#  1\. Executive Summary

This deliverable provides the SMARTool Data Management Plan (DMP) first
version and describes how the data collected or created within the project
will be handled and which are the standards and the methodology that is
followed for the collection and processing, whether and how these data will be
accessible for re-use and whether it will be shared [1]. SMARTool project, at
its present stage, is foreseen to collect and analyse a series of datasets,
including data from user requirements to the clinical and other patient
related data. Specifically, datasets are to be collected in two ways: (i) the
data collection related to the users' personal information, demographics,
clinical, imaging, molecular, omics and other health related data, (ii) the
collected data through the application of the multivariate analysis, the data
mining and the non-imaging classification algorithm for CAD stratification,
the multiscale and multilevel site specific models for plaque progression and
the non-invasive FFR, the pharmacological therapy modulation algorithms
(EVINCI database) using data mining techniques and the virtual stent
deployment approach. The former dataset will assist in the development of the
SMARTool platform, while the latter will be the actual datasets that are
foreseen to be the main output of the SMARTool platform.

This deliverable consolidates the feedback provided by the SMARTool partners
and the datasets they contribute to.

More specifically, the deliverable was circulated to the project partners
responsible for the different tasks, who defined the dataset descriptions
according to the plans of data collection and analysis as well as the methods
and processes which are followed to ensure adherence with the ethics
requirements. Given that the majority of SMARTool datasets include the data
collection from human participants, the produced and processed data are
handled with caution, following all the ethical and privacy issues involved in
such datasets. In addition, data are not shared or made accessible, if there
is a risk of compromising the participants’ privacy.

This is the initial version of the DMP of SMARTool and the datasets described
represent the reflection of the collected and processed data till M18. These
data will be enriched with more data (collected and processed), therefore
during the evolution of the project the context of the datasets is expected to
be continuously updated. However, the main principles as well the process of
data collection, processing and preservation are expected to be those, as
described in the current document. The deliverable is composed of different
chapters and sub-sections.

**Chapter 2** presents: (i) an overview of the SMARTool project, its overall
vision and objectives, (ii) the main scope of this deliverable including the
objectives of the DMP, (iii) the steps and actions involved in the data
management cycle and, (iv) the background of the SMARTool DMP.

**Chapter 3** presents: (i) the principle of SMARTool FAIR data management,
(ii) the main categories of the project data, (iii) the data access and
storage processes, (iv) the available databases accompanied with the level
roles and permissions, (v) a detailed description of the datasets collected
and processed per WP, the involved partners, the standards and metadata, the
data exploitation and sharing, the archiving and preservation (including
storage and backup) processes.

**Chapter 4** presents the specific methods and tools for ensuring data
security, including the data anonymization, encryption, storage, backups, etc.
The chapter refers also to the ethical considerations related to the
protection of the enrolled patients, the EU legislation and Ethics documents
and experts’ opinions and Ethical Committees, that are taken into
consideration during the lifecycle of SMARTool project.

#  2\. Introduction

A new element that has been included in the Horizon 2020 projects is the DMP
which is related to the data generated in the framework of the project and how
this data will be accessible [2]. The D4.1 – Data Management Plan v1 is the
first version of the DMP of the SMARTool project, which has received funding
by the European Union’s Horizon 2020 Programme under Grant Agreement number:
689068. The DMP is not a fixed process; it is an activity that will evolve
during the lifespan of the SMARTool project. This first version of the DMP
concerns an overview of the produced datasets and the specific conditions that
are related to them.

##  About the project

Coronary artery disease (CAD) is a chronic disease with a high prevalence and
epidemic proportions in the middle aged and elderly population, accounting for
about 50% of all deaths [3], [4]. Medical therapy, lifestyle and diet
suggestions and clinical risk factors, including dyslipidemia, hypertension,
diabetes, are among the key factors in CAD patient-specific management.
Currently, an integrated site specific and patient-specific comprehensive
predictive model of plaque progression is lagging. Although several algorithms
have been developed for primary and secondary prevention of CAD, a unified
platform that incorporates all local and systemic risk factors into a
personalized clinical decision support tool for stratification, diagnosis,
prediction and treatment is not available. SMARTool aims at the development of
a Cloud platform for enabling clinical decision support for prevention of CAD
and of associated major adverse cardiovascular events (MACE). This is achieved
through the standardization and integration of health data from heterogeneous
sources and existing patient/artery specific multiscale and multilevel
predictive models. Specifically, the SMARTool models rely on the extension of
the already available multiscale and multilevel ARTreat models for coronary
plaque assessment and progression over time through the utilization of non-
invasive imaging by coronary computed tomography angiography (CCTA) and are
extended with functional site-specific assessment (hemodynamically significant
plaques by noninvasive Fractional Flow Reserve (FFR) computation) and
additional heterogeneous patient-specific non-imaging data, such as patient
history, and lifestyle, exposome, biohumoral data, phenotyping and
genotyping). SMARTool allows:

  * **Patient-specific CAD stratification.** Specifically, the already available clinical stratification models (e.g Framingham, Genders, GES - Gene Expression Score) are implemented by patient genotyping and phenotyping (cellular/molecular markers) and examined in retrospective and prospective clinical data (EVINCI project population) towards stratifying the patients to the following categories: nonobstructive CAD, obstructive CAD and those without CAD categories. 

  

  * **Site specific plaque progression prediction.** Existing multiscale and multilevel models for plaque progression prediction (ARTreat project) are updated and refined through the incorporation of additional genotyping and phenotyping features and examined by retrospective/ prospective noninvasive CCTA imaging data plus non-imaging patient-specific information (follow-up - EVINCI population). 

  

  * **Patient-specific CAD diagnosis and CAD-related CHD treatment** Personalised and patientspecific therapeutical management (eg lifestyle changes, standard or high intensity medical therapy) are provided. Additionally, the interventional cardiologists are able to select the optimal stent type(s) and site(s) for appropriate stent deployment through the utilization of the SMARTool virtual angioplasty tool. 

  

Additionally, the final Clinical Decision Support System (CDSS) includes a
microfluidic lab-on-chip device for blood analysis of cellular/molecular
inflammatory markers. The overall platform will be assessed in the
participating clinical sites.

###  _**2.1 Purpose of the SMARTool Data Management Plan** _

The DMP is a cornerstone for ensuring good data management. DMP is related to
the data management life cycle for the data which will be collected, processed
and created during SMARTool project. More specifically, the DMP concerns the
following activities:

  * research data management during/ after the project end 

  

  * description of the type of the collected, processed and generated data 

  

  * definition of standards that are used for data storage, safety and security 

  

  * description of the data, which are shared/enable open access 

  

  * process of data storage 

  

  * assistance in streamlining the whole research process. The DMP provides _a priori_ the required data process and facilities to store data. 

  

The DMP: (i) includes in combination with ethical issues (section 4.2) the
availability of research data, (ii) defines the measures and processes in
order for the data to be properly anonymized and ensure their privacy, and
(iii) concerns the strategy that is followed for the open data, which does not
violate the conditions of the interlinked Research and Innovation projects.

Figure 1 presents the steps and actions involved in the SMARTool data
management cycle.

_**Figure 1:** Overview of the SMARTool data management cycle. _

As far as research data is concerned, SMARTool provides access to this data
through the CRFA platform. The imaging data is stored in the cloud-based
3DnetMedical.com DICOM compliant database from B3D’s UK based ISO27001
accredited datacenter – providing security, redundancy, reliability and
scalability. DICOM studies can be automatically uploaded from PACS systems
through 3Dnet Gateway using 2048 bit encryption, or can be uploaded manually
by the user using SSL encryption (Figure 2).

Non imaging clinical data is securely stored in a NoSQL MongoDB Database,
accessible only via locally installed application server. Data redundancy,
reliability and scalability is provided by the native MongoDB replica-set
feature. MongoDB cluster members communicate (for continuous synchronization)
via encrypted TLS/SSL channels. Data is uploaded via https (secure http)
application interface and can be accessed, from within the project
infrastructure via secure RESTful API (OAuth 2.0, OpenID session token). The
OAuth2 authentication and session token issuing, renewing and revoking, is
provided by a WSO2 Identity Server deployed in the project infrastructure.
Clinical documents are acquired and stored in a Document Repository according
to IHE XDS.b (Cross-Enterprise Document Sharing) and XDS-I.b (Cross-enterprise
Document Sharing for Imaging) standards [5]. Data security and privacy for
clinical documents is provided by implementing the IHE ATNA (Audit Trail and
Node Authentication) [6] and IHE XUA (Cross-Enterprise User Assertion)
profiles [7].

_**Figure 2:** Uploading DICOM studies to 3DnetMedical.com. _

The purpose of the clinical data collection/generation in relation to the
objectives of the project are:

  * To optimise the learning stage of SMARTool in order to extract and select discriminative markers (from any data source, ranging from history, genetics, circulating molecules and coronary anatomy by CT scan) of CAD severity/progression/association with MACE. 

  

  * To provide a "smart" data storage to the final CDSS which is the main outcome of the project, adequate for: (i) the external validation of the selected panel of markers by external cohorts, and (ii) CDSS application and exploitation in clinics. 

  

###  _**2.2 Background of the SMARTool Data Management Plan** _

SMARToool DMP will be in accordance with the following articles of the Grant
Agreement:

  * **Article 36 - Confidentiality.** During the project duration and for four years after the period set out in Article 3, the Consortium will keep confidential any data, documents or other relevant material that is defined as confidential. 

  

  * **Article 39.2 - Processing of personal data.** All personal data are processed in compliance with the applicable EU and national law on data protection. The Consortium provides adequate information and explanation to the personnel whose personal data are collected and processed and provide them with the specific privacy statement. 

  

#  3\. SMARTool Data Management Plan

##  3.1. FAIR Data

The SMARTool Consortium will be the end-responsible for the DMP and will make
the research and clinical data findable, accessible, interoperable and
reusable (FAIR), towards ensuring appropriate data management [8]. The
research data management is not an objective itself, but it is rather the way
to achieve knowledge discovery and innovation, and to accomplish data
extension, integration and reuse. More specifically, the FAIR principles will
promote the ability of machines to automatically discover and use the data, to
support its reuse by individuals (Figure 3).

_**Figure 3:** SMARTool FAIR data. _

To accomplish this, the data are available through well-defined APIs and the
CRFA web-based user interface. The developed within the project software
tools, which are used to create and process the data could be made available
under the open source Apache 2.0 license, whenever possible. To support the
FAIR principles, the following practices are followed in the SMARTool
platform.

  * Data discoverability and metadata provision 

  

  * Unique identifiers support persistent for a long time 

  

  * Data naming conventions 

  

  * Keywords search 

  

  * Clear versioning approach 

  

  * Standards for metadata: Documentation for future metadata will be created 

  

Τhe collected, processed and generated clinical and research data will be
preserved and stored in a specific format so as to ensure long-time
accessibility. To avoid file format obsolescence and avoid the risk of missing
useful information, specific actions are followed, such as the specific file
format selection with a high chance of being usable in the future.

In addition, it is foreseen that in the next update of this deliverable the
following issues will be addressed:  Specify/update the data which will be
open available. In case of closed data a rationale will be provided

  * Specify/update how the data and associated metadata, documentation and code will be stored  Specify/update the access rights and restrictions 

  

To support the data interoperability, specific actions are followed. In order
to acquire data from the legacy data sources, a specific HL7 compliant
integration layer using clinical data semantics is designed [9]. HL7
integration layer is written using HAPI (HL7 application programming
interface). The IHE Cross-Enterprise Document Sharing (XDS) Integration
Profile is adopted to allow the registration, distribution and access across
health enterprises of patient electronic health data. In any single Hospital,
the HIS (Hospital Information System) provides the enterprise specific patient
ID as well as the historical demographic data. For Patient Medical Records the
CDA Release 2 HL7 Standard (Clinical Reports) is used according to the

IHE Cardiology Technical Framework (Volume 1 (CARD TF-1): Integration Profiles
[10] and Volume 2 (CARD TF-2): Transactions [11]). For Genomics data exchange
we will adhere to HL7 IG CG_GENO, R1

Version 3 Genotype, Release1 - –January 2009, HL7 IG LOINCGENVA, R1 Version 2
Implementation

Guide: Clinical Genomics; Fully LOINC-Qualified Genetic Variation Model,
Release 1. For medical images, DICOM standard is adopted. For the refined
models, standards Μarkup language (ML) format is used; ML for the multiscale
and multilevel model, Predictive Model Markup language (PMML) for the data
mining and stratification models, Portable Format for Analytics (PFA), which
is an emerging standard for statistical models [12]. All the aforementioned
standards and protocols ensure the required security in information exchange
and anonymization as well as interoperability.

To support the data re-use the following aspects will be taken into
consideration:

  * Specific data will be licensed to permit its possible re-use 

  

  * The period of data embargo will be defined for the data which is available for re-use  The access to the data by third parties after the end of the project will be defined  The data quality assurance processes will be defined. 

  

  * The length of time for which the data is re-usable will be defined 

  

##  3.2. Datasets to be gathered and processed

The data of SMARTool project can be categorised in the following classes:

**Cat1.** Collected Data: Data that has not been subjected to quality
assurance or control

**Cat2** . Validated Collected Data: Data that has been assessed in terms of
completeness, correctness, integrity, credibility

**Cat3** . Analyzed Collected/ Generated Data: Data which has been validated,
analysed and processed

_**Table 1** : Overview of data sets. _  
  
<table>  
<tr>  
<th>

**Dataset**

</th>  
<th>

**Related WP**

</th>  
<th>

**Brief description**

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

Dataset 1

</td>  
<td>

WP1, WP2, WP3

</td>  
<td>

This dataset contains the SMARTool users' personal information, demographics,
clinical, imaging, molecular, omics and other health related data.

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

Dataset 2

</td>  
<td>

WP3

</td>  
<td>

This dataset contains the CAD stratification which is extracted through the
application of data mining techniques

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

Dataset 3

</td>  
<td>

WP4

</td>  
<td>

This dataset contains the results of the multiscale and multilevel site
specific models for plaque progression and the non-invasive FFR to be used in
the prognostic and diagnostic CDSS

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

Dataset 4

</td>  
<td>

WP4

</td>  
<td>

This dataset contains the results of the pharmacological therapy modulation
algorithms (EVINCI database) using data mining techniques. In addition, data
from the virtual stent deployment approach are also included.

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

Dataset 5

</td>  
<td>

WP5

</td>  
<td>

This dataset contains information on users’ requirements, use cases,
questionnaires, specifications and system architecture.

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

Dataset 6

</td>  
<td>

WP6

</td>  
<td>

The dataset contains the results of the research performed in SMARTool project
which are communicated through public deliverables, journal and conference
presentations, as well as other dissemination channels (website, social media,
etc).

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

Dataset 7

</td>  
<td>

WP7

</td>  
<td>

This dataset contains information related to the project management and
coordination.

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

##  3.3. Data Access Procedures

**Public (PU).** For the data that will be available in the public, the
project web page [13] will provide a description of the dataset and will allow
the user to download the relevant file.

**Protected Data (PR).** Data indicated as protected may be communicated out
of the consortium, as long as the interested parties ask for access from the
SMARTool consortium, after explaining and providing evidence on how this data
will be utilised, for instance for research or commercial purposes.

**Confidential/Private (CO).** Data which are denoted as private/confidential
will be stored at a specific space, namely the databases, to which only
selected partners will have access. In order for other SMARTool partners to
have access to these data, a proper written application to the partner
responsible for the data storage will be provided, accompanied with a
justification of the need for access.

##  3.4. Data Storage

During the lifespan of the project, the data will be collected and
systematically stored in a complex repository based on the following three
components:

  * Imaging data is stored in cloud-based 3DnetMedical.com DICOM compliant repository. 3DnetMedical.com conforms to the DICOM SOP’s of the Storage Service Class at Level 2 (Full). 3DnetMedical.com offers Vendor Neutral Archive (VNA) functionalities from a UK based ISO27001 accredited datacentre – providing security, redundancy, reliability and scalability through onshore outsourcing. 3DnetMedical.com follows specific privacy and security-conscious policies applicable to all of its information handling practices. The 3Dnet Dicom store is employed also for genomic data storage, following the Dicom. 

  

  * Structured clinical data (data acquired during the project for the trial population as well as data employed by the final platform) are stored in the MongoDB database; 

  

  * Clinical data acquired by means of HL7/IHE integrations with hospital sources are stored in an XDS/XDSi repository 

  

User data (such as usernames and application privileges) will be managed by a
platform's Identity Server (WSO2IS) and stored in the LDAP server embedded in
the Identity Server.

###  **3.4.1. Databases**

**3.4.1.1 Level roles and permissions in databases**

To easily manage the permissions in the databases, several roles have been
defined in the following three groups of roles; Administrator, WP leaders and
Researchers, Users (Table 2, Table 3, Table 4).

_**Table 2:** Level roles and permissions in the 3DnetMedical.com DICOM
compliant Database. _  
  
<table>  
<tr>  
<th>

**Description of permissions**

</th>  
<th>

**Administrator**

</th>  
<th>

**WP leaders & Researchers **

</th>  
<th>

**User**

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

Manage user accounts

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Manage user roles and access

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Manage folders, worklists and gateways.

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Upload studies

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Delete or assign a study*

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Visualisation and manipulation of imaging data from studies in accessible
worklists and folders*

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Download data*

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Report a study*

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Access to patient information*

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

*Depending on the user role 

_**Table 3** : Level roles and permissions in the CRFA NoSQL MongoDB Database.
_  
  
<table>  
<tr>  
<th>

**Description of permissions**

</th>  
<th>

**Administrator**

</th>  
<th>

**WP leaders & Researchers **

</th>  
<th>

**User**

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

Download anonymized non imaging clinical data

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Visualisation and editing of non-imaging data from studies

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Upload non imaging data for studies

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Delete and report a study*

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Access to patient information*

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

_**Table 4:** Level roles and permissions in the WSO2IS embedded LDAP User
Database. _  
  
<table>  
<tr>  
<th>

**Description of permissions**

</th>  
<th>

**Administrator**

</th>  
<th>

**WP leaders & Researchers **

</th>  
<th>

**User**

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

Manage user accounts

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Manage user roles and access

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Manage DB, Identity Server, Integration Bus and

Application Servers

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

**3.4.1.2 Process**

The 3DnetMedical.com conforms to DICOM SOP’s of the Storage Service Class at
Level 2 (Full). The DICOM Information Model is derived from the DICOM Model of
the Real World where the DICOM view of the Real-World that identifies the
relevant Real-World Objects and their relationships within the scope of the
DICOM Standard is shown. It provides a common framework to ensure consistency
between the various Information Objects defined by the DICOM Standard.

Clinical documents will be made available to the production SMARTool platform
by means of IHE XDS.b (Cross-Enterprise Document Sharing) and XDS-I.b (Cross-
enterprise Document Sharing for Imaging) integration profiles. An XDS Affinity
Domain will be defined for the platform, in order to ease clinical information
sharing with the clinical organizations that will use the platform. The
platform itself will act as a Document Consumer to query/retrieve documents
containing data used to model the patient, and as a Document Producer for
registering the documents generated by the platform.

Non imaging data will be collected directly during or after a patient's visit
via the platform's data entry application (CRFA). This data will be stored in
the SMARTool MongoDB database for processing and kept for future encounters
with the patient. In order to ease data entry activities, when non imaging
data is available in clinical documents stored in XDS.b repository the data
entry application (CRFA) will pre-fill retrievable data in the corresponding
form fields for user review. Non imaging data retention and clinical document
retention will be subject to the clinical organization policy.

###  **3.4.2. Datasets**

**3.4.2.1 Origin of WP1, WP2 and WP3 datasets**

_**Table 5:** Details of Dataset 1. _  
  
<table>  
<tr>  
<th>

**Data identification: Dataset 1**

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

**Description** This dataset contains the SMARTool users' personal
information, demographics, clinical, imaging, molecular, omics and other
health related data.

More specifically, the following categories of data are also included:

  * Blood tests/ Biohumoral 

  

  * Imaging - CTA scan visual/quantitative analysis: plaque composition (calcified, mixed, noncalcified) and features , nominal categories 

  

  * Circulating soluble proteins: consolidated biomarkers (hsTN, hsCRP, BNP, ALT) and inflammatory markers (IL6, IL 10, ICAM 1, VCAM, e-.selectin), values of blood concentration. 

  

  * Genetics: associated selected SNPs, selected RNA genes from bioinformatics analysis of DNA/RNA sequencing. 

  

  * Lipids: selected lipid species and concentrations in blood; names and values of plasma concentration from bioinformatics analysis. 

  

  * Circulating MN surface proteins to quantify Mon1 Mon 2 and Mon 3 subpopulations: relative and absolute concentrations in blood 

  

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

Category (Cat1, Cat2, Cat3)

</td>  
<td>

Cat1, Cat2

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

**Partners activities and responsibilities**

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

Partner owner of the data

</td>  
<td>

Clinical partners

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

Partner in charge of the data analysis

</td>  
<td>

CNR, LUMC, ALACRIS

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

Partner in charge of the data storage

</td>  
<td>

B3D, EHIT

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

Related WP(s) and task(s)

</td>  
<td>

WP1

Task 1.1 Clinical and imaging (CCTA) data

collection (eg Evinci baseline data)

Task 1.2 CCTA analysis under standardized criteria

WP2

Task 2.1 Biohumoral data collection and analysis at baseline and at follow-up

Task 2.2 Patient-specific phenotyping (cellular and molecular data) at
baseline and at follow-up

WP3

Task 3.1 Genomics and transcriptomics

Task 3.2 Omics data analysis

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

**Standards and metadata**

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

Standards, format, estimated volume of data

</td>  
<td>

RAW, DICOM, XLM, JSON, BSON

Clinical data will not contain any metadata.

Volume: To be calculated at the end of patient data collection

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

**Data exploitation and sharing**

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

Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the Commission Services) or Public

</td>  
<td>

Confidential

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

Data sharing, re-use, distribution, publication (How?)

</td>  
<td>

Internal validation of discriminative markers by bootstrapping techniques

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

Personal data protection: are they personal data?

If so, have you gained (written) consent from data subjects to collect this
information?

</td>  
<td>

Personal protection according to the project ethical and legal guidelines

Written consent received.

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

**Archiving and preservation (including storage and backup)**

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

Data storage (including backup): where? For how long?

</td>  
<td>

3DnetMedical.com DICOM compliant database

NoSQL MongoDB Database

Long term archival

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

**Methodologies for data collection.**

For the follow-up CT acquisition, in order to achieve optimal quality for the
3D reconstruction of the desired arterial segments, the 128/256/320 MSCT
scanners are used with interval between the slices not exceeding the threshold
of 0.5 mm. For the elimination of motion or other artifacts in the acquired
images, heart rate during scan should be less than 65 beats/min and optimally
less than 60 beats/min. Nitroglycerin prior to the CTA acquisition is
administered. Also, multiple cardiac phases are captured to choose different
phases for different coronary segments, if needed. The reconstructed field of
view is reduced to maximize the number of pixels devoted to depiction of the
heart, usually a field of view of 200–250mm for coronary CTA studies of native
coronary arteries.

For the blood sampling ( _samples to be sent to CNR_ ) the responsible
clinical partners collect for each enrolled patient during venipuncture:

  * N= 8/5 ml EDTA tubes (VIOLET tube) 

  

  * N=2/5ml Li-heparin tubes (GREEN tube) 

  

  * N= 2/5ml Clot activator tubes (RED tube) 

  

During blood sampling, details are recorded such as:

  * Samples collected (blood [which tubes, how many and in which order]). 

  

  * Date and time of blood sampling 

  

  * Last time of food/drink consumption and smoking. Special attention for 12h fasting and 12h refraining from smoking. 

  

The separation of blood aliquots is described below:

###  For the VIOLET tubes: EDTA N=8 tubes

 N=6 tubes/8

Ø EDTA-Plasma is separated by centrifugation at +4°C in a refrigerated
centrifuge, 10 minutes at 1500xg. The plasma is separated from cell pellet
within 1 hour from the withdrawing, keeping the vial over this time in an ice
bath.

Samples are subdivided in aliquots about 1 ml each (18 aliquots), by using
small plastic vials (VIOLET CAPS) and keeping all the vials in an ice-bath
during the whole procedure.  N=2 tubes/8

Ø EDTA-Whole-Blood tubes are stored at -20°C or -80°C without centrifugation
and NOT aliquoted.

###  For the RED tubes: Clot Activator N=2 tubes

 Ø Serum samples are separated by centrifugation, for 10 minutes at 1500xg.
Samples are subdivided in aliquots about 1 ml each (6 aliquots), by using
small plastic vials (RED CAPS) and keeping all the vials in an ice-bath during
the whole procedure.

###  For the GREEN tubes: Li-HEPARIN N=2 tubes

 Ø Heparin-Plasma are separated by centrifugation, for 10 minutes at 1500xg.
Samples should be subdivided in aliquots about 1 ml each (6 aliquots), by
using small plastic vials (GREEN CAPS) and keeping all the vials in an ice-
bath during the whole procedure.

###  For the TEMPUS tubes: N=3 tubes

  * Ø TEMPUS tubes (obtained by CNR) are stored at -20°C without centrifugation and NOT aliquoted For the blood storage, upon arrival in the lab, samples are maintained at room temperature (18-22 °C) for 2-4 hours before transferring to freezer, in an upright position. TEMPUS tubes can be left for up to72 hours at room temperature prior to freezing. The TEMPUS tubes should then be frozen at -20 °C upright until freezing and storage at -20°C or -80°C (if available) until shipping. 

  

The blood samples are packaged appropriately with sufficient dry ice or ensure
that samples do not thaw and packaged in a manner that prevents breakage or
leakage. For mandatory safety reasons the blood samples are tested for

  * HIV 

  

  * HEPATITIS B 

  

  * HEPATITIS C 

  

_**Table 6:** Details of Dataset 2. _  
  
<table>  
<tr>  
<th>

**Data identification: Dataset 2**

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

**Description:** This dataset contains the CAD stratification which is
extracted through the application of data mining techniques

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

Category (Cat1, Cat2, Cat3)

</td>  
<td>

Cat3

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

**Partners activities and responsibilities**

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

Partner owner of the data

</td>  
<td>

Clinical partners

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

Partner in charge of the data analysis

</td>  
<td>

FORTH

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

Partner in charge of the data storage

</td>  
<td>

EHIT

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

Related WP(s) and task(s)

</td>  
<td>

WP3

Task 3.4 Multivariate analysis, data mining and non-imaging classification
algorithm for CAD stratification

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

**Standards and metadata**

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

Standards, format, estimated volume of data

</td>  
<td>

RAW, JSON, ML,XML, PMML, PFA

Volume: ~5GB

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

**Data exploitation and sharing**

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

Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the Commission Services) or Public

</td>  
<td>

Confidential

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

Data sharing, re-use, distribution, publication (How?)

</td>  
<td>

No data sharing, Only the data mining and nonimaging classification algorithm
will be public available in peer review journals and conferences

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

Personal data protection: are they personal data?

If so, have you gained (written) consent from data subjects to collect this
information?

</td>  
<td>

Personal data protection according to the project ethical and legal guidelines

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

**Archiving and preservation (including storage and backup)**

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

Data storage (including backup): where? For how long?

</td>  
<td>

NoSQL MongoDB Database

Long term archival

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

**3.4.2.2 Origin of WP4 datasets**

_**Table 7:** Details of Dataset 3. _  
  
<table>  
<tr>  
<th>

**Data identification: Dataset 3**

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

**Description:** This dataset contains the results of the multiscale and
multilevel site specific models for plaque progression and the non-invasive
FFR to be used in the prognostic and diagnostic CDSS

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

Category (Cat1, Cat2, Cat3)

</td>  
<td>

Cat3

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

**Partners activities and responsibilities**

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

Partner owner of the data

</td>  
<td>

Clinical partners

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

Partner in charge of the data analysis

</td>  
<td>

FORTH

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

Partner in charge of the data storage

</td>  
<td>

EHIT

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

Related WP(s) and task(s)

</td>  
<td>

WP4

Task 4.1 Prognostic CDSS: Refinement of multiscale and multilevel site
specific models for plaque progression

Task 4.2 Diagnostic CDSS: Refinement of noninvasive FFR computation

Task 4.4 Validation of prognostic CDSS (Multiscale-multilevel site specific
plaque progression models)

Task 4.5 Validation of diagnostic CDSS (3D artery reconstruction, plaque
characterization and noninvasive FFR

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

**Standards and metadata**

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

Standards, format, estimated volume of data

</td>  
<td>

DICOM, RAW, XLM, STL, IGES

A data volume estimation will be later provided.

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

**Data exploitation and sharing**

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

Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public

</td>  
<td>

Confidential

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

Data sharing, re-use, distribution, publication (How?)

</td>  
<td>

No data sharing. Only the results from the plaque progression and the non-
invasive FFR will be public available in peer review journals and conferences

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

Personal data protection: are they personal data? If so, have you gained
(written) consent from data subjects to collect this information?

</td>  
<td>

Personal data protection according to the project ethical and legal guidelines

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

**Archiving and preservation (including storage and backup)**

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

Data storage (including backup): where? For how long?

</td>  
<td>

NoSQL MongoDB Database

Long term archival

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

_**Table 8:** Details of Dataset 4. _  
  
<table>  
<tr>  
<th>

**Data identification: Dataset 4**

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

**Description:** This dataset contains the results of the pharmacological
therapy modulation algorithms (EVINCI database) using data mining techniques.
In addition, data from the virtual stent deployment approach are also
included.

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

Category (Cat1, Cat2, Cat3)

</td>  
<td>

Cat3

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

**Partners activities and responsibilities**

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

Partner owner of the data

</td>  
<td>

Clinical partners

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

Partner in charge of the data analysis

</td>  
<td>

FINK, FORTH

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

Partner in charge of the data storage

</td>  
<td>

B3D, EHIT, FINK

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

Related WP(s) and task(s)

</td>  
<td>

WP4

Task 4.3 Treatment CDSS: Refinement of medical therapy and virtual angioplasty
decision support methods

Task 4.6 Validation of treatment CDSS

(Pharmacological therapy and virtual angioplasty tool)

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

**Standards and metadata**

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

Standards, format, estimated volume of data

</td>  
<td>

DICOM, STL, DAT, UNV, LST, BMP, AVI

A data volume estimation will be later provided.

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

**Data exploitation and sharing**

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

Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public

</td>  
<td>

Confidential, Public only some specific results

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

Data sharing, re-use, distribution, publication (How?)

</td>  
<td>

Data sharing, publication in peer review journal

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

Personal data protection: are they personal data?

If so, have you gained (written) consent from data subjects to collect this
information?

</td>  
<td>

Personal data protection according to the project ethical and legal guidelines

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

**Archiving and preservation (including storage and backup)**

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

Data storage (including backup): where? For how long?

</td>  
<td>

B3D, FINK

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

**3.4.2.3 Origin of WP5 datasets**

_**Table 9:** Details of Dataset 5. _  
  
<table>  
<tr>  
<th>

**Data Identification: Dataset 5**

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

Description: This dataset contains information related with T5.1 regarding
users’ requirements, use cases, questionnaires, functional specifications and
system architecture for the SMARTool Platform.

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

Category (Cat1, Cat2, Cat3)

</td>  
<td>

Cat 3

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

Partners activities and responsibilities

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

Partner owner of the data

</td>  
<td>

Technical partners

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

Partner in charge of the data analysis

</td>  
<td>

B3D, FORTH, FINK, EHIT

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

Partner in charge of the data storage

</td>  
<td>

B3D

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

Related WP(s) and task(s)

</td>  
<td>

WP5

Task 5.1 User requirements, functional specifications and architecture of the
SMARTool platform

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

Standards and metadata

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

Standards, format, estimated volume of data

</td>  
<td>

RAW, XML, UML, PDF, CSV

Volume~500ΜΒ

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

Data exploitation and sharing

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

Data access policy / Dissemination level: confidential (only for members of
the Consortium and the Commission Services) or Public

</td>  
<td>

Confidential

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

Data sharing, re-use, distribution, publication (How?)

</td>  
<td>

No data sharing.

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

Personal data protection: are they personal data?

If so, have you gained (written) consent from data subjects to collect this
information?

</td>  
<td>

This dataset does not include personal data. Questionnaires are anonymized.

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

Archiving and preservation (including storage and backup)

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

Data storage (including backup): where? For how long?

</td>  
<td>

Data will be available through D5.1 and its annexes. As WP leader B3D will
store and backup the data.

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

**3.4.2.4 Origin of WP6 datasets**

Each SMARTool partner should disseminate its results in accordance with
Article 29 of the Grant Agreement. The results of the research performed in
SMARTool project are communicated through public deliverables, journal and
conference presentations, as well as other dissemination channels (website,
social media, etc). On the other side, the exploitation is mainly achieved
through the main SMARTool exploitable products:

  * SMARTool 3DNet™-framework: a common, centralized shareable platform that provides a better control, management and streamlining of CAD clinical workflow based on CDSS. 

  

  * SMARTool CDSS: an integrated system for supporting the clinicians in diagnosis, prognosis and treatment of CAD patients and subjects at risk. 

  

  * SMARTool Point-Of-Care-Testing (POCT), a portable device for on chip blood analysis and patient phenotyping exploitable in diagnostic CDSS. 

  



**3.4.2.5 Origin of WP7 datasets**

_**Table 10:** Details of Dataset 7. _  
  
<table>  
<tr>  
<th>

**Data identification: Dataset 7.1**

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

Description: This dataset contains information related to the project
management and coordination

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

Category (Cat1, Cat2, Cat3)

</td>  
<td>

Cat 1, Cat 2, Cat 3 (by the Commission)

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

Partners activities and responsibilities

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

Partner owner of the data

</td>  
<td>

Joint Ownership

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

Partner in charge of the data analysis

</td>  
<td>

CNR

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

Partner in charge of the data storage

</td>  
<td>

CNR

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

Related WP(s) and task(s)

</td>  
<td>

WP7

T7.1 Project Management

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

Standards and metadata

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

Standards, format, estimated volume of data

</td>  
<td>

No specific standards for this data.

Files will be in Microsoft Office in doc, .docx, .xls, .xlsx, and .pdf
formats. Volume~1GB

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

Data exploitation and sharing

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

Data access policy/ Dissemination level: confidential (only for members of the
Consortium and the

Commission Services) or Public

</td>  
<td>

Confidential: information shared only among the

Consortium and between the Consortium and the Commission

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

Data sharing, re-use, distribution, publication (How?)

</td>  
<td>

Effort (quantified in person months – PMs) and financial data of each partner
is collected by CNR and compiled for monitoring purposes.

Data is entered/uploaded in the EC system SyGMa in order to allow the
Commission to oversee and assess use of the resources by the consortium.

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

Personal data protection: are they personal data?

If so, have you gained (written) consent from data subjects to collect this
information?

</td>  
<td>

No personal data collected

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

Archiving and preservation (including storage and backup)

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

Data storage (including backup): where? For how long?

</td>  
<td>

The data is collected for internal use in the project.

Standard daily offsite backup on CNR systems. Length:

10 years

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

#  4\. Data security and ethical considerations

##  4.1. Data security

The SMARTool project utilises and adopts specific methods and tools for
ensuring adequate field access and extended contact and communication between
the participants, who are sensitive in the activities that are related to
ethical concerns and research activities.

The following instructions will be used to ensure data security:  Perform
anonymization of personal data

  * Encrypt data, if necessary by the local researchers 

  

  * Data storage in two separate locations for avoiding data loss 

  

  * Perform frequent backups (every 24 hours) 

  

  * Ensure the final dataset coherence by marking up files in a structured way 

  

SMARTool databases for imaging and for non-imaging clinical data are hosted in
B3D’s secure data centre, collocated in dedicated spaces at top-tier
datacentre accredited withISO 27001 for Information Security Management, ISO
9001 for Quality Management, ISO 14001 for Environment Management. This
facility provides carrier-level support, including:

  * Access control and physical security 

  

  * Environmental controls 

  

  * Power 

  

  * Network 

  

  * Fire detection and suppression 

  

  * Network protection 

  

  * Disaster Recovery 

  

  * Security Monitoring 

  

B3D’s Information Governance has the purpose to ensure: (i) the patient
information confidentiality, and (ii) that the adherence to information
governance is built into the design of the 3DnetMedical service and derived
products provided to healthcare professionals. Information governance and
security underpins 3DnetMedical and, as an organisation, B3D strives to
achieve excellence in the services provided.

B3D operates under ISO 13485:2012 (quality management system). B3D complies
with ISO 62304 (medical software development) and incorporates ISO 14971 (risk
analysis) into product life cycle. All B3D products and operations conform to
industry standards including CE Annex II of directive 93/42/EEC, DICOM, HL7
and IHE.

SMARTool non imaging clinical data and clinical documents will be encrypted at
rest by means of a transparent filesystem encryption strategy with AES-256
symmetric key cypher. Disk encryption keys will be managed in a keystore
(vaultproject.io), and will be encrypted themselves with a master key which
will be separate from the data and the databases.

Network traffic carrying non imaging data will be encrypted via TLS/SSL
(HTTPS), with public key certificates issued and renewed by a root certificate
authority.

EHIT is an ISO 13485:2013 and ISO/IEC 27001 certified organization. This
ensures consistent information security management and product development
life cycle. Moreover, EHIT is certified for 21 IHE integration profiles.

##  4.2. Ethical issues

SMARTool will support the protection of the enrolled patients and make their
rights more visible by adopting the National Laws and EU Legislation and be in
compliance with the Directives as described in the following section.

**EU legislation and Ethics documents**

The Directives, Ethics Documents and experts’ opinions and Ethical Committees
are presented in the following table accompanied with a short reference note
on the general matter as well as the stated principles. All the European Union
countries of SMARTool adopted the European Directives. Additionally, the
partners of the SMARTool project follow the European Charter for researchers.

_**Table 11:** Ethical considerations in SMARTool project. _  
  
<table>  
<tr>  
<th>

**Directive 1995/46/EC of the European Parliament (October 1995):**

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

Protection of individuals related to personal data processing and free
movement. It concerns the protection for the individuals’ privacy and the free
movement of personal data within the European Union (EU). The Directive
defines specific criteria for the collection and utilisation of personal data.
Furthermore, the Directive stresses the obligation of each Member State to set
up an independent national authority for the monitoring of the Directive
application.

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

**Charter of Fundamental Rights of the EU that became legally binding on the
EU institutions and the national governments on 2009 with the entry into force
of the Treaty of Lisbon**

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

The Charter includes the rights and freedoms in terms of Dignity, Freedoms,
Equality, Solidarity, Citizens' Rights, and Justice.

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

**Opinion of the European Group on Ethics in Science and New Technologies No.
13 (July 1999)**

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

Protection of recognisable personal health data.

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

**Opinion of the European Group on Ethics in Science and New Technologies No.
26 (February 2012)**

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

Ethics of information and communication technologies.

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

**Opinion of the European Group on Ethics in Science and New Technologies No.
29 (13 October 2015)**

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

Examination of the principal health technologies and definition of a set of
recommendations for the EU and nationallevel policymakers, industry and other
stakeholders, towards maximising the benefits and minimise the issues
associated with new health technologies and citizen participation in health
policy, research and practice

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

**Directive 2001/20/EC of the European Parliament (April 2001)**

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

Performance of clinical trials and medicine under good clinical practice.

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

**Directive 2005/28/EC or Good Clinical Practice Directive of the European
Parliament and of the Council(8**

**April 2005)**

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

Presents the principles and detailed guidelines for good clinical practice
with regard to investigational medicinal products for human use, as well as
the requirements for authorisation of the manufacturing of such products.

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

**Universal Declaration on the human genome and human rights adopted by UNESCO
(1997)**

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

Refers to national and regional legislation on medicine, privacy and genetic
research.

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

**Clinical Trials Regulation (CTR) EU No 536/2014**

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

Ensures that the rules for conducting clinical trials are the same throughout
the EU. It is imperative to ensure that all Member States, in authorising and
supervising the conduct of a clinical trial, are based on identical rules.

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

**Italy**

</td>  
<td>

A number of ministerial decrees cover this area. A key one is Legislative
Decree no.211 of June 24, 2003 “Transposition of Directive 2001/20/EC relating
to the implementation of good clinical practice in the conduct of clinical
trials on medicinal products for clinical use”.

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

Law n.675 of 31 December 1996 Tutela delle persone e di altri soggetti
rispetto al trattamento dei dati personali (published on the G.U. n.5 of8
January 1996;

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

C. M. n. 6 of 2 September 2002, Attività dei comitati etici istituiti ai sensi
del decreto ministeriale 18 marzo 1998. (published on the G.U. n. 214 of 12
September 2002)

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

**Finland**

</td>  
<td>

Medical Research Act (L 488/1999);

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

Act of the Medical Use of Human Organs and Tissues (L 101/2001),

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

Act on the Status and Rights of Patients (L785/1992),

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

Personal Data Act (L 523/1999);

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

Good Clinical Practice (GCP) guidelines in accordance with the International

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

Conference on Harmonization (www.ich.org)

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

**Switzerland**

</td>  
<td>

Loi fédérale sur les médicaments et les dispositifs médicaux 812.21 du 15
décembre 2000 (Chapitre 4 Section 2 Essais cliniques, Section 4 Obligation de
garder le secret et la communication de donées)

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

Loi fédérale sur l’analyse génétique humaine 810.12 du 8 octobre 2004 (Section
2; Art. 5 Consentement, Art. 7 Protection des données génétiques, Art. 20.
Réutilisation du matériel biologique)

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

Local: Approval by the IRB Bundesgesetz 812.21 über Arzneimittel und
Medizinprodukte (Heilmittelgesetz, HMG) as of Dezember 15, 2000

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

Verordnung 812.214.2 über klinische Versuche mit Heilmitteln (VKlin) as of
October 17, 2001

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

Patientinnen- und Patientengesetz 813.13 as of April 5, 2004

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

Heilmittelverordnung (HMV) 812.1 as of May 21, 2008

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

**UK**

</td>  
<td>

Integrated Research Application Systems (IRAS)

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

National Research Ethics Service (NRES)

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

Department of Health’s Research Governance Framework for Health and Social
Care (2nd

Edition, 2005)

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

**France**

</td>  
<td>

Public Health Code (articles L. 1121-1 et seq.).6

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

The bioethics law of 2004 creating the French Biomedicine Agency

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

The Advisory Committee on the Treatment of Research Information in the Health

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

Field was created by a law n°94-548 1 July 1994

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

**Germany**

</td>  
<td>

Das Deutsche Referenzzentrum für Ethik in den Biowissenschaften - DRZE

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

Deutscher Ethikrat

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

Büro für Technikfolgen-Abschätzung beim Deutschen Bundestag - TAB

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

**Spain**

</td>  
<td>

Comités de Ética en Investigación Clínica, CEIC

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

Royal Decree 561/1993

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

Royal Decree 223/2004

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

Law of Biomedical Research (2007)

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

Asociación Nacional de Comités de Ética de la Investigación, ANCEI

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

**Poland**

</td>  
<td>

First Polish Code of Ethics (1977)

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

Extraordinary National Assembly of Delegates of the Polish Society of Medicine
in Szczecin on 22 June 1984

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

Code of Medical Ethics (1991)

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

First Polish Research Ethics Committee (1979)

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

Bioethics Committee at the Ministry of Health

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

Zbiór zasad i wytycznych pt. "Dobre obyczaje w nauce"

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

April 7, 2005 Order of the Minister of Health concerning the nature and extent
of inspection of clinical trials. Legislation Journal of the Republic of
Poland (2005) 69: pos. 623

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

January 3, 2007 Order of the Minister of Health concerning the application
form for authorization of clinical trials, payments for authorization and
final report of the clinical trial Legislation Journal of the Republic of
Poland (2010) 222: pos. 1453

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

February 11, 2011 Order of the Minister of Health concerning requirements
related with the basic documentation of clinical trials. Legislation Journal
of the Republic of Poland (2011) 40: pos. 210

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

May 12, 2012 Order of the Minister of Health concerning Good Clinical Practice
Legislation Journal of the Republic of Poland (2012) 0: pos. 489

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

**Netherlands**

</td>  
<td>

Personal Data Protection Act of 6 july 2000 (Wet bescherming persoonsgegevens)

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