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計量書誌学入門 / 20221109

Jiro Kikkawa
November 08, 2022

計量書誌学入門 / 20221109

筑波大学の「コンテンツ入門」というオムニバス形式の講義の1コマ分を担当したときの授業動画です.
授業概要: 文献の生産,流通,利用等に関する諸事象を計量的に扱う研究領域である「計量書誌学」の基礎を解説するとともに,最近の研究動向を紹介する.

Jiro Kikkawa

November 08, 2022
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  1. ܭྔॻࢽֶೖ໳ ίϯςϯπೖ໳ ୈճ ஜ೾େֶ ਤॻؗ৘ใϝσΟΞܥ ಛ೚ॿڭ ٢઒ ࣍࿠ ͖͔ͬΘ ͡Ζ͏

    [email protected] 1
  2. ֶज़࿦จ treatise ৽͍͠ݚڀ੒ՌΛ಺༰ͱ͠ɼҰఆͷߏ੒Λ࣋ͬͨ࿦จɽ Ұൠʹɼ࿦จ໊ɼஶऀ໊ɼং࿦ɼํ๏ͱ݁Ռɼߟ࡯ͱ݁࿦ɼҾ༻จݙ Ϧετ͔Βߏ੒͞ΕΔɽ௨ৗ͸ɼֶज़ࡶࢽʹܝࡌ͞Εͨ΋ͷΛֶज़࿦จ ͱݺΜͰ͍Δɽ౰ॳ͸ࡶࢽฤूऀ΁ͷखࢴ΍ใࠂͷܗࣜΛͱ͕ͬͨɼ 19ੈلʹݱࡏͷΑ͏ͳܗʹͳͬͨɽ 2 ୈճ ʲ٢઒ʳܭྔॻࢽֶ

    จݙͷੜ࢈ɼྲྀ௨ɼར༻౳ʹؔ͢Δॾࣄ৅Λܭྔతʹѻ͏ݚڀྖҬ Ͱ͋ΔʮܭྔॻࢽֶʯͷجૅΛղઆ͢Δͱͱ΋ʹɼ࠷ۙͷݚڀಈ޲Λ ঺հ͢Δɽ ͸͡Ίʹ: तۀ಺༰ͷ֓ཁ • ܭྔॻࢽֶ = bibliometrics (ϏϒϦΦϝτϦΫε) • ຊ೔ͷतۀͰ͸ɼʮจݙʯʮֶज़จݙʯͱͯ͠࿩ΛਐΊ·͢ɽ ֶज़࿦จ͸ɼֶज़จݙͷҰछͰ͢ɽ • ʮܭྔతʹʯ͸ʮྔత෼ੳʯ΍ʮ౷ܭ෼ੳʯΛ༻͍Δͱ͍͏ҙຯͰ͢ɽ γϥόεΑΓҾ༻ ਤॻؗ৘ใֶ༻ޠࣙయ* ΑΓҾ༻ * ೔ຊਤॻؗ৘ใֶձ༻ޠࣙయฤूҕһձ:ʮਤॻؗ৘ใֶ༻ޠࣙయʯ. ؙળग़൛, ୈ5൛, 2020.
  3. 3 ೑৯ڪཽʮεϐϊα΢ϧεʯlਫதͰੜ׆͍ͯͨ͠ Մೳੑߴ͍z 2022೥4݄2೔ 6࣌29෼ ࠷େڃͷ೑৯ڪཽͱ͞ΕഎதʹൕͷΑ͏ͳಛ௃తͳಥىͷ͋Δ ʮεϐϊα΢ϧεʯ͸ɺີ౓͕ߴͯ͘௜Έ΍͍͢ࠎΛ࣋ͪɺ ਫதʹજͬͯ㕒ΛͱΔͷʹదͨ͠ମΛ͍ͯͨ͠Մೳੑ͕͋Δ ͱ͍͏ݚڀ݁ՌΛΞϝϦΧͳͲͷݚڀάϧʔϓ͕ൃද͠·ͨ͠ɻ өըʮδϡϥγοΫɾύʔΫʯγϦʔζʹ΋ొ৔ͨ͠εϐϊα΢ϧε͸ɺ1ԯ೥΄Ͳલͷനѥلʹੜଉͨ͠

    ೑৯ڪཽͰɺԽੴͷಛ௃ͳͲ͔ΒਫதͰੜ׆͍ͯͨ͜͠ͱΛࢦఠ͢Δݟํ͕͋Γ·ͨ͠ɻ ΞϝϦΧͷϑΟʔϧυࣗવ࢙ത෺ؗͳͲͷݚڀάϧʔϓ͸ɺઈ໓ͨ͠΋ͷΛؚΊͯਫத΍཮্Ͱੜ׆ͨ͠ ͓Αͦ300छྨͷಈ෺ͷࠎͷີ౓Λௐ΂ɺͦͷ݁ՌΛΠΪϦεͷՊֶࡶࢽɺωΠνϟʔʹൃද͠·ͨ͠ɻ ͦΕʹΑΓ·͢ͱɺεϐϊα΢ϧεͷࠎ͸ີ౓͕ߴͯ͘௜Έ΍͘͢ɺϖϯΪϯ΍ϫχͳͲਫதʹજͬͯ ੜ׆͢Δ͜ͱͷଟ͍ಈ෺ͷಛ௃ͱࣅ͍ͯͨͱ͍͏͜ͱͰ͢ɻ ςΟϥϊα΢ϧεΛ͸͡Ί཮্Ͱੜ׆͍ͯͨ͠ಈ෺͸ൺֱతࠎͷີ౓͕௿͍܏޲͕ݟΒΕɺݚڀάϧʔϓ ͸ɺεϐϊα΢ϧε͸ਫதʹજͬͯङΓΛ͠ɺڕͳͲΛั৯͍ͯͨ͠Մೳੑ͕ߴ͍ͱ͍ͯ͠·͢ɻ ग़య: ೑৯ڪཽʮεϐϊα΢ϧεʯlਫதͰੜ׆͍ͯͨ͠Մೳੑߴ͍z | NHK https://www3.nhk.or.jp/news/html/20220402/k10013563731000.html ࠷ۙͷχϡʔε͔Β #1
  4. 4 ੈքେֶϥϯΩϯά ౦େ39Ґ ژେ68Ґ ͍ͣΕ΋ॱҐԼ͛Δ 2022೥10݄12೔ 15࣌27෼ ΠΪϦεͷڭҭઐ໳ࢽʮλΠϜζɾϋΠϠʔɾΤσϡέʔγϣϯʯ͸ɺ࠷৽ͷੈքେֶϥϯΩϯάΛ ൃද͠ɺ೔ຊͷେֶͰ࠷ߴॱҐͱͳͬͨ౦ژେֶ͕39Ґɺ͍࣍Ͱژ౎େֶ͕68ҐͱͳΓɺ͍ͣΕ΋ લͷ೥ΑΓॱҐΛԼ͛·ͨ͠ɻ

    ΠΪϦεͷڭҭઐ໳ࢽʮλΠϜζɾϋΠϠʔɾΤσϡέʔγϣϯʯ͸ɺݚڀ಺༰΍࿦จͷҾ༻ճ਺ɺ ࠃࡍੑͳͲͷࢦඪΛ΋ͱʹɺຖ೥ɺੈքͷେֶͷϥϯΩϯάΛൃද͍ͯ͠·͢ɻ ࠷৽ͷϥϯΩϯάͰ͸ɺ೔ຊ͸લͷ೥ΑΓɺ ˜౦ژେֶ͕4ͭॱҐΛԼ͛ͯ39Ґ ˜ژ౎େֶ͕ॱҐΛ7ͭԼ͛ͯ68ҐͱͳΓ·ͨ͠ɻ ্Ґ100Ґͷ͏ͪɺࠃͱ஍ҬผͰ͸ɺ ˜ΞϝϦΧ͕34ߍͱ࠷΋ଟ͘ɺ ΞδΞͰ͸ɺ ˜தࠃ͕7ߍ ˜߳ߓ͕5ߍ ˜ؖࠃ͕3ߍͰɺ ˜೔ຊͱγϯΨϙʔϧ͕ɺͦΕͧΕ2ߍͱͳ͍ͬͯ·͢ɻ ˜1Ґ͸ɺ7೥࿈ଓͰΠΪϦεͷΦοΫεϑΥʔυେֶ ˜2Ґ͸ɺΞϝϦΧͷϋʔόʔυେֶ ˜3Ґ͸ɺΠΪϦεͷέϯϒϦοδେֶͱΞϝϦΧͷελϯϑΥʔυେֶͰɺ ্Ґ10Ґ·ͰΛΠΪϦεͱΞϝϦΧͷେֶ͕઎Ί·ͨ͠ɻ ग़య: ੈքେֶϥϯΩϯά ౦େ39Ґ ژେ68Ґ ͍ͣΕ΋ॱҐԼ͛Δ | NHK | ڭҭ https://www3.nhk.or.jp/news/html/20221012/k10013856291000.html ・World University Rankings 2023 | Times Higher Education (THE) https://www.timeshighereducation.com/world-university-rankings/2023/world-ranking ࠷ۙͷχϡʔε͔Β #2
  5. Nature Volume 609 Issue 7929 2022೥9݄29೔߸ https://www.nature.com/nature/ volumes/609/issues/7929 Science Volume

    377 Issue 6614 2022೥9݄30೔߸ https://www.science.org/toc/sci ence/377/6614 5 Cell Volume 185 Issue 20 2022೥9݄29೔߸ https://www.cell.com/cell/issue? pii=S0092-8674(21)X0021-9 ֶज़ࡶࢽͷྫ
  6. 6 ֶज़࿦จͷྫ #1 Zhu, You-an; Li, Qiang; Lu, Jing, et.

    al.: “The oldest complete jawed vertebrates from the early Silurian of China”, Nature, Vol. 609, No. 7929, pp. 954–958, 2022. https://doi.org/10.1038/s41586-022-05136-8. Article The oldest complete jawed vertebrates from the early Silurian of China You-an Zhu1,2,8, Qiang Li3,4,8, Jing Lu1,2,5, Yang Chen1,4, Jianhua Wang3, Zhikun Gai1,2, Wenjin Zhao1,2,5, Guangbiao Wei6, Yilun Yu1,5, Per E. Ahlberg7  ✉ & Min Zhu1,2,5  ✉ Molecular studies suggest that the origin of jawed vertebrates was no later than the Late Ordovician period (around 450 million years ago (Ma))1,2. Together with disarticulated micro-remains of putative chondrichthyans from the Ordovician and early Silurian period3–8, these analyses suggest an evolutionary proliferation of jawed vertebrates before, and immediately after, the end-Ordovician mass extinction. However, until now, the earliest complete fossils of jawed shes for which a detailed reconstruction of their morphology was possible came from late Silurian assemblages (about 425 Ma)9–13. The dearth of articulated, whole-body fossils from before the late Silurian has long rendered the earliest history of jawed vertebrates obscure. Here we report a newly discovered Konservat-Lagerstätte, which is marked by the presence of diverse, well-preserved jawed shes with complete bodies, from the early Silurian (Telychian age, around 436 Ma) of Chongqing, South China. The dominant species, a ‘placoderm’ or jawed stem gnathostome, which we name Xiushanosteus mirabilis gen. et sp. nov., combines characters from major placoderm subgroups14–17 and foreshadows the transformation of the skull roof pattern from the placoderm to the osteichthyan condition10. The chondrichthyan Shenacanthus vermiformis gen. et sp. nov. exhibits extensive thoracic armour plates that were previously unknown in this lineage, and include a large median dorsal plate as in placoderms14–16, combined with a conventional chondrichthyan bauplan18,19. Together, these species reveal a previously unseen diversi cation of jawed vertebrates in the early Silurian, and provide detailed insights into the whole-body morphology of the jawed vertebrates of this period. Systematic palaeontology for X. mirabilis Here we describe two new species, X. mirabilis and S. vermiformis. Lagerstätte features many head-to-tail fishes with fine details such as the complete fin web and possible vertebral column cartilage (Extended Data Fig. 3e). Furthermore, fossils of eurypterids (Fig. 1c,d) and phyl- https://doi.org/10.1038/s41586-022-05136-8 Received: 20 October 2021 Accepted: 22 July 2022 Published online: 28 September 2022 Check for updates Systematic palaeontology for S. vermiformis Chondrichthyes Huxley, 1880 Figs. 3a–e, 4–6). B small compared w the total length of dorsoventrally co the head, and long all of which sugge ered by small, diam ridge scales or scu The post-thoracic combined, reachi the body. Two do by a spine, are pre possesses a round The dermatosk mélange of charac roof profile with p margin resemble (Extended Data Fi generally resemb only one pair of p of the skull roof re acanthothoracids tories, rather than example, petalich does not extend a to the condition f arctaspidid and w Xiushanosteus is sh the petalichthyid L lateral plates are r scute-like median dorsal tightly fits dermal neck conn In most placod paranuchals) tigh contact the trunk s crucial cervical ki tal dermal plates a b c d 2a 1a 1b 2b a Fig. 1 | Fossils from the Chongqing Lagerstätte. a, Slab containing the holotypes of S. vermiformis (1a, 1b) and X. mirabilis (2a, 2b). b, Slab showing the concentration of articulated fish fossils. c, Eurypterid Hughmilleria wangi preserved in articulation. d, Head of H. wangi showing the compound eyes. Scale bars, 5 mm.
  7. ݚڀ࿦จ Wikipedia ʹֶज़จݙͷࢀরهड़Λ௥Ճ͢Δฤूͷಛఆख๏ A Method to Identify the Edits Adding

    Bibliographic References to Wikipedia ٢઒ ࣍࿠ 1∗ɼߴٱ խੜ 2ɼ๕৊ ౙथ 2 Jiro KIKKAWA1∗, Masao TAKAKU2, Fuyuki YOSHIKANE2 1 ஜ೾େֶେֶӃ ਤॻؗ৘ใϝσΟΞݚڀՊ Graduate School of Library, Information and Media Studies, University of Tsukuba ˟ 305-8550 Ἒ৓ݝͭ͘͹ࢢय़೔ 1-2 E-mail: [email protected] 2 ஜ೾େֶ ਤॻؗ৘ใϝσΟΞܥ Faculty of Library, Information and Media Science, University of Tsukuba ˟ 305-8550 Ἒ৓ݝͭ͘͹ࢢय़೔ 1-2 E-mail: [email protected], [email protected] ∗ ࿈བྷઌஶऀ Corresponding Author Wikipedia ্Ͱͷֶज़จݙͷࢀরهड़ͷ௥Ճͱ͍͏ࣄ৅Λ໌Β͔ʹ͢ΔͨΊͷલఏͱͳΔํ๏࿦ͱͯ͠ɼ ࢀরهड़ͷॳग़࣌఺Λಛఆ͢ΔͨΊͷख๏ΛఏҊ͠ɼධՁ࣮ݧΛߦͬͨɽఏҊख๏͸ɼ·ͣɼࢀরهड़ͷ ࢀরઌΛ൑ఆ͠ɼϖʔδ৘ใɼจݙλΠτϧɼࣝผࢠΛऔಘ͢Δɽ࣍ʹɼର৅ͷϖʔδͷશฤूཤྺ͓Α ͼϖʔδຊจʹରͯࣝ͠ผࢠ·ͨ͸จݙλΠτϧΛ༻͍ͨख๏Λద༻͠ɼෳ਺ͷॳग़࣌఺ީิΛऔಘ͢Δɽ ࠷ޙʹɼީิ͔Βฤू೔͕࣌࠷ݹͷ΋ͷΛબ୒͢Δɽӳޠ൛ͷ DOI ϦϯΫͷॳग़࣌఺σʔληοτΛجʹ ධՁ࣮ݧΛߦͬͨ݁Ռɼਫ਼౓͸શମͰ 93.3%ɼ22 ෼໺த 20 ෼໺Ͱ 90%Ҏ্Ͱ͋Γɼݚڀ෼໺Λ໰Θͣ֓Ͷ ߴ͍ਫ਼౓Ͱࢀরهड़ͷॳग़࣌఺ΛಛఆͰ͖Δख๏Ͱ͋Δ͜ͱ͕໌Β͔ʹͳͬͨɽ We proposed a method to identify the edits adding bibliographic references to Wikipedia. The proposed method consists of the following steps. (1) The method extracts the references and matches them to a bibliographic database to build the basic data set. (2) It obtains the full revision history of the page that includes the references from dump data of Wikipedia. It also extracts identifiers and titles for each reference from the basic data set. (3) The method gets the candidate edits adding the references by using the ways, which use either identifiers or titles. (4) The method selects the oldest one as the edit adding the reference. We evaluated the proposed method by using the data set based on DOI links referenced on English Wikipedia. As a result, the accuracy was 93.3% as a whole and over 90% in 20 out of 22 research fields. We showed that the proposed method was able to identify the edits adding bibliographic references at a high accuracy regardless of research fields. Ωʔϫʔυ: WikipediaɼDigital Object Identifier (DOI)ɼֶज़৘ใྲྀ௨ Keywords: Wikipedia, Digital Object Identifier (DOI), Scholarly Communication 1. ͸͡Ίʹ ֶज़৘ใྲྀ௨ͷిࢠԽΛഎܠʹɼ΢Σϒ্Ͱֶज़ จݙ͕େن໛ʹࢀর͞Εɼྲྀ௨͍ͯ͠Δɽ΢ΣϒΛ ௨ֶͨ͡ज़৘ใج൫͕੔උ͞Εɼීٴ͢Δ͜ͱʹΑ Γɼैདྷͷֶज़จݙͷར༻ऀͰ͋Δݚڀऀ΍ઐ໳Ո ͳͲʹݶΒͣɼ༷ʑͳਓʑ΍ίϛϡχςΟʹΑΔֶ ज़จݙͷར׆༻͕ੜ͡Δͱߟ͑ΒΕΔɽͦͷͻͱͭ ʹɼWikipedia ͔Βͷֶज़จݙ΁ͷΞΫηε͕ڍ͛ ΒΕΔɽ࣮ࡍʹɼDOI (Digital Object Identifierɼ σδλϧΦϒδΣΫτࣝผࢠ) ͷੈք࠷େن໛ͷొ ࿥ػؔͰ͋Δ Crossref ͷ 2015 ೥࣌఺Ͱͷใࠂ [1] ʹΑΔͱɼWeb of Science ΍ Scopus ͳͲͷֶज़จ ݙσʔλϕʔεʹ͍࣍Ͱ 5 ൪໨ʹΞΫηεͷଟ͍ࢀ 情報知識学会誌 2020 Vol. 30, No. 3 7 ֶज़࿦จͷྫ #2 ٢઒࣍࿠; ߴٱխੜ; ๕৊ౙथ:ʮWikipediaʹֶज़จݙͷ ࢀরهड़Λ௥Ճ͢Δฤूͷಛఆख๏ʯ, ৘ใ஌ֶࣝձࢽ, Vol. 30, No. 3, pp. 370–389, 2020. https://doi.org/10.2964/jsik_2020_033. [37] Gusfield, Dan: “Algorithms on Strings, Trees, and Sequences: Computer Science and Com- putational Biology”. Cambridge University Press, 1997. https://doi.org/10.1017/ cbo9780511574931. [38] Levenshtein, V. I.: “Binary codes capable of correcting deletions, insertions, and rever- sals”, Soviet Physics-Doklady, Vol. 10, No. 8, pp. 707–710, 1966. [39] “Self-determination theory - Wikipedia”. https://en.wikipedia.org/w/index.php? oldid=332522310 (2009 ೥ 12 ݄ 8 ೔ 17 ࣌ 37 ෼࣌఺ͷ൛ɼ2020 ೥ 1 ݄ 15 ೔ࢀর). [40] Frodi, Ann; Bridges, Lisa; Grolnick, Wendy: “Correlates of Mastery-Related Behavior: A Short-Term Longitudinal Study of Infants in Their Second Year”, Child Development, Vol. 56, No. 5, pp. 1291–1298, 1985. https: //www.jstor.org/stable/1130244. [41] Crossref: “Crossref REST API”. https:// api.crossref.org/ (2019 ೥ 12 ݄ 31 ೔ࢀর). [42] Clarivate Analytics: “InCites Essential Sci- ence Indicators”, 2019. https://esi. clarivate.com/ (2019 ೥ 12 ݄ 31 ೔ࢀর). [43] Clarivate Analytics: “Journal List (In- Cites Essential Science Indicators Help)”. http://help.incites.clarivate.com/ incitesLiveESI/ESIGroup/overviewESI/ esiJournalsList.html (2019 ೥ 12 ݄ 31 ೔ ࢀর). [44] খ໺ࣉՆੜ: ʮ࿦จσʔλϕʔεʹ͓͚Δओ୊ ෼ྨʵ৘ใ෼ੳ΁ͷར༻ͷࢹ఺͔Βʵʯ , ৘ใͷ Պֶͱٕज़, Vol. 66, No. 6, pp. 272–276, 2016. https://doi.org/10.18919/jkg.66.6_272. [45] National Center for Biotechnology Informa- tion: “APIs - Develop”. https://www.ncbi. nlm.nih.gov/home/develop/api/ (2019 ೥ 12 ݄ 31 ೔ࢀর). [46] The SAO/NASA Astrophysics Data Sys- tem: “SAO/NASA ADS HELP: Direct Ac- cess”. http://ads.nao.ac.jp/abs_doc/ help_pages/linking.html (2019 ೥ 12 ݄ 31 ೔ࢀর). [47] Crossref: “Crossref Metadata API JSON Format”. https://github.com/CrossRef/ rest-api-doc/blob/master/api_format. md (2019 ೥ 12 ݄ 31 ೔ࢀর). [48] “Wikimedia database dump of the En- glish Wikipedia on March 01, 2017 : Wikimedia projects editors : Free Down- load, Borrow, and Streaming : Internet Archive”. https://archive.org/details/ enwiki-20170301 (2019 ೥ 12 ݄ 31 ೔ࢀর). [49] ʮHelp:ࠩ෼ - Wikipediaʯ. https://ja. wikipedia.org/w/index.php?title=H: DIFF (2020 ೥ 1 ݄ 15 ೔ࢀর). [50] Lawrence, Miles B.; Pelissier, Joseph M.: “Atlantic Hurricane Season of 1981”, Monthly Weather Review, Vol. 110, No. 7, pp. 852–866, 1982. https: //doi.org/10.1175/1520-0493(1982) 110%3C0852%3AAHSO%3E2.0.CO%3B2. (2020年 1月23日 受付) (2020年 6月 9日 採択) (2020年 7月10日 J-STAGE早期公開) ද 7: ݚڀ෼໺͝ͱͷख๏୯ମ͓Αͼ࠷ऴతͳਫ਼౓ (%) ৚݅/෼໺ ܦࡁ ࣾձ ਫ਼ਆ ໔Ӹ ෼ࢠ ಈ২ ඍੜ ੜ෺ ྟচ ༀֶ ೶ֶ ख๏ A 40.0 50.0 42.0 70.0 72.0 66.0 66.0 64.0 70.0 78.0 72.0 ख๏ B 88.0 76.0 94.0 88.0 92.0 82.0 80.0 80.0 86.0 84.0 86.0 ख๏ C 58.0 56.0 80.0 86.0 82.0 72.0 76.0 74.0 70.0 76.0 76.0 ख๏ A+B+C 94.0 90.0 98.0 96.0 96.0 94.0 92.0 90.0 98.0 96.0 94.0 ৚݅/෼໺ ଟྖ ਆܦ ؀ڥ Խֶ ஍ٿ ఱମ ਺ֶ ࡐྉ ෺ཧ ޻ֶ ܭࢉ શମ ख๏ A 74.0 62.0 58.0 70.0 60.0 76.0 44.0 70.0 60.0 62.0 58.0 62.9 ख๏ B 88.0 86.0 90.0 72.0 84.0 88.0 86.0 82.0 68.0 88.0 92.0 84.5 ख๏ C 80.0 76.0 74.0 56.0 78.0 78.0 64.0 68.0 58.0 58.0 60.0 70.7 ख๏ A+B+C 96.0 94.0 98.0 86.0 94.0 92.0 90.0 94.0 84.0 90.0 96.0 93.3 ਫ਼౓͕ 80%Ҏ্ͷ΋ͷ͸ଠࣈɼਫ਼౓͕ 90%Ҏ্Ͱ͋Δ΋ͷ͸໢ֻ͚Ͱ͍ࣔͯ͠Δɽ (56.0%)ɽͲͷ෼໺Ͱ΋ख๏ B ͕࠷΋ߴ͍ͨΊɼॳ ग़࣌఺ʹ͓͍ͯจݙλΠτϧ͕ࣔ͞Ε͍ͯΔέʔε ͕ଟ͍ͱߟ͑ΒΕΔɽ·ͨɼख๏ A ͱ B ͷൺֱͰ ͸ɼ͍ͣΕͷ෼໺Ͱ΋ख๏ A ͷ஋͕௿͍ͨΊɼ෼ ໺Λ໰ΘͣɼจݙλΠτϧΛؚΉطଘͷࢀরهड़ʹ ରͯ͠ޙ͔Βࣝผࢠ͕෇Ճ͞ΕΔέʔε͕ଟ͍ͱߟ ͑ΒΕΔɽ ࠷ऴతͳਫ਼౓͸ɼ෺ཧֶͱԽֶΛআ͘ 20 ෼໺Ͱ 90%Ҏ্Ͱ͋Δɽ஋͕࠷΋ߴ͍ͷ͸ਫ਼ਆֶɾ৺ཧֶɼ ྟচҩֶɼ ؀ڥ ɾ ΤίϩδʔͰ͋Δ (͍ͣΕ΋98.0%)ɽ ख๏୯ମͱൺֱ͢ΔͱɼͲͷ෼໺΋࠷ऴతͳਫ਼౓͕ ߴ͍ɽ͜ͷ͜ͱ͔Βɼฤू೔͕࣌࠷ݹͷ൛Λબ୒͢ Δ͜ͱͰख๏୯ମΑΓ΋ਫ਼౓͕޲্͢Δɽ20 ෼໺ Ͱ 90%Ҏ্ɼ࢒Δ 2 ෼໺Ͱ΋ 80%Ҏ্Ͱ͋ΔͨΊɼ ฤू೔͕࣌࠷ݹͷ൛Λબ୒͢Δ͜ͱʹΑΓɼॳग़࣌ ද 8: ख๏ͷ૊Έ߹Θͤผͷਫ਼౓ (%) ख๏ͷ૊Έ߹Θͤ ݶք஋ ࠷ݹͷ൛Λબ୒࣌ ࠩ ख๏ A ͷΈ 62.9 - - ख๏ B ͷΈ 84.5 - - ख๏ C ͷΈ 70.7 - - ख๏ A+B 92.5 91.3 -1.3 ख๏ B+C 88.5 87.5 -1.0 ख๏ A+C 83.3 82.9 -0.4 ख๏ A+B+C 94.8 93.3 -1.5 2%΄Ͳ޲্͢Δ͕ɼख๏ C Ͱਖ਼ղͰ͖Δ΋ͷ͸֓ Ͷख๏ A ·ͨ͸ B ʹΑΓਖ਼ղՄೳͱݴ͑Δɽ3 ൪ ໨ʹߴ͍૊Έ߹Θͤ͸ʮख๏ B+CʯͰ͋Γɼݶք ஋͸ 88.5%ɼ࠷ݹͷ൛Λબ୒࣌͸ 87.5%Ͱ͋Δɽ ʮख
  8. 8 ֶज़ࡶࢽʹ࿦จΛࡌͤΔʹ͸? → ࠪಡ ࠪಡ੍౓ referee system; peer review ֶज़ࡶࢽʹ౤ߘ͞Εͨ࿦จͷ಺༰Λࠪಡऀ

    (referee) ͕৹ࠪ͠ɼ౰֘ࡶࢽʹܝࡌ ͢Δ͔൱͔Λ൑ఆ͢Δ੍౓ɽ͜ͷ੍౓ʹΑͬͯɼ౤ߘ࿦จͱஶऀ͸ઐ໳తঝೝΛ ड͚ɼҰํ ֶज़ࡶࢽ͸࣭Λҡ࣋͢Δ͜ͱ͕Ͱ͖Δɽ ࠪಡ͸ɼࡶࢽͷฤूҕһ΍౤ߘ࿦จͷ಺༰ʹৄ͍͠ઐ໳Ոʹґཔ͢Δɽ ৹ࠪΛެਖ਼ʹߦ͏ͨΊʹɼ࿦จͷஶऀͱࠪಡऀͷ྆ऀʹޓ͍ͷࢯ໊Λ஌Βͤͣɼ ౤ߘ࿦จΛ৹ࠪ͠ɼͦΕʹԠͨ͡ॻ͖௚͠ΛٻΊΔ৔߹͕ଟ͍ɽ ࠪಡͷ݁Ռ٫Լ͞ΕΔ݅਺͸ɼֶ໰෼໺ʹΑͬͯଟগҟͳΔ͕ɼਤॻΛओͳൃද खஈͱ͍ͯ͠ΔਓจՊֶʹ͓͍ͯ΋ɼ͜ͷ٫Լ཰͸ߴ͍ɽ • QFFSೳྗͳͲ͕ಉ౳ͷਓɽ͜͜Ͱ͸ɼ࿦จ಺༰ʹৄ͍͠ݚڀऀɼಉۀऀͷ͜ͱ ˠ QFFS͕৹ࠪΛߦ͏ͷ͕ࠪಡ੍౓ • ֶज़ࡶࢽʹܝࡌ͢Δ͜ͱͰֶज़ͷਐల͕ظ଴Ͱ͖Δέʔε ࡌͤΔ΂͖Ͱ͋Δͱ ൑அ͞Εͨ৔߹ ͸࠾୒ ࡌͤΔɽͦ͏Ͱͳ͍৔߹͸ෆ࠾୒ ࡌͤͳ͍ • मਖ਼ʹΑͬͯ໰୊͕ղফ ಺༰ͷ࣭͕վળ͞ΕΔ৔߹͸ɼॻ͖௚͠ΛٻΊΒΕΔ ਤॻؗ৘ใֶ༻ޠࣙయΑΓҾ༻
  9. ࿦จ৹ࠪͷେ·͔ͳྲྀΕ ஶऀ͕ݪߘΛ౤ߘ͢Δ ฤूऀ ௕ ͕ݪߘΛ ࣄ຿ہͰड͚औΔ ฤू௕͕ݪߘΛ ࠪಡऀʹૹ෇͢Δ ʲࠪಡʳࠪಡऀ डཧ

    ٫Լ վగ ฤू௕ ࠪಡͳ͠Ͱ ฤू௕͕डཧ͢Δ ࠪಡͳ͠Ͱ ฤू௕͕٫Լ͢Δ डཧ ࠾୒ Accept ٫Լ ෆ࠾୒ Reject վగ मਖ਼ Revise ࠪಡޙɼ ฤू௕͕डཧ͢Δ editor/editorial kick ץߦ publish ஶऀ͕ݪߘΛվగ͢Δ ࠪಡޙɼ ฤू௕͕٫Լ͢Δ ૔ాܟࢠʮֶज़৘ใྲྀ௨ͱΦʔϓϯΞΫηεʯɽႻ૲ॻ๪ɼɽQɼਤΛൈਮͯ͠࡞੒ɽ ଠࣈɼӳޠ͓Αͼഁઢ໼ҹͷิهɼண৭౳͸͢΂ͯ٢઒ʹΑΔɽ 9 Received, ड෇೔ Accepted, ࠾୒೔
  10. Q. ੈք࠷ݹͷֶज़ࡶࢽ͸? • Le Journal des sçavans (δϡϧφϧɾσɾαϰΝϯ) • Philosophical

    Transactions of the Royal Society (ϑΟϩιϑΟΧϧɾτϥϯβΫγϣϯζ) • ˞ ྆ऀͱ΋ʹץߦ͸1665೥ Q. ੈͷதʹଘࡏ͢Δֶज़࿦จͷ݅਺͸? • ਖ਼֬ͳ݅਺͸෼͔Γ·ͤΜ͕ɼӳޠͰॻ͔Ε͓ͯΓɼ͔ͭɼ ΢Σϒ্ͰΞΫηεՄೳͳ΋ͷʹݶఆͨ͠৔߹ɼ2014೥࣌఺Ͱ গͳ͘ͱ΋1.14ԯ݅ͱͷਪܭ͕ࣔ͞Ε͍ͯ·͢ɽ Khabsa, Madian; Giles, C. Lee: “The Number of Scholarly Documents on the Public Web”, PLOS ONE, Vol. 9, No. 5, pp. 1–6, 2014. https://doi.org/10.1371/journal.pone.0093949. Q. Ͳ͜Ͱ/Ͳ͏΍ֶͬͯज़࿦จΛಡΉ͜ͱ͕Ͱ͖Δ? • ࡭ࢠମ (ࢴഔମ) ͸େֶਤॻؗɼిࢠ൛͸΢ΣϒͰಡΊ·͢ɽ ΢ΣϒͰखʹೖΒͳ͍৔߹͸େֶਤॻؗͰ୳͢೿͕ଟ͍͸ͣɽ • ۩ମతͳ୳͠ํ͸ɼਤॻؗͰͷߨशձ΍Ұ෦ͷतۀͰशಘΛ! ֶज़ࡶࢽ/࿦จʹؔ͢ΔQ&A #1 10
  11. Q. ݚڀऀ͕࿦จΛॻֶ͍ͯज़ࡶࢽʹ౤ߘ͢Δཧ༝͸? 1. ֶ໰Λ௥ڀ͢Δ / ਐาͤ͞ΔͨΊʹੜ͖͍ͯΔͨΊ  ࿦จΛॻ͔ͳ͍ͱੜ͖͍͚ͯͳ͍ͨΊ ˠPublish or

    Perish େֶڭһͷ࢓ࣄ͸ɼڭҭɼݚڀɼେֶӡӦ ࣾձߩݙ Q. Nature, Science, CellͳͲͷֶज़ࡶࢽʹ࿦จ͕ࡌΔͱ ੌ͍ͱ͞ΕΔͷ͸Ͳ͏ͯ͠? • ͻͱͭͷ౴͑͸ɼJournal Impact Factor ͱ͍͏Ҿ༻ʹجͮ͘ࢦඪͷ ஋͕ߴֶ͍ज़ࡶࢽ͔ͩΒͰ͢ɽ ֶज़ࡶࢽ/࿦จʹؔ͢ΔQ&A #2 11 ࡶࢽ໊ Journal Impact Factor Nature 69.504 Science 63.832 Cell 66.850 ද: 2021೥ͷJournal Impact Factorͷ۩ମྫ ※ΑΓݫີʹ͸ɼ2೥Impact Factor
  12. 12 Ҿ༻ͱ͸? #1 Ҿ༻ citation) จݙ"͕จݙ#ͷதͰݴٴ͞Ε͍ͯΔͱ͖ɼͦͷݴٴΛҾ༻ͱ͍͏ɽ ͦͷݴٴ͸ɼจݙBͷຊจதͰͳ͞ΕΔ͜ͱ΋͋Δ͠ɼ ޙ஫ɼ٭஫ɼॻࢽɼ͋Δ͍͸ࢀߟจݙҰཡͰͳ͞ΕΔ͜ͱ΋͋Δɽ * Diodato,

    Virgil Pasquale; ๕৊ౙथ; ؛ా࿨໌; খ໺ࣉՆੜ: ʮܭྔॻࢽֶࣙయʯ. ೔ຊਤॻؗڠձ, 2008. ܭྔॻࢽֶࣙయ* ΑΓҾ༻ จݙ# จݙ" จݙB ͸ จݙA ΛҾ༻͍ͯ͠Δ (cite) จݙA ͸ จݙB ʹΑͬͯҾ༻͞Ε͍ͯΔ (cited) Ҿ༻จݙ citing reference ඃҾ༻จݙ cited reference
  13. 13 Ҿ༻ͱ͸? #2 Analysis of the Deletions of DOIs 163

    2 Related Work Crossref DOI Statistics. Hendricks et al. [8] reported the statistics of Crossref DOIs in June 2019. More than 106 million Crossref DOIs had been registered, and the number of DOIs had increased by 11% on average over the past 10 years. As for the types of contents, 73% are journals, 13% are books, and 5.5% are conference papers and proceedings. Investigation of Duplicated Crossref DOIs. Tkaczyk [18] investigated Crossref DOIs not marked as an alias to other DOIs to consider their quantity and impact on citation-based metrics. Among DOIs randomly sampled from 590 publishers and academic societies with 5, 000 DOIs, 0.8% were duplicated, i.e., different DOI names but their metadata were the same or highly similar. The majority of them were caused by the re-registration of DOIs by the same publish- ers and academic societies. As for duplicated DOIs among different publishers and academic societies, one of the most frequent cases was content with DOIs initially registered by JSTOR and re-registered by new content holders. Incorrect DOIs Indexed by Scholarly Bibliographic Databases. Sev- eral studies have revealed errors in DOIs indexed by scholarly bibliographic databases. Franceschini et al. [7] analyzed DOIs in the records of Scopus and found that multiple DOIs were incorrectly assigned to the same record as rare cases. Zhu et al. [19] analyzed DOIs in the Web of Science records. They reported not only “wrong DOI names” but also “one paper with two different DOI names”. The former are similar errors, as reported by Franceschini et al. [7]. The latter are classified into the following two cases: (1) there were both correct and incor- rect DOIs in the records; (2) multiple correct DOIs were assigned to the same scholarly article. Analysis of Persistence of Crossref DOIs. Klein and Balakireva [12,13] examined the persistence of Crossref DOIs by analyzing their HTTP status codes. They randomly extracted 10,000 Crossref DOIs and examined the final status codes for each DOI link by using multiple HTTP request methods. More than half of the DOI links did not redirect to the content when an external net- work from academic institutions was used. However, the errors of all the DOI links were reduced to one-third when an internal network from academic institu- tions was used. These results indicate that the responses for the same DOI can differ according to conditions such as the HTTP request methods and network locations, which implies a lack of persistence of DOIs. Investigation of Duplicated Crossref DOIs. Tkaczyk [18] investigated Crossref DOIs not marked as an alias to other DOIs to consider their quantity and impact on citation-based metrics. Among DOIs randomly sampled from 590 publishers and academic societies with 5, 000 DOIs, 0.8% were duplicated, i.e., different DOI names but their metadata were the same or highly similar. The majority of them were caused by the re-registration of DOIs by the same publish- ers and academic societies. As for duplicated DOIs among different publishers and academic societies, one of the most frequent cases was content with DOIs initially registered by JSTOR and re-registered by new content holders. Incorrect DOIs Indexed by Scholarly Bibliographic Databases. Sev- eral studies have revealed errors in DOIs indexed by scholarly bibliographic databases. Franceschini et al. [7] analyzed DOIs in the records of Scopus and found that multiple DOIs were incorrectly assigned to the same record as rare cases. Zhu et al. [19] analyzed DOIs in the Web of Science records. They reported not only “wrong DOI names” but also “one paper with two different DOI names”. The former are similar errors, as reported by Franceschini et al. [7]. The latter are classified into the following two cases: (1) there were both correct and incor- rect DOIs in the records; (2) multiple correct DOIs were assigned to the same scholarly article. Analysis of Persistence of Crossref DOIs. Klein and Balakireva [12,13] examined the persistence of Crossref DOIs by analyzing their HTTP status codes. They randomly extracted 10,000 Crossref DOIs and examined the final status codes for each DOI link by using multiple HTTP request methods. More than half of the DOI links did not redirect to the content when an external net- work from academic institutions was used. However, the errors of all the DOI links were reduced to one-third when an internal network from academic institu- tions was used. These results indicate that the responses for the same DOI can differ according to conditions such as the HTTP request methods and network locations, which implies a lack of persistence of DOIs. Analysis of the Usage of DOI Links in Scholarly Articles. Regarding the usage of DOI links in the references of scholarly references, Van de Sompel et al. [16] examined references from 1.8 million papers published between 1997 and 2012. Consequently, they identified a problem that numerous scholarly articles were referenced using their location URIs instead of their DOI links. As described previously, researchers have reported duplicated Crossref DOIs [18,19], and some Crossref DOIs cause errors and are unable to lead to the Analysis of the Deletions of DOIs 173 Acknowledgments. This work was partially supported by JSPS KAKENHI Grant Numbers JP21K21303, JP22K18147, JP20K12543, and JP21K12592. We would like to thank Editage (https://www.editage.com/) for the English language editing. References 1. Cornell University: New arXiv articles are now automatically assigned DOIs | arXiv.org blog (2022). https://blog.arxiv.org/2022/02/17/new-arxiv-articles-are- now-automatically-assigned-dois/ 2. Crossref: January 2021 Public Data File from Crossref. Academic Torrents. https://doi.org/10.13003/gu3dqmjvg4 3. Crossref: Crossref Metadata API JSON Format (2021). https://github.com/ CrossRef/rest-api-doc/blob/master/api format.md 4. Crossref: Crossref REST API (2021). https://api.crossref.org/ 5. Crossref: crossref.org : : crossref stats (2022). https://www.crossref.org/ 06members/53status.html 6. Farley, I.: Conflict report - Crossref (2020). https://www.crossref.org/ documentation/reports/conflict-report/ 7. Franceschini, F., Maisano, D., Mastrogiacomo, L.: Errors in DOI indexing by bib- liometric databases. Scientometrics 102(3), 2181–2186 (2014). https://doi.org/10. 1007/s11192-014-1503-4 8. Hendricks, G., Tkaczyk, D., Lin, J., Feeney, P.: Crossref: the sustainable source of community-owned scholarly metadata. Quantit. Sci. Stud. 1(1), 414–427 (2020). https://doi.org/10.1162/qss a 00022 9. Himmelstein, D., Wheeler, K., Greene, C.: Metadata for all DOIs in Crossref: JSON MongoDB exports of all works from the Crossref API. figshare (2017). https://doi. org/10.6084/m9.figshare.4816720.v1 10. Kemp, J.: New public data file: 120+ million metadata records (2021). https:// www.crossref.org/blog/new-public-data-file-120-million-metadata-records/ 11. Kikkawa, J., Takaku, M., Yoshikane, F.: Dataset of the deleted DOIs extracted from the difference set between Crossref DOIs as of March 2017 and January 2021. Zenodo (2022). https://doi.org/10.5281/zenodo.6841257 Analysis of the Deletions of DOIs 173 Acknowledgments. This work was partially supported by JSPS KAKENHI Grant Numbers JP21K21303, JP22K18147, JP20K12543, and JP21K12592. We would like to thank Editage (https://www.editage.com/) for the English language editing. References 1. Cornell University: New arXiv articles are now automatically assigned DOIs | arXiv.org blog (2022). https://blog.arxiv.org/2022/02/17/new-arxiv-articles-are- now-automatically-assigned-dois/ 2. Crossref: January 2021 Public Data File from Crossref. Academic Torrents. https://doi.org/10.13003/gu3dqmjvg4 3. Crossref: Crossref Metadata API JSON Format (2021). https://github.com/ CrossRef/rest-api-doc/blob/master/api format.md 4. Crossref: Crossref REST API (2021). https://api.crossref.org/ 5. Crossref: crossref.org : : crossref stats (2022). https://www.crossref.org/ 06members/53status.html 6. Farley, I.: Conflict report - Crossref (2020). https://www.crossref.org/ documentation/reports/conflict-report/ 7. Franceschini, F., Maisano, D., Mastrogiacomo, L.: Errors in DOI indexing by bib- liometric databases. Scientometrics 102(3), 2181–2186 (2014). https://doi.org/10. 1007/s11192-014-1503-4 8. Hendricks, G., Tkaczyk, D., Lin, J., Feeney, P.: Crossref: the sustainable source of community-owned scholarly metadata. Quantit. Sci. Stud. 1(1), 414–427 (2020). https://doi.org/10.1162/qss a 00022 9. Himmelstein, D., Wheeler, K., Greene, C.: Metadata for all DOIs in Crossref: JSON MongoDB exports of all works from the Crossref API. figshare (2017). https://doi. org/10.6084/m9.figshare.4816720.v1 10. Kemp, J.: New public data file: 120+ million metadata records (2021). https:// www.crossref.org/blog/new-public-data-file-120-million-metadata-records/ 11. Kikkawa, J., Takaku, M., Yoshikane, F.: Dataset of the deleted DOIs extracted from the difference set between Crossref DOIs as of March 2017 and January 2021. Zenodo (2022). https://doi.org/10.5281/zenodo.6841257 12. Klein, M., Balakireva, L.: On the persistence of persistent identifiers of the scholarly web. In: Hall, M., Merˇ cun, T., Risse, T., Duchateau, F. (eds.) TPDL 2020. LNCS, vol. 12246, pp. 102–115. Springer, Cham (2020). https://doi.org/10.1007/978-3- 030-54956-5 8 13. Klein, M., Balakireva, L.: An extended analysis of the persistence of persistent identifiers of the scholarly web. Int. J. Digit. Libr. 23(1), 5–17 (2021). https://doi. org/10.1007/s00799-021-00315-w 174 J. Kikkawa et al. 18. Tkaczyk, D.: Double trouble with DOIs - Crossref (2020). https://www.crossref. org/blog/double-trouble-with-dois/ 19. Zhu, J., Hu, G., Liu, W.: DOI errors and possible solutions for web of science. Sci- entometrics 118(2), 709–718 (2018). https://doi.org/10.1007/s11192-018-2980-7 20. Ziegler, A.: halostatue/diff-lcs: generate difference sets between Ruby sequences (2022). https://github.com/halostatue/diff-lcs (লུ) (লུ) ࢀߟจݙࢀরจݙҰཡ ؔ࿈ݚڀઌߦݚڀ Kikkawa, Jiro; Takaku, Masao; Yoshikane, Fuyuki: “Analysis of the Deletions of DOIs”, Proceedings of the 26th International Conference on Theory and Practice of Digital Libraries (TPDL 2022), pp. 161-174. Springer International Publishing, 2022. http://doi.org/10.1007/978-3-031-16802-4_13.
  14. • ԿͷͨΊʹҾ༻͢Δͷ͔ • “If I have been able to see

    further, it was only because I stood on the shoulders of giants.” ― ࢲ͕ΑΓԕ͘ΛݟΔ͜ͱ͕Ͱ͖ͨͱ͢ΔͳΒ͹ɼ ͦΕ͸ࢲ͕ڊਓͷݞʹ৐͍͔ͬͯͨΒʹա͗·ͤΜɽ • ΞΠβοΫɾχϡʔτϯʹΑΔॻ؆ खࢴ ʹొ৔͢Δจݴͱͯ͠༗໊ɽ ͨͩ͠ɼ͜ͷݴ͍ճࣗ͠ମͷॳग़͸ΞΠβοΫɾχϡʔτϯͰ͸ͳ͍ͱ͞Ε͍ͯΔ ࢀߟ ʮڊਓͷݞͷ্ʹཱͭʯͱ͍͏ݴ༿ͷ͍ΘΕΛ஌Γ͍ͨɻ cϨϑΝϨϯεڠಉ σʔλϕʔε https://crd.ndl.go.jp/reference/detail?page=ref_view&id=1000151707 • Google Scholar https://scholar.google.com/ ڊਓͷݞͷ্ʹཱͭ • ධՁࢦඪͱͯ͠ͷҾ༻ • ͔͋ͪͪ͜ΒҾ༻͞Ε͍ͯΔ࿦จ͸ॏཁ౓͕ߴ͍ɼӨڹྗ͕େ͖͍ ͸ͣͰ͋Δͱ͍͏ߟ͑ํΛલఏͱ͍ͯ͠Δ • ࿦จؒͷҾ༻͔Β೿ੜͨ͠ධՁࢦඪ → PageRank (Google) • த਎ͦͷ΋ͷΛۛຯ͠ͳͯ͘΋ධՁͰ͖Δ • ೔ʑ૿͍͑ͯ͘େྔͷ৘ใ͔Β༗༻ͳ΋ͷΛޮ཰Α͘୳͢ʹ͸ ͱ͍͏ ໰୊Λղܾ͢ΔͨΊͷखஈͷͻͱͭ 14 Ҿ༻ͱ͸? #3
  15. 15 Impact Factor #1 • ͋Δֶज़ࡶࢽͷܝࡌ࿦จ͕ฏۉͰԿճҾ༻͞Ε͍ͯΔ͔ ʹجͮ͘ࢦඪ • ୯ʹImpact FactorͱݺͿͱ͖ɼJournal

    Impact FactorΛࢦ͢৔߹͕ଟ͍ • ҎԼɼImpact Factor = Journal Impact Factorͱͯ͠આ໌͢Δ ★ Impact Factorͷࢉग़ํ๏ ͋Δֶज़ࡶࢽ X ʹ͓͚Δ2021೥ͷImpact Factor͸ɼҎԼͷܭࢉʹΑΓٻΊΒΕΔ 2021೥ͷલ೥ɼલʑ೥ͷ2೥ؒ 2೥Impact Factorͱݺ͹ΕΔ 雑誌名 2019೥ͱ2020೥ͷܝࡌ࿦จ (A) ͷ݅਺ 2021೥ʹA͕Ҿ༻͞Εͨճ਺ Impact Factor Nature 1,964 (=905+1,059) 136,506 (=54,341+82,165) 69.504 Science 1,585 (=774+811) 101,173 (=39,656+61,517) 63.832 Cell 839 (=432+407) 56,087 (=21,088+34,999) 66.850 ද: 2021೥ʹ͓͚Δ2೥Impact Factorͷ۩ମྫ ग़య: Journal Citation Reports - https://jcr.clarivate.com/
  16. 16 Impact Factor #2 • ͋Δֶज़ࡶࢽͷܝࡌ࿦จ͕ฏۉͰԿճҾ༻͞Ε͍ͯΔ͔ ʹجͮ͘ࢦඪ • ຊདྷɼݸผͷ࿦จʹର͢ΔධՁࢦඪͰ͸ͳ͘ɼࡶࢽʹର͢ΔධՁࢦඪ Impact

    Factorͷྺ࢙ • ϢʔδϯɾΨʔϑΟʔϧυ Eugene Garfield ത͕࢜ൃҊ • ࣌୅എܠ1950೥୅ɼΠϯλʔωοτ͕ීٴ͢Δલ͔Β ࿦จͷ਺͸૿ՃͷҰ్ΛḷΔɽඞཁͳࡶࢽΛ୳͢ʹ͸ ˠ େن໛ͳҾ༻ࡧҾ (Citation Index) Λ࡞੒͢Δ͜ͱʹ ը૾ͷग़య: https://commons.wikime dia.org/wiki/File:Eugene_ Garfield_HD2007_Richar d_J._Bolte_Sr._Award.TIF ը૾ͷग़య: Citation indexing and indexes (IEKO) - https://www.isko.org/cyclo/citation
  17. 17 Impact Factor #3 • ͋Δֶज़ࡶࢽͷܝࡌ࿦จ͕ฏۉͰԿճҾ༻͞Ε͍ͯΔ͔ ʹجͮ͘ࢦඪ • ຊདྷɼݸผͷ࿦จʹର͢ΔධՁࢦඪͰ͸ͳ͘ɼࡶࢽʹର͢ΔධՁࢦඪ Impact

    Factorͷྺ࢙ • ϢʔδϯɾΨʔϑΟʔϧυ Eugene Garfield ത͕࢜ൃҊ • ࣌୅എܠ1950೥୅ɼΠϯλʔωοτ͕ීٴ͢Δલ͔Β ࿦จͷ਺͸૿ՃͷҰ్ΛḷΔɽඞཁͳࡶࢽΛ୳͢ʹ͸ ˠ େن໛ͳҾ༻ࡧҾ (Citation Index) Λ࡞੒͢Δ͜ͱʹ • ౰ॳ (1961೥) : ໿600ࢽʹ͓͚Δ140ສ݅ͷҾ༻ • ݱࡏ (2022೥) : ໿20,000ࢽʹ͓͚Δ8,500ສ݅௒ͷҾ༻ • ର৅ࢽͷܝࡌ࿦จҎ֎ʹ͓͚ΔҾ༻͸ूܭ͞Εͳ͍ɽ ੈͷதʹଘࡏ͢Δ͢΂ͯͷֶज़࿦จͷҾ༻Λ໢ཏ͢Δ΋ͷͰ͸ͳ͍ • Web of Science ͱ͍͏঎༻DBͱͯ͠ఏڙ → https://www.webofknowledge.com/ • Impact Factor͸Journal Citation Reports Ͱௐ΂Δ → https://jcr.clarivate.com/ • ஜ೾େֶ͕ػؔܖ໿͍ͯ͠ΔͨΊɼ͍ͣΕ΋ֶ಺ωοτϫʔΫ͔Βར༻Մೳ ը૾ͷग़య: https://commons.wikime dia.org/wiki/File:Eugene_ Garfield_HD2007_Richar d_J._Bolte_Sr._Award.TIF
  18. Impact Factor #4 18 • ͋Δֶज़ࡶࢽͷܝࡌ࿦จ͕ฏۉͰԿճҾ༻͞Ε͍ͯΔ͔ ʹجͮ͘ࢦඪ • ຊདྷɼݸผͷ࿦จʹର͢ΔධՁࢦඪͰ͸ͳ͘ɼࡶࢽʹର͢ΔධՁࢦඪ ඇৗʹ޿͘ීٴ͍ͯ͠ΔҰํͰɼޡ༻΋ʜʜ

    • IF͕5͔ͩΒ5ճҾ༻͞ΕΔ? → ฏۉ஋ͳͷͰɼݸʑͷܝࡌ࿦จ͕࣮ࡍʹԿճҾ༻͞ΕΔ͔Λҙຯ͠·ͤΜ • IFΛ଍͠߹ΘͤΔͱݚڀऀͷධՁ (ઓಆྗɼείΞ) ʹͳΔ? → ʮIF͕10ͷࡶࢽɼIF͕25ͷࡶࢽʹ1ฤͣͭͷ࿦จ͕ܝࡌ͞ΕͨͷͰɼ ࢲͷઓಆྗ͸35Ͱ͢ʯ͸ޡΓɽ • ҟͳΔݚڀ෼໺ɾݚڀྖҬͰൺֱ͢Δ? → ެද͞ΕΔ࿦จ਺ɼݚڀऀͷਓޱͳͲͷ৚͕݅ҟͳΔͷͰൺֱෆೳͰ͢ ͨͱ͑͹ɼਤॻؗ৘ใֶ෼໺Ͱ͸Impact Factor͕1ະຬͷࡶࢽ΋βϥͰ͢ • IF͕ߴֶ͍ज़ࡶࢽʹ࿦จ͕ܝࡌ͞ΕΔ = ੌ͍ɼҒ͍? → ͜Ε͸ඍົͳ࿩Ͱ͢ɽࢦඪͷࢉग़ํ๏͔Βݴ͏ͱݸʑͷ࿦จͷ࣭Λ ҙຯ͠ͳ͍ҰํɼݖҖ͕ߴ͍͜ͱͰಡऀ͕૿͑ɼڝ૪΋ܹ͘͠ͳΓ·͢
  19. Impact Factor #5 19 Further readings: ΋ͬͱৄ͘͠஌Γ͍ͨਓ޲͚ 1. ҳଜ༟; ஑಺༗ҝ:

    ʮΠϯύΫτϑΝΫλʔͷޭࡑ: Պֶऀࣾձʹ༩͑ͨӨڹͱ͔ͦ͜Β ੜ·Εͨ࿪Έʯ, Խֶ, Vol. 68, No. 12, pp. 32-36, 2013. https://hdl.handle.net/2241/120257. 2. ଙඤ: ʮҾ༻ʹجֶͮ͘ज़ݚڀͷΠϯύΫτධՁʯ, ৘ใͷՊֶͱٕज़, Vol. 70, No. 5, pp. 255-260, 2020. https://doi.org/10.18919/jkg.70.5_255. 3. ࠤ౻ᠳ: ʮֶज़৘ใΛΊ͙Δ৽ͨͳධՁࢦඪ: Impact Factor, h-index, Eigenfactor, Article Influence, Usage Factorʯ, ༀֶਤॻؗ, Vol. 54, No. 2, pp. 121-132, 2009. https://hdl.handle.net/2241/103230. 4. ๕৊ౙथ: ʮܭྔॻࢽֶݚڀͷಈ޲ ܭྔॻࢽֶతࢦඪʯ, ৘ใ؅ཧ, Vol. 53, No. 12, pp. 704-708, 2011. https://doi.org/10.1241/johokanri.53.704. 5. ༗ాਖ਼ن: ʮֶज़ग़൛ͷདྷͨಓʯ. ؠ೾ՊֶϥΠϒϥϦʔ. ؠ೾ॻళ, 2021. https://www.iwanami.co.jp/book/b591591.html. ※ୈ6ষ ֶज़ࢽϥϯΩϯάͷొ৔ 6. ೔ຊਤॻؗ৘ใֶձݚڀҕһձ (ฤ) ʮ৘ใͷධՁͱίϨΫγϣϯܗ੒ (Θ͔Δ! ਤॻؗ৘ใֶγϦʔζ ୈ2ר)ʯ. ษ੣ग़൛, 2015. https://bensei.jp/index.php?main_page=product_book_info&products_id=100515 ˞ୈ2ষ ֶज़৘ใͷධՁ খ໺ࣉՆੜ
  20. ʮݚڀऀʯͷධՁࢦඪ 20 • Ҿ༻ʹجͮ͘ʮֶज़ࡶࢽʯͷධՁࢦඪ ˠ Journal Impact Factor • Ҿ༻ʹجͮ͘ʮݚڀऀʯͷධՁࢦඪ

    ˠ Author Impact Factor ͋Δஶऀ ݚڀऀ ͷ࿦จ͕Ҿ༻͞Εͨճ਺Λɼͦͷஶऀͷ࿦จ਺Ͱׂͬͨ΋ͷ ˠh-index h-indexͱ͸Կ͔? • ݚڀऀͷݚڀۀ੷ΛଌΔࢦඪͷͻͱͭɽhࢦ਺ͱ΋ݺ͹ΕΔ • ෺ཧֶऀͷJorge E. Hirschത͕࢜2005೥ʹൃҊͨ͠ • ͋Δݚڀऀͷશൃද࿦จNp ݅ͷ͏ͪh͕݅ɼ֤ʑগͳ͘ͱ΋hճҾ༻͞Ε͓ͯΓɼ ͦͷଞͷ (Np -h) ͕֤݅ʑhճҎԼ͔͠Ҿ༻͞Ε͍ͯͳ͍৔߹ɼͦͷݚڀऀͷ hࢦ਺ͷ஋͸hͰ͋Δɼͱఆٛ͞ΕΔ • ݚڀऀͷൃද࿦จͷʮྔʯͱʮ࣭ʯͷ྆ํΛߟྀʹೖΕͨࢦඪ ग़య  ܭྔॻࢽֶࣙయɽ  Hirsch, J. E.: “An index to quantify an individual's scientific research output”, Proceedings of the National Academy of Sciences, Vol. 102, No. 46, pp. 16569-16572, 2005. https://doi.org/10.1073/pnas.0507655102.
  21. 21 h-indexͱ͸Կ͔? • ͋Δݚڀऀͷશൃද࿦จNp ݅ͷ͏ͪh͕݅ɼ֤ʑগͳ͘ͱ΋hճҾ༻͞Ε͓ͯΓɼͦͷଞͷ (Np -h) ͕݅ ֤ʑhճҎԼ͔͠Ҿ༻͞Ε͍ͯͳ͍৔߹ɼͦͷݚڀऀͷhࢦ਺ͷ஋͸hͰ͋Δɼͱఆٛ͞ΕΔ https://scholar.google.com/citations?user=B0AYLiUAAAAJ&hl=ja

    • ݚڀऀͷൃද࿦จͷʮྔʯͱ ʮ࣭ʯͷ྆ํΛߟྀʹೖΕͨ ࢦඪ • ඃҾ༻ճ਺ͷଟ͍ॱʹฒ΂ͯɼ ҎԼͷΑ͏ʹूܭ͢Δ͚ͩ ! ൪߸ ඃҾ༻਺ 1 59 2 38 3 29 4 27 5 18 6 16 7 10 8 10 9 9 10 8 < < < < < < < < ≦ > ★
  22. 22 https://scholar.google.com/citations?user=MFTif50AAAAJ&hl=ja https://www.webofscience.com/wos/author/record/GXZ-7953-2022 https://www.scopus.com/authid/detail.uri?authorId=57192369384

  23. 23 https://scholar.google.co.jp/citations?user=obhH0jkAAAAJ&hl=ja https://www.webofscience.com/wos/author/record/26635006 https://www.scopus.com/authid/detail.uri?authorId=36454884800

  24. 24 https://www.webofscience.com/wos/author/record/25541648 https://www.scopus.com/authid/detail.uri?authorId=7402694790

  25. 25 https://scholar.google.com/citations?user=k6hepIAAAAAJ&hl=ja https://www.webofscience.com/wos/author/record/1881319 https://www.scopus.com/authid/detail.uri?authorId=7202123309

  26. h-indexͷಛ௃ͱཹҙ఺ 26 • ݚڀऀͷݚڀۀ੷ΛଌΔࢦඪͷͻͱͭɽhࢦ਺ͱ΋ݺ͹ΕΔ • ݚڀऀͷൃද࿦จͷʮྔʯͱʮ࣭ʯͷ྆ํΛߟྀʹೖΕͨࢦඪ 1. ಉҰਓ෺Ͱ͋ͬͯ΋ɼσʔλϕʔε͕ҟͳΔͱh-indexͷ஋͸ҟͳΔ • Google

    ScholarɼScopusɼWeb of Scienceͷॱʹ஋͕ߴ͘ͳΔ܏޲͕͋Δ • ͳͥ? → ඃҾ༻਺ͷूܭର৅ͱͳΔ࿦จͷ৚݅ɾൣғ͕ҟͳΔͨΊ Google ScholarͳΒGoogle Scholarಉ࢜Ͱ஋Λൺֱ͢Δඞཁ͕͋Δ! 2. ݚڀ෼໺ɾྖҬ΍ɼݚڀऀͷΩϟϦΞʹΑͬͯh-indexͷ஋͸ҟͳΔ • ෼໺͝ͱʹ࿦จ਺΍׳श͕ҟͳΔͨΊ ಉ͡෼໺ɾྖҬͰ΋ҟͳΔ৔߹΋; • ܳज़΍ਓจࣾձֶͷྖҬͰ͸ɼ͋·Γ༻͍ΒΕ͍ͯͳ͍ࢦඪ • ࿦จ਺ɼඃҾ༻਺ͱ΋ʹɼ׆ಈͷ௕͍ਓ෺͸ߴ͘ɼएख͸௿͘ͳΔ܏޲ Further readings • ଙඤ: ʮҾ༻ʹجֶͮ͘ज़ݚڀͷΠϯύΫτධՁʯ, ৘ใͷՊֶͱٕज़, Vol. 70, No. 5, pp. 255-260, 2020. https://doi.org/10.18919/jkg.70.5_255. • ਗ਼ਫؽࢤ: ʮݚڀ׆ಈʹର͢Δ٬؍త͔ͭఆྔతͳධՁࢦඪʯ, ৘ใ؅ཧ, Vol. 52, No. 8, pp. 464-474, 2009. https://doi.org/10.1241/johokanri.52.464.
  27. ʮେֶʯͷධՁࢦඪ: ੈքେֶϥϯΩϯά 27 • શੈքͷେֶΛର৅ͱͨ͠ϥϯΩϯάɼॱҐɾ੒੷ධՁɽҎԼͷ΋ͷ͕༗໊ • World University Rankings |

    Times Higher Education (THE) https://www.timeshighereducation.com/world-university-rankings • QS World University Rankings: Top global universities | Top Universities https://www.topuniversities.com/qs-world-university-rankings ˞Quacquarelli Symonds (ΫΞΫΞϨϦɾγϞϯζ) ࣾ  ॱҐ େֶ໊  ॱҐ େֶ໊ 1 39 ౦ژେֶ 1 23 ౦ژେֶ 2 68 ژ౎େֶ 2 36 ژ౎େֶ 3 201–250 ౦๺େֶ 3 55 ౦ژ޻ۀେֶ 4 251–300 େࡕେֶ 4 68 େࡕେֶ 5 301–350 ໊ݹ԰େֶ 5 79 ౦๺େֶ 6 301–350 ౦ژ޻ۀେֶ 6 112 ໊ݹ԰େֶ 7 501–600 ๺ւಓେֶ 7 135 ۝भେֶ 8 501–600 ۝भେֶ 8 141 ๺ւಓେֶ 9 501–600 ౦ژҩՊࣃՊେֶ 9 197 ܚጯٛक़େֶ 10 501–600 ஜ೾େֶ 10 205 ૣҴాେֶ 11 601–800 ؔ੢ҩՊେֶ 11 312 ஜ೾େֶ 12 601–800 ࢈ۀҩՊେֶ 12 338 ޿ౡେֶ 13 601–800 ԣ඿ࢢཱେֶ 13 363 ਆށେֶ ද: ੈքେֶϥϯΩϯά2023ʹ͓͚Δࠃ಺େֶͷॱҐɽ྘͸THEɼΦϨϯδ͸QSΛࣔ͢ɽ
  28. ੈքେֶϥϯΩϯάʹ͓͚ΔධՁ߲໨ #1 28 Rank Name Overall શମ Teaching ڭҭ Research

    ݚڀ Citations ࿦จ ඃҾ༻ Industry Income ஌ࣝҠస International Outlook ࠃࡍԽ 39 ౦ژେֶ 75.9 88.1 91.4 55.5 86.7 43.3 68 ژ౎େֶ 68.0 77.5 79.1 52.3 88.6 40.5 501–600 ஜ೾େֶ 39.3–42.0 43.9 37.8 38.5 43.7 43.0 ද: Times Higher EducationʹΑΔੈքେֶϥϯΩϯά2023ʹ͓͚Δࠃ಺େֶͷείΞ ग़యWorld University Rankings 2023 | Times Higher Education (THE) https://www.timeshighereducation.com/world- university-rankings/2023/world-ranking#!/page/0/length/25/locations/JPN/sort_by/rank/sort_order/asc/cols/scores Rank Name Overall Academic Reputation Employer Reputation Faculty Student Citations per Faculty International Faculty International Students 23 ౦ژେֶ 85.3 100.0 99.7 91.9 73.3 10.4 27.8 33 ژ౎େֶ 81.4 98.6 98.9 94.8 54.2 14.9 22.1 285 ஜ೾େֶ 34.1 33.9 17.5 63.5 19.2 15.0 26.5 ද: QSʹΑΔੈքେֶϥϯΩϯά2023ʹ͓͚Δࠃ಺େֶͷείΞ ग़య: QS World University Rankings 2023: Top Global Universities | Top Universities https://www.topuniversities.com/university-rankings/world-university-rankings/2023 ʮInternational Research NetworkʯͱʮEmployment Outcomesʯʹ͍ͭͯ͸ɼ2023 editionͰ͸ॏΈ͕0%Ͱ͋ΔͨΊ লུͨ͠ (https://support.qs.com/hc/en-gb/articles/4405955370898-QS-World-University-Rankings)
  29. ੈքେֶϥϯΩϯάʹ͓͚ΔධՁ߲໨ #2 29 ؍఺ Times Higher Education (THE) QS ڭҭ

    30.0% 20.0% ɾڭҭͷධ൑ௐࠪ 15.0% ɾֶੜ / ڭһൺ཰ 20.0% ɾֶੜ / ڭһൺ཰ 4.5% ɾത࢜߸त༩ऀ਺ / ֶ࢜߸त༩ऀ਺ 2.25% ɾത࢜߸त༩ऀ਺ / ڭһ਺ 6.0% ɾେֶͷऩೖ / ڭһ਺ 2.25% ݚڀ 60.0% 20.0% ɾݚڀͷධ൑ௐࠪ 18.0% ɾڭһ1ਓ͋ͨΓͷඃҾ༻਺ 20.0% ɾݚڀඅऩೖ / ڭһɾݚڀελοϑ਺ 6.0% (Scopus) ɾ࿦จ਺ / ڭһɾݚڀελοϑ਺ Scopus 6.0% ɾ1࿦จ͋ͨΓͷඃҾ༻਺ Scopus 30.0% ࠃࡍԽ 7.5% 10.0% ɾ֎ࠃਓڭһൺ཰ 2.5% ɾ֎ࠃਓڭһൺ཰ 5.0% ɾ֎ࠃਓֶੜൺ཰ 2.5% ɾ֎ࠃਓֶੜൺ཰ 5.0% ɾࠃࡍڞஶ࿦จൺ཰ Scopus 2.5% ஌ࣝҠస 2.5% ɾ࢈ۀք͔Βͷݚڀඅऩೖ 2.5% ධ൑ௐࠪ 33.0% 50.0% ɾڭҭͷධ൑ௐࠪ 15.0% ɾݚڀऀʹΑΔධ൑ௐࠪ 40.0% ɾݚڀͷධ൑ௐࠪ 18.0% ɾޏ༻ओʹΑΔධ൑ௐࠪ 10.0% ද: Times Higher Education͓ΑͼQSʹ͓͚ΔධՁࢦඪͱ഑෼ͷҰཡ ੴ઒ਅ༝ඒʮੈքେֶϥϯΩϯάͱ஌ͷংྻԽେֶධՁͱࠃࡍڝ૪Λ໰͏ʯɽژ౎େֶֶज़ग़൛ձɼɽ QɼࢀߟᶅͷදΛجʹ࡞੒ɽଠࣈɼҰ෦จݴͷิهɼண৭౳͸͢΂ͯ٢઒ʹΑΔɽ
  30. ࠷ۙ (2010೥~ ) ͷಈ޲ 30 • Ҿ༻ʹجͮ͘ʮֶज़ࡶࢽʯͷධՁࢦඪ ˠ Journal Impact

    Factor • Ҿ༻ʹجͮ͘ʮݚڀऀʯͷධՁࢦඪ ˠh-index • ʮେֶʯͷධՁࢦඪ ˠ ੈքେֶϥϯΩϯά ࿦จ୯Ґ͔ͭଈ࣌తͳධՁࢦඪ altmetrics alternative + metricsɼΦϧτϝτϦΫε • ΢Σϒ্ (ओʹSNS্) ʹ͓͚Δ࿦จͷӾཡ΍ݴٴʹجͮ͘୅ସతͳධՁࢦඪ • ୅ସతͳධՁࢦඪ = Ҿ༻ʹجͮ͘఻౷తͳධՁࢦඪΛ୅ସɾิ׬͢Δ΋ͷ • TwitterɼWikipediaɼχϡʔεهࣄɼϒϩάɼιʔγϟϧϒοΫϚʔΫͳͲ ʹ͓͚ΔݴٴΛूܭ͢Δ • ࣮૷αʔϏεɼϓϩόΠμʔ͕ෳ਺ଘࡏ͢Δ • Altmetric.com https://www.altmetric.com/ ಛ௃ • ࿦จ୯Ґɼ͔ͭɼϦΞϧλΠϜͰͷूܭ͕ՄೳɽҾ༻ͷ৔߹͸͕࣌ࠩੜ͡Δ • ޿ൣͳࣾձతΠϯύΫτΛ؍ଌՄೳ: ݚڀऀҎ֎ɼඇֶज़తͳΠϯύΫτ • ࿦จɾݚڀͷධՁʹ࢖͑Δ͔Ͳ͏͔? ʹ͍ͭͯ͸ࢍ൱྆࿦ • ਫ૿͠ʹऑ͍
  31. πΠʔτ͸Ҿ༻Λ༧ଌͰ͖Δͷ͔? 31 n ࣾձతΠϯύΫτͱֶज़తΠϯύΫτͷؔ܎ɼ͢ͳΘͪɼaltmetricsͱඃҾ༻਺ ͳͲʹجͮ͘ैདྷͷࢦඪͱͷؔ܎Λ෼ੳͨ͠࿦จ • Journal of Medical Internet

    Researchͷܝࡌ࿦จ΁ͷϦϯΫΛؚΉπΠʔτΛऩूɼ 17-29ϲ݄ޙͷScopusͱGoogle ScholarͷҾ༻σʔλͱൺֱͨ͠ • ඃҾ༻਺ͷଟ͍ (Α͘Ҿ༻͞ΕΔ) ࿦จΛπΠʔτ͔Β༧ଌ͢ΔͨΊͷࢦඪͷݕ౼ • ஌ݟ (1)πΠʔτ਺ͱඃҾ༻਺ʹ͸ ͋Δఔ౓ͷ૬͕ؔ͋Δ ※૬ؔؔ܎ ≠ ҼՌؔ܎ (2)πΠʔτͷ݅਺͔ΒඃҾ༻਺ͷଟ͍࿦จΛ͋Δఔ౓༧ଌͰ͖Δɽ ۩ମతʹ͸ɼ࿦จͷެ։͔Β3೔Ҏ಺ͰɼඃҾ༻਺ͷଟ͍࿦จΛ༧ଌՄೳ • ੍໿ɾݶք఺ Journal of Medical Internet Researchͷܝࡌ࿦จʹର৅͕ݶఆ͞Ε͍ͯΔ͜ͱ ˠ Impact Factor͕ൺֱతߴ͘ɼΠϯλʔωοτؔ࿈ͷ಺༰Λѻ͍ͬͯΔӨڹ͕͋Δ? Science΍NatureͷΑ͏ͳࡶࢽ͸༗ྉͳͷͰɼݚڀऀ͙Β͍͔͠πΠʔτ͠ͳ͍? Ҿ༻ʹجͮ͘ࢦඪΛஔ͖׵͑ΔͷͰ͸ͳ͘ɼ޿ൣͳӨڹΛิ଍ɾิ׬͢Δࢦඪ Eysenbach, Gunther: “Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact”, Journal of Medical Internet Research, Vol. 13, No. 4, p. e123, 2011. https://doi.org/10.2196/jmir.2012.
  32. 32 https://www.altmetric.com/details/497618 Score Twitter Ͳͷࠃɾ஍Ҭ͔Β൓Ԡ͕͔͋ͬͨ

  33. 33 https://www.altmetric.com/details/497618

  34. 34 https://www.altmetric.com/details/497618

  35. 35 AltmetricείΞͷ ߴ͍࿦จͷྫ 2020೥: https://www.altmetric.com/top100/2020/ 2019೥: https://www.altmetric.com/top100/2019/ 2018೥: https://www.altmetric.com/top100/2018/ 2020

    • COVID-19 2018 • Fake news 2019 • Deepfake AI
  36. ࿦จ୯Ґ͔ͭଈ࣌తͳධՁࢦඪ altmetrics 36 alternative + metricsɼΦϧτϝτϦΫε • ΢Σϒ্ (ओʹSNS্) ʹ͓͚Δ࿦จͷӾཡ΍ݴٴʹجͮ͘୅ସతͳධՁࢦඪ

    • ୅ସతͳධՁࢦඪ = Ҿ༻ʹجͮ͘఻౷తͳධՁࢦඪΛ୅ସɾิ׬͢Δ΋ͷ • TwitterɼWikipediaɼχϡʔεهࣄɼϒϩάɼιʔγϟϧϒοΫϚʔΫͳͲ ʹ͓͚ΔݴٴΛूܭ͢Δ • ࣮૷αʔϏεɼϓϩόΠμʔ͕ෳ਺ଘࡏ͢Δ • Altmetric.com https://www.altmetric.com/ ಛ௃ • ࿦จ୯Ґɼ͔ͭɼϦΞϧλΠϜͰͷूܭ͕ՄೳɽҾ༻ͷ৔߹͸͕࣌ࠩੜ͡Δ • ޿ൣͳࣾձతΠϯύΫτΛ؍ଌՄೳ: ݚڀऀҎ֎ɼඇֶज़తͳΠϯύΫτ • ࿦จɾݚڀͷධՁʹ࢖͑Δ͔Ͳ͏͔? ʹ͍ͭͯ͸ࢍ൱྆࿦ • ਫ૿͠ʹऑ͍: ͍͍Ͷ! ΍πΠʔτ͸ߪೖͰ͖ΔͷͰɼૢ࡞Մೳ ↓ • Ҿ༻ʹجͮ͘ࢦඪͱ͸ผͷଆ໘͔Βݚڀ੒ՌͷӨڹΛଌΔ୅ସɾิ׬తࢦඪ Further readings • ଙඤ: ʮҾ༻ʹجֶͮ͘ज़ݚڀͷΠϯύΫτධՁʯ, ৘ใͷՊֶͱٕज़, Vol. 70, No. 5, pp. 255-260, 2020. https://doi.org/10.18919/jkg.70.5_255. • ٢ాޫஉ: ʮܭྔॻࢽֶͷ৽ͨͳ௅ઓ : ࠃ࢈ΦϧτϝτϦΫεܭଌαʔϏεͷ։ൃʯ, ৘ใͷՊֶͱٕज़, Vol. 64, No. 12, pp. 501-507, 2014. https://doi.org/10.18919/jkg.64.12_501.
  37. 37 ֶज़ࡶࢽɼֶज़࿦จɼࠪಡɼҾ༻ͱ͸Կ͔ ʹ͍ͭͯͦΕͧΕઆ໌ͷ͏͑ɼ ҎԼͷࢦඪʹ͍ͭͯ঺հͨ͠ɽ࠷ۙͷݚڀಈ޲ʹ͍ͭͯ΋গ͠঺հͨ͠ɽ • Ҿ༻ʹجͮ͘ʮֶज़ࡶࢽʯͷධՁࢦඪ ˠ Journal Impact Factor

    • Ҿ༻ʹجͮ͘ʮݚڀऀʯͷධՁࢦඪ ˠh-index • ʮେֶʯͷධՁࢦඪ ˠ ੈքେֶϥϯΩϯά • ࿦จ୯Ґ͔ͭଈ࣌తͳධՁࢦඪ ˠ altmetrics ୈճ ʲ٢઒ʳܭྔॻࢽֶ จݙͷੜ࢈ɼྲྀ௨ɼར༻౳ʹؔ͢Δॾࣄ৅Λܭྔతʹѻ͏ݚڀྖҬ Ͱ͋ΔʮܭྔॻࢽֶʯͷجૅΛղઆ͢Δͱͱ΋ʹɼ࠷ۙͷݚڀಈ޲Λ ঺հ͢Δɽ ͓ΘΓʹ: ·ͱΊ γϥόεΑΓҾ༻ ؔ࿈͢Δ಺༰Λѻ͍ͬͯΔतۀͷ঺հ n ৘ใධՁ (๕৊ઌੜ) n σΟδλϧυΩϡϝϯτ ߴٱઌੜ
  38. ܭྔॻࢽֶೖ໳ ίϯςϯπೖ໳ ୈճ ஜ೾େֶ ਤॻؗ৘ใϝσΟΞܥ ಛ೚ॿڭ ٢઒ ࣍࿠ ͖͔ͬΘ ͡Ζ͏

    [email protected] 38