#  1\. PRINCIPLES

Data management is under the responsibility of the coordinator and is planned
in agreement with beneficiary 14, UNITO. It is regulated by bilateral DTAs
(see below, item 6 and 7) and follows the Imperial College and UNITO rules for
_Data sharing, confidentiality and information governance_ _(item 8)._

#  2\. GENERAL PLAN FOR DATA MANAGEMENT

The general scheme for data management has been agreed upon at the kick-off
meeting (D1.1) and it includes:

  * The transfer of cohort data from single partners to (a) UNITO (with the exclusion of biomarkers) and (b) Imperial College for biomarkers. Both institutions have rules for data sharing, confidentiality and information governance 

  

  * The harmonization of relevant variables from all cohorts, depending on the needs of the WP, in particular in preparation of data analysis for the “decline phase” (Working Group 1, led by Stringhini - item 4 below), of the “build-up” phase (Working Group 2, led by Layte - item 5 below) and of the existing biomarkers (Working Group 3, led by Vineis). 

  

  * Harmonized variables will be made available to Work packages and Working Groups on request on the basis of the planned statistical analyses, reports and papers. 

  

#  3\. DEFINITION OF AGEING AND HARMONIZATION OF SES

The **workshop of WP7** (held on June 10 2015) led to definitions/refinements
of SES and healthy ageing that will be used in the consortium (a report is
prepared separately by M Kelly-Irving in deliverable D7.1).

The following simplified definition of healthy ageing has been proposed as a
starting point: “ **life expectancy at age 65 without activity limitations**
”. We will use both hard indicators (death) and functional indicators
(activity limitations), though whenever possible we will emphasize the second.

##  1\. Proposal for harmonization of adult SES variables (written by Fulvio
Ricceri, Angelo d’Errico, Silvia Stringhini)

**EDUCATIONAL LEVEL:**

Variable1 (3 levels) – _all cohorts_ :

  * primary or lower secondary school 

  

  * higher secondary school 

  

  * tertiary education (post-secondary) 

  

Variable 2 (4 levels) – _not all cohorts_ :

  * primary or lower secondary school 

  

  * vocational school 

  

  * higher secondary school 

  

  * tertiary education (post-secondary) 

  

**EMPLOYMENT STATUS:**

Variable 1 (2 levels) – _all cohorts_ :

  * employed 

  

  * not employed 

  

Variable 2 (5 levels) – _not all cohorts_ :

  * employed 

  

  * not employed: retired 

  

  * not employed: housewife 

  

  * not employed: unemployed 

  

  * not employed: disabled 

  

**OCCUPATIONAL CLASS:**

Variable 1 (5 levels) – _not all cohorts:_

  * higher professionals and managers (Class 1 ESEC – European Socio-economic 

  

Classification – 9 classes)

  * lower professionals and managers; higher clerical, services and sales workers (Class 2 and 3 ESEC) 

  

  * small employers and self-employed; farmers; lower supervisors and technicians (Class 4, 5, and 6 ESEC) 

  

  * lower clerical, services, and sales workers; skilled workers (Class 7 and 8 ESEC) - semi – and unskilled workers (Class 9 ESEC) 

  

##  INCOME

Variable 1 (3 levels):

  * tertiles within each cohort 

  

Variable 2 (4 levels) – _if possible_ :

  * quartiles within each cohort 

  

Variable 3 (5 levels) – _if possible_ :

  * quintiles within each cohort 

  

#  4\. PROPOSAL FOR HARMONISED SOCIO-ECONOMIC MEASURES IN THE BUILDUP PHASE
(WRITTEN BY RICHARD LAYTE)

For both papers and for other work in the growth phase working group we will
need to produce comparative data and this requires a harmonisation template
that can be applied across all of the cohorts contributing data. Whilst the
growth phase group could produce its own harmonisation guide, it makes sense,
where possible, to adopt that being used by the decline phase working group.
This will mean that should the same data be used in the different workgroups
we will not be creating more work for ourselves. Earlier in the summer Silvia
circulated an initial harmonisation guide for SES variables which I have
attached here for reference. This sets out harmonised variables for education,
income and social class and provides two/three levels of variable which can be
adopted depending on the level of information available. This is important as
data structures vary significantly across the cohorts and we will be forced to
use the lowest common denominator if we are looking to maximise the number of
countries in comparisons. Overall I think this is a good schema to use for the
SES variables although there are some questions about how these schemas would
be implemented in different countries and in different cohorts that I would
like to explore.

##  Education Variables

For education for example, in Ireland or the UK there is no analogue to the
‘vocational school’ listed although I fully recognise that there is a
differentiation between general and vocational tracks in other countries. In
the CASMIN schema (see attached) which has been used for a great deal of
social mobility research, there are higher and lower vocational qualifications
which are essentially analogues of lower secondary and higher secondary
educational qualifications. Should ‘vocational school’ by grouped with the
latter in the LIFEPATH three level variable?

There are similar issues around how to classify ‘tertiary education’. Many
countries have postsecondary courses in vocational subjects but these would
not be classed as tertiary education and indeed, do not lead to the advantage
that a bachelor’s degree would in the labour market. For example, nursing
qualifications or technical apprenticeships. In the CASMIN schema these are
classified as 2c_voc. Tertiary would usually include practically orientated
study programs like college technical diplomas and professional qualifications
like social workers.

A third issue is the amount of differentiation to be used depending on the age
of the cohort under investigation. Because of educational expansion in most
countries it is now quite rare to find a young person whose highest level of
education is primary. I imagine this is the reason why Silva and colleagues
have collapsed primary and lower secondary levels in their schema. Among older
cohorts though (those prior to 1967 in Ireland), leaving school before
secondary education was far more common and this track had significant impacts
on life trajectory. This would suggest keeping these two levels separate among
older cohorts.

Can I suggest that we adopt the following using the CASMIN groups attached?

  * Primary Education - 1a, 1b, 1c 

  

  * Lower Secondary School - 2a, 2b (‘Vocational School’ should be grouped here if education finished <=16) 

  

  * Primary and lower secondary can be grouped in younger cohorts. 

  

  * Higher Secondary School – 2c_gen, 2c_voc (‘Vocational School’ should be grouped here if education finished >16 & <=18) 

  

  * Tertiary Education (3a, 3b).ù 

  

##  Occupational Class

For the social (occupational) class variable Silva and colleagues have
suggested that we use an aggregated version of the European Socio-Economic
Classification (EsEC), a comparative schema created by David Rose based on the
Erikson/Goldthorpe/Portacarero schema from the early 1990s. EsEC is also close
to the ONS class scheme as used in the UK (which was also developed by David
Rose). This I think is a good choice as it is a theoretically based schema
that has proven to be a good predictor of outcomes (see
_https://irvapp.fbk.eu/sites/irvapp.fbk.eu/files/irvapp_seminar_2010_03_rose_harrison_slide.pdf_
). There are issues however in how teams are to allocate occupations to the
groups set out in the Harmonisation document. For example, there is likely to
be disagreement about which occupations are to be regarded as ‘professionals’
even within countries let alone across national borders and no clear way to
define ‘higher’ and ‘lower’ professionals. It is likely then that there would
be large discrepancies between the way different country teams would group
particular occupations. The usual response in comparative research is to apply
the International Standard Classification of Occupations (ISCO88, though there
is now a more recent version) and then group these on an agreed basis. It
looks from many of the submissions to Silvia that most studies do not ask for
occupational titles but instead ask respondents to allocate themselves to a
group at interview. In this situation we will have no choice but to apply a
different coding in each case and agree this across the team. However, I think
single occupation codes may be available in some cohorts and will check with
individual teams by email.

If we are to combine existing occupation/class groups could I suggest that we
adopt another aggregation of the EsEC classification that may lead to less
cross-national drift in allocation. The standard EsEC has 10 levels:

  * 'Large employers, higher mgrs/professionals' – (owners with 25+ employees, lawyers, doctors and judges plus corporate managers) 

  

  * 'Lower mgrs/professionals, higher supervisory/technicians' (secondary school teachers, academics, engineers, accountants) 

  

  * 'Intermediate occupations' (clerical and administrative occupations as well as associate professionals like social workers, primary school teachers, Montessori teachers, secretaries, etc). 

  

  * 'Small employers and self-employed (non-agriculture)' (shop keepers, self-employed artisans etc) 

  

  * 'Small employers and self-employed (agriculture)' (Small farmers) 

  

  * 'Lower supervisors and lower technician occupations' (supervisors of manual occupations and equipment operators) 

  

  * 'Lower sales, services and clerical ' (cashiers, cooks, firemen, police officers and salespeople) 

  

  * 'Lower technical' (skilled construction workers and other artisans) 

  

  * 'Routine Occupations' (unskilled manual labourers) 

  

  * Never worked and long-term unemployed 

  

I would suggest that we keep the professional classes together as they are
hard to differentiate and have outcomes which are quite similar anyway. The
intermediate occupations are often female but these women tend to be married
to men and have living standards like the skilled manuals and lower technical
groups so I would argue that 3 should be grouped with 6. I would argue for
keeping 4 and 5 separate as farmers vary hugely across countries in terms of
income and outcomes. It would also be good to differentiate between skilled
and unskilled manual occupations so I would suggest grouping 6, 7 and 8 and
having 9 and 10 separate. This gives us:

  1. Higher and lower professionals, large employers, higher technical and intermediate. (1 +2) 

  

  2. Smaller Employers and self-employed (non-agricultural) (4) 

  

  3. Smaller Employers and self-employed (agricultural) (5) 

  

  4. Manual supervisors, lower technical, sales and service plus intermediate). (3, 6, 7, 8) 

  

  5. Routine and never worked. (9+10). 

  

  

##  Income Categories

Ideally, each team would have access to a measure of household net income that
could be equivalised to take account of the number of people dependent on the
income which would then be categorised into groups such as tertiles or
quintiles. It looks from the documents circulated that many teams only have
income categories so as with occupational class we will need to agree how
these are grouped.

**5\. DATA TRANSFER AGREEMENT (FACSIMILE) BETWEEN EACH PARTNER**

#  (COHORTS) AND UNITO

##  DATA TRANSFER AGREEMENT

This Data Transfer Agreement ("Agreement") and the Memorandum of Understanding
(the “MOU”) included herein as Attachment 1 is between … (the ”Provider”) and
those who are acquiring Data (as defined hereinafter), the Lifepath network
and the University of Torino, Department of Clinical and Biological Sciences,
Orbassano Italy (the “Recipient”), under this Agreement. **I. Definitions:**

  1. PROVIDER: Organization providing the DATA. The name and address of this party will be specified herein. 

  

  2. PROVIDER SCIENTIST: The name and address of this party will be specified herein. 

  

  3. RECIPIENT: Organization receiving the DATA. The name and address of this party will be specified herein. 

  

  4. RECIPIENT SCIENTIST: The name and address of this party will be specified herein. 

  

  5. DATA: Data collected by PROVIDER. It includes specified non-identifiable data on individuals, in electronic format. 

  

  6. MODIFICATIONS: New data generated as a result of the analyses of the DATA. New data are a result of the harmonization of Data collected from PROVIDERs **II. Terms and Conditions of this Agreement:**

  

  

  1. The PROVIDER retains ownership of the DATA, including any DATA contained or incorporated in MODIFICATIONS. 

  

  2. The PROVIDER and RECIPIENT will have joint ownership of MODIFICATIONS (except that, the PROVIDER retains ownership rights to the DATA included therein). 

  

  3. The PROVIDER will only transfer DATA to the RECIPIENT in good standing and if the RECIPIENT has been approved by the PROVIDER. 

  

  4. The PROVIDER, the RECIPIENT, and the RECIPIENT SCIENTIST agree that the DATA and MODIFICATIONS: 

    1. are to be used solely for the agreed academic research purposes, as specified in the attached MOU; 

  

    2. will not be used for other than the agreed purposes without the prior written consent of the PROVIDER; 

  

    3. are to be used only at the RECIPIENT organization, and in the RECIPIENT SCIENTIST's department under the direction of the RECIPIENT SCIENTIST or others working under his/her direct supervision; and 

  

    4. will not be transferred to anyone else within the RECIPIENT organization or external to the RECIPIENT organization without the prior written consent of the PROVIDER. 

  
  

  

  5. Any DATA delivered pursuant to this Agreement is understood to be a complete and accurate copy of the data retained by the PROVIDER. 

  

  6. This agreement shall not be interpreted to prevent or delay publication of research findings resulting from the use of the DATA or the MODIFICATIONS. The RECIPIENT SCIENTIST agrees to provide appropriate acknowledgement of the source of the DATA in all publications. See MOU for further information. 

  

  7. The RECIPIENT agrees to use the DATA in compliance with all applicable statutes and regulations, including those relating to research involving the use of humans. 

  

  8. This Agreement will terminate on the earliest of the following dates: 

  

  

  1. on completion of the proposed research with the DATA, as described in the MOU, or 

  

  2. on 1 month written notice by either party to the other, prior to completion of the project, provided that 

    1. if termination should occur under 8(a) above, the the RECIPIENT will discontinue its use of the DATA and will, upon direction of the PROVIDER, retain the DATA for a period of 7 years or destroy it. The RECIPIENT, at their discretion, will retain the 

  
  

  

  

MODIFICATIONS for a period of 7 years.

  2. in the event the PROVIDER terminates this Agreement under 8(b), the RECIPIENT will discontinue its use of the DATA upon the effective date of termination and will, upon direction of the PROVIDER, return or destroy all DATA and modify the MODIFICATIONS by removal of the PROVIDER data only. 

  

  

  9. The DATA is provided at no cost. 

  

  10. The Parties agree to abide by the terms of this Data Transfer Agreement and the MOU incorporated herein as Attachment 1. In the event of conflict between this Data Transfer Agreement and the MOU, the terms of the Data Transfer Agreement will prevail. 

  

  11. This Data Transfer Agreement along with the MOU included as Attachment 1 constitutes the entire agreement between the parties and supersedes all communications, arrangements and agreements, either written or oral, between the parties with respect to the matter hereof, except where otherwise required in law. This agreement may be varied by exchange of letters between the parties. No variation or extension to this Data Transfer Agreement or MOU shall be binding upon either party unless in writing and acknowledged and approved by both parties in writing. 

  

  

##  (Signatures begin on the following page)

**Acknowledged and agreed to:**

_For RECIPIENT_

The Dept of Clinical and Biological Sciences, University of Torino, Orbassano,
agrees to the details of the collaboration described herein.

____________________________________________________

RECIPIENT SCIENTIST Signature Date, 26/06/2015

Name: Giuseppe Costa

Title: Professor

Address: Regione Gonzole n. 10, Orbassano (TO)

Phone: +39 0116705487

Fax: +39 0116705704

Email: giuseppe.costa@unito.it

_**For** PROVIDER _

… as the person responsible for the study from which the data is being
provided agrees to the details of the collaboration outlined herein.

________________________________________________________________________
Provider Scientist Signature Date

Name:

Title:

Address:

Phone:

Fax:

Email:

Attachment 1

##  6\. MEMORANDUM OF UNDERSTANDING

###  1\. Purpose

RECIPIENT and PROVIDER have agreed to collaborate on a pooled analysis project
under the auspices of the Lifepath Consortium.

This Memorandum of Understanding (MOU) and the Data Transfer Agreement (DTA)
describe the terms of the collaboration and the transfer of the data,
including intellectual property rights, publication, confidentiality, other
financial terms, and the specifics of the data and their transfer.

###  2\. Study

The LIFEPATH project answers the call “PHC1. Understanding Health, ageing and
disease: Determinants, risk factors and pathways; Scope Option (ii)”.

The specific and original objectives of LIFEPATH are:

a) To demonstrate that healthy ageing is strongly uneven in society, due to
multiple environmental, behavioural and social circumstances that affect
individuals’ life trajectories (text of the Scope of the Work Programme: “The
identification of determinants and pathways characteristic of healthy and
active ageing”). b) To improve the understanding of the mechanisms through
which healthy ageing pathways diverge by social circumstances, by
investigating life-course biological pathways using omic technologies. c) To
provide evidence on the reversibility of the poorer ageing trajectories
experienced by individuals exposed to the strongest adversities, by using an
experimental approach ("conditional cash transfer" experiment for poverty
reduction in New York City); and to analyse the health consequences of the
current economic recession in Europe (i.e. changes in social and economic
circumstances). d) To provide updated, relevant and innovative evidence for
underpinning future policies.

The collaborative arrangements under this MOU and described below and will be
carried out in accordance with the terms and conditions described therein.
Neither party will deviate from the description of the project without an
exchange of documents explaining, acknowledging and approving the deviation.

###  3\. Contact information

RECIPIENT who will be receiving DATA shall advise in writing of any change in
contact information. Upon receipt of DATA and MODIFICATIONS, the RECIPIENT
will retain responsibility for the security of the data and the scientific
rigour of any remaining statistical analyses to be performed.

###  4\. Data

The DATA needed for project consists of SES and health data relevant to the
Lifepath consortium.

The DATA will be labelled with a unique subject identification number that
must be retained. The DATA will include documentation of the DATA including
names of the columns and values of each of the levels within a column.

**5\. Data transfer**

The PROVIDER will send the DATA in electronic format, via encrypted email or
CD-ROM, to… .

###  6\. Statistical analysis

Research will be conducted in accordance with the RECIPIENT Institutional
Review Board. Additionally, the approval of the RECIPIENT Institution Review
Board will be obtained prior to the receipt of any data.

The analyses that will be performed will be based on de-identified datasets
and will include all the statistical analyses foreseen in the Lifepath DoA.
Data will be used to test the study hypotheses and estimate associations using
a variety of statistical techniques.

Any additional analyses must be proposed and agreed to in writing by all
parties.

###  7\. Publications

The Lifepath publication policy will be followed with respect to authorship on
any manuscript resulting from this project.

The collaborators will ensure the timely dissemination of research findings.

##  7\. DATA TRANSFER AGREEMENT (FACSIMILE) BETWEEN EACH PARTNER (COHORTS) AND
IMPERIAL COLLEGE (BIOMARKER DATA)

**DATA TRANSFER AGREEMENT**

This Data Transfer Agreement ("AGREEMENT") is by and between

  1. [name of providing institution] whose address is [address of supplying institution] (the “PROVIDER”); and 

  

  2. [name of receiving institution] whose address is [address of receiving institution] (the “RECIPIENT”). 

  

  

  1. **Definitions:**

    8. PROJECT: The Horizon 2020 multi-party project entitled “LIFEPATH: Lifecourse biological pathways underlying social differences in healthy ageing”. 

  

    9. GRANT AGREEMENT: Grant Agreement No. 633666 for the Project which was signed by Provider and Recipient. 

  

    10. CONSORTIUM AGREEMENT: The Consortium Agreement for the Project which was signed by Provider and Recipient. 

  

    11. PROVIDER’s SCIENTIST: [Name and institutional address of this individual] who is supplying the DATA. 

  

    12. RECIPIENT’s SCIENTIST: [Name and institutional address of this individual] who is receiving the DATA. 

  

    13. DATA: Data collected by PROVIDER in electronic format which includes specified nonidentifiable information on individuals. The PROVIDER’s SCIENTIST will send the DATA to the RECIPIENT’s SCIENTIST in electronic format via encrypted email or CD-ROM. 

  

    14. MODIFICATIONS: New data generated as a result of the analyses of the DATA either as a result of the harmonization of DATA collected from PROVIDER. 

  
  

  

  2. **Terms and Conditions:**

    12. The PROVIDER retains ownership of the DATA including any DATA contained or incorporated in MODIFICATIONS. 

  

    13. The PROVIDER and RECIPIENT will have joint ownership of MODIFICATIONS except, as noted above, the PROVIDER retains ownership rights to the DATA contained or incorporated in any MODIFICATIONS. 

  

    14. The PROVIDER, the RECIPIENT, and the RECIPIENT’s SCIENTIST agree that the DATA and MODIFICATIONS: 

      1. are to be used solely for the PROJECT as specified in the GRANT AGREEMENT’s Annex 1; 

  

      2. will not be used for any other purpose without the prior written consent of the PROVIDER; 

  

      3. are to be used only at the RECIPIENT organization, and in the RECIPIENT SCIENTIST's department under the direction of the RECIPIENT’s SCIENTIST or others working under his/her direct supervision; and 

  

      4. will not be transferred to anyone else within the RECIPIENT organization or external to the RECIPIENT organization without the prior written consent of the 

  
  

  
  

  

  

PROVIDER.

  15. The RECIPIENT and the RECIPIENT’s SCIENTIST shall acknowledge PROVIDER as the source of the DATA in any publication which mentions the DATA unless requested otherwise by the PROVIDER. 

  

  16. This AGREEMENT will terminate on the earliest of the following dates: 

    1. on completion of the proposed research with the DATA as described in the GRANT AGREEMENT’s Annex 1, or 

  

    2. on one (1) months’ written notice by either party to the other prior to completion of the PROJECT, provided that 

      1. if termination should occur under 5 (a) above, the RECIPIENT will discontinue its use of the DATA and will, upon direction of the PROVIDER, either retain the DATA for a period of 5 years or destroy it. The RECIPIENT, at their discretion, will retain the MODIFICATIONS for a period of 5 years. 

  

      2. in the event the PROVIDER terminates this Agreement under 5 (b), the RECIPIENT will discontinue its use of the DATA upon the effective date of termination and will, upon direction of the PROVIDER, return or destroy all DATA and modify the MODIFICATIONS by removal of the PROVIDER data only. 

  
  

  
  

  

  17. The DATA is provided at no cost. 

  

  18. The DATA will be labelled with a unique subject identification number that must be retained. The DATA will include documentation of the DATA including names of the columns and values of each of the levels within a column. 

  

  19. The parties agree to abide by the terms of this AGREEMENT, the GRANT AGREEMENT and the CONSORTIUM AGREEMENT. 

  

  20. This AGREEMENT along with the GRANT AGREEMENT and CONSORTIUM 

  

  

AGREEMENT constitutes the entire agreement between the parties. This agreement
may be varied by exchange of letters between the parties. No variation or
extension to this AGREEMENT shall be binding upon either party unless in
writing and acknowledged and approved by authorised signatories of both
parties.

  21. This AGREEMENT may be executed in two or more counterparts, each of which will be deemed an original, but all of which together shall constitute one and the same AGREEMENT. The PROVIDER and RECIPIENT acknowledge that an original signature or a copy thereof transmitted by PDF shall constitute an original signature for the purposes of this AGREEMENT. 

  

  

###  (Signatures begin on the following page)

**AGREED** by the PROVIDER and RECIPIENT through their authorised
signatories:-

_For and on behalf of the**PROVIDER** _

Signed:

Name:

Title:

Date:

_For and on behalf of the**RECIPIENT** _

Signed:

Name:

Title:

Date:

_Acknowledged and understood by the_

###  _PROVIDER’s SCIENTIST_

Signed:

Date:

_Acknowledged and understood by the_

###  _RECIPIENT’s SCIENTIST_

Signed:

Date:

##  8\. DATA SHARING, CONFIDENTIALITY AND INFORMATION GOVERNANCE: IMPERIAL
COLLEGE AND UNITO

Data sharing will be governed by multilateral Data Transfer Agreements
(template attached). The MRC-PHE Centre for Environment and Health at Imperial
College, where Lifepath is coordinated, has a strict policy on ethics, data
management and confidentiality (attached). Any studies initiated from within
the Centre are subject to national/international ethical review procedures. As
part of the Centre's research, considerable quantities of data on individuals
are held and analysed. In doing so the Centre complies with the **Data
Protection Act 1998 (UK)** and processes that information in accordance with
the eight Data Protection Principles set out in the Act. The Centre’s staff
includes the Data Protection Coordinator for the School of Public Health who
is responsible for maintaining a register of datasets and advising on
compliance. All PIs in the Centre have to undergo "information governance
training" and obtain a certificate. All data, whether held electronically or
manually, are securely stored. These rules apply to all partners in Lifepath.
In addition, all Lifepath data will be stored at the **Unito Center**
(University of Torino) after anonymization.

**_IT Policies – UNITO_ **

The following IT policies apply to data generated within the Lifepath action
and stored on the UNITO-Epi computer infrastructure. Giuseppe Costa, Angelo
d’Errico, and a to be defined person, have user accounts with extended rights
on the UNITO-Epi server and will need to obtain user accounts with extended
rights on the FTPS server at Imperial College for standard use and data
management purposes.

###  Logical User Access Rights and Identity Management

Each person who has access to the UNITO-Epi server has a unique username and
login credentials to access the server. This information is managed by
Microsoft Active Directory. Non-IT personnel are limited to their own login
and do not have administrative access to the server. Password requirements are
implemented and each user must change his/her password regularly. Failure to
do so results in lockout from the network. All administrative tasks (access
rights, account revocation, etc.) are performed by UNITO-Epi’s IT department.
Periodic review of logical access rights is done to ensure that the rights are
enforced over time.

###  Network Security (WAN/LAN)

The UNITO-Epi network is separated into two distinct segments: internal (non-
public) and external (Regional public administration network: Rupar). The
external network is composed of fiber channel access to Rupar network. Only
computers of the UNITO-Epi network have the ability to connect to the external
network No personal device can connect to the external network. Both networks
(internal & external) are protected by redundant firewalls. Internal switches
and routers are inaccessible by regular users, are password-protected and can
only be managed internally by IT personnel. Periodic review of firewall logs
is performed. No remote desktop access is allowed. Administrator/Root
passwords are changed on a periodic basis and are randomly generated
consisting of a minimum length, special and alphanumeric characters.

###  UNITO-Epi internal IT Acceptable Use Policy

Every UNITO-Epi employee has signed the internal IT Policy document ensuring
data security and protection for the company and its business partners. In
this document, the following activities are rated as strictly prohibited, with
no exceptions:

  * “Revealing your account password to others or allowing use of your account by others.” 

  

  * “Circumventing user authentication or security of any host, network or account.” 

  

  * “Distributing information deemed confidential by or under any agreement with UNITO-Epi or any agreement between UNITO-Epi and any other party.” 

  

###  Backup and Disaster Recovery

Three areas of concern in a disaster are data integrity, hardware availability
and physical infrastructure status. In the case of data integrity, data on the
server is tape-backed up once a month with incremental backups nightly.
Moreover, on the UNITO-Epi server is enabled daily the “shadow copy” service.
Tape backups are off-site in secure, fireproof locations. Server restoration
is possible and periodic testing of system restores including data recovery is
performed to ensure hardware and data integrity. The server is under service
contract with an external company for its lifespan. A comprehensive impact
analysis and risk assessment has been performed.

###  Data Exchange

Typically customers of UNITO-Epi provide their data to us in one of the
following ways:

  * Via secure HTTP (HTTPS) server 

  

  * Via secure FTP (FTPS) server 

  

  * Hand-delivered in person 

  

In all cases, the data is only handled by IT-personnel or the Project IT
Policies.