Background. To support the development of interdisciplinary research effectively, it is essential to provide research funding that takes into account the interdisciplinary characteristics of the field.
Objectives. We investigated the progress of interdisciplinarity between 1997 and 2017 for 12 selected target fields, from two points of view, focusing on the diversity of researchers’ specializations: (1) the number of researchers’ fields of specialization, and (2) the dispersion of the number of researchers among the fields of specialization.
Methods. For (1), we investigated different specialization fields, not only the target field as a whole but also for each researcher (author) and each article. For (2), we calculated the Gini coefficient and the coefficient of variation.
Results. Different characteristics were observed depending on the viewpoint and the index. The increase rate of (1) was especially high in Materials Science, Biomaterials. The rate of decline of (2) was particularly significant in Information Science & Library Science, Environmental Sciences and Anthropology. It was found that the interdisciplinarity of a field cannot be judged simply as high or low. Interdisciplinarity has gradually grown: not always increasing but also sometimes decreasing or stagnating at times.
Contributions. This study has offered a different perspective of interdisciplinarity, and also charted how it has strengthened over the past 20 years.

Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.

Referencing scholarly documents as information sources on Wikipedia is important because it supports or improves the quality of Wikipedia content. Several studies have been conducted regarding scholarly references on Wikipedia; however, little is known of the editors and their edits contributing to add the scholarly references on Wikipedia. In this study, we develop a methodology to detect the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable as the "first appearance of the scholarly reference." We identified the first appearances of 923,894 scholarly references (611,119 unique DOIs) in 180,795 unique pages on English Wikipedia as of March 1, 2017 and stored them in the dataset. Moreover, we assessed the precision of the dataset, which was highly precise regardless of the research field. Finally, we demonstrate the potential of our dataset. This dataset is unique and attracts those who are interested in how the scholarly references on Wikipedia grew and which editors added them.

We conducted a time-series analysis to clarify the sustainability for adding scholarly bibliographic references to English Wikipedia (enwiki). The results are summarized as follows: The characteristics of the trends between the edits in enwiki as a whole and the adding the references are different in the point that the latter keeps a certain scale at recent years. The number of editors adding the references has seasonal growth trends in April and November every year since 2011. The majority of new editors for adding the references contribute for single month only, but the scale of the editors remains flat in recent years. These characteristics in the editors adding the references seem to be affected by the participants of Wiki Education, so adding the references would be sustainable as long as these activities continue.

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.

We aimed at clarifying the characteristics of the Wikipedia editors who had added Digital Object Identifier (DOI) links to English Wikipedia as scholarly bibliographic references. The results are summarized as follows: (1) Approximately 930 thousand items classifiable into 22 research fields based on the Essential Science Indicators are identified by using DOI links. These items were added by 34,062 users, 31 bots, and 16,349 IP users. (2) Concentration in the number of DOI links on English Wikipedia added by Wikipedia editors is high as a whole (Gini coefficient 0.93), and most research fields'values are also high in common, due to the editors who added DOI links systematically to the existing references on a large scale.

In this paper, we analyzed Digital Object Identifier (DOI) links among English, Japanese, and Chinese Wikipedias (hereafter, enwiki, jawiki, and zhwiki, respectively), which possibly work as a bridge between the Web users and scholarly information. Most of the DOI links in these Wikipedias were revealed to be CrossRef DOIs. The second most-referenced in jawiki were JaLC DOIs, whereas those in zhwiki were ISTIC DOIs. JaLC DOIs were uniquely referenced in jawiki, and ISTIC DOIs tend to be referenced in zhwiki. In terms of DOI prefixes, Elsevier BV was the largest registrant in all languages. Nature Publishing Group and Wiley-Blackwell were also commonly referenced. The content hosted by these registrants was shared among the Wikipedia communities. Moreover, overlapping analysis showed that jawiki and zhwiki share the DOI links with enwiki at a similar high rate. The analysis of revision histories showed that the DOI links had been added to enwiki before they were included in jawiki and zhwiki — indicating that the majority of DOI links in jawiki and zhwiki were added by translating from enwiki. These findings imply that the DOI links in Wikipedia may result in multiple counts of altmetrics.

The authors extracted Digital Object Identifier (DOI) links on Japanese, English, and Chinese Wikipedias as of March 2015, 2016, and 2017 for a time series analysis. As a result, it is revealed that (1) the number of DOI links on each language Wikipedia is increasing year by year, (2) the number of DOI links uniquely referenced on each language Wikipedia is increasing year by year, (3) scholarly information of Japan tends to be referenced more on Japanese Wikipedia than other languages, and (4) there are unique references to scholarly information of Japan on each language Wikipedia and it is increasing year by year.

The authors extracted and analyzed DOI links from external links in Japanese Wikipedia. As of March 2015, there were 28,546 DOI links in all pages and 27,201 in main namespace pages. In terms of Registration Agencies, 97% of DOI Links were registered by CrossRef, and 2% by JaLC. From the aspect of Journal Titles， most journals were from scientific field. Some were Japanese journals.

Abstract: Referencing scholarly documents as information sources on Wikipedia is important because they complement and improve the quality of Wikipedia content. However, little is known about them, such as how rapidly they are added and how fresh they are. To answer these questions, we conduct a time-series analysis of adding scholarly references to the English Wikipedia as of October 2021. Consequently, we detect no tendencies in Wikipedia articles created recently to refer to more fresh references because the time lag between publishing the scholarly articles and adding references of the corresponding paper to Wikipedia articles has remained generally constant over the years. In contrast, tendencies to decrease over time in the time lag between creating Wikipedia articles and adding the first scholarly references are observed. The percentage of cases where scholarly references were added simultaneously as Wikipedia articles are created is found to have increased over the years, particularly since 2007-2008. This trend can be seen as a response to the policy changes of the Wikipedia community at that time that was adopted by various editors, rather than depending on massive activities by a small number of editors.

2022年11月22日 (火) 開催の "図書館総合展連続セミナー「学術コミュニケーション入門」を読む" の第3回にて発表に使用したスライド資料です。以下の2章分を担当しました。
・第10章: メトリクスとオルトメトリクス
・第11章: メタデータとその重要性

References:
・連続セミナー「学術コミュニケーション入門」を読む（全3回） | 図書館総合展 https://www.libraryfair.jp/forum/2022/535
・学術コミュニケーション入門: 知っているようで知らない128の疑問 | Rick Anderson, 宮入 暢子 |本 | 通販 | Amazon https://www.amazon.co.jp/dp/4904419952

Abstract: Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.

https://meta.wikimedia.org/wiki/WikiCite_2018

(予稿集での掲載ページは、p.25-28)
英語版WikipediaにおけるDOIリンクを通じた学術文献の参照記述を対象に、初出時点および研究分野に着目した分析を行なった。その結果、(1) 参照記述は2002年に初めて追加され、2007年から急増している、(2) 参照記述の追加は近年になればなるほど盛んになっている、(3) Scopus収録雑誌に対する論文参照率は全体で1.2%で、研究分野では「Multidisciplinary」が3.9%で最も高かった。

The authors extracted Digital Object Identifier (DOI) links on Japanese, English, and Chinese Wikipedias as of March 2015, 2016, and 2017 for a time series analysis.
As a result, it is revealed that
(1) the number of DOI links on each language Wikipedia is increasing year by year,
(2) the number of DOI links uniquely referenced on each language Wikipedia is increasing year by year,
(3) scholarly information of Japan tends to be referenced more on Japanese Wikipedia than other languages,
and (4) there are unique references to scholarly information of Japan on each language Wikipedia and it is increasing year by year.
http://www.jsik.jp/?forum2017

https://meta.wikimedia.org/wiki/WikiCite_2017

ジャパンリンクセンターでは、これまで「対話・共創の場」を2回開催してまいりました。第1回は「機関リポジトリのコンテンツへのDOI登録」、第2回は「研究データへのDOI登録」をテーマに開催し、大きな成果を得ました。
今回の「対話・共創の場」（第3回）は、「DOIの活用 ～ストラテジーの実現に向けて～」をテーマに開催します。平成29年3月でJaLC設立から5年が経過し、DOIの登録は着実に進んでいるため、今後はDOI登録後の活用についても検討を進めていく必要があります。 このような状況下、今年度策定した「ジャパンリンクセンター ストラテジー 2017-2022」には、最初に「DOIやメタデータがオープンに活用されることを推進します。」と掲げており、 これを実現させるための糸口を得ることを目的として、DOIの活用に関する現状の事例共有と、今後の方策を議論します。

We analyzed Digital Object Identifier (DOI) links among English, Japanese, and Chinese Wikipedia (hereafter, enwiki, jawiki, and zhwiki, respectively), that possibly work as a bridge between the web users and scholarly information. Most of DOI links in these Wikipedias were revealed to be CrossRef DOIs. The second most-referenced in jawiki were JaLC DOIs, whereas those in zhwiki were ISTIC DOIs. JaLC DOIs were uniquely referenced in jawiki and ISTIC DOIs tend to be referenced in zhwiki. Moreover, overlap analysis showed that jawiki and zhwiki share the DOI links at a high rate with enwiki. The analysis of revision histories showed the DOI links had been added to enwiki before they were included in jawiki and zhwiki, indicating that the majority of DOI links in jawiki and zhwiki were added through translation from enwiki. These findings imply the DOI links in Wikipedia may result in multiple counts in altmetrics.

近年、論文や研究データなどの多様な学術情報を扱う上で、DOI(Digital Object Identifier、デジタルオブジェクト識別子)がますます重要なものとなっている。日本国内の動向として、JaLC(Japan Link Center)における「研究データ利活用協議会」の発足など、DOI登録のみならず、その利活用を志向する段階に移行しつつあると言える。
しかし、研究者や図書館関係者にとって、あるいは、学術情報流通やウェブに関心をもつ人々にとって、DOIは「謎のハイパーリンク(あるいは謎の文字列)」というイメージを抱かれていることが少なくないのではないだろうか。DOIは学術情報にアクセスするための恒久的なハイパーリンクとしての機能のみならず、様々な機能を果たす。たとえば、Web APIによる書誌事項の提供を通じたアプリケーションやサービスとの連携や、定量的なアクセス分析のための基盤としての役割をもつ。
本発表では、「DOIなんて初めて聞いた」、「たまに耳にするけれど、あまり詳しくは知らない」といった人に向けて、実際のサービスや最新動向の紹介を交えながらDOIの基礎知識を共有する。さらに、新たなアイデアの検討やコミュニティの活性化へと繋げることを目指す。

本発表ではオンライン配信番組「図書館情報学チャンネル」の企画運営を通じて開発した番組プログラム編成支援システムを紹介する。図書館情報学チャンネルは、技術系の図書館情報学コミュニティ「Project Lie」が毎週1回配信しており、2010年から現在に至るまで6年間に及ぶ長寿オンライン配信番組である。内容としてはその週に起こった図書館情報学に関するニュースを紹介するという体裁をとっている。これまで番組プログラムはProject Lieメンバーの1人が作成する体制であったが、番組制作の負荷が集中し、番組の継続に支障をきたしつつあった。そこで負荷分散を目的として、番組プログラムを自動編纂するシステムを構築した。本発表ではその具体的な仕組みについて述べるとともに、それによる番組制作に与えた影響を紹介することで、今後の図書館情報学関係者間の情報共有のあり方について考察する。

In this paper, the authors have attempted to build a dataset of scholarly information on Wikipedia as Linked Open Data. As a data model, we refer to "DBpedia citations & references challenge", hereafter DBpedia method.
We extracted scholarly information from external links in Japanese Wikipedia. As of March 2016, there were 35,266 DOI links and 10,047 CiNii permalinks that identified with NAID in main namespace pages. As a result of comparison between DBpedia method and proposed method, DBpedia and proposed method extracted common DOI Links at high rate. DBpedia method extracted fewer CiNii permalinks than proposed method.

(予稿集掲載ページはp.39-42.)
あらまし： Wikipediaにおける日本国内の学術情報の参照について、研究分野および言語版ごとの特徴や差異を明らかにするため、日本語版、英語版、中国語版におけるJ-STAGEコンテンツの参照状況の分析を行った。結果、研究分野は生命科学分野の割合が共通して高く、日本語版は理学・工学分野の割合が他言語版に比べて高いこと、日本語版は和文誌や英和混在誌が多いのに対し、英語版および中国語版では英文誌が多いことが分かった。

掲載ページはp.17-20

機関リポジトリは学術情報流通基盤として重要な存在である。特に、平成25年4月の学位規則改定による博士論文のインターネット公表を背景に、今後ますます重要性が拡大することが予想されるほか、朝日新聞社刊行の「大学ランキング」において機関リポジトリランキングが掲載されるなど、機関リポジトリに対する社会的な認知の広がりや、機関リポジトリを大学に対する評価に用いる動きが見られる。したがって、機関リポジトリは学術情報流通基盤であると同時に、社会への情報発信や宣伝としての機能を果たしうる存在であると言える。
日本国内における機関リポジトリのメタデータを収集・分析するためのシステムとしては、国立情報学研究所提供の「IRDBコンテンツ分析システム(以下、IRDB)」が存在するが、同システムの主な提示内容は、分析対象機関全体または個別機関ごとのコンテンツの増減およびコンテンツ種別ごとの内訳などに留まっている。より具体的には、特定のコンテンツ種別について上位の機関名およびその割合を示す、A大学とB大学によるコンテンツの比較を行う、特定コンテンツ種別の搭載が特定年度以降どれくらい増えているかを示す、などの機能は提供されていない。これらを可視化することで、今後の学術情報基盤に関する議論の資料として活用が期待できるほか、大学図書館員の貢献を可視化することで大学図書館員の評価にも繋がることが期待できる。
以上の背景を踏まえ、本発表では、既存のIRDBに実装されていない分析の観点を検討したうえで、「Yet Another IRDB」として試験的に実装したシステムの提案を行う。

発表動画: https://www.youtube.com/watch?v=VV6kPKHzULg
ShowNotes: https://gist.github.com/corgies/f8a5c315eaa98a6fadc1
Slide: http://www.slideshare.net/jir_o/code4lib2015yairdbkikkawa

日本語版WikipediaにおけるDOIリンクが記述された経緯を明らかにするため、英語版Wikipediaの翻訳項目の特定およびそこに含まれるDOIリンクの分析を行った。その結果、DOIリンクが記述されている日本語版の約94%の項目が言語間リンクをもっていること、また、それらの項目間での共通のDOIリンクは、英語版の翻訳を通じて日本語版に記述されたものが大部分を占めることを示唆する結果が得られた。
http://www.jsims.jp/kenkyu-taikai/yokoku/14.html

本研究では,日本語版Wikipediaに含まれるDOIリンクの予備的分析を行った.2015年3月時点での日本語版Wikipediaには28,546件のDOIリンクがあり,そのうち標準名前空間ページに含まれる27,201件について,DOI登録機関はCrossRefが97%,JaLCが2%であった.日本国外の大手出版社が多く,雑誌タイトルレベルでは,Nature,Science,PNASなどの自然科学分野の有力誌が多く含まれていた.また,日本国内の出版者のDOIリンクが含まれていた.
http://www.jsik.jp/?2015program