Measuring governments’ R&D funding response to COVID-19: An application of the OECD Fundstat infrastructure to the analysis of R&D directionality
This paper presents new evidence on the size and direction of governments’ R&D funding response to the COVID-19 pandemic through the exploration of a novel data infrastructure, the OECD Fundstat initiative for the analysis of government-funded R&D projects. The document reports on the explor...
Gespeichert in:
| Veröffentlicht in: | OECD Science, Technology and Industry Working Papers Technology and Industry Working Papers, 2023-10, Vol.2023 (6) |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 6 |
| container_start_page | |
| container_title | OECD Science, Technology and Industry Working Papers |
| container_volume | 2023 |
| creator | Aristodemou, Leonidas Galindo-Rueda, Fernando Matsumoto, Kuniko Murakami, Akiyoshi |
| description | This paper presents new evidence on the size and direction of governments’ R&D funding response to the COVID-19 pandemic through the exploration of a novel data infrastructure, the OECD Fundstat initiative for the analysis of government-funded R&D projects. The document reports on the exploratory development and application of automatic classification tools to detect relevant COVID-19 R&D funding, map salient topics and classify and allocate project funding according to priorities in the WHO COVID-19 R&D Blueprint, as well as comparing results with similar analysis of scientific publication output data. The results provide new insights on which areas of enquiry were prioritised by governmental R&D funding bodies. |
| doi_str_mv | 10.1787/4889f5f2-en |
| format | Article |
| fullrecord | <record><control><sourceid>oecd_econi</sourceid><recordid>TN_cdi_oecd_workingpapers_10_1787_4889f5f2_en</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1787_4889f5f2_en</sourcerecordid><originalsourceid>FETCH-LOGICAL-e63n-2dfb54d0646f34ece553325d3e480f19f1bfc4569b55bf64d2283d0c452721633</originalsourceid><addsrcrecordid>eNo1kM1KAzEcxIMgWGpPvkBO3qL5-mezR9n6UagUpIi3sLv5pyza7JK0ijdfw9fzSdxSPQ3M_BiGIeRC8CtR2OJaW1sGCJJhPCETYQUwURo4I7Ocu4ZzbQHAmAkpH7HO-9TFDd3075jiFuMu_3x906fLOQ376A9Rwjz0MSPd9bRaPS_mY9s5OQ31W8bZn07J-u52XT2w5ep-Ud0sGRoVmfShAe250SYojS0CKCXBK9SWB1EG0YRWgykbgCYY7aW0yvPRkoUURqkpocdabPvYZTekblunTydsIawqTfEyIuyI9Nh699Gn13HzUA-YshPcHQ5x_4c4jOoXOnJUDw</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Measuring governments’ R&D funding response to COVID-19: An application of the OECD Fundstat infrastructure to the analysis of R&D directionality</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Aristodemou, Leonidas ; Galindo-Rueda, Fernando ; Matsumoto, Kuniko ; Murakami, Akiyoshi</creator><creatorcontrib>Aristodemou, Leonidas ; Galindo-Rueda, Fernando ; Matsumoto, Kuniko ; Murakami, Akiyoshi</creatorcontrib><description>This paper presents new evidence on the size and direction of governments’ R&D funding response to the COVID-19 pandemic through the exploration of a novel data infrastructure, the OECD Fundstat initiative for the analysis of government-funded R&D projects. The document reports on the exploratory development and application of automatic classification tools to detect relevant COVID-19 R&D funding, map salient topics and classify and allocate project funding according to priorities in the WHO COVID-19 R&D Blueprint, as well as comparing results with similar analysis of scientific publication output data. The results provide new insights on which areas of enquiry were prioritised by governmental R&D funding bodies.</description><identifier>EISSN: 1815-1965</identifier><identifier>DOI: 10.1787/4889f5f2-en</identifier><language>eng</language><publisher>Paris: OECD Publishing</publisher><subject>classification ; COVID-19 ; directionality ; Government funding ; large language models ; R&D ; Research and Development ; topic modelling</subject><ispartof>OECD Science, Technology and Industry Working Papers, 2023-10, Vol.2023 (6)</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,780,27902</link.rule.ids></links><search><creatorcontrib>Aristodemou, Leonidas</creatorcontrib><creatorcontrib>Galindo-Rueda, Fernando</creatorcontrib><creatorcontrib>Matsumoto, Kuniko</creatorcontrib><creatorcontrib>Murakami, Akiyoshi</creatorcontrib><title>Measuring governments’ R&D funding response to COVID-19: An application of the OECD Fundstat infrastructure to the analysis of R&D directionality</title><title>OECD Science, Technology and Industry Working Papers</title><addtitle>Documents de travail de l'OCDE sur la science, la technologie et l'industrie</addtitle><description>This paper presents new evidence on the size and direction of governments’ R&D funding response to the COVID-19 pandemic through the exploration of a novel data infrastructure, the OECD Fundstat initiative for the analysis of government-funded R&D projects. The document reports on the exploratory development and application of automatic classification tools to detect relevant COVID-19 R&D funding, map salient topics and classify and allocate project funding according to priorities in the WHO COVID-19 R&D Blueprint, as well as comparing results with similar analysis of scientific publication output data. The results provide new insights on which areas of enquiry were prioritised by governmental R&D funding bodies.</description><subject>classification</subject><subject>COVID-19</subject><subject>directionality</subject><subject>Government funding</subject><subject>large language models</subject><subject>R&D</subject><subject>Research and Development</subject><subject>topic modelling</subject><issn>1815-1965</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNo1kM1KAzEcxIMgWGpPvkBO3qL5-mezR9n6UagUpIi3sLv5pyza7JK0ijdfw9fzSdxSPQ3M_BiGIeRC8CtR2OJaW1sGCJJhPCETYQUwURo4I7Ocu4ZzbQHAmAkpH7HO-9TFDd3075jiFuMu_3x906fLOQ376A9Rwjz0MSPd9bRaPS_mY9s5OQ31W8bZn07J-u52XT2w5ep-Ud0sGRoVmfShAe250SYojS0CKCXBK9SWB1EG0YRWgykbgCYY7aW0yvPRkoUURqkpocdabPvYZTekblunTydsIawqTfEyIuyI9Nh699Gn13HzUA-YshPcHQ5x_4c4jOoXOnJUDw</recordid><startdate>20231016</startdate><enddate>20231016</enddate><creator>Aristodemou, Leonidas</creator><creator>Galindo-Rueda, Fernando</creator><creator>Matsumoto, Kuniko</creator><creator>Murakami, Akiyoshi</creator><general>OECD Publishing</general><scope>72Y</scope><scope>ARKBX</scope><scope>RSO</scope><scope>OQ6</scope></search><sort><creationdate>20231016</creationdate><title>Measuring governments’ R&D funding response to COVID-19</title><author>Aristodemou, Leonidas ; Galindo-Rueda, Fernando ; Matsumoto, Kuniko ; Murakami, Akiyoshi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-e63n-2dfb54d0646f34ece553325d3e480f19f1bfc4569b55bf64d2283d0c452721633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>classification</topic><topic>COVID-19</topic><topic>directionality</topic><topic>Government funding</topic><topic>large language models</topic><topic>R&D</topic><topic>Research and Development</topic><topic>topic modelling</topic><toplevel>online_resources</toplevel><creatorcontrib>Aristodemou, Leonidas</creatorcontrib><creatorcontrib>Galindo-Rueda, Fernando</creatorcontrib><creatorcontrib>Matsumoto, Kuniko</creatorcontrib><creatorcontrib>Murakami, Akiyoshi</creatorcontrib><collection>OECD iLibrary</collection><collection>OECD Working Paper Series</collection><collection>OECD iLibrary</collection><collection>ECONIS</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aristodemou, Leonidas</au><au>Galindo-Rueda, Fernando</au><au>Matsumoto, Kuniko</au><au>Murakami, Akiyoshi</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Measuring governments’ R&D funding response to COVID-19: An application of the OECD Fundstat infrastructure to the analysis of R&D directionality</atitle><jtitle>OECD Science, Technology and Industry Working Papers</jtitle><addtitle>Documents de travail de l'OCDE sur la science, la technologie et l'industrie</addtitle><date>2023-10-16</date><risdate>2023</risdate><volume>2023</volume><issue>6</issue><eissn>1815-1965</eissn><abstract>This paper presents new evidence on the size and direction of governments’ R&D funding response to the COVID-19 pandemic through the exploration of a novel data infrastructure, the OECD Fundstat initiative for the analysis of government-funded R&D projects. The document reports on the exploratory development and application of automatic classification tools to detect relevant COVID-19 R&D funding, map salient topics and classify and allocate project funding according to priorities in the WHO COVID-19 R&D Blueprint, as well as comparing results with similar analysis of scientific publication output data. The results provide new insights on which areas of enquiry were prioritised by governmental R&D funding bodies.</abstract><cop>Paris</cop><pub>OECD Publishing</pub><doi>10.1787/4889f5f2-en</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 1815-1965 |
| ispartof | OECD Science, Technology and Industry Working Papers, 2023-10, Vol.2023 (6) |
| issn | 1815-1965 |
| language | eng |
| recordid | cdi_oecd_workingpapers_10_1787_4889f5f2_en |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | classification COVID-19 directionality Government funding large language models R&D Research and Development topic modelling |
| title | Measuring governments’ R&D funding response to COVID-19: An application of the OECD Fundstat infrastructure to the analysis of R&D directionality |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T17%3A00%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-oecd_econi&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Measuring%20governments%E2%80%99%20R&D%20funding%20response%20to%20COVID-19:%20An%20application%20of%20the%20OECD%20Fundstat%20infrastructure%20to%20the%20analysis%20of%20R&D%20directionality&rft.jtitle=OECD%20Science,%20Technology%20and%20Industry%20Working%20Papers&rft.au=Aristodemou,%20Leonidas&rft.date=2023-10-16&rft.volume=2023&rft.issue=6&rft.eissn=1815-1965&rft_id=info:doi/10.1787/4889f5f2-en&rft_dat=%3Coecd_econi%3E10_1787_4889f5f2_en%3C/oecd_econi%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |