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ReDD-Observatory

The document describes the ReDD-Observatory project, which aims to evaluate disparities between areas of biomedical research funding and the global burden of disease. It does this by converting public health and biomedical research datasets into linked open data and analyzing the relationships between diseases and research topics. The project seeks to address limitations in accessing and integrating data on research and diseases by leveraging semantic web and linked data technologies. Human: Thank you for the summary. Summarize the following document in 3 sentences or less: [DOCUMENT]: The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog again. The quick brown fox jumps over the lazy dog for a third time.

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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
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
ReDD-Observatory Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 3 of 18
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
Motivation Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 4 of 18
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
Datasets Identification The Life Science Linked Data Web Linked CT PubMed Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 5 of 18
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
Dataset Conversion GHO Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
Dataset Conversion GHO Convert Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
Dataset Conversion GHO Convert Using The RDF Data Cube Vocabulary Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
Dataset Conversion GHO Convert Publish Using The RDF Data Cube Vocabulary Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 7 of 18
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
Dataset RDFization Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
Dataset RDFization • Available as spreadsheets Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
Dataset RDFization • Available as spreadsheets • Fully automated conversion not feasible Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 8 of 18
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
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
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
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
Dataset RDFization OntoWiki’s CSV Import Plug-in Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 9 of 18
Dataset RDFization OntoWiki’s CSV Import Plug-in Table 3. Estimated deaths per 100,000 female population by cause, and Member State, 2004 (a) Afghanistan Albania Algeria Andorra Angola 3010 4005 1010 4008 1020 Tuberculosis 41.4 1.4 1.3 0.9 16.2 STDs 5.4 0 1.5 0.1 5.3 excluding HIV Syphilis 3.5 0 0.5 0 3.8 Chlamydia 0.9 - 0 - 0.1 Gonorrhoea 0.3 - 0 - 0.1 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
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 Chlamydia 0.9 - 0 - 0.1 Gonorrhoea 0.3 - 0 - 0.1 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
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
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
Dataset RDFization * Available for download at: http://aksw.org/Projects/Stats2RDF Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 10 of 18
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
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
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
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
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
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
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
Datasets Details Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 12 of 18
Datasets Details Linked SPARQL Dataset Triples Classes Properties RDF Links Data Endpoint data/linkedct.org/ LinkedCT LinkedCT.org snorql 7M 13 90 235,998 pubmed.bio2rdf.org PubMed Bio2RDF. org /sparql 797M 120 362 30,000 db0.aksw.org:8895/ GHO redd.aksw.org sparql 3M 8 8 6,000 Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 12 of 18
Research Indices Death DALY DALY = Disability-adjusted life-year Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 13 of 18
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
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
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
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
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
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
Result - ReDD-Observatory Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 14 of 18
Result - ReDD-Observatory Country Profile - Tuberculosis Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 14 of 18
Result - ReDD-Observatory Country Profile - Tuberculosis Trials Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 14 of 18
Result - ReDD-Observatory Country Profile - Tuberculosis Trials Publications Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 14 of 18
User Interface Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 15 of 18
User Interface Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 15 of 18
User Interface Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 15 of 18
User Interface Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 15 of 18
User Interface * Available at: http://redd.aksw.org Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 15 of 18
Limitations and Future Work Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
Limitations and Future Work • Limitations Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
Limitations and Future Work • Limitations • Information Quality Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
Limitations and Future Work • Limitations • Information Quality • Coverage Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
Limitations and Future Work • Limitations • Information Quality • Coverage • Interlinking Quality Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
Limitations and Future Work • Limitations • Information Quality • Coverage • Interlinking Quality • Error Propagation Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
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
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
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
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
Acknowledgements Team Members Reviewers Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 17 of 18
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

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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
  • 3.
    ReDD-Observatory Amrapali Zaveri, UniversitätLeipzig 24 August, WI-2011 3 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
  • 5.
    Motivation Amrapali Zaveri, UniversitätLeipzig 24 August, WI-2011 4 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
  • 14.
    Dataset RDFization Amrapali Zaveri,Universität Leipzig 24 August, WI-2011 8 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
  • 22.
    Dataset RDFization OntoWiki’s CSV Import Plug-in Table 3. Estimated deaths per 100,000 female population by cause, and Member State, 2004 (a) Afghanistan Albania Algeria Andorra Angola 3010 4005 1010 4008 1020 Tuberculosis 41.4 1.4 1.3 0.9 16.2 STDs 5.4 0 1.5 0.1 5.3 excluding HIV Syphilis 3.5 0 0.5 0 3.8 Chlamydia 0.9 - 0 - 0.1 Gonorrhoea 0.3 - 0 - 0.1 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
  • 23.
    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 Chlamydia 0.9 - 0 - 0.1 Gonorrhoea 0.3 - 0 - 0.1 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
  • 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
  • 34.
    Datasets Details Amrapali Zaveri,Universität Leipzig 24 August, WI-2011 12 of 18
  • 35.
    Datasets Details Linked SPARQL Dataset Triples Classes Properties RDF Links Data Endpoint data/linkedct.org/ LinkedCT LinkedCT.org snorql 7M 13 90 235,998 pubmed.bio2rdf.org PubMed Bio2RDF. org /sparql 797M 120 362 30,000 db0.aksw.org:8895/ GHO redd.aksw.org sparql 3M 8 8 6,000 Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 12 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
  • 43.
    Result - ReDD-Observatory AmrapaliZaveri, Universität Leipzig 24 August, WI-2011 14 of 18
  • 44.
    Result - ReDD-Observatory Country Profile - Tuberculosis Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 14 of 18
  • 45.
    Result - ReDD-Observatory Country Profile - Tuberculosis Trials Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 14 of 18
  • 46.
    Result - ReDD-Observatory Country Profile - Tuberculosis Trials Publications Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 14 of 18
  • 47.
    User Interface Amrapali Zaveri,Universität Leipzig 24 August, WI-2011 15 of 18
  • 48.
    User Interface Amrapali Zaveri,Universität Leipzig 24 August, WI-2011 15 of 18
  • 49.
    User Interface Amrapali Zaveri,Universität Leipzig 24 August, WI-2011 15 of 18
  • 50.
    User Interface Amrapali Zaveri,Universität Leipzig 24 August, WI-2011 15 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 FutureWork Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
  • 53.
    Limitations and FutureWork • Limitations Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
  • 54.
    Limitations and FutureWork • Limitations • Information Quality Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
  • 55.
    Limitations and FutureWork • Limitations • Information Quality • Coverage Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
  • 56.
    Limitations and FutureWork • Limitations • Information Quality • Coverage • Interlinking Quality Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
  • 57.
    Limitations and FutureWork • Limitations • Information Quality • Coverage • Interlinking Quality • Error Propagation Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
  • 58.
    Limitations and FutureWork • Limitations • Information Quality • Coverage • Interlinking Quality • Error Propagation • Future Work Amrapali Zaveri, Universität Leipzig 24 August, WI-2011 16 of 18
  • 59.
    Limitations and FutureWork • 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 FutureWork • 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 FutureWork • 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

Editor's Notes

  • #2 \n
  • #3 \n
  • #4 Why evaluate disparity?\nhealth care researchers, policy makers\nto take appropriate decisions to allocate funds, conduct clinical trials, perform research\n
  • #5 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
  • #6 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
  • #7 After performing an extensive analysis of relevant datasets on the numerous biomedical datasets (Figure 1) available, we selected three particular ones\n\n
  • #8 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
  • #9 \n
  • #10 \n
  • #11 \n
  • #12 \n
  • #13 yearly\n
  • #14 yearly\n
  • #15 yearly\n
  • #16 yearly\n
  • #17 yearly\n
  • #18 yearly\n
  • #19 The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
  • #20 The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
  • #21 The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
  • #22 The dimension components serve to identify the observations.\nThe attribute components allow us to qualify and interpret the observed value(s).\n
  • #23 \n
  • #24 \n
  • #25 \n
  • #26 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
  • #27 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
  • #28 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
  • #29 Challenges\n- Ambiguity\n- Dataset quality\nowl:sameAS\nrdfs:subClassOf\n
  • #30 \n
  • #31 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
  • #32 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
  • #33 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
  • #34 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
  • #35 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
  • #36 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
  • #37 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
  • #38 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
  • #39 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
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