analysis and results of the survey of grid...

International Cooperative Action on Grid Computing and Biomedical Informatics

between the

European Union, Latin America, the Western Balkans and North Africa

www.vph-action-grid.eu

Analysis and Results of the Survey of Grid

Initiatives

Deliverable 2.2

v 1.7

Authors (affiliation): Adriana Dawidoski (HIBA)

Ana Maria Gomez Saldaño (HIBA)

Danilo Gonzalez (UTALCA)

Daniel Luna (HIBA)

Diana de la Iglesia (UPM)

Paula Otero (HIBA)

Silvana Figar (HIBA)

Sergio Guiñez Molinos (UTALCA)

Stefano Chiesa (UPM)

Sonia Elizabeth Benitez (HIBA)

Tomas Perez-Acle (UTALCA)

Project Reference: 224176

Call: FP7-ICT-2007-2

Submission Date: 26/05/2009

http://www.vph-action-grid.eu/

FP7-ICT-224176

Del. 2.2 – Analysis and Results of the Survey of Grid Initiatives

WP2: Survey of Grid initiatives-Resourceome

Level: PU Version: v1.7

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Table of Contents

1. INTRODUCTION ......................................................................................................... 6

1.1. Population ......................................................................................................... 6

1.2. Sampling Strategy ............................................................................................ 6

1.2.1. Sampling strategy based on convenience ................................................. 6

1.3. Survey Administration .................................................................................... 11

1.3.1. Instrument .............................................................................................. 11

1.4. Survey Design ................................................................................................ 12

1.4.1. Survey domain ....................................................................................... 12

2. RESULTS ..................................................................................................................... 15

2.1. Baselines Characteristics ................................................................................ 15

2.1.1. Geographical distribution ....................................................................... 15

2.1.2. Characteristics of the participants .......................................................... 16

2.1.3. Characteristic of research groups ........................................................... 17

2.1.4. Research area distribution ...................................................................... 19

2.2. Medical Informatics ........................................................................................ 21

2.3. Bioinformatics ................................................................................................ 28

2.4. High Performance and Grid Computing ......................................................... 37

2.5. NanoMedicine and Nanoinformatics. ............................................................. 43

3. DISCUSSION .............................................................................................................. 53


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3.2. Bioinformatics ................................................................................................ 55

3.3. GRID and High Performance Computing ...................................................... 58

3.4. Medical NanoInformatics ............................................................................... 61

4. REFERENCES ............................................................................................................ 65

5. ANNEX ......................................................................................................................... 71


5.2 Bioinformatics ................................................................................................ 84

5.3 High Performance and Grid Computing ....................................................... 103

5.4 Nanoinformatics ........................................................................................... 115

5.5 Optional Sections .......................................................................................... 127

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DOCUMENT HISTORY

Name Date Version Description

P. Otero (HIBA) ,

S. Figar (HIBA),

A. Dawidoski (HIBA),

A. M. Gomez Saldaño (HIBA),

S. E. Benitez (HIBA),

S. Chiesa (UPM)

27/04/09 v0.0 First Draft (Tables &

UPM Questions )

P. Otero (HIBA),

S. Figar (HIBA),



S. E. Benitez (HIBA)

29/04/09 v1.0 Document creation

S. Figar (HIBA),

P. Otero (HIBA) ,

D. Luna (HIBA),



S. E. Benitez (HIBA)

30/04/09 v1.1 Medical Informatics

Discussion

D. Gonzalez (UTALCA),

S. Guinez-Molinos (UTALCA),

T. Perez-Acle (UTALCA)

08/05/09 v1.2

NanoBioMedical

Informatics, Grid and

HPC Discussion

D. Gonzalez (UTALCA),

S. Guinez-Molinos (UTALCA),

T. Perez-Acle (UTALCA),


S. Chiesa (UPM)

12/05/09 v1.3 Edition

S. Chiesa (UPM),

D. de la Iglesia (UPM),

D. Gonzalez (UTALCA)

14/05/09 v1.4 Deliverable review

ACTION-Grid partners 16/05/09 v1.5 Deliverable review

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Name Date Version Description

Quality Committee 19/05/09 v1.6 Quality review

V. Maojo (UPM),

S. Chiesa (UPM),

D. de la Iglesia (UPM)

26/05/09 v1.7 Final Review

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1. INTRODUCTION

1.1. Population

Reference population: groups that work with BMI and Nanotechnology oriented to

Health Area in Latin American, North Africa, the Western Balkans and the European

Union.

Eligible Population: Research groups focused at least in one of the following areas:

Medical Informatics: Information technology applied to the Health field

Bioinformatics applied to the Medical field

Nanotechnology with current or potential applicability in healthcare.

Eligible population is the result of the combination of the following:

Working groups or centers (contacts) provided by the partners and the experts

of ACTION-Grid. This includes centers registered in National/International

Societies listed under the areas mentioned above.

Study population and analysis Unit: Research groups defined in the eligible

population, contacted by the partners of ACTION-Grid.

1.2. Sampling Strategy

1.2.1. Sampling strategy based on convenience

A convenience sampling method was applied.

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This sampling strategy aims to include relevant contacts for the survey, provided by

ACTION-Grid partners.

Research groups were invited by members of the ACTION-Grid consortium to

participate and answer the online survey. E-mail invitations to answer the survey were

sent to each eligible contact.

Countries without significant working groups and centers without relevant

publications in Medline in the involved areas in the last five years were not included in

the convenience list. In these countries it was assumed that no research is being carried

out and thus, they are not eligible to fill the survey. (*)

(*) Search Strategy:

Research groups are defined as those that have published at least 1 paper in the last 5

years. For searching purposes, the affiliation of the main author of each publication

was considered independently of the number and different affiliations of the rest of the

authors. Medline was used for this purpose and the following MeSH terms have been

included:

Informatics (Dental Informatics, Medical Informatics, Nursing Informatics,

Public Health Informatics)

Bioinformatics

Nanotechnology (it includes Nanoinformatics), Nanostructures and

Macromolecular Substances.

Contacts were stratified according to the following geographic areas:

Conglomerate A: European Union

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Conglomerate B: Latin America

Conglomerate C: North Africa

Conglomerate D: Western Balkans

Conglomerate A

Countries

European

Union

(area number 1)

1 Federal Republic of Germany

2 Republic of Austria

3 Republic of Bulgaria

4 Belgium

5 Republic of Cyprus

6 Denmark

7 Slovak Republic

8 Republic of Slovenia

9 Spain

10 Republic of Estonia

11 Finland

12 French Republic

13 Greece

14 Republic of Hungary

15 Ireland

16 Italy

17 Republic of Latvia

18 Republic of Lithuania

19 Luxembourg

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Countries

20 Republic of Malta

21 Netherlands

22 Republic of Poland

23 Portugal

24 United Kingdom

25 Czech Republic

26 Romania

27 Kingdom of Sweden

Conglomerate B

Countries

Latin America

(area number 2)

1 Argentina

2 Brazil

3 Chile

4 Colombia

5 Costa Rica

6 Cuba

7 México

8 Panamá

9 Uruguay

10 Bolivia

11Dominican Republic

12 Ecuador

13 Salvador

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Countries

14 Guatemala

15 Haiti

16 Honduras

17 Nicaragua

18 Paraguay

19 Peru

20 Venezuela

Conglomerate C

North Africa

(area number 3)

Countries

1 Lebanon

2 Algeria

3 Egipt

4 Morocco

5 Tunisia

6 Sudan

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Conglomerate D

Western Balkans

(area number 4)

Countries

1 Albania

2 Bosnia and Herzegovina

3 Republic of Macedonia

4 Croatia

5 Serbia

6 Montenegro

1.3. Survey Administration

1.3.1. Instrument

The survey was administrated via WEB (http://fragaria.utalca.cl/actiongrid/).

At least one e-mail invitation to answer the survey was sent to each eligible person.

As strategy to enhance the rate of response to the survey, the information in the web

page and the invitation mails included:

Goals and purposes of the survey.

Current state of the art.

More than one e-mail was sent, depending on the expert consideration.

An agreement was signed in the web page before answering the survey. This

agreement enhances confidentiality and ethical issues.

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Data Management of the survey database

Data was analyzed in a centralized way.

The response rate was defined according to the number of countries that answered the

survey (with at least 1 survey answer by country), by number of countries that are

currently doing research in Medical Informatics, Biomedical Informatics or Medical

Nanoinformatics (with at least 1 publication indexed in PubMed in the last 5 years).

1.4. Survey Design

The survey contains the following domains:

1.4.1. Survey domain

1.4.1.1. Characterization of the research group:

a. People interviewed and Institution contact data.

b. Characterization of interviewed people (title, age, sex, etc.)

c. Type of institution.

d. Dimension and activities of research group (type and number of human

resources, projects and research lines, etc)

e. Identification of the main area/s of research: Medical Informatics,

Bioinformatics, HPC & Grid Computing, and Nanoinformatics.

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1.4.1.2. Characterization of the current situation and resources of the

research group:

f. Years of experience in the research area.

g. Type of research topics within the area of research.

h. For Medical Informatics, Bioinformatics and Nanoinformatics.

i. Used and developed software (e.g. applications and tools).

ii. Kind of data (data sources), used and developed.

iii. Taxonomies/ontologies, used and developed.

i. Specific for Grid

i. Current computational resources and equipments.

ii. Current shared Grid resources

iii. Years of experience in specifics subareas of Grid computing

1.4.1.3. Needs of Research group needs:

j. Identification of development plans: Medical Informatics,

Bioinformatics, HPC & Grid Computing, and Nanoinformatics

k. For Medical Informatics, Bioinformatics and Nanoinformatics

i. Types of applications, software, services and tools needed

ii. Data types needed (data sources)

iii. Needed taxonomies/ontologies.

l. Specific for Grid

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i. Planned uses of Grid: needed resources, resources to offer.

ii. Needed equipment and technology.

iii. Technical, legal and political barriers for joining a Grid

computing infrastructure.

1.4.1.4. Human Resources and Mobility (Optional section)

m. Human resources needs.

n. Needs for mobility (quantitatively and qualitatively)

o. Barriers and expected impacts of the mobility.

1.4.1.5. Training Needs (Optional section)

p. Needs of training (quantitatively and qualitatively)

q. Barriers and expected impacts of training

1.4.1.6. Current hardware resources (Optional section)

r. Processing capacity

s. Storage capacity

t. Interconnection capacity

u. Administration capacity

v. Main hardware needs

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2. RESULTS

A total of 702 research groups from 59 countries were invited at least once by e-mail

to answer the survey on the web page. Luxemburg, Republic of Malta, Panamá,

Uruguay, Bolivia, Dominican Republic, Ecuador, Salvador, Guatemala, Haiti and

Honduras countries were not considered, as there were no research significant groups

either in the contact mailing or in the PubMed search. Thus these countries were

removed from the denominator and forty eight countries remained for the rate

construction. Researchers from twenty-nine countries answered the Survey. The global

response rate was, considering the countries, 60,4% (29/48). For the European Union

was 52 % (13/25), 72 % (8/11) for Latin America, 66,7% (4/6) for West Balkans and

50% (3/6) for North Africa.

2.1. Baselines Characteristics

2.1.1. Geographical distribution

A total of 96 investigators from 48 countries answered the survey. The majority were

from European Union (n 39), Latin American (n 33) and Western Balkans (n 15).The

following figure shows the world distribution according to the number of participants.

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Fig. 1 – Geographical distribution of participants

2.1.2. Characteristics of the participants

Participants had a mean age of 43 years (Standard Deviation 9). 84% were male, 59%

PhD, 44% seniors and 69% were mainly working at a public institution. The

characteristics according to geographical area are shown in table 1.

Table 1: Characteristics of participants.

Global

n=96

European

Union

n= 39

Latina

American

n=33

North Africa

n=9

Western

Balkans

n=15

Age years

(X) (DS) 43 (9,1) 44,6 (9,7) 41,9 (7,17) 39,11 (10, 3) 45, 7 (10,4)

Sex

Male, n (%) 81 (84,4) 35 (89,7) 28 (84,8) 6 (66,7) 12 (80)

Title

PhD, n (%) 57 (59,4) 26 (66,6) 19 (57,6) 7 (77,8) 5 (33,3)

Master, n (%) 21 (21,9) 7 (17,9) 7 (21,2) 1 (11,1) 6 (40)

Medial Doctor, 10 (10,4) 2 (5,1) 4 (12,1) 1 (11,1) 3 (20)

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Global

n=96

European

Union

n= 39

Latina

American

n=33

North Africa

n=9

Western

Balkans

n=15

n (%)

BSc, n (%) 1 (1) 1 (2,6 ) 0 0 0

Other, n (%) 7 (7,3) 3 (7,7) 3 (9,1) 0 1 (6,7)

Rank

Director,n (%) 29 (30,2) 12 (30,8) 12 (36,4) 1 (11,1) 4 (26,7)

Administrator,

n (%) 4(4,2) 3 (7,7) 1 (3) 0 (0) 0

Senior, n (%) 42 (43,8) 17 (43,6) 14 (42,4) 5 (55,6) 6 (40,6)

Junior

researcher,

n (%)

8 (8,3) 1 (2,6) 4 (12,1) 2 (22,6) 1 (6,7)

Other, n (%) 12 (12,5)

Institution type,

n (%)

Public, n (%) 66 (68,8) 30 (76,9) 21 (63,6) 6 (66,7) 9 (60)

Private (not for

profit), n (%) 10 (10,4) 1 (2,6) 8 (24,2) 1 (11,1) 0

Private, n (%) 15 (15,6) 7 (17,9) 4 (12,1) 1 (11,1) 3 (20)

2.1.3. Characteristic of research groups

All the research groups are currently involved in scientific research (basic, applied,

product development, technology development). 96 percent of the groups are

investigating in health domain, with a median number of researchers of 10 (QI 5-15).

Most of the groups are composed by few researchers (77% “up to 5 PhDs” and 50%

“up to 5 non PhDs”), No difference among geographical areas was found.

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The most frequent medical areas, in which groups are working, are shown in table 2.

Table 2: Medical areas in which research is being developed by the participants.

Global

n=96

European

Union

n= 39

Latina

American

n=33

North

Africa

n=9

Western

Balkans

n=15

Biomedical

Engineering,

n (%)

23 (24) 12 (33,3) 4(12) 3(33,3) 3(20)

Biotechnology,

n (%) 23 (24) 8 (20,5) 10 (30,3) 3 (33,3) 2 (13,3)

Templates, n (%) 21 (21,8) 11 (28,2) 7 (21,2) 1 (11,1) 2 (13,3)

Genome, n (%) 19 (19,8) 10 (25,6) 7 (21,2) 2 (22,2) 0

Biochemistry,

n (%) 15(15,6) 7(21) 7(17) 0 1(6,6)

Cardiovascular

Physiology,

n (%)

14 (14,6) 9 (23,1) 1 (3) 1 (11,1) 3 (20)

Health Services,

n (%) 12 (12,5) 3 (7,7) 4 (12,1) 1 (11,1) 4 (26,6)

Biology, n (%) 12(12,5) 4(10,3) 4(12,1) 2(22) 2(13,3)

Gene Library,

n (%) 11 (11,5) 6 (15,4) 4 (12,1) 1 (11,1) 0

Biophysics, n (%) 11 (11,5) 5 (12,8) 4 (12,1) 0 (0) 2 (13)

Radiology, n (%) 10 (10,4) 9 (23) 0 1 (11,1) 0

Evidence, n (%) 9 (9,4) 5 (12,8) 2 (6,1) 0 2 (13,3)

In the European Union, 58 projects had public funding (among them, 12 projects

belong to a single research group), 28 in Latin America (range of number of projects

per group from 0 to 5), 2 in North Africa and 7 in Western Balkans. On the other hand,

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14 projects had private financing in the European Union, 16 in Latin America, 2 in

North Africa, 11 in Western Balkans.

Finally, 17 groups in the European Union, 17 groups in Latin American, 6 in North

Africa and 9 in Western Balkans did not have any kind of funds, neither public nor

private.

Projects are described in table 3.

Table 3: Kind of projects (collaborative, website or other) that are being

developed.

Global European

Union

Latina

American

North

Africa

Western

Balkans

Collaborative

Projects, n (%) 85 (88,5) 36 (92,3) 29 (87) 7 (77,8) 13 (86,7)

Website, n (%) 19 (19,8 ) 4 (10,3) 7 (21,2) 2 (22,2) 6 (40)

Others, n (%) 19 (19,8) 8 (20,5) 7 (21,2) 1 (11,1) 3 (20 )

No group referred to have any project results that have been patented or registered.

Sixty percent of the institutions have an Ethical Committee. A similar proportion was

found in all the geographical areas.

2.1.4. Research area distribution

From those who answered the survey 55 (57%) referred to carry out research in

Medical Informatics (MI), 42 (48%) in Bioinformatics (BI), 6 (6%) in Medical

Nanoinformatics (MNI) and 29 (30%) in Grid and High Performance Computing

(GHPC).

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Considering the four areas (MI, NMI, BI and GHCP), the following graph shows the

percentage of groups that are involved in one research area, in two, in three or in all

these areas.

Number Areas in which groups are involved

number of areas (MI, BMI,MNI, GHPC)

4321

Fre

cu

en

cy

gro

up

s (

%)

inv

olv

ed

70

60

50

40

30

20

10

0

Fig. 2 - Percentage of research groups according to the number of research areas

where they work.

60% referred to need resources to improve research in Medical Informatics (MI), 52%

in Bioinformatics (BI), 6% in Medical Nanoinformatics (MNI) and 37% in Grid and

High Performance Computing (GHPC).

The following graph shows the number of groups who referred to have needs in one,

two or more areas.

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Needs in Investigation Areas

Number of Investigation Areas (MI,BMI,MNI,GHPC)

Four Areas

Three Areas

Two Areas

One Area

Fre

cu

en

cy

Gro

up

s w

ith

ne

ed

s

60

50

40

30

20

10

0

Fig. 3 - Number of groups with needs in one, two or more areas

2.2. Medical Informatics

The areas where most groups are conducting research are Health Information Systems,

Imaging or biological signal data processing and Clinical Decision Support System.

However we found some geographical differences.

In average, sixty percent of the groups referred to carry out research projects in Health

Information Systems (HIS). Latin America is the geographical area with the highest

percentage of groups working in HIS. However, the difference in percentage of groups

working in this field Latin America is not considerably higher compared with the

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European Union, North Africa and the Western Balkans countries. (73% vs. 54%, 40%

and 61% respectively; p 0.523).

In spite of lack of statistical significance, imaging or biological signal data processing

was found to be the area more frequently investigated among groups from North

Africa, compared to the global measure. (60% vs. 38%; p 0.385).

Western Balkans had the highest percentage of groups investigating in education.

(46% vs. 33% ; p 0.523)

Groups from the European Union usually carry out research evenly distributed in most

of the topics mentioned above.

Table 4 shows the frequency of research areas in Medical Informatics according to

geographical areas.

Table 4: Frequencies of Research Areas by Geographical Areas

Global

n=57

European Union

n=24

Latin America

n=15

North Africa

n=5

Western

Balkans n=13

Health Information

Systems, n (%) 34 (59,6) 13 (54,2) 11 (73,3) 2 (40) 8 (61,5)

Public Health, n (%) 12 (21,1) 3 (12,5) 5 (33,3) 0 4 (30,8)

Imaging or biological

signal data processing, n

(%)

22 (38,6) 11 (45,8) 6 (40) 3 (60) 2 (15,4)

Clinical Decision Support

System, n (%) 22 (38,6) 11 (45,8) 7 (46,7) 0 4 (30,8)

Nursing, n (%) 2 (3,5) 0 0 1 (20) 1 (7,7)

Interoperability, n (%) 18 (31,6) 7 (29,2) 7 (46,7) 2 (40) 2 (15,4)

Telemedicine, n (%) 20 (35,1) 8 (33,3) 6 (40) 2 (40) 4 (30,8)

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Global

n=57

European Union

n=24

Latin America

n=15

North Africa

n=5

Western

Balkans n=13

Organization and

Management, n (%) 14 (24,6) 4 (16,7) 5 (33,3) 0 5 (38,5)

Education, n (%) 19 (33,3) 7 (29,2) 5 (33,3) 1 (20) 6 (46,2)

Standardization, n (%) 12 (21,2) 6 (25) 3 (20) 1 (20) 2 (15,4)

Other, n (%) 5 (8,8) 1 (4,2) 2 (13,3) 0 2 (15,4)

Table 5 shows that the majority of the groups, except those from Latin American, are

doing research for more than ten years. This seems to be correlated with the presence

of a Health Information System (HIS) at the institution where the groups do research,

as it is shown in table 6. Latin American groups are more involved in the HIS

development. (Table 7)

Table 5: Time from when groups are doing research.

Global

n=57

European

Union

n=24

Latin America

n=15

North

Africa

n=5

Western

Balkans

n=13

Less than 5 years, n (%) 13 (22,8) 3 (12,5) 8 (53,3) 0 2 (15,4)

5-10 years, n (%) 16 (28,1) 5 (20,8) 4 (26,7) 2 (40) 5 (38,5)

More than 10 years, n (%) 23 (40,4) 14 (58,3) 2 (13,3) 3 (60) 4 (30,8)

Table 6: Presence of Health Information System (HIS) at the institution.

Global

n=57

European Union

n=24

Latin America

n=15

North Africa

n=5

Western

Balkans

n=13

Yes, n (%) 17 (29,8) 5 (20,8) 7 (46,7) 2 (40) 3 (23,1)

No, n (%) 35 (61,4) 17 (70,8) 7 (46,7) 3 (60) 8 (61,5)

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Table 7: Group participation in the institutional HIS development.

Global

n=57

European

Union

n=24

Latin

America

n=15

North Africa

n=5

Western

Balkans

n=13

Yes, n (%) 29 (50,9) 10 (41,7) 11 (73,3) 3 (60) 5 (38,5)

No, n (%) 23 (40,4) 12 (50) 3 (20) 2 (40) 6 (46,2)

In table 8, the percentages of MI groups working with different kind of data are

shown. European groups had the highest proportions for Genomic and proteomic data.

Only few groups were using Metabolomic and Nanotechnology data. Clinical Data is

globally the most used.

Table 8: Kind of data used for research.

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western Balkans

n=13

Clinical Data, n (%) 40 (70,2) 17 (70,8) 13 (86,7) 2 (40) 8 (61,5)

Biological signal Data,

n (%) 17 (29,8) 9 (37,5) 4 (16,7) 0 4 (30,8)

Genomic Data, n (%) 17 (29,8) 11 (45,8) 3 (20) 0 3 (23,1)

Proteomic Data, n (%) 9 (15,8) 5 (20,8) 2 (13,3) 0 2 (15,4)

Metabolomic Data, n (%) 5 (8,8) 1 (4,2) 1 (6,7) 1 (20) 2 (15,4)

Molecular structure Data, n

(%) 6 (10,5) 1 (4,2) 1 (6,7 1 (20) 3 (23,1)

Imaging Data, n (%) 21 (36,8) 13 (54,2) 1 (6,7) 4 (80) 3 (23,1)

Nanotechnology data, n (%) 1 (1,8) 0 0 0 1 (7,7)

Laboratory data, n (%) 19 (33,3) 11 (45,8) 4 (26,7) 0 4 (30,8)

Public Health data, n (%) 16 (28,1) 5 (20,8) 5 (33,3) 2 (40) 4 (30,8)

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Public and Private data were mainly applied for investigation in all geographical areas,

while commercial sources were less used.

Table 9: source/s of data used in research.

Global

n=57

European

Union

n=24

Latin

America

n=15

North Africa

n=5

Western

Balkans

n=13

Public Domain, % 36 (63,2) 14 (58,3) 9 (60) 3 (60) 10(76,9)

Private, % 31 (54,4) 15 (62,5) 7 (46,7) 4 (80) 5 (38,5)

Commercial, % 7 (12,3) 2 (8,3) 0 2 (40) 3 (23,1)

Approximately 60% of the research efforts in Medical Informatics generate new data.

This was similar for all groups and it was mainly Clinical Data (31.6%). Sixty percent

of the groups referred to have not created any taxonomies and/or ontologies, derived

from their research on Medical Informatics. Those who did create taxonomies and/or

ontologies, referred mainly to have done it in Anatomy.

Table 10: New data generated from research.

Global

n=57

European Union

n=24

Latin America

n=15

North Africa

n=5

Western

Balkans n=13

Yes, % 32 (56,1) 14 (58,3) 9 (60) 3 (60) 6 (46,2)

No, % 19 (33,3) 7 (29,2) 5 (33,3) 2 (40) 5 (38,5)

The type of data mainly needed for research purpose was Clinical data. Other kinds of

needed data are expressed in table 11.

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Table 11: Data needed for research purpose.

Global

n=51

European

Union

n=21

Latin America

n=14

North Africa

n=4

Western

Balkans

n=12

Clinical Data, n (%) 37 (72,5) 14 (66,7) 11 (78,6) 4 (100) 8 (66,7)

Biological signal Data, n (%) 20 (39,2) 10 (47,6) 4 (28,6) 2 (50) 4 (33,3)

Genomic Data, n (%) 17 (33,3) 10 (47,6) 2 (14,3) 1 (25) 4 (33,3)

Proteomic Data, n (%) 10 (19,6) 4 (19) 3 (21,4) 1 (25) 2 (16,7)

Metabolomic Data, n (%) 6 (11,8) 2 (9,5) 2 (14,3) 1 (25) 1 (8,3)

Molecular structure Data, n (%) 4 (7,8) 1 (4,8) 0 1 (25) 2 (16,7)

Imaging Data, n (%) 23 (45,1) 11 (52,4) 4 (28,6) 3 (75) 5 (41,7)

Nanotechnology data, n (%) 3 (5,9) 1 (4,8) 0 1 (25) 1 (8,3)

Public Health data, n (%) 23 (45,1) 10 (47,6) 4 (28,6) 2 (50) 7 (58,3)

Needs related to Health Information Systems are shown in table 12. Those groups that

referred not to have a HIS and not having the need are 50% of the European and North

Africa groups. Fifty percent of Latin American groups referred to have a HIS but they

need to add new services.

Related to Information Systems, Decision Support Systems (Clinical and Management

ones) and Clinical Laboratory Information Systems are mostly needed; while related

to Telemedicine, Remote consultation is the mostly needed.

Table 12: About Health Information System (HIS)

Global

n=51

European

Union

n=21

Latin

America

n=14

North Africa

n=4

Western

Balkans

n=12

my group does not have a

HIS and does not need it, n

(%)

19 (37,5) 11 (52,4) 2 (14,3) 2 (50) 4 (33,3)

my group does not have a 9 (17,6) 3 (14,3) 3 (21,4) 0 3 (25)

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Global

n=51

European

Union

n=21

Latin

America

n=14

North Africa

n=4

Western

Balkans

n=12

HIS but it needs it, n (%)

my group has a HIS but we

need to add new services, n

(%)

14 (27,5) 4 (19) 7 (50) 1 (25) 2 (16,7)

my group has a HIS but we

do not need to modify it, n

(%)

7 (13,7) 2 (9,5) 2 (14,3) 1 (25) 2 (16,7)

Among those who referred to need development in Taxonomies and Ontologies (half

of the groups) table 13, shows the mostly needed.

Table 13: Description of needed Taxonomies and Ontologies

European Union Latin America North Africa Western Balkans

ACGT Master Ontology

Gene Ontology

OWL

ICD-10

Clinical data

Any that may be applied

in Clinical DSS

Cardiology and

angiology: anatomy,

diseases, processes

Stomatology

Cardiovascular

SNOMED - MedDRA

SNOMED CT

Snomed as taxonomy

Dicom pathology wg-26

standards

Special Materials

(Orthesis and Prosthesis)

Loinc

ICD

Telehealth

Also localized on Croatian

General medical ontologies

Specialized clinical ontologies

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Clinical data

Ontology engines for the

creation of ontologies

Public health

Physiology, anatomy

2.3. Bioinformatics

Current Resources for Bioinformatics

This section of the survey wanted to determine the impact of bioinformatics along the

selected regions. As seen on Table 14, sequence analyses are the most common assays

performed. This tendency is maintained when reviewing at the region level. In

accordance to this kind of analyses and the requirements they produce, data mining is

the second most common type of study among researchers. It is interesting to note, the

scarce number of groups doing research in organism modelling and complex systems

outside the European Union (at least within the sample).

Table 14. Bioinformatics areas in which groups perform research.

Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Sequence analysis, n (%) 24 (51,1) 10 (52,6) 10 (52,6) 3 (50) 1 (33,3)

Image processing, n (%) 5 (10,6) 1 (5,3) 1 (5,3) 2 (33,3) 1 (33,3)

Genomic Annotation, n (%) 13 (27,7) 6 (31,6) 5 (16,7) 1 (33,3) 1 (33,3)

Molecular Simulation,n (%) 14 (29,8) 6 (31,6) 6 (31,6) 1 (16,7) 1 (33,3)

Computer aided drug 9 (19,1) 2 (10,5) 5 (26,3) 1 (16,7) 1 (33,3)

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Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

design, n (%)

Systems biology, n (%) 10 (21,3) 5 (26,3) 3 (15,8) 1 (16,7) 1 (33,3)

Organism modelling, n (%) 3 (6,4) 2 (10,5) 0 0 1 (33,3)

Comparative genomics,

n (%) 11(23,4) 3 (15,8) 5 (26,3) 2 (33,3) 1 (33,3)

Metabolic reconstruction, n

(%) 6 (12,8) 2 (10,5) 3 (15,8) 0 1 (33,3)

Molecular phylogeny,

n (%) 11 (23,4) 4 (21,1) 5 (26,3) 1 (16,7) 1 (33,3)

Functional genomic,

n (%) 14 (29,8) 6 (31,6) 6 (31,6) 1 (16,7) 1 (33,3)

Gene regulation network,

n (%) 11 (23,4) 5 (26,3) 4 (21,1) 1 (16,7) 1 (33,3)

Proteomic,

n (%) 8 (17) 4 (21,2) 2 (10,5) 1 (16,7) 1 (33,3)

Molecular modelling,

n (%) 10 (21,3) 3 (15,8) 6 (31,6) 0 1 (33,3)

Databases,

n (%) 22 (46,8) 9 (47,4) 10 (52,6) 2 (33,3) 1 (33,3)

Complex systems,

n (%) 3 (6,4) 2 (10,5) 0 0 1 (33,3)

Data mining,

n (%) 19 (40,4) 10 (52,6) 6 (31,6) 2 (33,3) 1 (33,3)

Visualization,

n (%) 12 (25,5) 4 (21,1) 4 (21,1) 3 (50) 1 (33,3)

other

Chemogenom

ics

Farmacoinfor

matics

Experiment

al work to

support

simulations

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Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Chemoinform

atics

epigenetic

analysis

When analyzing how old are the groups that perform research in bioinformatics, Table

15 shows that the majority of the groups are between 5 to 10 years old. This is in

agreement with the recent development of the human genome project and their derived

sciences and technologies.

Table 15. Time performing research in bioinformatics

Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Less than 5 years, n (%) 13 (27,6) 5 (26,7) 6 (31,6) 2 (33,3) 0

5-10 years, n (%) 19 (40,4) 7 (36,8) 9 (47,4) 2 (33,3) 1 (33,3)

More than 10 years, n (%) 8 (17,2) 4 (21) 2 (10,5) 2 (33,3) 0

Missing, n (%) 7 (14,9) 3 (15,8) 2 (10,5) 0 2 (66,7)

The types of data used by researchers using bioinformatics tools are depicted in table

16. As expected, genomic data is the most abundant type, being followed by molecular

structure data. Interestingly, the European Union and North Africa, reported none

groups within the sample producing data for nanotechnology using bioinformatics

tools.

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Table 16. Types of data used by research in bioinformatics

Global

n=47

Europea

n Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Clinical Data, n (%) 14 (9,8) 9 (47,4) 3 (15,8) 1 (16,7) 1 (33,3)

Biological signal

Data, n (%) 13 (27,7) 5 (26,3) 4 (21,1) 3 (50) 1 (33,3)

Genomic Data, n (%) 28 (59,6) 12 (63,2) 11 (57,9) 4 (66,7) 1 (33,3)

Proteomic Data, n

(%) 13 (27,7) 6 (31,6) 3 (15,8) 3 (50) 1 (33,3)

Metabolomic Data,

n (%) 10 (21,3) 4 (21,1) 4 (21,1) 1 (16,7) 1 (33,3)

Molecular structure

Data, n (%) 16 (34) 3 (15,8) 10 (52,6) 2 (33,3) 1 (33,3)

Imaging Data, n (%) 7 (14,9) 3 (15,8) 1 (5,3) 2 (33,3) 1 (33,3)

Nanotechnology data,

n (%) 4 (8,5) 0 3 (15,8) 0 1 (33,3)

Public Health data, n

(%) 4 (8,5) 1 (5,3) 2 (10,5) 0 1 (33,3)

Other

qrt-PCR

data

Experimental

data from

XRR, SEM,

EDS, AFM,

VHRE

Table 17, shows the main sources of data being exploited by groups doing research in

bioinformatics. As expected, the main source is public data.

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Table 17. Data sources used for bioinformatics research

Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Public, n (%) 31 (33) 11 (57,9) 16 (84,2) 4 (66,7) 0

Private, n (%) 7 (7,30) 4 (21,1) 1 (5,3) 2 (33,3) 0

No, n (%) 2 (2,1) 1 (5,3) 0 0 1 (33,3)

Other We use Private

and Public data

The number of groups reporting to have developed bioinformatics tools is depicted in

Table 18. In accordance to the applied nature of bioinformatics, the majority of groups

have actually developed tools.

Table 18. Number of groups that developed bioinformatics tools

Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Yes, n (%) 28 (59,8) 14 (73,7) 11 (57,9) 2 (33,3) 1 (33,3)

No response, n (%) 6 (12,8) 3 (15,8) 2 (10,5) 0 1 (33,3)

As well as the majority of groups have developed tools (Table 18), the majority of

groups have also generated data. The distribution per regions is shown in Table 19.

Table 19. Number of groups that generated data by bioinformatics research

Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Yes, n (%) 25 (53,2) 8 (42,1) 14 (73,7) 2 (33,3) 1 (33,3)

No response, n (%) 8 (17) 3 (15,8) 3 (15,8) 0 2 (66,7)

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Bioinformatics needs

Table 20 shows the areas in which research in bioinformatics will be focused. Once

again, the majorities of the groups are interested in data mining and sequence analysis.

Table 20. Areas in which groups will perform bioinformatics research

Global

n=47

European

Union

n=19

Latin

America

n=17

North

Africa

n=6

Western

Balkans

n=5

Sequence analysis,

n (%)

18 (38,3) 8 (42,1) 6 (35,3) 3 (50) 1 (20)

Image processing,

n (%)

7 (14,9) 2 (10,5) 1 (5,9) 2 (33,3) 2 (40)

Genomic Annotation,

n (%)

11 (23,4) 4 (21,1) 5 (29,4) 0 2 (40)

Molecular Simulation,

n (%)

8 (17) 3 (15,3) 4 (23,5) 0 1 (20)

Computer aided drug

design, n (%)

10 (21,3) 4 (21,1) 3 (17,6) 1 (16,7) 2 (40)

Systems biology, n (%) 16 (34) 6 (31,6) 7 (41,2) 1 (16,7) 2 (40)

Organism modeling,

n (%)

4 (8,5) 1 (5,3) 1 (5,9) 1 (16,7) 1 (20)

Comparative genomics,

n (%)

8 (17) 1 (5,3) 3 (17,6) 1 (16,7) 3 (60)

Metabolic

reconstruction,

n (%)

6 (12,8) 1 (5,3) 3 (17,6) 1 (16,7) 1 (20)

Molecular phylogeny,

n (%)

6 (12,8) 1 (5,3) 4 (23,5) 0 1 (20)

Functional genomic,

n (%)

10 (21,3) 2 (10,5) 5 (29,4) 0 3 (60)

Gene regulation 12 (25,5) 6 (31,6) 4 (23,5) 1 (16,7) 1 (20)

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Global

n=47

European

Union

n=19

Latin

America

n=17

North

Africa

n=6

Western

Balkans

n=5

network, n (%)

Proteomic,

n (%)

6 (12,8) 3 (15,8) 1 (5,3) 0 2 (40)

Molecular modelling, n

(%)

9 (19,1) 3 (15,8) 5 (29,4) 0 1 (20)

Databases, n (%) 16 (34) 6 (31,6) 6 (35,3) 2 (33,3) 2 (40)

Complex systems, n (%) 6 (12,8) 3 (15,8) 0 1 (16,7) 2 (40)

Data mining, n (%) 21 (44,7) 7 (36,8) 7 (41,2) 3 (50) 4 (80)

Visualization, n (%) 14 (29,8) 6 (31,6) 3 (17,6) 3 (50) 2 (40)

other Large scale

global

health

promotion

systems

Analysis

tools:

Raman-

AFM, low

and high

Temperatu

re stage for

AFM

Data types needed to conduct research in bioinformatics are depicted in Table 21.

Table 21. Data types needed to conduct bioinformatics research

Global

n=47

European

Union

n=19

Latin

America

n=17

North

Africa

n=6

Western

Balkans

n=5

Clinical Data,

n (%)

21 (44,7) 11 (57,9) 5 (29,4) 2 (33,3) 3 (60)

Biological

signal, n (%)

Data

20 (42,6) 10 (52,6) 7 (41,2) 1 (16,7) 2 (40)

Genomic Data, 26 (55,3) 11 (57,9) 10 (58,8) 2 (33,3) 3 (60)

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Global

n=47

European

Union

n=19

Latin

America

n=17

North

Africa

n=6

Western

Balkans

n=5

n (%)

Proteomic Data,

n (%)

15 (31,9) 6 (31,6) 5 (29,4) 2 (33,3) 2 (40)

Metabolomic

Data, n (%)

10 (21,3) 2 (10,5) 6 (35,3) 1 (16,7) 1 (20)

Molecular

structure Data,

n (%)

14 (29,8) 5 (26,3) 6 (35,3) 1 (16,7) 2 (40)

Imaging Data,

n (%)

10 (21,3) 4 (21,1) 1 (5,9) 3 (5) 2 (40)

Nanotechnology

data, n (%)

4 (8,4) 0 2 (11,8) 1 (16,7) 1 (20)

Public Health

data, n (%)

10 (21,3) 4 (21,1) 2 (11,8) 1 (15,7) 3 (60)

Other Chemical

(chemogeno

mics) data)

In spite that the majority of the researchers produce bioinformatics tools (Table 18), a

very important fraction of the groups declare to need new bioinformatics tools, as can

be seen in Table 22.

Table 22. Bioinformatics tools requirements

Global

n=47

European

Union

n=19

Latin

America

n=19

North

Africa

n=6

Western

Balkans

n=3

Yes, n (%) 23 (48,9) 7 (36,8) 10 (58,8) 4 (66,7) 2 (66,7)

No response,

n (%)

8 (17) 4 (21,2) 2 (11,2) 1 (16,7) 1 (33,3)

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When analyzing the need for ontologies or taxonomies to conduct research in

bioinformatics, there is no global clear tendency.

Table 23. Number of groups that needs taxonomies to develop research in

bioinformatics

Global

n=47

European

Union

n=19

Latin America

n=17

North

Africa

n=6

Western

Balkans

n=5

Yes, n (%) 8 (17) 3 (15,8) 2 (11,8) 2 (33,3) 1 (33,3)

No response,

n (%)

8 (17) 4 (21,2) 2 (11,8) 1 (16,7) 1 (33,3)

Despite there is no clear tendency indicating the necessity of antologies or taxomines

(see table 23; 50% yes, 50% no), it is interesting to note that many efforts in this

direction were mentioned by groups doing research in bioinformatics, as can be seen

on table 24.

Table 24. Taxonomies and/or ontologies used by groups

European Union Latin America

North Africa

Western

Balkans

Genotype, Phenotype,

Disease,Symptoms/

GO/

GO, KEGGs, etc/

Gene ontologies/

GO, UML, MESH/

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2.4. High Performance and Grid Computing

Mainly, the questions in High Performance and Grid Computing (GHPC) areas in this

survey were focused to find out research groups that use GHPC in health care areas.

Specifically Medical Informatics, Bioinformatics and Nanoinformatics applied to

medicine.

In this scenario, and considering all the groups who answered the survey, only twenty

nine percent of the surveyed individuals referred to use GHPC in health care areas.

The European Union and Latin America have the major percentage of participation

(59% and 24% respectively) compared with North Africa and Western Balkans (7%

and 10% respectively).

The four main research areas where GHPC is used are; Software Developing (48%),

Image Processing (38%), Databases (34%) and Sequence Analysis (31%). These

results are shown in Table 25. The rest of the areas and percentages are in Annex 1.

Table 25. Main areas where High Performance and Grid Computing is used

Global

n=29

European

Union

n=17

Latin

America

n=7

North

Africa

n=2

Western Balkans

n=3

Sequence analysis,

n (%) 9 (31) 4 (23,5) 3 (42,9) 1 (50 1 (33,3)

Databases, n (%) 10 (34,5) 4 (23,5) 3 (42,9) 1 (50) 2 (66,7)

Image processing,

n (%) 11 (37,9) 9 (52,9) 1 (14,3) 0 1 (33,3)

Software developing,

n (%) 14 (48,3) 7 (41,2) 4 (57,1) 0 3 (100)

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Parallel computing and distributed computing are prevalently used as computational

resource for the most part of the research groups, both in EU and LA. 72% of the

European group surveyed use Grid Computing to solve health care computational

problem. Latin America has a 57% for the same goals. Table 26 shown results of the

computational resources available in each geographical region.

Table 26. Computational resources available in the groups

Global

n=29

European

Union

n=17

Latin

America

n=7

North

Africa

n=2

Western Balkans

n=3

Parallel computing

(Infiniband,

CrayLink,etc.),n (%)

14 (48,3) 9 (52,9) 2 (28,6) 1 (50) 2 (66,7)

GRID Computing,

n (%) 21 (72,4) 13 (76,5) 4 (57,1) 2 (100) 2 (66,7)

Distributed

computing (web

services or other not-

GRID system), n (%)

15 (51,7) 7 (41,2) 4 (57,1) 1 (50) 3 (100)

Multiprocessor

symmetric

computation systems

(SMP) , n (%)

11 (37,9) 7 (41,2) 2 (28,6) 0 2 (66,7)

Other Access to the

HellasGrid/

GPU/MPI

Cluster

Although a high percentage of groups are working with grid (Table 26), this

percentage drops considerably (Table 27) when the question is if they are willing to

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share it, especially in Latin America and Africa, in Medical Informatics and

Bioinformatics areas. (Annex 1).

Table 27. Computational resources available to share in a GRID

Global

n=29

European

Union

n=17

Latin America

n=7

North

Africa

n=2

Western

Balkans

n=3

Storage in Medical

Informatics, n (%)

6 (20,7) 4 (23,5) 0 0 2 (66,7)

Specific hardware in

Medical Informatics , n

(%)

3 (10,3) 2 (11,8) 0 0 1 (33,3)

Specific Software in

Medical Informatics , n

(%)

5 (17,2) 3 (17,6) 0 0 2 (66,7)

Storage in

Bioinformatics, n (%)

4 (13,8) 2 (11,8) 0 0 2 (66,7)

Computational power

in Bioinformatics, n

(%)

7 (24,1) 4 (23,5) 1 (14,3) 0 2 (66,7)



2 (6,9) 1 (5,9) 0 0 1 (33,3)



7 (24,7) 2 (11,8) 3 (42,9) 0 2 (66,7)

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Global

n=29

European

Union

n=17

Latin America

n=7

North

Africa

n=2

Western

Balkans

n=3

Storage in

Nanoinformatics, n (%)

2 (6,9) 0 1(14,3) 0 1 (33,3)

Computational power

in Nanoinformatics, n

(%)

2 (6,9 ) 1 (5,9) 0 0 1 (33,3)



3 (10,3) 1 (5,9) 1 (14,3) 0 1 (33,3)



3 (10,3) 1 (5,9) 1 (14,3) 0 1 (33,3)

Regarding the experience of the groups in each research areas, the distribution in

Europe is between 5 to 10 years (41,2%) being overtaken by the segment Less than 5

years (35,5%). In Latin America is concentrated in the segment less than 5 years

(85%) in GHPC. Western Balkans and North Africa are also equally distributed

between the two segments (Table 28).

Table 28. Experience in a High Performance Computing and GRID Computing

Global

n=29

Europea

n Union

n=17

Latin

America

n=7

North

Africa

n=2

Western Balkans

n=3

Less than 5 years, n

(%)

15 (51,7) 7 (41,2) 6 (85,7) 1 (50) 1 (33,3)

5-10 years, n (%) 10 (35,5) 7 (41,2) 0 1 (50) 2 (66,7)

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Global

n=29

Europea

n Union

n=17

Latin

America

n=7

North

Africa

n=2

Western Balkans

n=3

More than 10 years,

n (%)

3 (10,5) 2 (11,8) 1 (11,8) 0 0

No response, n (%) 1 (3,4) 1 (5,8) 0 0 0

The majority of the research groups have a data centre and know the main

characteristics of the equipment that they are currently using, such us type of

equipment and bandwidth (Annex 1 table 5.5, 5.6, 5.7). The areas with a major

development of computing centers are distributed in Europe, Latin America, Western

Balkans and North Africa, respectively (Table 29).

Table 29. Data Centre (Equipped room for servers)

Global

n=29

Europea

n Union

n=17

Latin

America

n=7

North

Africa

n=2

Western Balkans

n=3

Yes, n (%) 21 (72,4) 13 (76,5) 5 (71,4) 1 (50) 2 (66,7)

No response, n (%) 1 (3,4) 1 (5,9) 0 0 0

On the other hand, a high percentage of research groups manifest interest joining a

Grid Infrastructure (Annex 1. table 9.1). In Europe 86%, Latin America 87%, North

Africa and Western Balkans with a 100%. In the same way, the entire group manifest

interest to learn more about Grid technologies. Only the Europe does not show a

percentage over 50% related to the interest in this need.

Related to distribution of areas where the groups will join a Grid Infrastructure, some

groups manifest interest in all areas surveyed, except in Nanoinformatics. Thereby, the

main areas with resources to offer and share using Grid Infrastructure, both in Europe

Union and Latin America, are Medical Informatics and Bioinformatics (Annex 1 Table

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9.1.3). Moreover, if the research group has interest in joining a Grid Infrastructure, the

main needs in these areas (Medical Informatics and Bioinformatics) are computational

power (42% and 45% respectively) and storage (35% and 14% respectively). North

Africa and Western Balkans manifest the same intention, with respect to the areas and

needs to integrate and join to Grid.

In general, when asking each group of the surveyed areas about their most urgent

needs, the answers are diverse and distributed in the different areas. Many of surveyed

group manifested the necessity to increase their computational power. However, all

the groups showed the necessity to Develop Software, increase Human Resources and

access to Training. It is important to note that these needs in Latin America and North

Africa exceed in percentage the needs in European Union. (Table 30).

Table 30. Most needed by the research groups

Global

N=31

Europea

n Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

Increase computational

power, n (%)

24 (77,4) 15 (83,3) 6 (85,7) 2 (100) 1 (25)

Increase storage capacity, n

(%)

14 (45,2) 9 (50) 2 (28,6) 2 (100) 1 (25)

Increase the bandwidth, n

(%)

5 (16,1) 2 (11,1) 1 (14,3) 0 2 (50)

Develop software, n (%) 16 (51,6) 9 (50) 5 (71,4) 0 2 (50)

Human resources, n (%) 18 (58,1) 9 (50) 5 (71,4) 2 (100) 2 (50)

Training, n (%) 18 (58,1) 4 (44,4) 5 (71,4) 2 (100) 3 (75)

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2.5. Nanomedicine and Nanoinformatics.

This section presented the data that refers to the Nanomedical Informatics. While the

survey results may be indicative or even suggestive, we have to highlight the overall

low achieved response rate. The reason could be in targeting (or achieving) primarily

the (bio) medical informatics groups, not the Nanomedicine/Nanoinformatics groups,

or, as we suggested already at the original DoW and the ATR in Brussels, the

difficulty in obtaining a high response, given the large number of related surveys (with

a number of questions) that have been made during the last years in the EC context.

Also, Nanoinformatics area was the last one in our questionnaire and possibly some

groups did not completed the form.

In this survey, we will describe the development stage of nanomedicine and

nanoinformatics in four different geographical regions.

Only a 6% of the groups are working in projects related with Medical Nanoinformatics

(nano-medicine or nanoinformatics).

Table 31: Frequencies of Research Areas by Geographical Areas

Global

N=8

European

Union

N=2

Latin America

N=2

North

Africa

N=1

Western

Balkans

N=3

Implantable materials, n (%) 1 (12,5) 0 0 0 1 (33,3)

Implantable devices, n (%) 2 (25) 1 (50) 0 0 1 (33,3)

Surgical Aids, n (%) 1 (12,5) 0 0 0 1 (33,3)

Diagnostic tools, n (%) 2 (25) 0 0 1 (100) 1 (33,3)

Modelling and simulation, n (%) 3 (37,5) 1 (50) 1 (50) 0 1 (33,3)

Databases, n (%) 2 (25) 0 1 (50) 0 1 (33,3)

Understanding basic life

processes, n (%)

2 (25) 0 0 1 (100) 1 (33,3)

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Global

N=8

European

Union

N=2

Latin America

N=2

North

Africa

N=1

Western

Balkans

N=3

Molecule detection, n (%) 1 (12,5) 0 0 0 1 (33,3)

Filtering systems, n (%) 1 (12,5) 0 0 0 1 (33,3)

Signal transduction systems, n

(%)

1 (12,5) 0 0 0 1 (33,3)

Drug delivery systems, n (%) 2 (25) 0 0 0 2 (66,7)

Drug discovery, n (%) 2 (25) 0 0 0 2 (66,7)

Targeting systems, n (%) 2 (25) 0 0 0 2 (66,7)

Molecule based machineries, n

(%)

1 (12,5) 0 0 0 1 (33,3)

Nanosystems, n (%) 3 (37,5) 0 2 (100) 0 1 (33,3)

Quantum dots 3 (37,5) 0 2 (100) 0 1 (33,3)

Gold nanoparticles 2 (25) 0 1 (50) 0 1 (33,3)

Silica Nanoparticles 2 (25) 0 1 (50) 0 1 (33,3)

Lipoparticles 1 (12,5) 0 0 0 1 (33,3)

Micelles 1 (12,5) 0 0 0 1 (33,3)

Paramagnetic and

Superparamagnetic

nanoparticles

2 (25) 0 1 (50) 0 1 (33,3)

Fluorescent nanoparticles 1 (12,5) 0 0 0 1 (33,3)

Cubosomes 1 (12,5) 0 0 0 1 (33,3)

Dendrimers 2 (25) 0 1 (50) 0 1 (33,3)

DNA-nanoparticles 1 (12,5) 0 0 0 1 (33,3)

Fullerenes 1 (12,5) 0 0 0 1 (33,3)

Nanoshells 1 (12,5) 0 0 0 1 (33,3)

Nanotubes 2 (25) 0 1 (50) 0 1 (33,3)

Nanopores 2 (25) 0 1 (50) 0 1 (33,3)

Other No

response

No

response

No response No

response

No

response

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Table 31 showed that the number of groups working in this field is quite similar; 2

groups in the European Union, 2 in Latin America, 1 in North Africa and 3 in Western

Balkans, being this last one the most active and interested in almost all research areas

related to nanomedicine.

Some interesting geographical differences were found. The Western Balkans - and in

less proportion North Africa and European Union – are more interested in implantable

material, while in Latin America are more interested on systems at the molecular level

with potential pharmaceutical applications. Given the limited number of groups who

answered on Nanoinformatics, it is not possible to conclude whether a region has more

or less interest in any particular aspect of Nanoinformatics.

Table 32: Time performing research in nanotechnology

Global

N=8

European

Union

N=2

Latin America

N=2

North

Africa

N=1

Western

Balkans

N=3

Less than 5 years, n (%) 1 (12,5) 0 1 (50) 0 0

5-10 years, n (%) 4 (50) 1 (50) 1 (50) 0 2 (66,7)

More than 10 years,

n (%)

1 (12,5) 0 0 1 (100) 0

No response, n (%) 2 (25) 1 (50) 0 0 1 (33,3)

Table 32 shows the experience by geographical area. Despite the fact that

Nanoinformatics is a new area, all of them have more of 5 years experience in this

field, with the exception of Latin America, that shows 1 case under 5 years.

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Table 33: Tools developed for research in nanomedicine (or in developing

process)

Global

N=8

European

Union

N=2

Latin

America

N=2

North

Africa

N=1

Western

Balkans

N=3

yes, n (%) 5 (62,5) 1 (50) 2 (100) 0 2 (66,7)

No response,

n (%)

2 (25) 1 (50) 0 1 (100) 1 (33,3)

62,5% of the groups has developed tools for research in nanomedicine or are in

developing process.

It is interesting to observe that - despite the lack of statistical significance – it was

found in Modelling and Simulations a common field among the European Union,

Latin America and Western Balkans. Because of this, we understand that properties of

nanodevices at molecular level are a key knowledge to develop new applications in

nanomedicine (see Table 34).

Table 34: Tools for nanomedicine research

Global

N=8

European

Union

N=2

Latin

America

N=2

North

Africa

N=1

Western

Balkans

N=3

Implantable materials, n (%) 1 (12,5) 0 0 0 1 (33,3)

Implantable devices, n (%) 2 (25) 1 (50) 0 0 1 (33,3)

Surgical Aids, n (%) 1 (12,5) 0 0 0 1 (33,3)

Diagnostic tools, n (%) 0 0 0 1 (33,3) 1 (12,5)

Modelling and simulation, n

(%)

4 (50) 1 (50) 2 (100) 0 1 (33,3)

Databases, n (%) 2 (25) 0 1 (50) 0 1 (33,3)

Understanding basic life 1 (12,5) 0 0 0 1 (33,3)

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Global

N=8

European

Union

N=2

Latin

America

N=2

North

Africa

N=1

Western

Balkans

N=3

processes, n (%)

Molecule detection, n (%) 2 (25) 0 1 (50) 0 1 (33,3)


Signal transduction systems, n

(%)

2 (25) 0 1 (50) 0 1 (33,3)

Drug delivery systems, n (%) 2 (25) 0 0 0 2 (66,7)

Drug discovery, n (%) 2 (25) 0 0 0 2 (66,7)

Targeting systems, n (%) 2 (25) 0 0 0 2 (66,7)

Molecule based machineries, n

(%)

1 (12,5) 0 0 0 1 (33,3)

When it is asked about nanoinformatics, the groups associate this discipline to

sequence analysis (25%), comparative genomics (100%), metabolomic reconstruction

(100%) and molecular phylogeny (100%). Considering only the groups who answered

about nanoinformatics, most of them associate this discipline to sequence analysis

(25%), comparative genomics (100%), metabolomic reconstruction (100%) and

molecular phylogeny (100%).

This association between Nanoinformatics and genomics is probably related to reports

where nanotechnology is used as a tool for diagnostic, to improve personalized

medicine. The use of genomics pattern in the personalized medicine and how the

nanomedicine can improve the diagnostics and therapy strategies is probably one of

the most exciting challenges in the personalized medicine. Genomics and the

Nanomedicine should converge in the next generation of medical therapy (see Table

35).

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Table 35: Methods and Tools associated to nanoinformatics research.

Global

N=8

European

Union

N=2

Latin

America

N=2

North

Africa

N=1

Western

Balkans

N=3

Methods and or tools from others

areas

Sequence analysis, n (%) 2 (25) 0 0 1 (100) 1 (33,3)

Image processing , n (%) 4 (50) 1 (50) 1 (50) 1 (1009 1 (33,3)

Genomic Annotation, n (%) 2 (25) 0 0 1 (100) 1 (33,3)

Molecular Simulation, n (%) 5 (62,5) 1 (50) 2 (100) 0 2 (66,7)

Computer aided drug design, n (%) 1 (12,5) 0 0 0 1 (33,3)

Systems biology, n (%) 2 (25) 1 (50) 0 0 1 (33,3)

Organism modelling, n (%) 1 (12,5) 1 (50) 0 0 0

Comparative genomics, n (%) 8 (100) 2 (100) 2 (100) 1 (100) 3 (100)

Metabolic reconstruction, n (%) 8 (100) 2 (100) 2 (100) 1 (100) 3 (100)

Molecular phylogeny, n (%) 8 (100) 2 (100) 2 (100) 1 (100) 3 (100)

Functional genomic, n (%) 1 (12,5) 0 0 0 1 (33,3)

Gene regulation network, n (%) 1 (12,5) 0 0 0 1 (33,3)

Proteomic, n (%) 1 (12,5) 0 0 0 1 (33,3)

Molecular modelling, n (%) 2 (25) 0 1 (50) 0 1 (33,3)

Databases, n (%) 3 (37,5) 0 2 (100) 0 1 (33,3)

Complex systems, n (%) 1 (12,5) 0 0 0 1 (33,3)

Data mining, n (%) 1 (12,5) 0 0 0 1 (33,3)

Visualization, n (%) 2 (25) 0 1 (50) 0 1 (33,3)

Others:

XRR,SEM,

EDS/AFM/

STM/VHR

E

Specific nanomedicine tools, n (%)

Implantable materials, n (%) 2 (25) 0 0 1 (100) 1 (33,3)

Implantable devices, n (%) 3 (37,5) 1 (50) 0 1 (100) 1 (33,3)

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Global

N=8

European

Union

N=2

Latin

America

N=2

North

Africa

N=1

Western

Balkans

N=3

Surgical Aids, n (%) 2 (25) 0 0 1 (100) 1 (33,3)

Diagnostic tools, n (%) 2 (25) 0 0 1 (100) 1 (33,3)

Modelling and simulation, n (%) 3 (37,5) 1 (50) 1 (50)

0

1 (33,3)

Databases, n (%) 2 (25) 0 1 (50) 0 1 (33,3)

Understanding basic life processes,

n (%) 1 (33,3) 2 (100) 0 1 (50) 0

Molecule detection, n (%) 1 (12,5) 0 0 0 1 (33,3)


Signal transduction systems, n (%) 1 (12,5) 0 0 0 1 (33,3)

Drug delivery systems, n (%) 1 (12,5) 0 0 0 1 (33,3)

Drug discovery, n (%) 1 (12,5) 0 0 0 1 (33,3)

Targeting systems, n (%) 1 (12,5) 0 0 0 1 (33,3)

Molecule based machineries,

n (%) 1 (12,5) 0 0 0 1 (33,3)

Other

No

descripti

on

Table 36: kind of data does each group need for your research:

Global

N=11

European Union

N=3

Latin

America

N=3

Africa

N=1

Western

Balkans

N=4

Clinical Data, n

(%)

4 (36,4) 1 (33,3) 1 (33,3) 1 (100) 1 (25)

Biological

signals Data, n

(%)

4 (36,4) 2 (66,7) 1 (33,3) 1 (100) 0

Genomic Data, n

(%)

4 (36,4) 2 (66,7) 0 1 (100) 1 (25)

Proteomic Data, 1(9,1) 0 0 1 (100) 0

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Global

N=11

European Union

N=3

Latin

America

N=3

Africa

N=1

Western

Balkans

N=4

n (%)

Metabolomic

Data, n (%)

1(9,1) 0 0 1 (100) 0

Molecular

structure Data, n

(%)

3 (27,3) 0 1 (33,3) 1 (100) 1 (25)

Imaging Data, n

(%)

5 (45,5) 2 (66,7) 0 1 (100) 2 (50)

Nanotechnology

system data, n

(%)

3 (27,3) 1 (33,3) 1 (33,3) 1 (100) 0

Public Health

data, n (%)

4 (36,4) 1 (33,3) 1 (33,3) 1 (100) 1 (25)

Specific

Nanoparticles,

n (%)

5 (45,5) 0 2 (66,7) 1 (100) 2 (50)

Other, n (%) 2 (18,2) 0 0 1 (100) 1 (25)

Other

(description)

SIN

DESCRIPCI

ON

Table 36 shows that when it is asked about what kind of data does the group need, it is

find that there exists two areas as common pattern for these groups, which are; 1)

Clinical Data, Biological signals data and genomics data and 2) imaging data,

nanotechnology system data, public health data, among others. When it is asked about

some specific nanoparticle, only Latin America responds, showing clear interest on

systems at molecular level.

Table 37: Does your group need tools for research in some nanoinformatics area?

Global

N=11

European

Union

N=3

Latin

America

N=3

North Africa

N=1

Western

Balkans

N=4

Yes, n (%) 7 (63,6) 2 (66,7) 1 (33,3) 1 (100) 3 (75)

No response, n (%) 3 (27,3) 1 (33,3) 1 (33,3) 0 1 (33,3)

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Table 38: Does your group need any taxonomy or ontology for Nanoinformatics

research?

Global

N=11

European

Union

N=3

Latin

America

N=3

North Africa

N=1

Western

Balkans

N=4

Yes, n (%) 4 (36,4) 1 (33,3) 0 1 (50) 4 (36,4)

No response, n (%) 3 (27,3) 1 (33,3) 2 (66,7) 1 (25) 3 (27,3)

63,6 % of the groups needs tools for research in some nanoinformatics area (Table 37),

but only 36,4% of the groups need some taxonomy or ontology for nanoinformatics

research (Table 38), probably because the research groups are not related with this

kind of tools and in the current stage they are more interested on aspects and

properties at molecular level. This fact is another proof that currently it does not exist

a well-defined policy to concentrate information about nanotechnology applied in

medicine.

Regarding to human resources in nanomedicine, all groups are interested in mobility

and collaboration, but the lack of grants are the main difficulty for researched mobility

in all geographical areas, except in European Union (Table 39).

Table 39: Main difficulties for researchers´ mobility 1

Global European

Union

Latina

America

North

Africa

Western

Balkans

P

Lack of grants, n (%) 59 (70,2) 17 (54,8) 25 (83,3) 7 (77,8) 10 (71,4)

Lack of time*, n (%) 28 (33,3) 9 (29 ) 9 (30 ) 0 10 (71,4) 0, 3

Lack of interest of

other institutions, n

(%)

13 (15,5) 3 (9,7) 4 (13,3) 1 (11,1) 5 (35,7)

Communication 7 (8,3) 1 (3,2) 1 (3,3) 2 (22,2) 3 (21,4)

1 The sums of each column are greater than the total (84) because each interviewee had the possibility to

choose several options.

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Global European

Union

Latina

America

North

Africa

Western

Balkans

P

problems, n (%)

Institutional interests

conflict, n (%)

6 (7,1) 1 (3,2) 2 (6,7) 0 3 (21,4)

Politic interests

conflict*, n (%)

4 (4,8) 0 1 (3,3) 0 3 (21,4) 0,13

Discrimination

(Racial, religious,

…), n (%)

3 (3,6) 0 1 (3,3) 0 2 (14,3)

Other*, n (%) 4 (4,8) 0 1 (3,3) 0 3 (21,4) 0, 13

Total, n (%) 84 31 30 9 14

Description of other

problems

- Lack of

knowledge

regarding

other groups

- Our

group is

young

one and

just one

year old

- Visa regime

- Lack of

strategy of

interoperab.

- Education in

data and media

semantification

- Lack of

competence in

governmental

bodies

- Lack of

elasticity in

European views

about

innovations

(*) Significant differences between areas were founded

More details about the survey in Medical Nanoinformatics are available in Annex 1,

section 5.4, Nanoinformatics.

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3. DISCUSSION


The growth of Medical Informatics in the last decades has been demonstrated by the

growing number of research on areas that are directly connected with applied clinical

informatics, such as Health Information Systems [1, 2], Clinical Decision Support

Systems, [3] Imaging or Biological Signals processing [4] which are shown on the

results of the survey, although with geographical differences [5].

We have found that - in average - sixty percent of the groups included in the survey

carry out research projects on Health Information Systems; however there is a non

significant higher percentage of groups working in this field in Latin America

compared to the European Union, North Africa and Western Balkans. (73% vs. 54%,

40% and 61% respectively; p 0.523).

We have to point out that, although it does not show statistical significance, Imaging

or Biological Signal Data processing were the most frequently researched topics

among groups from North Africa, compared to the global measure (60% vs. 38%; p

0.385), denoting the vast amount of available data in electronic records to be

exploited.

In the field of Education in Medical Informatics, the Western Balkans had the highest

percentage of groups investigating in education (46% vs. 33%; p 0.523). The other

groups still have not developed an important amount of research on this field. In

relation with education and health information systems there is a need for further

research on teaching health informatics to healthcare professionals [6].

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All groups from the European Union do research distributed in all topics.

As expected, the number of research groups doing Medical Nanoinformatics is scarce,

with only a 6%. This fact denotes the novelty of this area, as well as the need to

develop a critical mass of scientists to produce impact on society.

The stage of development in the field of Medical Informatics for each region is shown

by the number of years dedicated to research on the topic. The majority of the groups,

except those from Latin American, had carried out research projects for more than ten

years. This seems to be correlated with the presence of a Health Information System

(HIS) at the institution [7] where the groups do research, as it is shown in table 6.

Clinical data is the most frequent type of data used in all the groups. The use of other

types of data is quite variable by region. In table 8, the percentages of MI groups

working with different type of data are shown. European groups had the highest

proportions for Genomic and proteomic data. Only few groups were using

Metabolomic and Nanotechnology data.

Public data and/or private own data from health information systems, is the main

source of information used for their investigations in all geographical areas; however

commercial sources were less used. Approximately 60% of research efforts in Medical

Informatics generates new data [8], mainly referred as Clinical Data (31.6%).

Terminology, ontologies and taxonomies are one of the challenges of Medical

Informatics and the processing of data for its applications [9-11]. The Unified Medical

Language System (UMLS) project coordinated by the US National Library of

Medicine, through its metathesaurus and semantic network with lexical applications

have tried to overcome problems caused by differences in terminology, but still cannot

fulfill all the groups needs [12]. We have found that sixty percent of the groups

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referred not to have created any taxonomies and/or ontologies, derived from their

research on Medical Informatics. Those who developed ontologies were mainly in the

field of Anatomy [13]. Half of the groups have referred they need development in

Taxonomies and Ontologies, table 13 shows the most needed.

Health Information Systems aim to provide support for the improvement of health care

systems by the integration of clinical, ancillary services and administrative systems

working not separately but as one [14]. The needs related to Health Information

Systems are shown in table 12. Those groups that referred that did not have a Health

Information Systems and does not need them are 50% of the European and North

Africa groups. This could be attributed to the fact that in some healthcare institutions,

academics that carry out research on the fields are not generally involved in the

institutions’ definition of implementing a Health Information Systems. On the other

hand, fifty percent of Latin American groups referred to have a Health Information

Systems but they need to add new services, these could be related in part that the

groups in this region due to the lack of human resources, the people involved in the

development of a HIS also are the ones that conduct the research projects related.

Since most HIS that are still under development need the addition of more

components, we have found that Decision Support Systems (Clinical and Management

ones) and Clinical Laboratory Information Systems are mostly needed; while related

to Telemedicine: Remote consultation is the most needed.

3.2. Bioinformatics

Bioinformatics can be defined as the use of computational tools to study, model and

predict the behavior of biological systems. When analyzing the data produced by the

majority of the groups that participated in the survey (Table 16), it is important to note

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the diverse nature of the analyzed data. As expected, most of the data comes from

genomics experiments, denoting the great development of this area around the world.

It is also interesting to note that there are a significant proportion of groups doing

research in topics related with molecular structure within Latin America. When

inquiring about nanotechnology data, none of the groups reported using this type of

data in the European Union.

When analyzing data composition and structure (Table 17) it is not surprising that the

majority if the groups were using public data. The number of private sources becomes

relevant only in the case of the researchers coming from the European Union.

The number of groups that developed tools for bioinformatics is shown in Table 18. In

accordance to the applied nature of bioinformatics, the majority of the surveyed

groups reported this kind of contributions. Probably due to the early stage of

bioinformatics in North Africa, none group reported development of tools in that

region.

Table 19 shows the number of groups that generate data by conducting bioinformatics

research. In spite that the majority of the groups have generated data, it is interesting

to note the high number of Latin America groups that contributed with data to the

scientific community.

The needs for conducting bioinformatics research are depicted in tables 20 to 24. As

seen in Table 20, the majorities of the groups are interested in data mining and

sequence analysis topics. However, Computer Based Drug Design and Systems

Biology appear as interesting fields for bioinformatics. It is worth noting that there are

some groups interested in nanotechnology research, as seen in the “others” row in

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table 20. Instruments like AFM and Raman spectroscopy are fundamental tools for

nanotechnology.

As seen in Table 21, and in accordance with the previous results, genomic data is, by

far, the most required data type. In second order, biological signal data become

relevant for researchers, implying the relevance of data capturing systems for

bioinformatics. In spite if the third position, clinic data are also highly relevant for

groups doing research in bioinformatics.

Table 22 shows that, in spite of the fact that the majority of the researchers do actually

produce bioinformatics tools (Table 18), the majority of them also declare to need new

bioinformatics tools. This interplay between the need for new tools, the production and

the usage of them by groups performing bioinformatics, is one of the most unique

ways to advance the scientific knowledge, and is a typical manifestation of

bioinformatics.

As occurred with computer sciences, the need to interoperate, at the programmatic

level, and the need to intercommunicate results, has promoted the adoption of

standards, controlled vocabularies and ontologies. Table 23 and 24 demonstrate that,

there is a growing need for the production of such ontologies, but also that many

efforts are currently being conducted. The need for an interoperation language is being

solved by XML-related standards such as SBML [15] and other efforts, but in the case

of ontologies for nanoparticles and other nanosystems, the fast advance in new

synthetic methods [16, 17, 18] and self assembly structures [19, 20], rapidly obsoletes

y possible ontology.

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3.3. GRID and High Performance Computing

“A computational grid is a hardware and software infrastructure that provides

dependable, consistent, pervasive and inexpensive access to high-end computational

capabilities" [21, 22]. In the context of the Health Care areas, the Action Grid Survey

reveals the reality in the different areas, where the research groups use GHPC in

multiple contexts (Current resources, necessities, human resources & training and

mobility). As example, Software Developing (48%), Image Processing, (38%),

Databases (34%) and Sequence Analysis (31%), just to mention the principal ones.

In this scenario, and considering all the groups who answered the survey, only 29% of

the surveyed mentioned to use GHPC in health care areas. The European Union and

Latin America have the major percentage of participation (59% and 24% respectively)

compared with North Africa and Western Balkans (7% and 10% respectively).

The main areas above-mentioned are insert into two major areas of research: Medical

Informatics and Bioinformatics. In these research areas, GHPC is fundamental for the

productivity of the research, especially considering the following aspects [22]:

The massive and continuing increase in the power of affordable computing.

The rapid deployment of the high-bandwidth communications, key for the

transmission of large amount of information.

The rapid deployment of the global networks, hardware, and application to

Internet.

All these issues have been conducted in very different ways. The GHPC offers great

capability for many applications in Medical Informatics and Bioinformatics, which

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will potentially lead to improvements in Health Care areas and the quality of life. In

Medical Informatics, the processing and storage of medical imaging and Software

Development lead the survey [23]. In Bioinformatics, the focus was on biological

problems, simple and complex biological molecules. More complex systems, for

example: cells, organs, and complete organism associated to very large datasets.

Moreover, in Bioinformatics, Bioinformaticians have developed large collections of

tools, to make sense of the rapidly growing pool of molecular biological data [24].

This is reflected in the high percentage of GHPC technologies used on Software

Developing (48%) and Sequence Analysis (31%). Nowadays, Bioinformaticians

develop tools to access data and tools through the web, using web services, which play

an important role for bioinformatics in several research groups [25, 26].

Also, there exist other areas of research presenting important percentages to be

considered in this survey, where they need to perform expensive calculations or to

store large amount of information generated by research. The main research areas

using GHPC by research groups are:

Molecular Simulation (21% of research groups in the GHPC area), distributed

in EU (18%) and Latin America (28%).

Systems Biology (21% of research groups in the GHPC area). This research

field is present in all surveyed geographic areas, with 18% in EU, 14% in Latin

America, 50% in North Africa and 33% in Western Balkans. However the last

two surveyed locations has no statistical significance, because of the reduced

number of answers.

With 17%, we found multiples research areas: Organism modeling,

comparative genomics, molecular phylogeny, molecular modeling and

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visualization. Distributed mainly between EU and Latin America.

Computer aided drug design (14% of research groups in the GHPC area).

Other areas have a low participation rate in this survey, approximated 3%. (See

more details in Annex 1. section 5, table 5.1).

On the other hand, a high percentage of research groups manifest interest to join a

Grid Infrastructure (Annex 1. table 9.1). In Europe 86%, Latin America 87%, North

Africa and Western Balkans with a 100%. In the same way, all the groups manifest

interest to learn more about Grid technologies. Only European Union groups in the

survey do not exceed 50% of interest in this necessity. The main necessities in these

areas (Medical Informatics and Bioinformatics) are computational power (42% and

45% respectively by area) and storage (35% and 14% respectively by area).

Regarding the experience of the groups working with GHPC in each research area, the

distribution in Europe is between 5 to 10 years (35%) being dominated by the segment

Less than 5 years (52%). In Latin America is concentrated in the segment 5-10 years

(85%). In the Western Balkans and North Africa, these two segments were distributed

equally. It is important to consider this factor, specially taking into account the lack of

the human resources to research in GHPC areas.

The necessities of Human Resources are high in all the areas, but mainly concentrated

in Computer Science Engineering, Medicine and Bioinformatics and distributed in the

following research areas (Medical Informatics, Bioinformatics, GHPC and in minor

scale de Nanoinformatics). The necessities of human resources have significant

differences depending on the geographic areas:

Computer science engineering apply to Bioinformatics (Europe: 22,6 ; Latin

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America: 36,7; North Africa: 33,3 ; Western Balkans : 35,7)

Medicine apply to Nanoinformatics (Europe: 6,5 ; Latin America: 6,7 ; North

Africa: 0; Western Balkans : 14,3 )

As regards to the lack of human resources and necessities of experts in GHPC, it is

mainly needed Computer Sciences Engineering applied to Bioinformatics, considering

that all surveyed areas have close links with human health.

3.4. Medical Nanoinformatics

Due to the fact that any discussion on the data obtained should not lead to any relevant

conclusion, we decided to report here just some general considerations about the

current state of the art of Medical Nanoinformatics that we could extract from our

expertise knowledge. This means that this section is not based on the data obtained in

the survey (since the sample is very low), but on the experience and previous works

done by ACTION-Grid members.

Nanobiology is a fast-emerging discipline that brings the tools of nanotechnology to

the biological sciences. The introduction of new techniques may accelerate the

development of highly specific biomedical treatments, increase their efficiency, and

minimize their secondary effects [27]. Introducing foreign bodies into the complex

machinery of the human body is, however, a great challenge.

Nanomedicine during the last 5 years has grown very fast. A simple analysis of the

number of links related with term “nanomedicine” shown an exponential interest (see

the following figure).

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Fig. 4 - Number of links related with term Nanomedicine

Most of this research is done in USA; however many of new initiatives are growing all

over the world. For example, in Germany it was organized the cluster

Nanotechnology, which includes other initiatives like nano-initiative Bayer GMBH

(http://www.nanoinitiative-bayern.de/), among others. There also exist several new

academics initiatives, which in many cases are associated with industries.

Computer-aided methods are the natural option to speed up the development of these

technologies. However, the procedures for annotation and simulation of nanoparticle

properties should be developed and analyzed the computational limitation methods

that could be useful in this field.

Yet, the procedures for annotation and simulation of nanoparticle properties must be

developed and their limitations understood before computational methods can be fully

exploited. The use of computer methods to organize, analyze and interpret

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nanotechnological data is called nanoinformatics. Development of databases and the

use of text mining methods are still at an early stage. The need for a common language

is being solved by XML standards, such as SBML and other efforts, but in the case of

ontologies for nanoparticles and other nanosystems is not trivial.

Medical applications of Nano focused the attention of society in recent years, as it was

shown before. The first nanomaterials designed for its application in the biomedical

sciences was done on 1985 and from 2004 the term “nanomedicine” was widespread.

The concept of nanoinformatics is still developing and in very early stage.

The emergence of the modern nanobiomedical sciences is dominated by the

convergence in the application to biomedical problems of nanomaterials, coming from

technologies product of breakthrough synthetic methods in lipid (liposomes), polymer

(dendrimers) and colloid chemistry (metal colloids) and the discovery of entirely new

chemical processes like fullerene and quantum-dot synthesis methods. All these new

molecular systems have a potential application in diagnostic and therapy [28]. Also the

new nanomaterial with potential application in implants or another medical device has

important interest in Europe.

Lack of a common language as a mechanism for sharing knowledge is a huge

impediment. This situation is common in a new discipline. In short, lack of a common

model and data makes difficult the development of rational approaches. The reliability

of unpaid surveys is minimal under these circumstances. Similar results have been

cited in reports of FDA [29], NSF [30], etc.

Dr. Britt E. Erickson, in his article titled “Nanomaterial Data Remain Scarce” [31],

explained that company participation in EPA's voluntary Nanoscale Materials

Stewardship Program (NMSP) remains low, according to an interim report released by

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EPA on Jan. 12. EPA launched NMSP in January 2008 and gave companies the option

of joining a basic program, in which they would submit only information that they

already have about their nanoscale materials, or a more in-depth program, in which -

as time pass by - they would provide additional information, such as exposure, fate,

and transport data. The program had a weak start, and although participation has

improved since the six-month mark, the number of companies participating is still

about 10 times less than EPA had predicted. As of Dec. 8, 2008, 29 companies had

submitted information on 123 nanoscale materials under the basic program, and only

four companies had agreed to participate in the in-depth program. EPA and the

chemical industry consider the program successful, but critics argue that the poor

participation underscores the need for mandatory reporting and testing of these

materials.

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4. CONCLUSIONS

The results obtained with this survey have a very limited statistical relevance for some

of its components. For Bioinformatics, Medical Informatics and High Performance

and Grid computing the extracted data allows us to define some general conclusions.

Low response rate was expected, but in case of Nano(Medical) Informatics no firm

conclusions can be made from the available data, due to the overall low response rate

which does not facilitate any recommendation to be made from this data.

Possible reasons for overall low response rate might include:

A large number of recent EC projects and research activities in this area

included a survey. This causes a flood of requests for completing forms that

may lead to a passive attitude of researchers. It is getting more difficult to

obtain an answer when a large number of requests are made every month.

Answering groups may have been mainly focused on other research areas, thus

further decreasing the number of responses provided for Nano(Medical)

Informatics.

Some of the groups that accessed the survey may have considered the survey

too long to be answered and did not complete the form.

As we stated initially in the Description of Work of the ACTION-Grid project, we

always have considered the task of obtaining a high response rate in the survey as a

very risky task. For this reason we decided (from the original DoW) to associate to the

survey an innovative application known as the “Resourceome”. This tool could help us

to fill the gaps in the survey and, using text mining based methods, try to solve the

problems we are currently facing.

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The results of this effort will be included in the final report of the White Paper

(Deliverable 4.3).

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5. REFERENCES

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4. Dreyer, K.J., PACS : a guide to the digital revolution. 2nd ed. 2006, New

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Lechleitner, F. Leiner, R. Weber, A. Winter, and A.C. Wolff, Medical

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9. Bodenreider, O., Biomedical ontologies in action: role in knowledge

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10. Cimino, J.J., Terminology tools: state of the art and practical lessons. Methods

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11. Cimino, J.J. and X. Zhu, The practical impact of ontologies on biomedical

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12. Campbell, K.E., D.E. Oliver, and E.H. Shortliffe, The Unified Medical

Language System: toward a collaborative approach for solving terminologic

problems. J Am Med Inform Assoc, 1998. 5(1): p. 12-6.

13. Kalet, I.J., J.L. Mejino, V. Wang, M. Whipple, and J.F. Brinkley, Content-

specific auditing of a large scale anatomy ontology. J Biomed Inform, 2009.

14. Kuhn, K.A. and D.A. Giuse, From hospital information systems to health

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15. http://sbml.org. Last visit may 10, 2009

16. Zhu, Y., et al., Assembly of polymeric micelles into hollow microcapsules with

extraordinary stability against extreme pH conditions. Langmuir, 2008.

24(15): p. 7810-6.

17. Winklhofer, M., Nanobiotechnology: putting cobalt on the menu. Nat

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http://sbml.org/

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18. Torres, A.J., et al., Nanobiotechnology and cell biology: micro- and

nanofabricated surfaces to investigate receptor-mediated signaling. Annu Rev

Biophys, 2008. 37: p. 265-88.

19. Zhang, S., Designer self-assembling Peptide nanofiber scaffolds for study of 3-

d cell biology and beyond. Adv Cancer Res, 2008. 99: p. 335-62.

20. Schneider, A., J.A. Garlick, and C. Egles, Self-assembling peptide nanofiber

scaffolds accelerate wound healing. PLoS ONE, 2008. 3(1): p. e1410.

21. Foster, C. Kesselman, and S. Tuecke, "The Anatomy of the Grid: Enabling

Scalable Virtual Organizations," International Journal of High Performance

Computing Applications, vol. 15, pp. 200-222, 2001.

22. Foster, C. Kesselman, J. M. Nick, and S. Tuecke, "The Physiology of the

Grid," An Open Grid Services Architecture for Distributed Systems

Integration, 2002.

23. Fran Berman, Geoffrey Fox , Anthony J. G. Hey , Tony Hey, Grid Computing:

Making the Global Infrastructure a Reality, John Wiley & Sons, Inc., New

York, NY, 2003.

24. Neerincx, PBT, Evolution of web services in bioinformatics, BRIEFINGS IN

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25. Heinz Stockinger, Teresa Attwood, Shahid Nadeem Chohan, Richard Côté,

Philippe Cudré-Mauroux, Laurent Falquet, Pedro Fernandes, Robert D. Finn,

Taavi Hupponen, Eija Korpelainen, Alberto Labarga, Aurelie Laugraud, Tania

Lima, Evangelos Pafilis, Marco Pagni, Steve Pettifer, Isabelle Phan, and

Nazim Rahman, Experience using web services for biological sequence

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analysis, Briefings in Bioinformatics Advance Access published on July 11,

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26. M. Papazouglou and D. Georgakopoulos. Special issue on service oriented

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27 USA House of Representatives Report on Nanotechnologies and Health, June

2008

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29 Food and Drug Administration Report, December 2008

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Engineering News, Jan 19, 2009, 87(3), 42.

32 ERA Nanoforum, Nano in Europe (Scandinavian Countries), 2006

33 ERA FP7 ObservatoryNANO (contract 218528, comprehensive surveys to be

released April 2009)

34 Workshop on Enabling Standards for Nanomaterial Characterization report (to

be submitted), NIST, October 2008

35 National Cancer Institute Strategic Plan, January, 2006

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6. ANNEX

This section includes the survey` raw data in table format by research area. The

utterance of the questions and their corresponding numeration are showing in the

captions of each tables. Absolute number of observation and its percentage in brackets

are expressed in each cell: n (%).


Section 3: Current Resources for Medical Informatics

3.1 In which one of the following areas does your group carry out research

projects?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Health Information

Systems

34 (59,6) 13 (54,2) 11 (73,3) 2 (40) 8 (61,5)

Public Health 12 (21,1) 3 (12,5) 5 (33,3) 0 4 (30,8)

Imaging or

biological signal

data processing

22 (38,6) 11 (45,8) 6 (40 ) 3 (60) 2 (15,4)

Clinical Decision

Support System

22 (38,6) 11 (45,8) 7 (46,7) 0 4 (30,8)

Nursing 2 (3,5) 0 0 1 (20) 1 (7,7)

Interoperability 18 (31,6) 7 (29,2) 7 (46,7) 2 (40 ) 2 (15,4)

Telemedicine 20 (35,1) 8 (33,3) 6 (40 ) 2 (40) 4 (30,8)

Organization and

Management

14 (24,6) 4 (16,7) 5 (33,3) 0 5 (38,5)

Education 19 (33,3) 7 (29,2) 5 (33,3) 1 (20) 6 (46,2)

Standardization 12 (21,1) 6 (25) 3 (20) 1 (20) 2 (15,4)

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Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Other: 5 (8,8) 1 (4,2) 2 (13,3) 0 2 (15,4)

Other (description) Collaboration

and semantic

grid

Electronic

Health

Records

Text mining

Basic research

into

computational

models of

biological

processes

3.2 How long have you done research on Medical Informatics?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Less than 5

years

13 ( 22,8) 3 ( 12,5) 8 ( 53,3) 0 ( 0 ) 2 ( 15,4)

5-10 years 16 ( 28,1) 5 ( 20,8) 4 ( 26,7) 2 ( 40 ) 5 ( 38,5)

More than

10 years

23 ( 40,4) 14 ( 58,3) 2 ( 13,3) 3 ( 60) 4 ( 30,8)

Missing 5 ( 8,8) 2 ( 8,3) 1 ( 6,7) 0 2 ( 15,4)

3.3 Does your group belong to an institution that has a Health Information

System (HIS)?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Yes 17 (29,8) 5 (20,8) 7 (46,7) 2 (40 ) 3 (23,1)

No response 5 (8,8) 2 (8,3) 1 (6,7) 0 2 (15,4)

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Information Systems

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Clinical Laboratory

Information Systems

7 (12,3) 3 (12,5) 4 (26,7) 0 0

Databases as Topic 2 (3,5) 1 (4,2) 1 (6,7) 0 0

Databases, Bibliographic 3 (5,3) 0 3 (20) 0 0

Databases, Factual 1 (1,8) 0 1 (6,7) 0 0

Database Management

Systems

6 (10,5) 1 (4,2) 5 (33,3) 0 0

Decision Support

Systems, Clinical

5 (8,8) 0 5 (33,3) 0 0

Hospital Information

Systems

10 (17,5) 4 (16,7) 3 (20) 1 (20) 2 (15,4)

Medical Order Entry

Systems

7 (12,3) 3 (12,5) 4 (26,7) 0 0

Management

Information Systems

8 (14 ) 2 (8,3) 5 (33,3) 1 (20) 0

Ambulatory Care

Information Systems

5 (8,8) 1 (4,2) 3 (20) 0 1 (7,7)

Clinical Laboratory

Information Systems

7 (12,3) 2 (8,3) 5 (33,3) 0 0

Clinical Pharmacy

Information Systems

4 (7) 0 4 (26,7) 0 0

Decision Support

Systems, Management

4 (7 ) 1 (4,2) 3 (20) 0 0

Healthcare Common

Procedure Coding

System

2 (3,5) 1 (4,2) 1 (6,7) 0 0

Hospital Information 8 (14 ) 2 (8,3) 4 (26,7) 0 2 (15,4)

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Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Systems

Personnel Staffing and

Scheduling Information

Systems

3 (5,3) 0 3 (20) 0 0

Radiology Information

Systems

7 (12,3) 3 (12,5) 2 (13,3) 1 (20) 1 (7,7)

Medical Records

Systems, Computerized

10 (17,5) 2 (8,3) 6 (40 ) 0 2 (15,4)

Online Systems 7 (12,3) 2 (8,3) 4 (26,7) 0 1 (7,7)

Libraries, Digital 2 (3,5) 0 1 (6,7) 0 1 (7,7)

Reminder Systems 4 (7 ) 2 (8,3) 2 (13,3) 0 0

Telemedicine

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Remote

Consultation

7 (12,3) 2 (8,3) 3 (20) 1 (20) 1 (7,7)

Telepathology 2 (3,5) 1 (4,2) 1 (6,7) 0 0

Teleradiology 3 (5,3) 1 (4,2) 1 (6,7) 0 1 (7,7)

Other 3 (5,3) 1 (4,2) 1 (6,7) 0 1 (7,7)

Other (description) Mobile

telemedicine

Archetype-

based EHR

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3.4 Has your group developed any HIS service?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Yes 29 (50,9) 10 (41,7) 11 (73,3) 3 (60 ) 5 (38,5)

No response 5 (8,8) 2 (8,3) 1 (6,7) 0 2 (15,4)

Information Systems

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Clinical Laboratory

Information Systems

6 (10,5) 2 (8,3) 3 (20) 1 (20) 0

Databases as Topic 8 (14 ) 4 (16,7) 3 (20) 1 (20) 0

Databases, Bibliographic 1 (1,8) 1 (4,2) 0 0 0

Databases, Factual 1 (1,8) 1 (4,2) 0 0 0

Database Management

Systems

8 (14 ) 2 (8,3) 5 (33,3) 1 (20) 0

Decision Support

Systems, Clinical

12 (21,1) 5 (20,8) 5 (33,3) 1 (20) 1 (7,7)


Systems

10 (17,5) 3 (12,5) 4 (26,7) 2 (40 ) 1 (7,7)

Medical Order Entry

Systems

8 (14 ) 4 (16,7) 4 (26,7) 0 0

Management

Information Systems

7 (12,3) 2 (8,3) 3 (20) 2 (40 ) 0

Ambulatory Care

Information Systems

7 (12,3) 3 (12,5) 3 (20) 1 (20) 0

Clinical Laboratory

Information Systems

5 (8,8) 1 (4,2) 3 (20) 1 (20) 0

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Clinical Pharmacy

Information Systems

5 (8,8) 1 (4,2) 4 (26,7) 0 0

Decision Support

Systems, Management

6 (10,5) 2 (8,3) 3 (20) 1 (20) 0

Healthcare Common

Procedure Coding

System

1 (1,8) 0 1 (6,7) 0 0


Systems

8 (14 ) 2 (8,3) 4 (26,7) 1 (20) 1 (7,7)



Systems

3 (5,3) 1 (4,2) 2 (13,3) 0 0


Systems

4 (7 ) 2 (8,3) 1 (6,7) 1 (20) 0

Medical Records


13 (22,8) 5 (20,8) 5 (33,3) 0 3 (23,1)

Online Systems 9 (15,8) 5 (20,8) 3 (20) 0 1 (7,7)

Reminder Systems 6 (10,5) 2 (8,3) 4 (26,7) 0 0

Telemedicine

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Remote Consultation 9 (15,8) 4 (16,7) 3 (20) 1 (20) 1 (7,7)

Telepathology 3 (5,3) 2 (8,3) 1 (6,7) 0 0

Teleradiology 3 (5,3) 2 (8,3) 1 (6,7) 0 0

Other 4 (7 ) 0 2 (13,3) 1 (20) 1 (7,7)

Other (description) Archetype-

based EHR

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3.5 What kind of data is your group using in its research?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Clinical Data 40 (70,2) 17 (70,8) 13 (86,7) 2 (40 ) 8 (61,5)

Biological signal Data 17 (29,8) 9 (37,5) 4 (26,7) 0 4 (30,8)

Genomic Data 17 (29,8) 11 (45,8) 3 (20) 0 3 (23,1)

Proteomic Data 9 (15,8) 5 (20,8) 2 (13,3) 0 2 (15,4)

Metabolomic Data 5 (8,8) 1 (4,2) 1 (6,7) 1 (20) 2 (15,4)

Molecular structure

Data

6 (10,5) 1 (4,2) 1 (6,7) 1 (20) 3 (23,1)

Imaging Data 21(36,8) 13 (54,2) 1 (6,7) 4 (80 ) 3 (23,1)

Nanotechnology data 1 (1,8) 0 0 0 1 (7,7)

Laboratory data 19(33,3) 11 (45,8) 4 (26,7) 0 4 (30,8)

Public Health data 16(28,1) 5 (20,8) 5 (33,3) 2 (40) 4 (30,8)

Other 4 (7 ) 1 (4,2) 0 1 (20) 2 (15,4)

Others (description) Patient diary

data

Business

Management

data

3.6 What is/are the source/s of data are you using in your researches?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Public Domain 36 (63,2) 14 (58,3) 9 (60 ) 3 (60) 10 (76,9)

Private 31 (54,4) 15 (62,5) 7 (46,7) 4 (80) 5 (38,5)

Commercial 7 (12,3) 2 (8,3) 0 2 (4 ) 3 (23,1)

Others 7 (12,3) 3 (12,5) 1 (6,7) 1 (20) 2 (15,4)

Others (description) Generated by

projects

no real data

data are

simulated

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3.7 Has your group generated data derived from your research on Medical

Informatics?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Yes 32 (56,1) 14 (58,3) 9 (60 ) 3 (60 ) 6 (46,2)

No response 6 (10,5) 3 (12,5) 1 (6,7) 0 2 (15,4)

List data

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Clinical Data 18 (31,6) 9 (37,5) 5 (33,3) 2 (40) 2 (15,4)

Biological signal Data 7 (12,3) 4 (16,7) 0 2 (40) 1 (7,7)

Genomic Data 8 (14 ) 5 (20,8) 2 (13,3) 1 (20) 0

Proteomic Data 5 (8,8) 2 (8,3) 2 (13,3) 1 (20) 0

Molecular structure

Data

1 (1,8) 0 1 (6,7) 0 0

Imaging Data 10 (17,5) 7 (29,2) 1 (6,7) 2 (40) 0

Laboratory data 7 (12,3) 3 (12,5) 2 (13,3) 0 2 (15,4)

Public Health data 9 (15,8) 3 (12,5) 2 (13,3) 0 4 (30,8)

Others 2 (3,5) 1 (4,2) 0 0 1 (7,7)

Others (description) Functional

data

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3.8 Has your group created any taxonomies and/or ontologies, derived from your

research on Medical Informatics?

Global

n=57

European

Union

n=24

Latin

America

n=15

North

Africa

n=5

Western

Balkans

n=13

Yes 17 (29,8) 9 (37,5) 3 (20) 1 (20) 4 (30,8)

No response 5 (8,8) 2 (8,3) 1 (6,7) 0 2 (15,4)

3.9 Which ontologies did your group generate?


bioinformatics (datatypes

and services

anatomy chemistry terms,

pathology terms

Brain Tumour One that represents the

data for the Brazilian

National Health Card

Project

heart failure

cardiology, dentistry Telehealth

Cervical Cancer

cervical cancer ontology

Classification of tumours

by location and type

General ontology in health

and health information

description

oncology

ontologies in neuroimaging

and neuranatomy

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Section 7: Medical Informatics Needs

7.1 What kind of data does your group need for your research purposes?

Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12

Clinical Data 37 (72,5) 14 (66,7) 11 (78,6) 4 (100) 8 (66,7)

Biological signal Data 20 (39,2) 10 (47,6) 4 (28,6) 2 (50) 4 (33,3)

Genomic Data 17 (33,3) 10 (47,6) 2 (14,3) 1 (25 ) 4 (33,3)

Proteomic Data 10 (19,6) 4 (19 ) 3 (21,4) 1 (25) 2 (16,7)

Metabolomic Data 6 (11,8) 2 (9,5) 2 (14,3) 1 (25) 1 (8,3)

Molecular structure Data 4 (7,8) 1 (4,8) 0 1 (25) 2 (16,7)

Imaging Data 23 (45,1) 11 (52,4) 4 (28,6) 3 (75) 5 (41,7)

Nanotechnology data 3 (5,9) 1 (4,8) 0 1 (25) 1 (8,3)

Public Health data 23 (45,1) 10 (47,6) 4 (28,6) 2 (50) 7 (58,3)

Others 3 (5,9) 1 (4,8) 0 1 (25) 1 (8,3)

7.2 About Health Information System (HIS)

Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12


HIS and does not need it

19 (37,3) 11 (52,4) 2 (14,3) 2 (50 ) 4 (33,3)


HIS but it needs it

9 (17,6) 3 (14,3) 3 (21,4) 0 3 (25)

my group has a HIS but

we need to add new

services

14 (27,5) 4 (19 ) 7 (50) 1 (25) 2 (16,7)

my group has a HIS but

we do not need to modify it

7 (13,7) 2 (9,5) 2 (14,3) 1 (25) 2 (16,7)

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Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12

Missing 2 (3,9) 1 (4,8) 0 0 1 (8,3)

7.2.1 Which services are you interested to add in your Health Information System

for research in Medical Informatics?

Information Systems

Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12

Clinical Laboratory

Information Systems

8(15,7) 3 (14,3) 2 (14,3) 0 3 (25)

Databases as Topic 3(5,9) 0 0 0 3 (25)

Databases,

Bibliographic

5(9,8) 1 (4,8) 2 (14,3) 0 2 (16,7)

Databases, Factual 2(3,9) 0 0 0 2 (16,7)

Database Management

Systems

8(15,7) 2 (9,5) 4 (28,6) 0 2 (16,7)

Decision Support

Systems, Clinical

10(19,6) 3 (14,3) 4 (28,6) 0 3 (25)

Medical Order Entry

Systems

5(9,8) 0 2 (14,3) 0 3 (25)

Management

Information Systems

5(9,8) 0 3 (21,4) 0 2 (16,7)

Ambulatory Care

Information Systems

4(7,8) 0 2 (14,3) 0 2 (16,7)

Clinical Laboratory

Information Systems

4(7,8) 0 2 (14,3) 0 2 (16,7)

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Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12

Clinical Pharmacy

Information Systems

4(7,8) 0 2 (14,3) 0 2 (16,7)

Decision Support

Systems, Management

10(19,6) 0 7 (50 ) 0 3 (25)

Healthcare Common

Procedure Coding

System

6(11,8) 0 3 (21,4) 0 3 (25)


Systems

9(17,6) 3 (14,3) 3 (21,4) 0 3 (25)



Systems

3(5,9) 0 1 (7,1) 0 2 (16,7)


Systems

5(9,8) 1 (4,8) 2 (14,3) 0 2 (16,7)

Medical Records


9(17,6) 2 (9,5) 4 (28,6) 0 3 (25)

Online Systems 5(9,8) 0 2 (14,3) 0 3 (25)

Libraries, Digital 5(9,8) 1 (4,8) 2 (14,3) 0 2 (16,7)

Reminder Systems 5(9,8) 1 (4,8) 2 (14,3) 0 2 (16,7)

Telemedicine

Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12

Remote Consultation 8(15,7) 0 6 (42,9) 0 2 (16,7)

Telepathology 7(13,7) 1 (4,8) 4 (28,6) 0 2 (16,7)

Teleradiology 5(9,8) 0 3 (21,4) 0 2 (16,7)

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Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12

Other 3(5,9) 1 (4,8) 0 0 2 (16,7)

Others (description) Unified research

information

management

infrastructure (e.g.

clinical

trials+imaging)

Tele-

education

7.3 Does your group need any taxonomies or ontologies for your research

purposes in Medical Informatics?

Global

n=51

European

Union

n=21

Latin

America

n=14

North

Africa

n=4

Western

Balkans

n=12

Yes 21(41,2) 11 (52,4) 6 (42,9) 0 4 (33,3)

No 28(54,9) 9 (42,9) 8 (57,1) 4 (100) 7 (58,3)

Missing 2(3,9) 1 (4,8) 0 0 1 (8,3)

Total 51 (100) 21 (100) 14 (100) 4 (100) 12 (100)

7.4 What taxonomies or ontologies does your group need for your research?


ACGT MAster Ontology ,

Gene Ontology, ICD-10,

SNOMED - MedDRA also localized on

Croatian

Any that may be applied in

Clinical DSS

snomed as taxonomy,

dicom pathology wg-

26 standards

general medical

ontologies, specialized

clinical ontologies

cardiology and angiology:

anatomy, diseases,

processes

SNOMED CT

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cardiology, stomatology Special Materials

(Orthesis and

Prosthesis), Loinc and

ICD

Cardiovascular Telehealth

Clinical data

ICD10

Ontology engines for the

creation of ontologies

OWL

physiology, anatomy

Public health

5.2 Bioinformatics

Section 4: Current Resources for Bioinformatics

4.1 Which one of the following areas does your group develop research projects?

Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Sequence

analysis 24 (51,1) 10 (52,6) 10 (52,6) 3 (50) 1 (33,3)

Image

processing 5 (10,6) 1 (5,3) 1 (5,3) 2 (33,3) 1 (33,3)

Genomic

Annotation 13 (27,7) 6 (31,6) 5 (26,3) 1 (16,7) 1 (33,3)

Molecular

Simulation 14 (29,8) 6 (31,6) 6 (31,6) 1 (16,7) 1 (33,3)

Computer 9 (19,1) 2 (10,5) 5 (26,3) 1 (16,7) 1 (33,3)

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

aided drug

design

Systems

biology 10 (21,3) 5 (26,3) 3 (15,8) 1 (16,7) 1 (33,3)

Organism

modelling 3 (6,4) 2 (10,5) 0 0 1 (33,3)

Comparative

genomics 11(23,4) 3 (15,8) 5 (26,3) 2 (33,3) 1 (33,3)

Metabolic

reconstructio

n

6 (12,8) 2 (10,5) 3 (15,8) 0 1 (33,3)

Molecular

phylogeny 11(23,4) 4 (21,1) 5 (26,3) 1 (16,7) 1 (33,3)

Functional

genomic 14(29,8) 6 (31,6) 6 (31,6) 1(16,7) 1 (33,3)

Gene

regulation

network

11(23,4) 5 (26,3) 4 (21,1) 1 (16,7) 1 (33,3)

Proteomic 8 (17) 4 (21,2) 2 (10,5) 1 (16,7) 1 (33,3)

Molecular

modelling 10 (21,3) 3 (15,8) 6 (31,6) 0 1 (33,3)

Databases 22 (46,8) 9 (47,4) 10 (52,6) 2 (33,3) 1 (33,3)

Complex

systems 3 (6,4) 2 (10,5) 0 0 1 (33,3)

Data mining 19 (40,4) 10 (52,6) 6 (31,6) 2 (33,3) 1 (33,3)

Visualization 12 (25,5) 4 (21,1) 4 (21,1) 3 (50) 1 (33,3)

other

Chemogenomi

cs,

farmacoinfor

Experimental

work to

support

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3 matics,

Chemoinform

atics/

epigenetic

analysis

simulations

4.2 How long have you done research in Bioinformatics?

Global

N=47

European

Union

N=19

Latin America

N=19

North

Africa

N=6

Western

Balkans

N=3

Less than 5

years 13 (27,6) 5 (26,7) 6 (31,6) 2 (33,3) 0

5-10 years 19 (40,4) 7 (36,8) 9 (47,4) 2 (33,3) 1 (33,3)

More than 10

years 8 (17,2) 4 (21) 2 (10,5) 2 (33,3) 0

missing 7 (14,9) 3 (15,8) 2 (10,5) 0 2 (66,7)

4.3 - Which tools does your group use in its research activity?

Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

DNA

Annotation 23 (48,9) 12 (63,2) 8 (42,1) 2 (33,3) 1 (33,3)

Gene

Prediction

14 (29,8) 5 (26,3) 7 (36,8) 1 (16,7) 1 (33,3)

Mapping and

Assembly

12 (25,5) 4 (21,1) 4 (21,1) 3 (50) 1 (33,3)

Phylogeny

Reconstruction

16 (34) 6 (31,6) 6 (31,6) 3 (50) 1 (33,3)

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Sequence

Feature

Detection

17 (36,2) 5 (26,3)

8 (42,1) 3 (50) 1 (33,3)

Sequence

Polymorphisms

15 (31,9) 5 (26,3) 7 (36,8) 2 (33,3) 1 (33,3)

Sequence

Retrieval and

Submission

14 (29,8) 5 (26,3)

7 (36,8) 1 (16,7) 1 (33,3)

Protein

2-D Structure

Prediction

10 (21,3) 1 (5,3) 6 (31,6) 2 (33,3) 1 (33,3)

2-D Structural

Features

14 (29,8) 3 (15,8) 8 (42,1) 2 (33,3) 1 (33,3)

3-D Structure

Comparison

11 (23,4) 2 (10,5) 6 (31,6) 2 (33,3) 1 (33,3)

3-D Structure

Prediction

17 (36,2) 5 (26,3) 9 (47,4) 2 (33,3) 1 (33,3)

3-D Structure

Retrieval,

Viewing

11 (23,4) 3 (15,8)

5 (26,3) 2 (33,3) 1 (33,3)

Biochemical

Features Do-it-

all Tools for

Protein

6 (12,8) 0

4 (21,1) 1 (16,7) 1 (33,3)

Domains and

Motifs

Function

15 (31,9) 5 (26,3)

6 (31,6) 3 (50) 1 (33,3)

Interactions,

Pathways,

13 (27,7) 3 (15,8) 7 (36,8) 2 (33,3) 1 (33,3)

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Enzymes

Localization

and Targeting

Molecular

Dynamics and

Docking

12 (25,5) 5 (26,3)

5 (26,3) 1 (16,7) 1 (33,3)

Phylogeny

Reconstruction

13 (27,7) 3 (15,8) 7 (35,8) 2 (33,3) 1 (33,3)

Presentation

and Format

4 (8,5) 1 (5,3) 2 (10,5) 0 1 (33,3)

Protein

Expression

7 (14,9) 2 (10,5) 2 ()10,5 2 (33,3) 1 (33,3)

Proteomics 7 (14,9) 3 (15,8) 2 (10,5) 1 (16,7) 1 (33,3)

Sequence

Comparison

16 (34) 4 (21,2) 8 (42,1) 3 (50) 1 (33,3)

Sequence Data 13 (27,7) 2 (10,5) 8 (42,1) 2 (33,3) 1 (33,3)

Sequence

Features

9 (19,1) 1(5,3) 5 (26,3) 2 (33,3) 1 (33,3)

Sequence

Retrieval

12 (25,5) 3 (15,8) 6 (31,6) 2 (33,3) 1 (33,3)

Sequence

Comparison

Alignment

Editing and

Visualization

23 (48,9) 8 (42,1)

11 (57,9) 3 (50) 1 (33,3)

Analysis of

Aligned

Sequences

20 (42,6) 8 (42,1)

9 (47,4) 2 (33,3) 1 (33,3)

Comparative 15 (31,9) 5 (26,3) 7 (36,8) 2 (33,3) 1 (33,3)

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Genomics

Multiple

Sequence

Alignments

23 (48,9) 10 (52,6)

9 (47,4) 3 (50) 1 (33,3)

Alignment

Tools

15 (31,9) 5 (26,3) 6 (31,6) 3 (50) 1 (33,3)

Pairwise

Sequence

Alignments

17 (36,2) 6 (31,6)

7 (36,8) 3 (50) 1 (33,3)

Similarity

Searching

21 (44,7) 7 (36,8) 9 (47,4) 4 (66,7) 1 (33,3)

RNA

Functional

RNAs

8 (17) 3 (15,8) 3 (15,8) 1 (16,7) 1 (33,3)

General

Resources

6 (12,8) 3 (15,8) 1 (5,3) 1 (16,7) 1 (16,7)

Motifs 10 (21,3) 4 (21,1) 3 (15,8) 2 (33,3) 1 (33,3)

Sequence

Retrieval

11 (23,4) 3(15,8) 4 (21,1) 3 (50) 1 (33,3)

Structure

Prediction,

Visualization,

and Design

7 (14,9) 2 (10,5)

1 (5,3) 3 (50) 1 (33,3)

Transcriptomic

s / Gene

expression

(ESTs, cDNA,

SAGE,

Microarrays)

16 (34) 6 (31,6)

7 (36,8) 2 (33,3) 1 (33,3)

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Gene

Regulation adn

Networks

11 (23,4) 3 (15,8)

5 (26,3) 2 (33,3) 1 (33,3)

4.4 What kind of data is your group using in its researches?

Global

N=47

European

Union

N=19

Latin America

N=19

North

Africa

N=6

Western

Balkans

N=3

Clinical Data 14 (9,8) 9 (47,4) 3 (15,8) 1 (16,7) 1 (33,3)

Biological signal

Data 13 (27,7) 5 (26,3) 4 (21,1 3 (50 1(33,3)

Genomic Data 28 (59,6) 12 (63,2) 11 (579 4 (66,7 1 (33,3)

Proteomic Data 13 (27,7) 6 (31,6) 3 (15,8 3 (50 1 (33,3)

Metabolomic

Data 10 (21,3 4 (21,1 4 (21,1 1 (16,7 1 (33,3)

Molecular

structure Data 16 (34) 3 (15,8) 10 (52,6) 2 (33,3) 1 (33,3)

Imaging Data 7 (14,9) 3 (15,8) 1 (5,3) 2 (33,3) 1 (33,3)

Nanotechnology

data 4 (8,5) 0 3 (15,8) 0 1 (33,3)

Public Health

data 4 (8,5) 1 (5,3) 2 (10,5) 0 1 (33,3)

other

qrt-PCR

data

Experimental

data from XRR,

SEM, EDS,

AFM, VHRE

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4.5 The data you can access are public, private, commercial, other?

Global

N=47

European

Union

N=19

Latin America

N=19

North

Africa

N=6

Western

Balkans

N=3

Public 31 (33) 11 (57,9) 16 (84,2) 4 (66,7) 0

Private 7(7,30) 4 (21,1) 1 (5,3) 2 (33,3) 0

No 2 (2,1) 1 (5,3) 0 0 1 (33,3)

Other we use

Private and

Public data

4.6 Has your group developed any tool for bioinformatics research?

Global

N=47

European

Union

N=19

Latin America

N=19

North

Africa

N=6

Western

Balkans

N=3

Yes 28 (59,8) 14 (73,7) 11 (57,9) 2 (33,3) 1 (33,3)

No response 6(12,8) 3 (15,8) 2 (10,5) 0 1 (33,3)

4.6.a Tool for bioinformatics

Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

DNA

Annotation 8 (17) 5 (26,3) 3 (15,8) 0 0

Gene Prediction 5 (10,6) 3 (15,8) 2 (10,5) 0 0

Mapping and

Assembly

2 (4,3) 0 1 (5,3) 1 (16,7) 0

Phylogeny

Reconstruction

5 (10,6) 3 (15,8) 2 (10,5) 0 0

Sequence

Feature

Detection

4 (8,5) 3 (15,8) 0 1 (16,7) 0

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Sequence

Polymorphisms

5 (10,6) 2 (10,5) 2 (10,5) 1 (16,7) 0

Sequence

Retrieval and

Submission

6 (12,8) 3 (15,8) 2 (10,5) 1 (16,7) 0

Structure

Prediction

3 (93,6) 1 (5,3) 2 (10,5) 0 0

Protein

3-D Structural

Features

1 (2,1) 1 (5,3) 0 0 0

3-D Structure

Comparison

1 (2,1) 1 (5,3) 0 0 0

3-D Structure

Prediction

3 (6,4) 2 (10,5 `) 1 (5,3) 0 0

3-D Structure

Retrieval,

Viewing

1 (2,1) 1 (5,3) 0 0 0

Biochemical

Features Do-it-

all Tools for

Protein

2 (4,3) 0 2 (10,5) 0 0

Domains and

Motifs Function

3 (6,4) 1 (5,3) 2 (10,5) 0 0

Interactions,

Pathways,

Enzymes

Localization and

Targeting

3 (6,4) 2 ()10,5 1 (5,3) 0 0

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Molecular

Dynamics and

Docking

5 (10,5) 3 (15,8) 2 (10,5) 0 0

Phylogeny

Reconstruction

5 (10,5) 2 (10,5) 3 (14,8) 0 0

Presentation and

Format

3 (6,4) 1 (5,3) 2 (10,5) 0 0

Protein

Expression

No

answer

Proteomics 2 (4,3) 1 (5,3) 1 (5,3) 0 0

Sequence

Comparison

4 (8,5) 2 (10,5) 1 (5,3) 1 (16,7) 0

Sequence Data 1 (2,1) 0 1 (5,3) 0 0

Sequence

Features

2 (4,3) 0 1 (5,3) 1 (16,7) 0

Sequence

Retrieval

3 (6,4) 1 (5,3) 2 (10,5) 0 0

Sequence

comparison

Alignment

Editing and

Visualization

4 (8,5) 3 (15,8) 0 1 (16,7) 0

Analysis of

Aligned

Sequences

4 (8,5) 3 (15,8) 1 (5,3) 0 0

Comparative

Genomics

3 (6,4) 2 (10,5) 0 1 (16,7) 0

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Multiple

Sequence

Alignments

4 (8,5) 2 (10,5) 0 2 (33,3) 0

Other Alignment

Tools

3 (6,4) (10,5 `) 0 1 (16,7) 0

Pairwise

Sequence

Alignments

5 (10,6) 2 (10,5) 1 (5,3) 2 (33,3) 0

Similarity

Searching

6 (12,8) 4 (21,1) 0 2 (33,3) 0

RNA

Functional

RNAs

2 (4,3) 2 (10,5) 0 0 0

General

Resources

2 (4,3) 1 (5,3) 1 (5,3) 0 0

Motifs 3 (6,4) 3 (15,8) 0 0 0

Sequence

Retrieval

1 (2,1) 1 (5,3) 0 0 0

Structure

Prediction,

Visualization,

and Design

3 (6,4) 3 (15,8) 0 0 0

Transcriptomics

/ Gene

expression

(ESTs, cDNA,

SAGE,

Microarrays)

7 (14,9) 4 (21,1) 3 (15,8) 0 0

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Gene Regulation

ADN Networks

6 (12,8) 3 (15,8) 3 (15,8) 0 0

4.7. Has your group generated data derived from your research in

Bioinformatics?

Global

N=47

European

Union

N=19

Latin America

N=19

North

Africa

N=6

Western

Balkans

N=3

Yes 25 (53,2) 8 (42,1) 14 (73,7) 2 (33,3) 1 (33,3)

No response 8 (17) 3 (15,8) 3 (15,8) 0 2 (66,7)

4.7.a Data derived from your research in Bioinformatics

Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Clinical Data 3 (6,4) 3 (15,8 0 0 0 0

Biological signal

Data

4 (8,5) 2 (10,5) 2 (10,5) 0 0

Genomic Data 12 (25,5) 4 (21,1) 8 (42,1) 0 0

Proteomic Data 5 (10,6) 2 (10,5) 3 (15,8) 0 0

Metabolomic

Data

2 (4,3) 0 2 (10,5) 0 0

Molecular

structure Data

7 (14,9) 0 7 (36,8) 0 0

Imaging Data 2 (4,3) 0 1 (5,3) 1 (16,7) 0

Nanotechnology

data

2 (4,3) 0 2 (10,5) 0 0

Public Health

data

no response no response no response no response no response

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Global

N=47

European

Union

N=19

Latin

America

N=19

North

Africa

N=6

Western

Balkans

N=3

Others Functional

data

Orthology

data/

Sequence

variation data,

integration

information

from

disparate data

sources

4.8 Is your group generating or did your group generate any ontology based on

your bioinformatics research?

Global

N=47

European

Union

N=19

Latin America

N=19

North

Africa

N=6

Western

Balkans

N=3

Yes 3 (6,4) 2 (10,5) 0 0 1 (33,3)

No response 7(14,9) 3 (15,8) 2 (10,5) 0 2 (66,7)

4.9 -Which ontologies did your group generate?

Global

N=47

European Union

N=19

Latin America

N=19

North Africa

N=6

Western Balkans

N=3

bioinformatics

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Section 8 Bioinformatics needs

8.1 –Please, select the areas in which you are interested and in which you are

planning to begin to do research soon. (you can select more than one option).

Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Sequence

analysis

18 (38,3) 8 (42,1) 6 (35,3) 3 (50) 1 (20)

Image

processing

7 (14,9) 2 (10,5) 1 (5,9) 2 (33,3) 2 (40)

Genomic

Annotation

11 (23,4) 4 (21,1) 5 (29,4) 0 2 (40)

Molecular

Simulation

8 (17) 3 (15,3) 4 (23,5) 0 1 (20)

Computer aided

drug design

10 (21,3) 4 (21,1) 3 (17,6) 1 (16,7) 2 (40)

Systems biology 16 (34) 6 (31,6) 7 (41,2) 1 (16,7) 2 (40)

Organism

modelling

4 (8,5) 1 (5,3) 1 (5,9) 1 (16,7) 1 (20)

Comparative

genomics

8 (17) 1 *5,3 ( 3 (17,6) 1 (16,7) 3 (60)

Metabolic

reconstruction

6 (12,8 `) 1 (5,3) 3 (17,6) 1 (16,7) 1 (20)

Molecular

phylogeny

6 (12,8) 1 (5,3) 4 (23,5) 0 1 (20)

Functional

genomic

10 (21,3) 2 (10,5) 5 (29,4) 0 3 (60)

Gene regulation

network

12 (25,5) 6 (31,6) 4 (23,5) 1 (16,7) 1 (20)

Proteomic 6 (12,8) 3 (15,8) 1 (5,3) 0 2 (40)

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Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Molecular

modelling

9 (19,1) 3 (15,8) 5 (29,4) 0 1 (20)

Databases 16 (34) 6 (31,6) 6 (35,3) 2 (33,3) 2 (40)

Complex

systems

6 (12,8) 3 (15,8) 0 1 (16,7) 2 (40)

Data mining 21 (44,7) 7 (36,8) 7 (41,2) 3 (50) 4 (80)

Visualization 14 (29,8) 6 (31,6) 3 (17,6) 3 (50) 2 (40)

other Large scale

global health

promotion

systems

Analysis tools:

Raman-AFM,

low and high

Temperature

stage for AFM

8.2 What kind of data is your group need in its researches?

Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Clinical Data 21 (44,7) 11 (57,9) 5 (29,4) 2 (33,3) 3 (60)

Biological signal

Data

20 (42,6) 10 (52,6) 7 (41,2) 1 (16,7) 2 (40)

Genomic Data 26 (55,3) 11 (57,9) 10 (58,8) 2 (33,3) 3 (60)

Proteomic Data 15 (31,9) 6 (31,6) 5 (29,4) 2 (33,3) 2 (40)

Metabolomic

Data

10 (21,3) 2 (10,5) 6 (35,3) 1 (16,7) 1 (20)

Molecular

structure Data

14 (29,8) 5 (26,3) 6 (35,3) 1 (16,7) 2 (40)

Imaging Data 10 (21,3) 4 (21,1) 1 (5,9) 3 (5-) 2 (40)

Nanotechnology

data

4 (8,4) 0 2 (11,8) 1 (16,7) 1 (20)

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Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Public Health

data

10 (21,3) 4 (21,1) 2 (11,8) 1 (15,7) 3 (60)

Other Chemical

(chemogenomic

s) data)

8.3- Does your group need any tool for Bioinformatics research?

Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Yes 23 (48,9) 7 (36,8) 10 (58,8) 4 (66,7) 2 (40)

No response 8 (17) 4 (21,2) 2 (11,2) 1 (16,7) 1 (20)

Needed tools for Bioinformatics research

Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

DNA

Annotation 10 (21,3) 3 (15,8) 4 (23,5) 2 (33,3) 1 (20)

Gene Prediction 7 (14,9) 2 (10,5) 3 (17,6) 1 (16,7) 1 (20)

Mapping and

Assembly

8 (17) 3 (25,8) 3 (17,6) 1 (16,7) 1 (20)

Phylogeny

Reconstruction

5 (10,6) 1 (5,3) 1 (5,9) 1 (16,7) 2 (40)

Sequence

Feature

Detection

8 (17) 1 (5,3) 2 (11,8) 3 (50) 2 (40)

Sequence

Polymorphisms

7 (14,9) 2 (10,5) 2 (11,8) 2 (33,3) 1 (20)

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Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Sequence

Retrieval and

Submission

10 (21,3) 2 (10,5) 3 (17,6) 4 (66,7) 1 (20)

Protein

2-D Structure

Prediction

4 (8,5) 1 (5,9) 1 (5,9) 1 (16,7) 1 (20)

2-D Structural

Features

7 (14,9) 2 (10,5) 3 (17,6) 1 (16,7) 1 (20)

3-D Structure

Comparison

8 (17) 2 (10,5) 3 (17,6) 2 (33,3) 1 (20)

3-D Structure

Prediction

8 (17) 2 (10,5) 4 (23,5) 1 (16,7) 1 (20)

3-D Structure

Retrieval,

Viewing

6 (12,8) 1 (5,3) 3 (17,6) 1 (16,7) 1 (20)

Biochemical

Features Do-it-

all Tools for

Protein

5 (10,6) 2 (10,5) 2 (11,8) 0 1 (20)

Domains and

Motifs Function

8 (17) 3 (15,8) 2 (11,8) 1 (16,7) 2 (40)

Interactions,

Pathways,

Enzymes

Localization

and Targeting

8 (17) 3 (15,8) 2 (11,8) 1 (16,7) 2 (40)

Molecular

Dynamics and

Docking

5 (10,6) 1 (5,3) 3 (17,6) 0 1 (20)

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Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Phylogeny

Reconstruction

4 (8,5) 1 (5,3) 2 (11,8) 0 1 (20)

Presentation

and Format

3 (6,4) 1 (5,3) 1 (5,9) 0 1(20)

Protein

Expression (19)

7 (14,9) 3 (15,8) 1 (5,9) 1 (16,7) 2 (40)

Proteomics 3 (6,4) 1 (5,3) 1 (5,9) 0 1 (20)

Sequence

Comparison

7 (14,9) 1 (5,3) 3 (17,6) 2 (33,3) 1 (20)

Sequence Data 9 (19,1) 2 (10,5) 3 (17,6) 3 (50) 1 (20)

Sequence

Features

8 (17) 1 (5,3) 2 (11,8) 3 (50) 2 (40)

Sequence

Retrieval

8 (17) 1 (5,3) 3(17,6) 3 (50) 1 (20)

Sequence

Comparison

Alignment

Editing and

Visualization

13(27,7) 4 (21,1) 4 (23,5) 4 (66,7) 1 (20)

Analysis of

Aligned

Sequences

8 (17) 2 (10,5) 3 (17,6) 2 (33,3) 1 (20)

Comparative

Genomics

8 (17) 1 (5,3) 3 (17,6) 3 (50) 1 (20)

Multiple

Sequence

Alignments

8 (17) 2 (10,5) 3 (17,6) 3 (50) 0

Alignment

Tools

5 (10,6) 1 (5,3) 2 (11,8) 1 (16,7) 1 (20)

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Global

N=47

European

Union

N=19

Latin

America

N=17

North

Africa

N=6

Western

Balkans

N=5

Pairwise

Sequence

Alignments

9 (19,1) 1 (5,3) 4 (23,5) 3 (50) 1 (20)

Similarity

Searching

8 (17) 2 (10,5) 2 (11,8) 3 (50) 1 (20)

RNA 5 (10,6) 1 (5,3) 2 (11,8) 1 (16,7) 1 (20)

Functional

RNAs 33

General

Resources

5 (10,6) 2 (10,5) 1 (5,9) 1 (16,7) 1 (20)

Motifs 7 (14,9) 1 (5,3) 3 (17,6) 2 (33,3) 1 (20)

Sequence

Retrieval

6 (12,8) 1 (5,3) 2 (11,8) 2 (33,3) 1 (20)

Structure

Prediction,

Visualization,

and Design

5 (10,6) 1 (5,3) 2 (11,8) 1 (16,7) 1 (20)

Transcriptomics

/ Gene

expression

(ESTs, cDNA,

SAGE,

Microarrays)

8 (17) 3 (15,8) 3 (17,6) 1 (16,7) 1 (20)

Gene

Regulation and

Networks

9 (19,1) 4 (21,1) 3 (17,6) 1 (16,7) 1 (20)

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8.4 Does your group need any taxonomy or ontology for Bioinformatics research?

Global

N=47

European

Union

N=19

Latin America

N=17

North

Africa

N=6

Western

Balkans

N=5

Yes 8 (17) 3 (15,8) 2 (11,8) 2 (33,3) 1 (20)

No response 8 (17) 4 (21,2) 2 (11,8) 1 (16,7) 1 (20)

8.5 Which taxonomies or ontologies do you need for your research?


North Africa

Western

Balkans

Genotype, Phenotype,

Disease, Symptoms/

GO/

GO, KEGGs, etc/

Gene ontologies/

GO, UML, MESH/

5.3 High Performance and Grid Computing

Section 5: Current Resources for High Performance and Grid Computing

5.1 Select the areas where your group is doing research:

Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Sequence

analysis (n)

9 (31) 4 (23,5 3 (42,9) 1 (50 1 (33,3

Image

processing

11 (37,9 9 (52,9) 1 (14,3 0 1 (33,3)

Genomic

Annotation

1 (3,4) 0 0 0 1 (33,3

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Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Molecular

Simulation

6 (20,7 3 (17,6 2 (28,6 0 1 (33,3)

Computer aided

drug design

4 (13,8 1 (5,9) 2 (28,6) 0 1 (33,3

Systems biology 6 (20,7) 3 (17,6) 1 (14,3) 1 (50) 1 (33,3)

Organism

modelling

5 (17,2) 2 (11,8) 2 (28,6) 0 1 (33,3)

Comparative

genomics

5 (17,2) 2 (11,8) 2 (28,6) 0 1 (33,3)

Metabolic

reconstruction

1 (3,4) 0 0 0 1 (33,3)

Molecular

phylogeny

5 (17,2) 2 (11,8) 2 (28,6) 0 1 (33,3)

Functional

genomic

3 (10,3) 1 (5,9) 1 (14,3) 0 1 (33,3)

Gene regulation

network

2 (6,9) 1 (5,9) 0 0 1 (33,3)

Proteomic 2 (6,9) 1 ()5,9 0 0 1 (33,3)

Molecular

modelling

5 (17,9) 2 (11,8) 2 (28,6) 0 1 (33,3)

Databases 10 (34,5) 4 (23,5) 3 (42,9) 1 (50) 2 (66,7)

Complex

systems

3 (10,3) 1 (5,9) 0 0 2 (66,7)

Visualization 5 (17,2) 4 (23,5) 0 0 1 (33,3)

Software

developing

14 (48,3) 7 (41,2) 4 (57,1) 0 3 (100)

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Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Other Cardiac

modelling and

simulations/

CFD

Simulation/

Clinical trials/

Compliance

management/

Computationa

l fluid

dynamic/Mid

dleware

Biological

signal data/

5.2 Which kind of Computational resources is available in your group?

Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3 Parallel computing

(Infiniband,

CrayLink, etc.)

14 (48,3) 9 (52,9) 2 (28,6) 1 (50) 2 (66,7)

GRID Computing 21 (72,4) 13 (76,5) 4 (57,1) 2 (100) 2 (66,7)

Distributed

computing (web

services or other not-

GRID system)

15 (51,7)) 7 (41,2) 4 (57,1) 1 (50) 3 (100)

Multiprocessor

symmetric

computation systems

(SMP?s)

11 (37,9)

7 (41,2) 2 (28,6) 0 2 (66,7)

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Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3 Other: Access to

the

HellasGrid/

GPU/MPI

Cluster

5.3 Does your research group currently belong to any GRID computing Project?

Global

N=29

Europea

n Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Yes (n ) 14 (51,7) 9 (52,9) 2 (34,9) 0 2 (66,7)

5.3.1 GRIDs computing Project your group is connected with …

Global

N=29

European Union

N=17

Latin America

N=7

North Africa

N=2

Western Balkans

N=3

@neurST (6th FP)/

EGEE/

EGEE-III, SEE Grid-

SCI/GiSHEO/

EELA, EELA2,

EGEE/

NL-GRID (BIG-

GRID

CRO NGI

Croatian National

Grid/

5.3.2 In which research areas is your group currently offering resources, using

GRID technology?

Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Storage in

Medical

Informatics

6 (20,7) 4 (23,5)) 0 0 2 (66,7)

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Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Specific hardware

in Medical

Informatics

3 (10,3) 2 (11,8)) 0 0 1 (33,3)

Specific Software

in Medical

Informatics

5 (17,2) 3 (17,6) 0 0 2 (66,7)

Storage in

Bioinformatics

4 (13,8) 2 (11,8) 0 0 2 (66,7)

Computational

power in

Bioinformatics

7 (24,1) 4 (23,5) 1 (14,3) 0 2 (66,7)

Specific hardware

in Bioinformatics

2 (6,9) 1 (5,9) 0 0 1 (33,3)

Specific Software

in Bioinformatics

7 (24,7) 2 (11,8) 3 (42,9) 0 2 (66,7)

Storage in

Nanoinformatics

2 (6,9) 0 1(14,3) 0 1 (33,3)

Computational

power in

Nanoinformatics

2 (6,9 ) 1 (5,9) 0 0 1 (33,3)

Specific hardware

in

Nanoinformatics

3 (10,3) 1 (5,9) 1 (14,3) 0 1 (33,3)

Specific Software

in

Nanoinformatics

3 (10,3) 1 (5,9) 1 (14,3) 0 1 (33,3)

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5.4 How long have you done research on High Performance and Grid computing

in Software Development?

Global

N=29

Europea

n Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Less than 5

years

15 (51,7) 7 (41,2) 6 (85,7) 1 (50) 1 (33,3)

5-10 years 10 (35,5) 7 (41,2) 0 1 850 9 2 (66,7)

More than 10

years

3 (10,5) 2 (11,8) 1 (11,8) 0 0

No response 1 (3,4) 1 (50) 0 0 0

5.4.a How long have you done research on High Performance and Grid

computing in Hardware Administration?

Global

N=29

European

Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Less than 5

years

15 (51,7) 8 (47,1)) 6 (85,8) 0

1 (33,3)

5-10 years 5 (17,2) 4 (23,5) 0 0 1 (33,3)

More than 10

years

3 (10,3) 2 (11,8) 1 (14,3) 0 0

No response 6 (20,7) 3 (17,6) 0 2 (100) 1 (33,3 )

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5.4.b. How long have you done research on High Performance and Grid

computing in Databases?

Global

N=29

Europea

n Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Less than 5

years

12 (41,4) 7 (41,2) 4 (57,1) 0 1 (33,3)

5-10 years 5 (17,2) 3 (17,6) 2 (28,6) 0 0

More than 10

years

4 (13,8) 3 (17,6) 0 0 1 (33,3)

No response 7 (24,1) 4 (23,5) 1 (14,3) 2 (100) 1 (33,3)

5.5 Does your research group have a Data Centre (a dedicated and specifically

equipped room for servers)?

Global

N=29

Europea

n Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

Yes 21 (72,4) 13 (76,5) 5 (71,4) 1 (50) 2 (66,7)

No response 1 (3,4) 1 (5,9) 0 0 0

5.6 What kind of equipment are you currently using in your research group?

Global

N=29

Europea

n Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

yes 24 (82,8) 14 (82,4) 6 (85,7) 1(50) 3 (100

I don’t know 4 (13,4) 2 (11,8) 1(14,3) 1 (50) 0

No response 1 (3,4) 1 (5,9) 0 0 0

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5.6.a What kind of equipment are you currently using in your research group?

IBM 10 (34, 5) 4 (23,5) 3 (42,9) 1 (50) 2 (66,7)

NEC 2 (6,9) 1 (5,9 0 0 1 (33,3

DELL 8 (27,6) 3 (27,6 2 (28,6) 1 (50 2 (66,7)

SUN 7 (24,1) 2 (11,8) 2 (28,6) 0 3 (100)

CRAY 0 0 0 0 1 (33,3)

Fujitsu 2 (6,9) 1 (5,9) 0 0 1 (33,3)

Hitachi 2 (6,9) 0 0 0 2 (66,7)

SGI 5 (17,2) 3 (17,6) 1 (14,3) 0 1 (33,3)

HP 8 (27,6) 4 (23,5) 2 (28,6) 0 2 (66,7)

NVIDIA-

TESLA

5 (17,2) 3 (17,6) 1 (14,3) 0 1 (33,3)

SELF-MADE 5 (17,2) 2 (11,8) 2 (28,6) 0 1 (33,3)

Other ACGT,

Advancing

Clinic Genoma

Trial on

Cancer/

EGEE/ EGEE-

III,SEE-Grid-

SCI,GiSHEO,S

CIENSE

CRO NGI-

Croatian

National

Infrastructure,

EGEE-III

(EU FP7 Proj)

5.7.1 How much download bandwidth has your research group?

Global

N=29

Europea

n Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

up to 512 kbps 2 (6,9) 0 2 (28,6) 0 0

up to 1Mbps 2 (6,9) 1 (5,9) 1 (14,3) 0 0

up to 2 Mbps 2 (6,9) 1 (5,9) 0 1 (50) 0

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More than 4

Mbps

21 (72,4) 13 (76,5) 4 (57,1) 1 (50) 3 (100)

I do not know 1 (3,4) 1 (5,9) 0 0 0

No response 1 (3,4) 1 (5,9) 0 0 0

5.7.2 How much upload bandwidth has your research group?

Global

N=29

Europea

n Union

N=17

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=3

up to 512 kbps 1 (3,4) 0 1 (14,3) 0 0

up to 1Mbps 3 (10,3) 1 (5,9) 2 (28,6) 0 0

up to 2 Mbps 3 (10,3) 2 (11,8) 0 1 (50) 0

up to 4 Mbps 1 (3,4) 0 0 0 1 (33,3)

More than 4

Mbps

18 (62,1) 12 (70,6) 3 (42,9) 1 (50) 2 (66,7)

I do not know 2 (6,9) 1 (5,9) 1 (14,3) 0 0

No response 1 (3,4) 1 (5,9) 0 0 0

Section 9: High Performance and Grid Computing Needs

9.1 Is your group interest in joining a GRID infrastructure?

Global

N=31

Europea

n Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

Yes (n ) 25 (80,6) 13 (72,2) 6 (87,7) 2 (100) 4 (100)

No response 1 (3,2) 1 (5,6) 0 0 0

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9.2 Is your group interest in learning more about GRID technologies?

Global

N=31

Europea

n Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

Yes (n) 21 (67,7) 9 /(50) 6 (85,7) 2 (100 4 (100)

No response 1 (3,2) 1 (5,6) 0 0 0

9.1.3 How do you think you would use the Grid technology for each of the

following areas?

We offer:

Global

N=31

European

Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

Medical

Informatics

Storage 6 (19,4) 4 (22,2) 0 1 (50) 1 (25)

Computational

power

9 (29) 3 (16,7) 3 (42,9) 1 (50) 2 (50)

Specific hardware 3 (9,7) 2 (11,1) 1 (14,3) 0 0

Specific Software 9 (29) 6 (33,3) 2 (28,6) 0 1 ()

Bioinformatics

storage 5 (16,1) 1 (5,6) 2 (28,6) 1 (50) 1 (25)

Computational

power

10 (32,2) 2 (11,1) 4 (57,1) 2 (100) 2 (50)

Specific hardware 2 (6,5) 1 (5,6) 1 (14,3) 0 0

Specific Software 7 (22,6) 4 (22,2) 3 (42,9) 0 0

Nanoinformatics

Storage No response No response No response No response No response

Computational

power

2 (6,5) 0 1 (14,3) 0 1 (25)

Specific hardware No response No response No response No response No response

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Global

N=31

European

Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

Specific Software 3 (9,7) 2 (11,1) 1 (14,3) 0 0

We need:

Medical

Informatics

storage 11 (35,5) 4 (22,2) 4 (57,1) 1 (50) 2 (50)

Computational

power

13 (41,9) 6 (33,3) 4 (57,1) 1 (50) 2 (50)

Specific hardware 7 (22,6) 1 (5,6) 3 (42,9) 0 3 (75)

Specific Software 8 (25,8) 3 (16,7) 2 (28,6) 0 3 (75)

Bioinformatics

storage 9 (29) 1 (5,6) 4 (57,1) 2 (100) 2 (50)

Computational

power

14 (45,2) 5 (27,8) 5 (71,4) 2 (100) 2 (50)

Specific hardware 6 (19,4) 0 4 (57,1) 0 2 (50)


Nanoinformatics

Storage 2 (6,5) 0 1 (14,3) 0 1 (25)

Computational

power

4 (12,9) 2 (11,1) 1 (14,3) 0 1 (25)

Specific hardware 2 (6,5) 0 1 (14,3) 0 1 (25)


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9.2 Which options are the most needed by your research group?

Global

N=31

Europea

n Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

Increase

computational

power

24 (77,4) 15 (83,3) 6 (85,7) 2 (100) 1 (25)

Increase storage

capacity

14 (45,2) 9 (50 &) 2 (28,6) 2 (100) 1 (25)

Increase the

bandwidth

5 (16,1) 2 (11,1) 1 (14,3) 0 2 (50)

Develop

software

16 (51,6) 9 (50) 5 (71,4) 0 2 (50)

Human

resources

18 (58,1) 9 (50) 5 (71,4) 2 (100) 2 (50)

Training 18 (58,1) 4 (44,4) 5 (71,4) 2 (100) 3 (75)

9.2 Do you Think that in your group exists any restriction or problem that

impedes you join a GRID?

Global

N=31

Europea

n Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

YES 9 (29) 5 (27,8) 1 (14,3) 1 (50) 2 (50)

No 1 (3,2) 1 (5,6) 0 0 0

I do not know 20 (64,5) 11 (61,1) 6 (84,7) 1 (50) 2 (50)

No response 2 (6,5) 2 (11,1) 0 0 0

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Restriction or problem

Global

N=31

European

Union

N=18

Latin

America

N=7

North

Africa

N=2

Western

Balkans

N=4

Technologic

limits

4 (12,9) 2 (11,1) 1 (14,3) 0 1(25)

Lack of human

resources

4 (12,9) 3 (16,7) 0 0 1 (25)

Security

problems

2 (6,5) 1 (5,6) 0 0 1 (25)

Legal problems 2 (6,5) 1 (5,6) 0 0 1 (25)

Politic problems 4 (12,9) 3 (16,7) 0 0 1 (25)

Other problems 1 (3,2) 0 0 0 1 (25)

5.4 Nanoinformatics

Section 6: Current Resources in Nanoinformatics

6.1- Please, select in which nanomedicine areas your group is doing research

Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

Implantable

materials

1 (12,5) 0 0 0 1 (33,3)

Implantable

devices

2 (25) 1 (50) 0 0 1 (33,3)

Surgical Aids 1 (12,5) 0 0 0 1 (33,3)

Diagnostic tools 2 (25) 0 0 1 (100) 1 (33,3)

Modelling and

simulation

3 (37,5) 1 (50) 1 (50) 0 1 (33,3)

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Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

Databases 2 (25) 0 1 (50) 0 1 (33,3)

Understanding

basic life

processes

2 (25) 0 0 1 (100) 1 (33,3)

Molecule

detection

1 (12,5) 0 0 0 1 (33,3)

Filtering systems 1 (12,5) 0 0 0 1 (33,3)

Signal

transduction

systems

1 (12,5) 0 0 0 1 (33,3)

Drug delivery

systems

2 (25) 0 0 0 2 (66,7)

Drug discovery 2 (25) 0 0 0 2 (66,7)

Targeting

systems

2 (25) 0 0 0 2 (66,7)

Molecule based

machineries

1 (12,5) 0 0 0 1 (33,3)

Nanosystems 3 (37,5) 0 2 (100) 0 1 (33,3)

Quantum dots 3 (37,5) 0 2 (100) 0 1 (33,3)

Gold

nanoparticles

2 (25) 0 1 (50) 0 1 (33,3)

Silica

Nanoparticles

2 (25) 0 1 (50) 0 1 (33,3)

Lipoparticles 1 (12,5) 0 0 0 1 (33,3)

Micelles 1 (12,5) 0 0 0 1 (33,3)

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Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

Paramagnetic

and

Superparamagn

etic

nanoparticles

2 (25) 0 1 (50) 0 1 (33,3)

Fluorescent

nanoparticles

1 (12,5) 0 0 0 1 (33,3)

Cubosomes 1 (12,5) 0 0 0 1 (33,3)

Dendrimers 2 (25) 0 1 (50) 0 1 (33,3)

DNA-

nanoparticles

1 (12,5) 0 0 0 1 (33,3)

Fullerenes 1 (12,5) 0 0 0 1 (33,3)

Nanoshells 1 (12,5) 0 0 0 1 (33,3)

Nanotubes 2 (25) 0 1 (50) 0 1 (33,3)

Nanopores 2 (25) 0 1 (50) 0 1 (33,3)

Other No

response

No

response

No response No

response

No response

6.2 How long have you done research on Nanomedicine?

Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

Less than 5

years

1 (12,5) 0 1 (50) 0 0

5-10 years 4 (50) 1 (50) 1 (50) 0 2 (66,7)

More than 10

years

1 (12,5) 0 0 1 (100) 0

No response 2 (25) 1 (50) 0 0 1 (33,3)

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6.3 Is your group developing or did it develop tools for nanomedicine research?

Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

yes 5 (62,5) 1 (50) 2 (100) 0 2 (66,7)

No response 2 (25) 1 (50) 0 1 (100) 1 (33,3)

Tools for nanomedicine research

Global

European

Union

Latin

America

North

Africa

Western

Balkans

Implantable

materials

1 (12,5) 0 0 0 1 (33,3)

Implantable

devices

2 (25) 1 (50) 0 0 1 (33,3)

Surgical Aids 1 (12,5) 0 0 0 1 (33,3)

Diagnostic tools 0 0 0 1 (33,3) 1 (12,5)

Modelling and

simulation

4 (50) 1 (50) 2 (100) 0 1 (33,3)

Databases 2 (25) 0 1 (50) 0 1 (33,3)

Understanding

basic life

processes

1 (12,5) 0 0 0 1 (33,3)

Molecule

detection

2 (25) 0 1 (50) 0 1 (33,3)

Filtering systems 1 (12,5) 0 0 0 1 (33,3)

Signal

transduction

systems

2 (25) 0 1 (50) 0 1 (33,3)

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Global

European

Union

Latin

America

North

Africa

Western

Balkans

Drug delivery

systems

2 (25) 0 0 0 2 (66,7)

Drug discovery 2 (25) 0 0 0 2 (66,7)

Targeting

systems

2 (25) 0 0 0 2 (66,7)

Molecule based

machineries

1 (12,5) 0 0 0 1 (33,3)

6.4 In your nanoinformatics research your group is using:

Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

Methods and or

tools from others

areas

Sequence

analysis 2 (25)

0

0

1 (100) 1 (33,3)

Image

processing 4 (50) 1 (50) 1 (50) 1 (1009 1 (33,3)

Genomic

Annotation 2 (25)

0

0

1 (100) 1 (33,3)

Molecular

Simulation 5 (62,5) 1 (50) 2 (100)

0

2 (66,7)

Computer aided

drug design 1 (12,5)

0

0

0

1 (33,39

Systems

biology 2 (25) 1 (50)

0

0

1 (33,3)

Organism

modelling 1 (12,5) 1 (50) 0 0 0

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Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

Comparative

genomics 8 (100) 2 (100) 2 (100) 1 (100) 3 (100)

Metabolic

reconstruction 8 (100) 2 (100) 2 (100) 1 (100) 3 (100)

Molecular

phylogeny 8 (100) 2 (100) 2 (100) 1 (100) 3 (100)

Functional

genomic 1 (12,5) 0 0 0 1 (33,3)

Gene regulation

network 1 (12,5) 0 0 0 1 (33,3)

Proteomic 1 (12,5) 0 0 0 1 (33,3)

Molecular

modelling 2 (25) 0 1 (50) 0 1 (33,3)

Databases 3 (37,5) 0 2 (100) 0 1 (33,3)

Complex

systems 1 (12,5) 0 0 0 1 (33,3)

Data mining 1 (12,5) 0 0 0 1 (33,3)

Visualization 2 (25) 0 1 (50) 0 1 (33,3)

Others:

XRR,SEM,

EDS/AFM/

STM/VHR

E

Specific

nanomedicine

tools

Implantable

materials 2 (25)

0

0 1 (100) 1 (33,3)

Implantable

devices 3 (37,5) 1 (50) 0 1 (100) 1 (33,3)

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Global

n=8

European

Union

n=2

Latin America

n=2

North

Africa

n=1

Western

Balkans

n=3

Surgical Aids 2 (25)

0

0 1 (100) 1 (33,3)

Diagnostic

tools 2 (25)

0

0 1 (100) 1 (33,3)

Modelling and

simulation 3 (37,5) 1 (5) 1 (50)

0

1 (33,3)

Databases 2 (25) 0 1 (50) 0 1 (33,3)

Understanding

basic life

processes

1 (33,3) 2 (25) 0 1 (50) 0

Molecule

detection 1 (12,5) 0 0 0 1 (33,3)

Filtering

systems 1 (12,5) 0 0 0 1 (33,3)

Signal

transduction

systems

1 (12,5) 0 0 0 1 (33,3)

Drug delivery

systems 1 (12,5) 0 0 0 1 (33,3)

Drug discovery 1 (12,5) 0 0 0 1 (33,3)

Targeting

systems 1 (12,5) 0 0 0 1 (33,3)

Molecule based

machineries 1 (12,5) 0 0 0 1 (33,3)

Other No description

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6.5- Is your group generating or did your group generate data derived by your

nanoinformatics research?

Global

n=8

European

Union

n=2

Latin

America

n=2

North

Africa

n=1

Western

Balkans

n=3

Yes 5 (62,5) 1 (50) 1 (50) 1 (100) 2 (66,7)

No response 2 (25) 1 (50) 0 0

Data derived by your Nanoinformatics research

Global

European

Union

Latin

America

North

Africa

Western

Balkans

Implantable materials 2 (25) 0 0 1 (100) 1 (33)

Implantable devices 2 (25) 0 0 1 (100) 1 (33)

Surgical Aids 2 (25) 0 0 1 (100) 1 (33)

Diagnostic tools 1 (12,5) 0 0 0 1 (33)

Modelling and

simulation

3 (37,5) 1(50) 1(50) 0 1 (33)

Databases 2 (25) 0 1(50) 0 1 (33)

Understanding basic life

processes

1 (12,5) 0 0 0 1 (33)

Molecule detection 1 (12,5) 0 0 0 1 (33)

Filtering systems 1 (12,5) 0 0 0 1 (33)

Signal transduction

systems

1 (12,5) 0 0 0

Drug delivery systems 2 (25) 0 0 0 1 (33)

Drug discovery 1 (12,5) 0 0 0 1 (33)

Targeting systems 1 (12,5) 0 0 0 1 (33)

Molecule based

machineries

1 (12,5) 0 0 0 1 (33)

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6.6- Is your group generating or did your group generate any taxonomy and/or

ontology based on your nanoinformatics research?

Global

n=8

European

Union

n=2

Latin

America

n=2

North

Africa

n=1

Western

Balkans

n=3

Yes 3 (37,5) 1 (50) 0 1 (100) 1 (33,3)

No response 2 (25) 1 (50) 0 0 1 (33,3)

6.7- Which taxonomies and/or ontologies did your group generate?


North Africa

Western Balkans

Cerebral Aneurism ontology,

cardiac anatomy taxonomy No description No description No description

Section 10: Nanoinformatics Needs

10.1 What kind of data does your group need for your research?

Global

n=11

European

Union

n=3

Latin America

n=3

North

Africa

n=1

Western

Balkans

n=4

Clinical Data 4 (36,4) 1 (33,3) 1 (33,3) 1 (100) 1 (25)

Biological

signals Data

4 (36,4) 2 (66,7) 1 (33,3) 1 (100) 0

Genomic Data 4 (36,4) 2 (66,7) 0 1 (100) 1 (25)

Proteomic Data 1(9,1) 0 0 1 (100) 0

Metabolomic

Data

1(9,1) 0 0 1 (100) 0

Molecular

structure Data

3 (27,3) 0 1 (33,3) 1 (100) 1 (25)

Imaging Data 5 (45,5) 2 (66,7) 0 1 (100) 2

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Global

n=11

European

Union

n=3

Latin America

n=3

North

Africa

n=1

Western

Balkans

n=4

Nanotechnology

system data

3 (27,3) 1 (33,3) 1 (33,3) 1 (100) 0

Public Health

data

4 (36,4) 1 (33,3) 1 (33,3) 1 (100) 1 (25)

Specific

Nanoparticles:

below

5 (45,5) 0 2 (66,7) 1 (100) 2 (50)

Other 2 (18,2) 0 0 1 (100) 1 (25)

Other

(description)

SIN

DESCRIPCION

Specific Nanoparticles:

Global

n=11

European

Union

n=3

Latin

America

n=3

North

Africa

n=1

Western

Balkans

n=4

Quantum dots 2 (18,2) 0 2 (66,7) 0 0

Gold nanoparticles 2 (18,2) 0 2 (66,7) 0 0

Silica

Nanoparticles

1 (9,1) 0 1 (33,3) 0 0

Lipoparticles No response No response No response No response

Paramagnetic and

Superparamagnetic

nanoparticles

1 (9,1) 0 1 (33,3) 0 0

Fluorescent

nanoparticles

1 (9,1) 0 1 (33,3) 0 0

Cubosomes No response No response No response No response

Dendrimers 1 (9,1) 0 1 (33,3) 0 0

DNA-nanoparticles No response No response No response No response

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Global

n=11

European

Union

n=3

Latin

America

n=3

North

Africa

n=1

Western

Balkans

n=4

Fullerenes 1 (9,1) 0 0 0 1 (25)

Nanoshells 1 (9,1) 0 1 (33,3) 0

10.2- Does your group need tools to research in some nanoinformatics area?

Global

n=11

European

Union

n=3

Latin

America

n=3

North Africa

n=1

Western

Balkans

n=4

Yes 7 (63,6) 2 (66,7) 1 (33,3) 1 (100) 3 (75)

No response 3 (27,3) 1 (33,3) 1 (33,3) 0 1 (33,3)

Global

European

Union

Latin America

North

Africa

Western

Balkans

Implantable

materials

0 0 0 1 (33,3) 1 (12,5)

Implantable

devices

1 (50) 0 0 1 (33,3) 2 (25)

Surgical Aids 0 0 0 1 (33,3) 1 (12,5)

Diagnostic tools 0 0 1 (100) 1 (33,3) 2 (25)

Modelling and

simulation

1 (50) 1 (50) 0 1 (33,3) 3 (37,5)

Databases 0 1 (50) 0 1 (33,3) 2 (25)

Understanding

basic life processes

0 0 1 (100) 1 (33,3) 2 (25)

Molecule detection 0 0 0 1 (33,3) 1 (12,5)

Filtering systems 0 0 0 1 (33,3) 1 (12,5)

Signal transduction

systems

0 0 0 1 (33,3) 1 (12,5)

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Global

European

Union

Latin America

North

Africa

Western

Balkans

Drug delivery

systems

0 0 0 2 (66,7) 2 (25)

Drug discovery 0 0 0 2 (66,7) 2 (25)

Targeting systems 0 0 0 2 (66,7) 2 (25)

Molecule based

machineries

0 0 0 1 (33,3) 1 (12,5)

Nanosystems: 0 2 (100) 0 1 (33,3) 3 (37,5)

Other 0 0 1 0

10.3-Does your group need any taxonomy or ontology for Nanoinformatics

research?

Global

n=11

European

Union

n=3

Latin

America

n=3

North Africa

n=1

Western

Balkans

n=4

Yes 4 (36,4) 1 (33,3) 0 1 (50) 4 (36,4)

No response 3 (27,3) 1 (33,3) 2 (66,7) 1 (25) 3 (27,3)

10.4-Which taxonomies or ontologies do your group need for your research?


Cardiology and

angiology: anatomy,

Diseases,

Processes

Fulleren derivatives

nano structure

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5.5 Optional Sections

Section 11: Human Resources and Mobility

11.1: Human Resources Needs

To apply in....

Medical Informatics

Bioinformatics HPC / Grid Nanoinformatics

Needed HHRR from...

Computer science

engineering

35 (41,7) 26 (31) 35 (41,7) 4 (4,8)

Medicine 35 (41,7) 11 (13,1) 6 (7,1) 5 (6 )*

Bioinformatics 14 (16,7) 26 (31 ) 10 (11,9) 4 (4,8)

Biology 6 (7,1) 21 (25) 3 (3,6) 3 (3,6)

Chemistry 3 (3,6) 6 (7,1) 3 (3,6) 3 (3,6)

Physics 7 (8,3) 6 (7,1) 6 (7,1) 3 (3,6)

Biochemistry 4 (4,8) 13 (15,5) 5 (6 ) 4 (4,8)

Description of other

HHRR needed

- Mathematics

- Electrical engineering

- Educational

professionals

- Nursing

- Pharmacist

- Semantic media

specialist

- Knowledge engineers

- Mathematics

- Agriculture

Engineer

- Statistician

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11.2 Did any member of your group travel to another group OR did any external

researcher come for a period in your group, in the last 5 years?

Global

n 84

European

Union

n 31

Latin America

n 30

North Africa

n 9

Western

Balkans

n 14

Yes 42 (50 ) 16 (51,6) 18 (60 ) 2 (22,2) 6 (42,9)

No 28 (33,3) 8 (25,8) 8 (26,7) 5 (55,6) 7 (50 )

Missing Data 14 (16,7) 7 (22,6) 4 (13,3) 2 (22,2) 1 (7,1)

Total 84 (100) 31 (100) 30 (100) 9 (100) 14 (100)

11.2.1: N° of researchers hosted / visitors in the last 5 hear

Global European

Union

Latin America North Africa Western

Balkans

median (RIG) median (RIG) median (RIG) median (RIG) median (RIG)

Visitors

(Who stayed

in another

group)

2 (1-4) 3 (1-5) 2 (0,5-3,5) * 1 (0-3)

Hosted 2 (0-2) 2 (0-5,5) 1 (0-3) * 1 (0-4,5)

Total 42 16 18 2 6

Missing Data 1 1

* No enough data to estimate the median.

11.2.2: Area of interaction with other groups in the last 5 years §

Researchers Hosted Researchers that Travel

Medical Informatics 10 (11,9) 10 (11,9)

Bioinformatics 7 (8,3) 11 (13,1)

High Performance and GRID

Computing

4 (4,8) 6 (7,1)

Nanoinformatics 1 (1,2) 1 (1,2)

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11.2.3: Perceived Impact of the Researchers Mobility

Global

n 84

European

Union

n 31

Latin America

n 30

North

Africa

n 9

Western

Balkans

n 14

Very high impact 16 (19 ) 4 (12,9) 9 (30 ) 1 (11,1) 2 (14,3)

High impact 14 (16,7) 6 (19,4) 7 (23,3) 0 2 (14,3)

Moderate impact 8 (9,5) 5 (16,1) 3 (10 ) 0 0

Small impact 3 (3,6) 1 (3,2) 0 1 (11,1) 1 (7,1)

Missing Data 42 (50) 15 (48,4) 11 (36,7) 7 (77,8) 9 (64,3)

Total 84 (100) 31 (100) 30 (100) 9 (100) 14 (100)

11.3: Main difficulties for researchers mobility §

Global

n 84

European

Union

n 31

Latin

America

n 30

North

Africa

n 9

Western Balkans

n 14

Lack of grants 59 (70,2) 17 (54,8) 25 (83,3) 7 (77,8) 10 (71,4)

Lack of time* 28 (33,3) 9 (29 ) 9 (30) 0 10 (71,4)

Lack of interest of

other institutions

13 (15,5) 3 (9,7) 4 (13,3) 1 (11,1) 5 (35,7)

Communication

problems

7 (8,3) 1 (3,2) 1 (3,3) 2 (22,2) 3 (21,4)

Institutional

interests conflict

6 (7,1) 1 (3,2) 2 (6,7) 0 3 (21,4)

Politic interests

conflict*

4 (4,8) 0 1 (3,3) 0 3 (21,4)

Discrimination

(Racial, religious,

…)

3 (3,6) 0 1 (3,3) 0 2 (14,3)

Other* 4 (4,8) 0 1 (3,3) 0 3 (21,4)

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Global

n 84

European

Union

n 31

Latin

America

n 30

North

Africa

n 9

Western Balkans

n 14

Description of

other problems

- Lack of

knowledge

regarding

other groups

- Our

group is

young

one and

just one

year old

- Visa regime

- Lack of strategy of

interoperab.

- Education in data

and media

semantification

- Lack of competence

in governmental

bodies

- Lack of elasticity in

European views

about innovations

11.4: Wishes for travelling and possibilities of hosting§

Whishes for travelling Possibility of hosting

Area:

Medical Informatics 36 (42,9) 34 (40,5)

Bioinformatics 42 (50 ) 37 (44 )

High performance and GRID

Computation

28 (33,3) 21 (25)

Nanoinformatics 8 (9,5) 5 (6 )

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Section 12: Training

12.1: Areas in which investigators had received training in the last 5 years

according to geographical areas.

Global European

Union

Latin

America

North

Africa

Western

Balkans

Any training 47 (49) 18 (75) 21 (87) 2 (29) 6 (50)*

Medical Informatics 18 (19) 5 (17) 9 (32) 1 (12) 3 (23)

Bioinformatics 29 (30) 13 (43) 12 (43) 2 (25) 2 (15)

High Performance and Grid

Computing

16 (17) 8 (27) 4 (14) 1 (12) 3 (23)

Nanoinformatics 1 (1) 0 0 1 (12) 0

*P value 0.008

12.1.1: How much impact has had the received training in the developing of new

products and new research?

Global European

Union

Latin

America

North

Africa

Western

Balkans

Very high 7 (7) 3 (17) 4 (19) 0 0

High 25 (26) 10 (56) 11 (52) 1 (50) 3 (60)

Moderate 11 (11) 5 (28) 5 (24) 0 1 (20)

Small 3 (3) 0 1 (5) 1 (50) 1 (20)

No impact

This was an optional section. Forty nine percent (n 47) refers to have had any training

in any of the following areas: Medical informatics, bioinformatics, High Performance

and Grid Computing and Nanoinformatics. 21 answered not to have had any training

while the rest (30) did not answer this section.

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12.1.2: Main difficulties for training

Global European

Union

Latin

America

North Africa Western

Balkans

Lack of courses 22 (23) 6 (20) 11 (39) 1 (12.5) 4 (31)

Poor quality of

courses

13 (13) 6 (20) 6 (21) 1 (12.5) 0

Poor quality of

professors,

6 (6) 2 (7) 3 (11) 1 (12.5) 0

Lack of time 23 (24) 7 (23) 11 (39) 1 (12.5) 4 (31)

Interest of

researchers

6 (6) 2 (7) 2 (7) 1 (12.5) 6 (8)

Training facility

and equipment

9 (9) 4 (13) 3 (11) 1 (12.5) 1 (8)

Other 6 (6) 1 (3) 2 (7) 2 (25) 1 (8)

12.2: Kind of training offered/need

Kind of training offered by the groups surveyed.

Global

European

Union

Latin

America North Africa

Western

Balkans

Medical

Informatics,

29 (30) 10 (33) 9 (32) 3 (37) 7 (54)

Bio 33 (34) 13 (43) 14 (50) 3 (37) 3 (21)

Grid 15 (16) 8 (27) 4 (14) 1 (12.5) 2 (15)

Nano 3 (24) 0 0 1 (12.5) 2 (15)

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Kind of training may your group need in the next 5 years

Global

n 96

European

Union

Latin

America

North Africa Western

Balkans

Medical

Informatics

33 (34) 10 (33) 10 (36) 4 (50) 9 (69)

Bio 36 (37) 15 (50) 12 (42) 5 (62) 4 (31)

Grid 34 (35) 15 (50) 10 (36) 3 (37) 6 (42)

Nano 12 (12) 3 (10) 3 (11) 1 (12.5) 5 (38)

Kind of training offered and needed in different subjects. (Because of few

number of answers they are not stratified by geographical area)

Training offered Training needed

Medical Informatics

Health information system 20 (20) 18 (19)

Public health 7 (7) 11 (11)

Imaging or biological signal data processing 10 (10) 14 (15)

Clinical Decision support systems 8 (8) 15 (16)

Nursing 15 (15) 4 /4)

Interoperability 6 (6) 14 (15)

Telemedicine 5 (5) 14 (15)

Organization and management 10 (10) 9 (9)

Education 6 (6) 7 (7)

Standardization 12 (12) 14 (15)

Bio informatics

Sequence Analysis 16 (17) 5 (5)

Image processing 4 (4) 7 (7)

Genomic annotation 8 (8) 8 (8)

Molecular simulation 7 (7) 5 (5)

Computer aided drug design 6 (6) 9 (9)

Systems Biology 3 (3) 16 (16)

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Organism Modelling 1 (1) 9 (9)

Comparative genomics 7 (7) 10 (10)

Metabolic reconstruction 3 (3) 6 (6)

Molecular Phylogeny 4 (4) 5 (5)

Functional genomic 7 (7) 9 (9)

Gene regulation network 6 (6) 11 (11)

Proteomic 3 (3) 8 (8)

Molecular modelling 7 (7) 4 (4)

Databases 18 (19) 14 (15)

Complex systems 2 (2) 7 (7)

Data mining 15 (16) 18 (19)

visualization 9 (9) 11 (11)

High performance and grid computing

Sequence analysis 5 (5) 8 (8)

Image processing 4 (4) 9 (9)

Genomic annotation 1 (1) 6 (6)

Molecular simulation 4 (4) 6 (6)

Computer assisted design 2 (2) 8 (8)

Systems biology 2 (2) 13 (13)

Decision support systems 3 (3) 12 (12)

Comparative genomic 3 (3) 7 (7)

Metabolic reconstruction 1 (1) 5 (5)

Molecular phylogeny 4 (4) 4 (4)

Functional genomic 1 (1) 7 (7)

Gene regulation networks 1 (1) 5 (5)

Proteomic 1 (1) 7 (7)

Molecular modelling 2 (2) 5 (5)

Databases 5 (5) 16 (17)

Complex systems 3 (3) 11 (11)

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Visualization systems 2 (2) 9 (9)

Software developing 7 (7) 17 (18)

Nanoinformatics

Implantable materials 1 (1) 4 (4)

Implantable devices 1 (1) 7 (7)

Surgical aids 1 (1) 3 (3)

Diagnostic tools 1 (1) 7 (7)

Modelling and simulation 1 (1) 8 (8)

Databases 1 (1) 5 (5)

Understanding basic life processes 1 (1) 4 (4)

Molecule detection 1 (1) 3 (3)

Filtering systems 1 (1) 2 (2)

Signal transduction systems 1 (1) 4 (4)

Drug delivery systems 2 (2) 5 (5)

Drug discovery 2 (2) 5 (5)

Targeting systems 2 (2) 5 (5)

Molecule based machineries 1 (1) 5 (5)

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Section 13: Hardware

13.1: N° of processors in the research group

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Up to 64 43 (54,4) 12 (38,7) 20 (74,1) 5 (62,5) 6 (46,2)

65 to 256 6 (7,6) 3 (9,7) 3 (11,1) 0 0

257 to 1024 5 (6,3) 2 (6,5) 1 (3,7) 0 2 (15,4)

> 1024 6 (7,6) 4 (12,9) 0 1 (12,5) 1 (7,7)

Don't know 10 (12,7) 4 (12,9) 2 (7,4) 1 (12,5) 3 (23,1)

Missing 9 (11,4) 6 (19,4) 1 (3,7) 1 (12,5) 1 (7,7)

13.2: Type of processors

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Specific processor 51 (64,6) 19 (61,3) 20 (74,1) 5 (62,5) 7 (53,8)

Don't know 19 (24,1) 6 (19,4) 6 (22,2) 2 (25) 5 (38,5)

Missing Data 9 (11,4) 6 (19,4) 1 (3,7) 1 (12,5) 1 (7,7)

Total 79 (100) 31 (100) 27 (100) 8 (100) 13 (100)

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Description of the processors§

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Pentium 38 (48,1) 10 (32,3) 18 (66,7) 4 (50 ) 6 (46,2)

Xeon 23 (29,1) 12 (38,7) 7 (25,9) 2 (25) 2 (15,4)

Opteron 17 (21,5) 6 (19,4) 5 (18,5) 3 (37,5) 3 (23,1)

Itanium 6 (7,6) 4 (12,9) 0 1 (12,5) 1 (7,7)

PowerPC 5 (6,3) 3 (9,7) 0 1 (12,5) 1 (7,7)

POWER 3 (3,8) 1 (3,2) 0 1 (12,5) 1 (7,7)

PowerXCell 3 (3,8) 1 (3,2) 0 1 (12,5) 1 (7,7)

Other 5 (6,3) 2 (6,5) 0 2 (25) 1 (7,7)

Other :

Description

- Cray vector

- MIPS

- Sony

- Toshiba

13.3: Storage capacity

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Up to 10 TB 44 (55,7) 16 (51,6) 19 (70,4) 4 (50 ) 5 (38,5)

11 to 50 TB 6 (7,6) 3 (9,7) 2 (7,4) 0 1 (7,7)

51 to 100 TB 2 (2,5) 0 0 0 2 (15,4)

> 100 TB 5 (6,3) 2 (6,5) 1 (3,7) 1 (12,5) 1 (7,7)

Don't know 13 (16,5) 4 (12,9) 4 (14,8) 2 (25) 3 (23,1)

Missing data 9 (11,4) 6 (19,4) 1 (3,7) 1 (12,5) 1 (7,7)

13.5: Kind of equipment currently using

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Specific

equipment

46 (58,2) 15 (48,4) 19 (70,4) 4 (50 ) 8 (61,5)

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Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Don't know 24 (30,4) 10 (32,3) 7 (25,9) 3 (37,5) 4 (30,8)

Missing data 9 (11,4) 6 (19,4) 1 (3,7) 1 (12,5) 1 (7,7)

Description of the used equipment§

Global European

Union

Latin

America

North

Africa

Western

Balkans

DELL 25 (31,6) 8 (25,8) 10 (37) 4 (50 ) 3 (23,1)

HP 19 (24,1) 7 (22,6) 6 (22,2) 0 6 (46,2)

IBM 16 (20,3) 5 (16,1) 5 (18,5) 1 (12,5) 5 (38,5)

SELF-

MADE

12 (15,2) 2 (6,5) 7 (25,9) 0 3 (23,1)

SUN 8 (10,1) 1 (3,2) 4 (14,8) 1 (12,5) 2 (15,4)

NEC 2 (2,5) 1 (3,2) 0 0 1 (7,7)

CRAY 3 (3,8) 2 (6,5) 0 0 1 (7,7)

FUJITSU 5 (6,3) 1 (3,2) 1 (3,7) 0 3 (23,1)

HITACHI* 2 (2,5) 0 0 0 2 (15,4)

SGI 5 (6,3) 3 (9,7) 1 (3,7) 0 1 (7,7)

NVIDIA-

TESLA

5 (6,3) 2 (6,5) 2 (7,4) 0 1 (7,7)

OTHER 5 (6,3) 3 (9,7) 1 (3,7) 0 1 (7,7)

OTHER

Description

- Apple - Super

Micro

computer

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13.6: Bandwidth

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

up to 512 kbps 4 (5,1) 0 2 (7,4) 1 (12,5) 1 (7,7)

512 to 1Mbps 12 (15,2) 1 (3,2) 8 (29,6) 1 (12,5) 2 (15,4)

1 to 2 Mbps 6 (7,6) 3 (9,7) 1 (3,7) 1 (12,5) 1 (7,7)

2 to 4 Mbps 4 (5,1) 1 (3,2) 3 (11,1) 0 0

> 4 Mbps 32 (40,5) 14 (45,2) 9 (33,3) 3 (37,5) 6 (46,2)

Don't know 12 (15,2) 6 (19,4) 3 (11,1) 1 (12,5) 2 (15,4)

Missing data 9 (11,4) 6 (19,4) 1 (3,7) 1 (12,5) 1 (7,7)

13.7: Interconnection system

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Specific

interconnection

system

49 (62 ) 17 (54,8) 20 (74,1) 4 (50 ) 8 (61,5)

Don't know 21 (26,6) 8 (25,8) 6 (22,2) 3 (37,5) 4 (30,8)

Missing data 9 (11,4) 6 (19,4) 1 (3,7) 1 (12,5) 1 (7,7)

Description of the interconnection system used

Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

Ethernet

(10 to 100mpbs

24 (30,4) 5 (16,1) 11 (40,7) 3 (37,5) 5 (38,5)

Ethernet

(1 Gbps)

32 (40,5) 15 (48,4) 11 (40,7) 0 6 (46,2)

InfiniBand 8 (10,1) 4 (12,9) 2 (7,4) 0 2 (15,4)

NumaLink 1 (1,3) 0 0 0 1 (7,7)

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Global

n 79

European

Union

n 31

Latina

America

n 27

North Africa

n 8

Western

Balkans

n 13

CrayLink 1 (1,3) 0 0 0 1 (7,7)

Other 2 (2,5) 1 (3,2) 0 0 1 (7,7)

Other: Description Myrinet

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3.8: Dedicated Data Centre

Data Centre Data Centre Administrator

Yes 47 (59,5) 40 (50,6)

No 21 (26,6) 6 (7,6)

Don't know 2 (2,5) 1 (1,3)

Missing data 9 (11,4) 32 (40,5)

Total 79 (100 ) 79 (100 )

13.9: Most important aspects to improve in the next 5 years2

Global

n 79

European

Union

n 31

Latina

America

n 27

North

Africa

n 8

Western

Balkans

n 13

Number of

processor

(cores)

40 (50,6) 15 (48,4) 17 (63 ) 3 (37,5) 5 (38,5)

Storage capacity 37 (46,8) 16 (51,6) 14 (51,9) 4 (50) 3 (23,1)

Data Centre 36 (45,6) 9 (29 ) 13 (48,1) 6 (75) 8 (61,5)

Bandwidth 34 (43 ) 8 (25,8) 17 (63 ) 6 (75) 3 (23,1)

Number of

workstations

26 (32,9) 5 (16,1) 12 (44,4) 5 (62,5) 4 (30,8)

Internal

connection

16 (20,3) 2 (6,5) 5 (18,5) 6 (75) 3 (23,1)

2 The sums of each column are greater than the total (79) because each interviewee had the possibility to

choose several options.

analysis and results of the survey of grid...

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