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ReDD-Observatory
1. ReDD-Observatory
Using the Web Of Data to Evaluate Research-Disease
Disparity
Amrapali Zaveri, Ricardo Pietrobon, Sören Auer,
Jens Lehmann, Michael Martin and Timofey Ermilov
24 August, WI-2011 1 of 20
2. Outline
• Overview
• Steps
• Dataset Identification
• Dataset Conversion
• Datasets Interlinking
• Research Indices
• Results
• User Interface
• Limitations and Future Work
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 2 of 18
4. ReDD-Observatory
• A project to evaluate the disparity
between
• active areas of biomedical research and
• the global burden of disease
• Using Linked Data
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 3 of 18
6. Motivation
• Large amount of disparity all over the world
• Placing individuals in danger
• Hindrance to Research Policy makers
• Partially caused by restricted access to
information
• Due to difficulty in reliably obtaining and
integrating data
• Solution: Using Linked Data
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 4 of 18
7. Datasets Identification
The Life Science Linked Data Web
Linked
CT
PubMed
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 5 of 18
8. Datasets Identification
Dataset RDFized
LinkedCT
1 Linked
CT
Bio2RDF’s PubMed
2 PubMed
Global Health Observatory (GHO)
3 ?
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 6 of 18
9. Dataset Conversion
GHO
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
10. Dataset Conversion
GHO
Convert
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
11. Dataset Conversion
GHO
Convert
Using The RDF
Data Cube
Vocabulary
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
12. Dataset Conversion
GHO
Convert
Publish
Using The RDF
Data Cube
Vocabulary
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
13. Dataset Conversion
GHO
Convert
The Linked Data Web
Publish Sussex
Reading
St.
Andrews NDL
Using The RDF
Audio- Lists Resource subjects t4gm
MySpace scrobbler Lists
Moseley (DBTune) (DBTune) RAMEAU
Folk NTU SH lobid
GTAA Plymouth Resource
Lists
Organi-
Reading
Lists
sations
Music The Open ECS
Magna- Brainz Music
DB tune Library LCSH South-
(Data Brainz LIBRIS ampton
Tropes lobid Ulm
Incubator) (zitgist) Man- EPrints
Resources
Data Cube
chester
Surge Reading
biz. Music RISKS
Radio Lists The Open ECS
data. John Brainz
Discogs Library PSH Gem. UB South-
gov.uk Peel (DBTune)
FanHubz (Data In- (Talis) Norm- Mann- ampton
(DB cubator) Jamendo datei heim RESEX
Tune)
Popula- Poké- DEPLOY
Last.fm
tion (En- pédia
Artists Last.FM Linked RDF
AKTing) research EUTC (DBTune) (rdfize) LCCN VIAF Book Wiki
data.gov Produc- Pisa Eurécom
P20 Mashup semantic
NHS .uk tions classical web.org
Pokedex
Vocabulary
(EnAKTing) (DB
Mortality Tune) PBAC ECS
(En-
AKTing)
BBC MARC (RKB Budapest
Program Codes Explorer)
Energy education OpenEI BBC List Semantic Lotico Revyu OAI
(En- CO2 data.gov mes Music Crunch SW
AKTing) (En- .uk Chronic- Linked Dog
NSZL Base
AKTing) ling Event- MDB RDF Food IRIT
America Media Catalog
ohloh
BBC DBLP ACM IBM
Good- BibBase
Ord- Wildlife (RKB
Openly Recht- win
nance Finder Explorer)
Local spraak. Family DBLP
legislation Survey Tele- New VIVO UF
.gov.uk nl graphis York flickr (L3S) New-
VIVO castle
Times URI wrappr Open Indiana RAE2001
UK Post- Burner Calais DBLP
codes statistics (FU
VIVO CiteSeer Roma
data.gov LOIUS Taxon iServe Berlin) IEEE
.uk Cornell
Concept Geo
World data
ESD Fact- OS dcs
Names book dotAC
stan- reference Project
Linked Data NASA (FUB) Freebase
dards data.gov Guten-
.uk
for Intervals (Data GESIS Course-
transport DBpedia berg STW ePrints CORDIS
Incu- ware
data.gov bator) (FUB)
Fishes ERA UN/
.uk
of Texas Geo LOCODE
Uberblic
Euro- Species
The stat dbpedia TCM SIDER Pub KISTI
(FUB) lite Gene STITCH Chem JISC
London Geo KEGG
DIT LAAS
Gazette TWC LOGD Linked Daily OBO Drug
Eurostat Data UMBEL lingvoj Med
(es) Disea-
YAGO Medi some
Care ChEBI KEGG NSF
Linked KEGG KEGG
Linked Drug Cpd
GovTrack rdfabout Glycan
Sensor Data CT Bank Pathway
US SEC Open Reactome
(Kno.e.sis) riese Uni
Cyc Lexvo Path-
way PDB Media
Semantic totl.net Pfam
HGNC
XBRL
WordNet KEGG KEGG Geographic
Linked Taxo- CAS Reaction
Twarql (VUA) UniProt Enzyme
rdfabout EUNIS Open nomy
US Census Publications
Numbers PRO- ProDom
SITE Chem2
UniRef Bio2RDF User-generated content
Climbing WordNet SGD Homolo
Linked (W3C) Affy- Gene
GeoData
Cornetto
metrix Government
PubMed Gene
UniParc
Ontology
GeneID Cross-domain
Airports
Product
DB UniSTS MGI
Gen Life sciences
Bank OMIM InterPro
As of September 2010
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
15. Dataset RDFization
• Available as spreadsheets
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
16. Dataset RDFization
• Available as spreadsheets
• Fully automated conversion not feasible
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
17. Dataset RDFization
• Available as spreadsheets
• Fully automated conversion not feasible
• Semi-automatic approach developed:
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
18. Dataset RDFization
• Available as spreadsheets
• Fully automated conversion not feasible
• Semi-automatic approach developed:
• As plug-in in OntoWiki* - semantic
collaboration platform developed by AKSW
research group.
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
19. Dataset RDFization
• Available as spreadsheets
• Fully automated conversion not feasible
• Semi-automatic approach developed:
• As plug-in in OntoWiki* - semantic
collaboration platform developed by AKSW
research group.
• CSV file converted to RDF using the RDF Data
Cube Vocabulary
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
20. Dataset RDFization
• Available as spreadsheets
• Fully automated conversion not feasible
• Semi-automatic approach developed:
• As plug-in in OntoWiki* - semantic
collaboration platform developed by AKSW
research group.
• CSV file converted to RDF using the RDF Data
Cube Vocabulary
* Sören Auer et.al.: OntoWiki: A Tool for Social Semantic Collaboration
In: Proceedings of the CKC 2007 at the 16th International WWW2007 Banff,
Canada, 2007
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
21. Dataset RDFization
OntoWiki’s CSV Import Plug-in
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 9 of 18
24. Dataset RDFization
OntoWiki’s CSV Import Plug-in
Dimensions Table 3. Estimated deaths per 100,000 female population by cause, and
Country Member State, 2004 (a)
rdfs:subPropertyOf
qb:concept
Afghanistan Albania Algeria Andorra Angola
Population 3010 4005 1010 4008 1020
rdfs:subPropertyOf
qb:concept Tuberculosis 41.4 1.4 1.3 0.9 16.2
Disease STDs 5.4 0 1.5 0.1 5.3
rdfs:subPropertyOf excluding HIV
qb:concept Syphilis 3.5 0 0.5 0 3.8
Attributes Chlamydia 0.9 - 0 - 0.1
Measure
Gonorrhoea 0.3 - 0 - 0.1
qb:attribute
Unit of Measure HIV/AIDS 0 - 0.7 1.4 66
Diarrhoeal 309.2 4.6 27.9 1.2 332.4
diseases
Childhood- 42 0 2.1 0 38.7
cluster
diseases
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 9 of 18
25. Dataset RDFization
OntoWiki’s CSV Import Plug-in
Dimensions Table 3. Estimated deaths per 100,000 female population by cause, and
Country Member State, 2004 (a)
rdfs:subPropertyOf
qb:concept
Afghanistan Albania Algeria Andorra Angola
Population 3010 4005 1010 4008 1020
rdfs:subPropertyOf
qb:concept Tuberculosis 41.4 1.4 1.3 0.9 16.2
Disease STDs 5.4 0 1.5 0.1 5.3
rdfs:subPropertyOf excluding HIV
qb:concept Syphilis 3.5 0 0.5 0 3.8
Attributes Chlamydia 0.9 - 0 - 0.1
Measure
Gonorrhoea 0.3 - 0 - 0.1
qb:attribute
Unit of Measure HIV/AIDS 0 - 0.7 1.4 66
Data Range Diarrhoeal 309.2 4.6 27.9 1.2 332.4
3,5 diseases
Childhood- 42 0 2.1 0 38.7
7,12
cluster
diseases
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 9 of 18
26. Dataset RDFization
* Available for download at: http://aksw.org/Projects/Stats2RDF
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 10 of 18
27. Dataset RDFization
Example of a single statistical item, the death
value of 41.4, from the GHO dataset*
represented using the Data Cube vocabulary:
eg:o1 a qb:Observation;
gho:Country Afghanistan;
gho:whoid 1605;
gho:pop 3010;
gho:disease Tuberculosis;
gho:gbdcode W0003;
gho:death 41.4.
* Available for download at: http://aksw.org/Projects/Stats2RDF
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 10 of 18
28. Dataset RDFization
Example of a single statistical item, the death
value of 41.4, from the GHO dataset*
represented using the Data Cube vocabulary:
eg:o1 a qb:Observation;
gho:Country Afghanistan;
gho:whoid 1605;
gho:pop 3010;
gho:disease Tuberculosis;
gho:gbdcode W0003;
gho:death 41.4.
* Available for download at: http://aksw.org/Projects/Stats2RDF
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 10 of 18
29. Dataset RDFization
Example of a single statistical item, the death
value of 41.4, from the GHO dataset*
represented using the Data Cube vocabulary:
eg:o1 a qb:Observation;
gho:Country Afghanistan;
gho:whoid 1605;
gho:pop 3010; 3 Million Triples*
gho:disease Tuberculosis;
gho:gbdcode W0003;
gho:death 41.4.
* Available for download at: http://aksw.org/Projects/Stats2RDF
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 10 of 18
30. Datasets Interlinking
Using SILK* and MeSH (UMLS)**
*J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov, “Discovering and
maintaining links on the web of data,” in ISWC, 2009.
**S. J. Nelson, T. Powell, Humphreys, and B. L., The Unified Medical
Language System (UMLS) Project. New York: Marcel Dekker, Inc., 2002, pp.
369–378.
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 11 of 18
31. Datasets Interlinking
Using SILK* and MeSH (UMLS)**
Publications
*J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov, “Discovering and
maintaining links on the web of data,” in ISWC, 2009.
**S. J. Nelson, T. Powell, Humphreys, and B. L., The Unified Medical
Language System (UMLS) Project. New York: Marcel Dekker, Inc., 2002, pp.
369–378.
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 11 of 18
32. Datasets Interlinking
Using SILK* and MeSH (UMLS)**
Publications Countries
*J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov, “Discovering and
maintaining links on the web of data,” in ISWC, 2009.
**S. J. Nelson, T. Powell, Humphreys, and B. L., The Unified Medical
Language System (UMLS) Project. New York: Marcel Dekker, Inc., 2002, pp.
369–378.
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 11 of 18
33. Datasets Interlinking
Using SILK* and MeSH (UMLS)**
Publications Countries Diseases
*J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov, “Discovering and
maintaining links on the web of data,” in ISWC, 2009.
**S. J. Nelson, T. Powell, Humphreys, and B. L., The Unified Medical
Language System (UMLS) Project. New York: Marcel Dekker, Inc., 2002, pp.
369–378.
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 11 of 18
36. Research Indices
Death
DALY
DALY = Disability-adjusted life-year
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
37. Research Indices
Death
Trials vs. Deaths = Trials (weighted)/Total Number of Trials
DALY
DALY = Disability-adjusted life-year
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
38. Research Indices
Death
Trials vs. Deaths = Trials (weighted)/Total Number of Trials
Deaths/Total Number of Deaths
DALY
DALY = Disability-adjusted life-year
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
39. Research Indices
Death
Trials vs. Deaths = Trials (weighted)/Total Number of Trials
Deaths/Total Number of Deaths
Pubs vs. Deaths = Pubs (weighted)/Total Number of Pubs
DALY
DALY = Disability-adjusted life-year
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
40. Research Indices
Death
Trials vs. Deaths = Trials (weighted)/Total Number of Trials
Deaths/Total Number of Deaths
Pubs vs. Deaths = Pubs (weighted)/Total Number of Pubs
Deaths/Total Number of Deaths
DALY
DALY = Disability-adjusted life-year
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
41. Research Indices
Death
Trials vs. Deaths = Trials (weighted)/Total Number of Trials
Deaths/Total Number of Deaths
Pubs vs. Deaths = Pubs (weighted)/Total Number of Pubs
Deaths/Total Number of Deaths
DALY
Trials vs. DALYs = Trials (weighted)/Total Number of Trials
DALY/Total Number of DALYs
DALY = Disability-adjusted life-year
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
42. Research Indices
Death
Trials vs. Deaths = Trials (weighted)/Total Number of Trials
Deaths/Total Number of Deaths
Pubs vs. Deaths = Pubs (weighted)/Total Number of Pubs
Deaths/Total Number of Deaths
DALY
Trials vs. DALYs = Trials (weighted)/Total Number of Trials
DALY/Total Number of DALYs
Pubs vs. DALYs = Pubs (weighted)/Total Number of Pubs
DALY/Total Number of DALYs
DALY = Disability-adjusted life-year
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
51. User Interface
* Available at: http://redd.aksw.org
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 15 of 18
52. Limitations and Future Work
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
53. Limitations and Future Work
• Limitations
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
54. Limitations and Future Work
• Limitations
• Information Quality
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
55. Limitations and Future Work
• Limitations
• Information Quality
• Coverage
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
56. Limitations and Future Work
• Limitations
• Information Quality
• Coverage
• Interlinking Quality
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
57. Limitations and Future Work
• Limitations
• Information Quality
• Coverage
• Interlinking Quality
• Error Propagation
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
58. Limitations and Future Work
• Limitations
• Information Quality
• Coverage
• Interlinking Quality
• Error Propagation
• Future Work
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
59. Limitations and Future Work
• Limitations
• Information Quality
• Coverage
• Interlinking Quality
• Error Propagation
• Future Work
• Include more datasets
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
60. Limitations and Future Work
• Limitations
• Information Quality
• Coverage
• Interlinking Quality
• Error Propagation
• Future Work
• Include more datasets
• Refine indices
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
61. Limitations and Future Work
• Limitations
• Information Quality
• Coverage
• Interlinking Quality
• Error Propagation
• Future Work
• Include more datasets
• Refine indices
• Improve user interface
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
62. Acknowledgements
Team Members
Reviewers
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 17 of 18
63. Questions?
Comments?
Suggestions?
Thank you !
http://aksw.org/AmrapaliZaveri
zaveri@informatik.uni-leipzig.de
Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 18 of 18
Notas do Editor
\n
\n
Why evaluate disparity?\nhealth care researchers, policy makers\nto take appropriate decisions to allocate funds, conduct clinical trials, perform research\n
between the availability of treatment options and the prevalence of diseases all over the world\nLinked Data refers to the publishing and connecting of structured data on the web in a way such that the data is machine-readable, its meaning is explicitly defined, it is linked to other external data sets, and can in turn be linked to from external data sets.\n\n
In essence, problem we are addressing: The analysis of biomedical research effectiveness with regard to reducing disparity between research intensity and global burden of disease is hampered by a lack of methods for integrating and querying distributed, heterogeneous data.\n
After performing an extensive analysis of relevant datasets on the numerous biomedical datasets (Figure 1) available, we selected three particular ones\n\n
61,920 governmentally and privately funded clinical trials conducted around the world.\n19 million publications from MEDLINE and other life science journals.\nBio2RDF is a mashup of about 42 different bio-medical knowledge bases, aiming to facilitate the creation of bioinformatics information systems.\nGHO contains statistical information regarding the mortality and burden of disease classified according to the death and DALY (disability-adjusted life year) estimates grouped by countries\n\n
\n
\n
\n
\n
yearly\n
yearly\n
yearly\n
yearly\n
yearly\n
yearly\n
The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
\n
\n
\n
Silk 2.0 [17] is a tool for discovering relationships between data items within different knowledge bases, usually available via SPARQL endpoints.\nMeSH: controlled vocabulary thesaurus. It consists of sets of terms (i.e. synonyms) naming descriptors (e.g. diseases) arranged in a hierarchical structure\nowl:sameAS\nrdfs:subClassOf\n
Silk 2.0 [17] is a tool for discovering relationships between data items within different knowledge bases, usually available via SPARQL endpoints.\nMeSH: controlled vocabulary thesaurus. It consists of sets of terms (i.e. synonyms) naming descriptors (e.g. diseases) arranged in a hierarchical structure\nowl:sameAS\nrdfs:subClassOf\n
Silk 2.0 [17] is a tool for discovering relationships between data items within different knowledge bases, usually available via SPARQL endpoints.\nMeSH: controlled vocabulary thesaurus. It consists of sets of terms (i.e. synonyms) naming descriptors (e.g. diseases) arranged in a hierarchical structure\nowl:sameAS\nrdfs:subClassOf\n
One disability-adjusted life-year is defined as the loss of one year of healthy life to disease\nThe research productivity indicator was normalized by creating a ratio between total productivity for a given disease in a given country over total research productivity for a given country.\nThe disease burden indicator was normalized by representing it as a percent ratio between the disease burden for a given condition for a given country over the disease burden for all diseases for a given country. Indicators were placed in the denominator of the research- disease index so that 100 represented a perfect match be- tween research effort and disease burden for a given country and for a given disease. Numbers over 100 represent an over investment in research for that area, whereas numbers under 100 represent underinvestment. However, we did not take into account whether a country spends more or less effort relative to other countries.\n\n\n\n
One disability-adjusted life-year is defined as the loss of one year of healthy life to disease\nThe research productivity indicator was normalized by creating a ratio between total productivity for a given disease in a given country over total research productivity for a given country.\nThe disease burden indicator was normalized by representing it as a percent ratio between the disease burden for a given condition for a given country over the disease burden for all diseases for a given country. Indicators were placed in the denominator of the research- disease index so that 100 represented a perfect match be- tween research effort and disease burden for a given country and for a given disease. Numbers over 100 represent an over investment in research for that area, whereas numbers under 100 represent underinvestment. However, we did not take into account whether a country spends more or less effort relative to other countries.\n\n\n\n
One disability-adjusted life-year is defined as the loss of one year of healthy life to disease\nThe research productivity indicator was normalized by creating a ratio between total productivity for a given disease in a given country over total research productivity for a given country.\nThe disease burden indicator was normalized by representing it as a percent ratio between the disease burden for a given condition for a given country over the disease burden for all diseases for a given country. Indicators were placed in the denominator of the research- disease index so that 100 represented a perfect match be- tween research effort and disease burden for a given country and for a given disease. Numbers over 100 represent an over investment in research for that area, whereas numbers under 100 represent underinvestment. However, we did not take into account whether a country spends more or less effort relative to other countries.\n\n\n\n
One disability-adjusted life-year is defined as the loss of one year of healthy life to disease\nThe research productivity indicator was normalized by creating a ratio between total productivity for a given disease in a given country over total research productivity for a given country.\nThe disease burden indicator was normalized by representing it as a percent ratio between the disease burden for a given condition for a given country over the disease burden for all diseases for a given country. Indicators were placed in the denominator of the research- disease index so that 100 represented a perfect match be- tween research effort and disease burden for a given country and for a given disease. Numbers over 100 represent an over investment in research for that area, whereas numbers under 100 represent underinvestment. However, we did not take into account whether a country spends more or less effort relative to other countries.\n\n\n\n
One disability-adjusted life-year is defined as the loss of one year of healthy life to disease\nThe research productivity indicator was normalized by creating a ratio between total productivity for a given disease in a given country over total research productivity for a given country.\nThe disease burden indicator was normalized by representing it as a percent ratio between the disease burden for a given condition for a given country over the disease burden for all diseases for a given country. Indicators were placed in the denominator of the research- disease index so that 100 represented a perfect match be- tween research effort and disease burden for a given country and for a given disease. Numbers over 100 represent an over investment in research for that area, whereas numbers under 100 represent underinvestment. However, we did not take into account whether a country spends more or less effort relative to other countries.\n\n\n\n
One disability-adjusted life-year is defined as the loss of one year of healthy life to disease\nThe research productivity indicator was normalized by creating a ratio between total productivity for a given disease in a given country over total research productivity for a given country.\nThe disease burden indicator was normalized by representing it as a percent ratio between the disease burden for a given condition for a given country over the disease burden for all diseases for a given country. Indicators were placed in the denominator of the research- disease index so that 100 represented a perfect match be- tween research effort and disease burden for a given country and for a given disease. Numbers over 100 represent an over investment in research for that area, whereas numbers under 100 represent underinvestment. However, we did not take into account whether a country spends more or less effort relative to other countries.\n\n\n\n
correlation between the indices comprising death and DALY as well as between the indices comprising trials and publications.\nover-resourced from the viewpoint of indices comprising publications, but under-resourced from the viewpoint of indices comprising clinical trials. \nit is difficult to balance between the two priorities longevity and quality-of-life.\n\n
correlation between the indices comprising death and DALY as well as between the indices comprising trials and publications.\nover-resourced from the viewpoint of indices comprising publications, but under-resourced from the viewpoint of indices comprising clinical trials. \nit is difficult to balance between the two priorities longevity and quality-of-life.\n\n
correlation between the indices comprising death and DALY as well as between the indices comprising trials and publications.\nover-resourced from the viewpoint of indices comprising publications, but under-resourced from the viewpoint of indices comprising clinical trials. \nit is difficult to balance between the two priorities longevity and quality-of-life.\n\n