td>

**A 2  **

</td>  
<td>

**AI 3  **

</td>  
<td>

**U 4  **

</td>  
<td>

**I 5  **

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

</td>  
<td>

</td>  
<td>

clinicopathologic

</td>  
<td>

PrECISE project

</td>  
<td>

</td>  
<td>

be integrated with proteomic information from the same samples.

</td>  
<td>

Indentifiers

(DOIs) will be provided to ensure

that data and

algorithms are

discoverable.

</td>  
<td>

see

Chapter

5

</td>  
<td>

presently

analysed and

assembled

for

publication in a high-

impact peerreviewed journal.

</td>  
<td>

Chapter 5

</td>  
<td>

adhere to

commonly used

scientific

standards (WHO/ISUP201

6, GCP).

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

7

</td>  
<td>

UZH

</td>  
<td>

CRPC_he

terogeneit y_ampliS eq

</td>  
<td>

Members of the PrECISE project

</td>  
<td>

Similar data was produced in the framework of The Cancer Genome Atlas project

(http://cancerge nome.nih.gov/ca ncersselected/pr ostatecancer)

</td>  
<td>

Yes. Data can be integrated with proteomic information from the same patients.

</td>  
<td>

Digital Object

Indentifiers

(DOIs) will be provided to ensure

that data and

algorithms are

discoverable.

</td>  
<td>

Yes, see

Chapter

5

</td>  
<td>

Data is being presently

analysed and

assembled

for

publication in a high-

impact peerreviewed journal.

</td>  
<td>

Yes, see Chapter 5

</td>  
<td>

Data formats adhere to

commonly used

scientific

standards (e.g.

BAM format)

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

Table 1: PrECISE data generation

#  Chapter 4 Processing and explanation of generated data

The following sections provide some additional information to the listed data
introduced in Chapter 3. This information includes the entity, which is
responsible for the data, how the data is collected, an identification of the
end-users of the data, and research data identification.

##  4.1 PPM39_rnaSeq

This data set consists of RNA sequencing data for 39 prostate cancer patients
of the University Hospital Zurich. More specifically, for all 39 patients, the
RNA of prostate cancer tissue and the matching normal tissue was sequenced. In
addition, for 27 of the patients, a second cancer tissue sample from the same
prostate tumour was analysed the same way.

_**4.1.1 Responsible Beneficiary** _

The data was produced by UZH.

###  4.1.2 Gathering Process

RNA sequencing was performed at the Functional Genomics Center Zurich. RNAseq
libraries were generated using the TruSeq RNA stranded kit with PolyA-
enrichment (Illumina, San Diego, CA, USA). Libraries were sequenced in paired-
end mode on an Illumina HiSeq 2500 platform. After base calling, de-
multiplexing and in-silico adaptor-trimming, the data was aligned to the hg19
reference genome (UCSC version) using STAR aligner v.2.2.3 (Dobin et al.,
2013). Expression values were calculated using the FeatureCount software (Liao
et al, 2014).

###  4.1.3 End-User of the Data

Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.

###  4.1.4 Research Data Identification

Within the scientific community, several initiatives have been launched to
cover the minimal standards for the annotation of a sequencing project, such
as MIGS or MISEEQE. However these standards are rather preliminary and quite
extensive.

For practical reasons, we decided to follow the example of the public data
repository Gene Expression Omnibus (GEO), which requires a minimal set of
metadata to be uploaded with every sequencing experiment that is submitted to
the archive. This set contains descriptive information and protocols for the
overall study and individual samples, and references to processed and raw data
file names. A template for this set can be found under links (see
bibliography, links - Chapter 8). The most important part of the metadata
belonging to each data file is covered by the clinical data (e.g. age, tissue
type, survival data, time to relapse, BSA levels etc.), which allows for
setting individual samples into context with each other.

A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No, _Blood) to share data with AstridBio and
other members of the consortium. Only UZH can unblind these data. This
numbering system provides a unique indentifyer to each sample, linking all
omics, clinical and pathological data. This PathoPro identifier will be
replaced by AstridBio when setting up the data on the PrECISE server.

##  4.2 PPM39_exomeSeq

This data set consists of DNA sequencing data for 39 prostate cancer patients
of the University Hospital Zurich. More specifically, for all 39 patients, the
DNA of prostate cancer tissue, the matching normal tissue and blood was
sequenced. In addition, for 27 of the patients, a second cancer tissue sample
from the same prostate tumour was analysed the same way.

_**4.2.1 Responsible Beneficiary** _

The data was produced by UZH.

###  4.2.2 Gathering Process

DNA samples were sequenced in paired-end mode on an Illumina HiSeq 2500
platform. After base calling, de-multiplexing and in-silico adaptor-trimming
(using Trimmomatic, Bolder et al., 2014), the data was aligned to the hg19
reference genome (UCSC version) using the Bowtie2 software (Langmead et al.,
2012). Bam files containing the mapped reads were preprocessed in the
following way: Indel information was used to realign individual read using the
RealignerTargetCreator and IndelRealigner option of the Genome Analysis Tools
Kit (McKenna et al., 2010). Mate-pair information between mates was verified
and fixed using

Picard tools (http://picard.sourceforge.net) and single base were recalibrated
using GATK BaseRecalibrator. After preprocessing, variant calling was carried
out by comparing normal or tumor prostate tissue samples with matched blood
samples using the programs MuTect (Cibulskis et al., 2013 ) and ,
independently, Strelka (Saunders et al., 2012). Somatic variants that were not
detected by both programs were filtered out using CLC Genomics Workbench (CLC
Genomics Workbench 8.0.3, https://www.qiagenbioinformatics.com) as well as
those that had an entry in the dbsnp (Sherry et al., 2001) common database or
those that represented synonymous variants without predicted effects on
splicing.

###  4.2.3 End-User of the Data

Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.

###  4.2.4 Research Data Identification (see also 4.1.4)

A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No, _Blood) to share data with AstridBio and
other members of the consortium. Only UZH can unblind these data. This
numbering system provides a unique indentifyer to each sample, linking all
omics, clinical and pathological data. This PathoPro identifier will be
replaced by AstridBio when setting up the data on the PrECISE server.

##  4.3 PPM39_swathMS

This data set consists of Swath-MS proteomics data for 39 prostate cancer
patients of the University Hospital Zurich. More specifically, for all 39
patients, the protein lysates of prostate cancer tissue, and the matching
normal tissue was analysed. In addition, for 27 of the patients, a second
cancer tissue sample from the same prostate tumour was analysed the same way.

###  4.3.1 Responsible Beneficiary

The data was produced by the Institute for Molecular Systems Biology of the
ETH Zurich (ETHZ).

_**4.3.2 Gathering Process** _

Production of data has been described in detail by Tiannan et al., Nature
Medicine 2015.

###  4.3.3 End-User of the Data

Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.

###  4.3.4 Research Data Identification

A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No) to share data with AstridBio and other
members of the consortium. Only UZH can unblind these data. This numbering
system provides a unique indentifyer to each sample, linking all omics,
clinical and pathological data. This PathoPro identifier will be replaced by
AstridBio when setting up the data on the PrECISE server.

##  4.4 PPM39_clinico-pathologic

This data set consists of clinical follow-up data (age at diagnosis,
Progression-free survival time and status) and pathological data (UICC TNM
classification, Gleason grade, WHO/ISUP 2016 grade group), for 39 prostate
cancer patients of the University Hospital Zurich. More specifically, for all
39 patients, prostate cancer tissue was graded. In addition, for 27 of the
patients, a second (lower grade) cancer tissue area from the same prostate
tumour was selected and grade the same way.

_**4.4.1 Responsible Beneficiary** _

The data was produced by UZH.

###  4.4.2 Gathering Process

The 2016 WHO/ISUP classification of urological tumors was used to stage and
grade the respective lesions. Clinical follow-up data of patients of where
retrieved from the Zurich proCOC ( **Pro** state **C** ancer **O** utcomes
**C** ohort) study.

###  4.4.3 End-User of the Data

Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.

###  4.4.4 Research Data Identification

A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX) to share data with AstridBio and other members of the
consortium. Only UZH can unblind these data. This numbering system provides a
unique indentifyer to each sample, linking all omics, clinical and
pathological data. This PathoPro identifier will be replaced by AstridBio when
setting up the data on the PrECISE server.

##  4.5 PPP500_swathMS

This data set consists of Swath-MS proteomics data for 500 prostate cancer
patients of the University Hospital Zurich. More specifically, for all 500
patients, the protein lysates of prostate cancer tissue and matching normal
tissue was analysed.

###  4.5.1 Responsible Beneficiary

The data was produced by the Institute for Molecular Systems Biology of the
ETH Zurich (ETHZ).

_**4.5.2 Gathering Process** _

Production of data has been described in detail by Tiannan et al., Nature
Medicine 2015.

###  4.5.3 End-User of the Data

Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project

###  4.5.4 Research Data Identification

A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1, _TA2, _No) to share data with AstridBio and other
members of the consortium. Only UZH can unblind these data. This numbering
system provides a unique indentifyer to each sample, linking all omics,
clinical and pathological data. This PathoPro identifier will be replaced by
AstridBio when setting up the data on the PrECISE server.

##  4.6 PPP500_clinico-pathologic

This data set consists of clinical follow-up data (age at diagnosis,
Progression-free survival time and status) and pathological data (UICC TNM
classification, Gleason grade, WHO/ISUP 2016 grade group), for 500 prostate
cancer patients of the University Hospital Zurich. More specifically, for all
500 patients, prostate cancer tissue was graded.

_**4.6.1 Responsible Beneficiary** _

The data was produced by UZH.

###  4.6.2 Gathering Process

The 2016 WHO/ISUP classification of urological tumors was used to stage and
grade the respective lesions. Clinical follow-up data of patients of where
retrieved from the Zurich proCOC ( **Pro** state **C** ancer **O** utcomes
**C** ohort) study.

###  4.6.3 End-User of the Data

Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.

###  4.6.4 Research Data Identification

A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX) to share data with AstridBio and other members of the
consortium. Only UZH can unblind these data. This numbering system provides a
unique indentifyer to each sample, linking all omics, clinical and
pathological data. This PathoPro identifier will be replaced by AstridBio when
setting up the data on the PrECISE server.

##  4.7 CRPC_heterogeneity_ampliSeq

This data set consists of DNA sequencing data for 10 prostate cancer patients
of the University Hospital Zurich. More specifically, for all 10 patients, the
genomic DNA of 10 different tumour areas (CRPC) and of a matching normal
tissue area will be sequenced.

_**4.7.1 Responsible Beneficiary** _

The data will be produced by UZH.

###  4.7.2 Gathering Process

DNA samples will sequenced using Amplicon sequencing on the Ion Torrent Proton
System (genes _PTEN, AR, TP53, SPOP, FOXA1_ ).

###  4.7.3 End-User of the Data

Biofinformatics researchers of the PhosphonetPPM (SystemsX.ch) project and the
PrECISE project.

###  4.7.4 Research Data Identification (see also 4.1.4)

A pseudonym will be provided by the lab information system PathPro of UZH
(format: GXX.XXXX_TA1-12, _No) to share data with AstridBio and other members
of the consortium. Only UZH can unblind these data. This numbering system
provides a unique indentifyer to each sample, linking all omics, clinical and
pathological data. This PathoPro identifier will be replaced by AstridBio when
setting up the data on the PrECISE server.

#  Chapter 5 Accessibility – Data sharing, archiving and preservation

We will store clinical and experimental data on separate servers. Experimental
data will be accessible for up- and download using encrypted standard file
transfer protocols. We will separate data produced within the PrECISE project
and data uploaded from other data repositories (e.g. TCGA data). The
respective meta-data will be stored together with the clinical data. Clinical
data will be accessible via web applications using encryption. The system for
the data management will be based on the SmartBioBank software (
_http://www.smartbiobank.com_ ). This software allows for integration of
clinical and experimental/genomic data and enables data sharing and database
merging between independent research groups. The clinical data and the meta-
data should be searchable via a web-based interface.

In the beginning of the project, data will be shared exclusively between
members of the PrECISE project. As a sharing platform for small amounts of
data up to 100MB the already existing project SVN repository
_https://precise.technikon.com_ is used. This allows easy synchronization
between the partners as well as data versioning. It has to be noted that only
project partners have access to the project SVN. A data transfer agreement has
to be signed for getting access to unpublished data (see Chapter 9).

The PrECISE Executive Board will make decisions on the publication of the
data. Most journals in the biomedical field require the submission of genomic
data together with standard meta-data to public repositories. This means that
in case of publishing our findings in scientific journals, we will make the
data available to the public. In this case, the data sets will get a
persistent identifier.

External researchers will be able to apply for access to the PrECISE data
before the general release to the public. In this case an application form
needs to be filled detailing a research plan, ethical approval etc.. The
application form will be available on the PrECISE project website together
with information on approval process and data content of the PrECISE database.
The Executive board can grant access to the entire database or restrict access
to selected data sets. Non-profit organizations should cover the expenses of
the consortium related to the data migration etc. For-profit organizations
will have to pay an additional fee for data access.

The data can be used to discover new biomarkers or sets of biomarkers, which
allow for improved diagnostics of prostate cancer. In addition, different
levels of data were acquired of a complex biological systems transitioning
from one developmental stage (i.e. normal tissue) to another (i.e. tumour
tissue). Integrating these data sets may result in important discoveries that
will elucidate the dynamics of biological systems that fails to control
homeostasis resulting in disease.

No data will be deleted. Instead, preservation of raw and meta-data will be
reached by two efforts: first, setup of a data server by AstridBio; second,
publication of raw and meta-data in journals specialized in long-term
preservation of biomedical data (e.g. Nature Scientific Data using the
DataVerse platform). Through this, no costs (time/effort) to prepare the data
for sharing/preservation are involved, and data will be publicly available for
a very long time period (decades).

#  Chapter 6 Summary and Conclusion

This data management plan outlines the handling of data generated within the
PrECISE project, during and after the project lifetime. As the deliverable
will be kept as a living document it will be regularly updated by the
consortium (the next time in M18 within the periodic report). The partners put
into write their plans and guarded expectations regarding valuable and
publishable data.