OF TECHNOLOGY • 2000 • Exchange Student in Malaysia • 2002-2009 • CLARAONLINE, INC. • ICT Hosting Company, nowadays called Cloud system supplier • 2009-2015 • Institute of Innovation Research, HITOTSUBASHI UNIVERSITY • 2015-2017 • Science for RE-Designing Science, Technology and Innovation Policy Center, National Graduate Institute for Policy Studies (GRIPS) / NISTEP / Hitotsubashi UNIVERSITY/MANAGEMENT INNOVATION CENTER • 2018-2019 • EHESS Paris – CEAFJP/Michelin Research Fellow • OECD Expert Advisory Group: Digital Science and Innovation Policy and Governance (DSIP) and STI Policy Monitoring and Analysis (REITER) project • 2019- • TDB Center for Advanced Empirical Research on Enterprise and Economy, Faculty of Economics, Hitotsubashi University
Industry/firm level (University/Company) Micro Individual Level (Scientist/Inventor) PATENT - Inventor - Assignee - Patent Number - IPC - Patent Family - Non Patent Literature PAPER - Author - Organization - Category - Acknowledgement DESIGN - No. - Designer Name FUND - No. - Tied Patent/Paper N. Scienc e Linkag e Economic Census Innovation Survey(NISTEP) INPUT-OUTPUT TABLE (I/O) Macro Economic Model Funding Database Press Release Survey of Research and Development (Statistics JAPAN) SNA (System of National Accounts; GDP)
Science J-global Data ・# of paper ・# of cited ・Research Categories Convincing three Paper databases to capture scientific activities in global/local journal. Star Scientist Cohort Data Method: Converting XML -> SQL, then Creating Panel Data in the unit of Researcher/Organization (b.)Patent DB PATSTAT (EPO) Patents View(USPTO) J-global (JPO) IIP PatentDB (JPO) Data ・ # of patent ・ # of patent cited ・FI code/IPC code Using three major Patent Office (USPTO, EPO and JPO) to manage Patent Families. Matching DB bet. Patent = Paper Using disambiguation algorism to normalize researcher and his/her organization information. Using Mecab to coordinate Japanese characters (c.)Academic Funding DB SPIAS (SciREX/NISTEP/JST) KAKEN-DB (NII/JST) RePORT (NIH) Nanobank COMMETS (Z&D) Data ・Amount of Fund Budget ・Direct/Indirect Ratio ・Type of Funding Agency Covering Japan (SPIAS, KAKEN-DB) and US (RePORT, Nanobank) Fund data simultaneously (d.) Venture Company Info DB Entrepedia Crunchbase Data ・Carrier/Position of Scientist in Venture Capital Covers Japan (Entrepedia) and US (Crunchbase) database simultaneously, evaluate the economic impact of star scientist Method. Retrieving the data via API, CSV or JSON format. Creating Panel Data in the unit of Researcher/Organization Method. Retrieve the data from Web interface. Method: Converting XML -> SQL, then Making Panel Data in the unit of Researcher/Organization Press Release News Paper
with a 16-digit number that is compatible with the ISO Standard (ISO 27729), also known as the International Standard Name Identifier (ISNI), e.g. https://orcid.org/0000-0001- 2345-6789 • Initially ORCID iDs will be randomly assigned by the ORCID Registry from a block of numbers that will not conflict with ISNI-formatted numbers assigned in other ways. ORCID iDs always require all 16 digits of the identifier; they can not be shortened to remove leading zeros if they exist. • No information about a person is encoded in the ORCID iD. The identifiers were designed to be usable in situations where personally- identifiable information should/can not be shared. Also, since the ORCID iD is designed to be a career-long identifier, no information that can change over a person's career is embedded in the iD, e.g., country, institution, field of study. https://support.orcid.org/hc/en-us/articles/360006897674
is a unique identifier for researchers on Publons. Register on Publons and import your publications from the Web of Science to become eligible for a Web of Science ResearcherID. • Each night, Publons assigns a Web of Science ResearcherID to any profiles with one or more Web of Science-indexed publications that do not yet have a ResearcherID. • Any publications you add to your Publons profile will then be linked to your Web of Science ResearcherID when anyone searches for you on Web of Science. Please allow up to two weeks for changes you make on Publons to be reflected on Web of Science. https://publons.freshdesk.com/support/solutions/articles/1200003828 1-what-is-my-web-of-science-researcherid-
the Web of Science Core collection. It assigns author ids to the authorships of papers. • There are four major components to DAIS • Initial Clustering – Starting from scratch, take our whole database without an authority list of known authors, identify the different authors. • Ongoing – As new data comes into the database, assign author ids. • RID Integration – Integrates manually created publication lists with DAIS • Reevaluation – Does a fresh, full clustering on a per name basis; discovers new authors not known at the time of the initial clustering
ID is an author ID that Scopus automatically assigns to each author in its database to group publications of the same author together. For the set of documents grouped under the profile of an author ID, Scopus provides bibliometric information such as citation counts, h-index, and h-graph via its citation overview function. Scopus Author ID is now ORCID compliant. • 結局, 複数の Author ID が単一の研究者に紐づけられている可能性があ る • “Because of author name ambiguity issues and other reasons such as prior affiliations, the automatic matching algorithm of Scopus may generate another new ID for the same author when a new paper is included in the database. ” https://libguides.library.cityu.edu.hk/aim/scopus
our machine to mimic how we need to tell one John Smith from another: run a few search queries. This is particularly feasible because we sit on top of Bing that has indexed many CVs and user homepages that can provide valuable clues. With the entire web at our disposable, we are able to group authors together when doing so will contribute to less than 3% of errors. For more details, please see our January 2018 blog.” • 利用している情報 • information about author affiliation, publication venues, and co-author network. • Our data scientists have developed a method for mining data from authors’ web sites and online CVs. Taking advantage of Microsoft’s web-scale infrastructure, by analyzing billions of documents found on the web, the team has taught the machine to recognize web pages that belong to researchers or may be CVs. https://www.microsoft.com/en- us/research/project/academic/articles/microsoft-academic-uses- knowledge-address-problem-conflation-disambiguation/
• Scholar (論文データ) には Author Identifiers を用意 • “By default, author values are grouped by their display name, which can result in the aggregation of scholarly works from different authors with the same name. Enabling author identifiers uses the identifiers available in our data sources to group authors, which can help disambiguate different authors with the same name. The author identifiers used currently include Microsoft Academic, and ORCID identifiers if they are available in data from CrossRef or PubMed. • N.B. Author disambiguation algorithms can incorrectly assign more than one identifier for the same person. In this case, you may wish to disable this feature to match purely on name alone, or select the different identifiers belonging to an individual author. • 実質的には, Microsoft Academics の Author ID を利用 • ”
NISTEP Discussion Paper, 162, http://hdl.handle.net/11035/3215 • Hussain, I., Asghar, S. (2017) A survey of author name disambiguation techniques: 2010–2016, The Knowledge Engineering Review, 32, e22. • Li, Guan-Cheng & Lai, Ronald & D’Amour, Alexander & Doolin, David M. & Sun, Ye & Torvik, Vetle I. & Yu, Amy Z. & Fleming, Lee. (2014) Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010), Research Policy, Elsevier, 43, 6, pp.941-955.
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