In Japan, national universities were turned into independent administrative entities, namely national university corporations, in April 2004 with the aim of further developing education and research while making use of their individual characteristics. This study examined international patent applications through the Patent Cooperation Treaty (PCT) filed by Japanese national universities after their incorporation for the purpose of elucidating the similarities between universities in terms of trends in technology fields and their differences in the joint application network including industry–academia collaboration. As an information source for patent documents, we used the database PATENTSCOPE. 18,217 PCT applications filed by Japanese national universities from April 1, 2004 to March 31, 2021 were extracted for our analyses. As for trends in technology fields, we created a feature vector whose components were calculated on the basis of the frequency of assigned classifications for each university, and measured the closeness of trends in technology fields between universities using the cosine similarity between their feature vectors. Furthermore, a correspondence analysis was performed based on the feature vectors of the universities in order to visualize the closeness of trends in technology fields between them. As for trends in joint applications, on the other hand, we constructed and visualized a joint application network, wherein applicants are nodes and joint applicants are connected by edges. Moreover, the importance of universities in the joint application network was measured by degree centrality and betweenness centrality.
Our findings are as follows. With regard to trends in the technology fields of PCT applications, it was shown that, as for medicine and engineering, all universities of the same specialty were fairly similar to each other. This is not necessarily consistent with the trends of overall applications, not limited to the PCT applications, by Japanese national universities that have been illustrated in previous research. That is to say, universities of the same specialty seem to be more similar in application trends regarding the technology fields where PCT applications are preferred (i.e., fields where universities are presumed to put their efforts in obtaining rights in foreign countries) than regarding the other fields. With regard to trends in PCT joint applications, it was shown that University of Tokyo, Osaka University, Kyoto University, and Tohoku University occupied particularly important positions both as hubs and bridges. However, the difference in the tendency for co-applicants was clearly observed between Osaka University and Tohoku University, with the former having established a strong network with universities and the latter with companies.

Scholarly communication through YouTube videos has been increasing. Although Altmetric provides the dataset on such references, its coverage is unclear, and it does not contain the original external links in each video. Further investigation is needed to understand the characteristics of scholarly references as external links in YouTube videos. To address this gap, we propose a method to identify scholarly references by searching for domain names and building a dataset by applying this method. Subsequently, we compare this dataset with the Altmetric dataset and analyze the external link characteristics. Using the proposed method and targeting six types of domain names, we identified approximately 480,000 references among 230,000 videos posted on 55,000 channels. Notably, over half of these references were not covered by the Altmetric dataset, resulting in a 150% increase in the number of references when combining the dataset constructed by the proposed method with the Altmetric dataset, compared to the Altmetric dataset alone. Regarding external links, PubMed and DOI links were prominent; however, a substantial number of direct links to publisher platforms were observed. Most channels and videos contained external links to a single platform, scattered across each platform. The method proposed in this study is helpful for identifying and analyzing scholarly references on YouTube. In addition, the findings on external link characteristics raise concerns about the long-term accessibility and fact-checking of information sources for YouTube video content.

Background. Scholarly documents referenced on Wikipedia play important roles in improving the quality of Wikipedia articles, and the references using DOI links are desirable for permanent access and correct identification. However, few studies have been conducted to analyze long-term observation and analysis of the addition of DOI links for scholarly references on Wikipedia.
Objectives. The study aims to clarify the long-term trends in the scholarly references on the English and Japanese Wikipedia from 2015 to 2023 using DOI links and associated Wikipedia articles.
Methods. We extracted DOI links and Wikipedia articles containing them from Wikipedia dump files. Additionally, we obtained DOI Registration Agency (RA), Crossref metadata, and Essential Science Indicators categories, integrating them accordingly. We investigated (1) the statistics of DOI links and associated Wikipedia articles, (2) the difference and product sets from Wikipedias, (3) the top contents highly referenced on each Wikipedia, and (4) distributions of the DOI registration years/published years on DOI links newly added to English and Japanese Wikipedia.
Results. (1) The diversities of RAs for DOI links increased in both Wikipedia from 2015 to 2023. (2) From 2015 to 2023, DOI links referenced in both Wikipedias increased five times. Notably, unique DOI links referenced in Japanese Wikipedia (not overlapping with English Wikipedia) increased ten times. (3) DataCite DOIs have emerged among the top five contents in English and Japanese Wikipedia as of 2023. Three of the top five contents in Japanese Wikipedia were JaLC DOIs, all written in Japanese. (4) For DOI links added after 2015, most references in both Wikipedia point to scholarly articles published before 2015.
Contributions. We clarified the long-term progress of adding DOI links as scholarly references on English and Japanese Wikipedia from 2015 to 2023. The findings of this study contribute to understanding the characteristics of DOI links referenced on Wikipedia and the trend of activities related to scholarly content on Wikipedia communities, as well as applications to Altmetrics studies.

Scholarly communication through video by various people and institutions has spread more and more in recent years and impacts the dissemination of scholarly knowledge and people's daily lives. However, most of the previous works were conducted to measure the impacts of these research outputs as Altmetrics studies; little is known about characteristics and trends of scholarly articles and their associated videos, e.g., how many of these articles and videos are available since when?, and who has been publishing these videos? Hence, we analyzed 410,101 articles with 228,192 unique DOIs referenced in 137,629 videos uploaded by 39,416 channels, using data provided by Altmetric.com. As a result, the total number of scholarly references on YouTube has increased rapidly since 2016 and has continued to grow. The top research fields of scholarly articles were Clinical Medicine and Multidisciplinary consistently. The most common channels in the number of videos associated with scholarly references were hosted by publishers and academic societies, SciShow, and other individuals/organizations. On the other hand, the most common channels in the number of scholarly references were individuals/organizations who uploaded videos related to the categories such as nutrition and dietetics, sports sciences, and alternative medicine, along with the SciShow series, rather than publishers and academic societies.

国立情報学研究所 (NII) のイベントである「NII OPEN HOUSE 2018」において、武田英明先生、大向一輝先生の研究グループの研究紹介ポスターとして作成・掲示。

Scholarly communication through YouTube videos has been increasing. Although Altmetric (https://altmetric.com/) provides the dataset on such references, its coverage is unclear, and it does not contain the original external links in each video. Considering this background, we built and published a dataset of scholarly bibliographic references on YouTube videos by using YouTube Data API v3, targeting six types of domain names: "doi.org," "ncbi.nlm.nih.gov," ieeexplore.ieee.org," "link.springer.com," "onlinelibrary.wiley.com," and "sciencedirect.com." As a result, we identified approximately 480,000 references associated with Crossref DOIs among 230,000 videos published by December 31, 2023, posted on 55,000 channels. Notably, over half of these references were not covered by the Altmetric dataset, resulting in a 150% increase in the number of references when combining the dataset constructed by the proposed method with the Altmetric dataset, compared to the Altmetric dataset alone. Regarding external links, PubMed and DOI links were prominent; however, a substantial number of direct links to publisher platforms were observed. Most channels and videos contained external links to a single platform, scattered across each platform. This dataset is helpful for identifying and analyzing scholarly references on YouTube.

Data Records
The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we identified the deleted DOIs by from the difference set between Crossref DOIs as of March 2017 and January 2021. We note that the file "00_Non-Crossref_DOIs.jsonl.gz" is not deleted DOIs but other files are deleted DOIs. Please refer the following conference paper: Kikkawa, J., Takaku, M. & Yoshikane, F. "Analysis of the deletions of DOIs: What factors undermine their persistence and to what extent?", Proceedings of the 26th International Conference on Theory and Practice of Digital Libraries (TPDL 2022), (to appear), 2022.

We developed 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) in180,795 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 and it was a high precision regardless of the research field. In this version, it is available not only the dataset of English Wikipedia as of March 1, 2017, but also English Wikipedia as of October 1, 2021, generated by using the same methodology.

https://speakerdeck.com/corgies/jsims2018

Scholarly communication through YouTube videos has been increasing. Although Altmetric provides the dataset on such references, its coverage is unclear, and it does not contain the original external links in each video. Further investigation is needed to understand the characteristics of scholarly references as external links in YouTube videos. To address this gap, we propose a method to identify scholarly references by searching for domain names and building a dataset by applying this method. Subsequently, we compare this dataset with the Altmetric dataset and analyze the external link characteristics. Using the proposed method and targeting six types of domain names, we identified approximately 480,000 references among 230,000 videos posted on 55,000 channels. Notably, over half of these references were not covered by the Altmetric dataset, resulting in a 150% increase in the number of references when combining the dataset constructed by the proposed method with the Altmetric dataset, compared to the Altmetric dataset alone. Regarding external links, PubMed and DOI links were prominent; however, a substantial number of direct links to publisher platforms were observed. Most channels and videos contained external links to a single platform, scattered across each platform. The method proposed in this study is helpful for identifying and analyzing scholarly references on YouTube. In addition, the findings on external link characteristics raise concerns about the long-term accessibility and fact-checking of information sources for YouTube video content.

論文などの学術リソースの発見には、これまでWeb of Science, Scopus, Google Scholar, CiNiiなど、収集された学術データベースの検索を使うことが通例であった。しかし、オープンサイエンスの浸透により、学術リソースも多様化するとともに研究のあり方も多様化してきてきている。そこで、オープンサイエンスにおけるリソース発見の現在と未来について幅広く議論する。

取り上げるトピックス：
プリプリントにみる学術リソースの多様化
Wikipedia / YouTubeでの学術論文の利用にみる学術リソースの利用の多様化
学術データベースの今後のあり方

登壇予定者：
金沢 輝一（国立情報学研究所）
小柴 等（科学技術・学術政策研究所）
吉川 次郎（筑波大学）
武田 英明（国立情報学研究所）

Scholarly communication through video by various people and institutions has spread more and more in recent years and impacts the dissemination of scholarly knowledge and people’s daily lives. However, most of the previous works were conducted to measure the impacts of these research outputs as Altmetrics studies; little is known about characteristics and trends of scholarly articles and their associated videos. E.g., how many of these articles and videos are available since when?, and who has been publishing these videos? Hence, we analyzed 410,101 articles with 228,192 unique DOIs referenced in 137,629 videos uploaded by 39,416 channels, using data provided by Altmetric.com. As a result, the total number of scholarly references on YouTube has increased rapidly since 2016 and has continued to grow. The top research fields of scholarly articles were Clinical Medicine and Multidisciplinary consistently. The most common channels in the number of videos associated with scholarly references were hosted by publishers and academic societies, SciShow, and other individuals/organizations.

Background. Scholarly documents referenced on Wikipedia play important roles in improving the quality of Wikipedia articles, and the references using DOI links are desirable for permanent access and correct identification. However, few studies have been conducted to analyze long-term observation and analysis of the addition of DOI links for scholarly references on Wikipedia.
Objectives. The study aims to clarify the long-term trends in the scholarly references on the English and Japanese Wikipedia from 2015 to 2023 using DOI links and associated Wikipedia articles.
Methods. We extracted DOI links and Wikipedia articles containing them from Wikipedia dump files. Additionally, we obtained DOI Registration Agency (RA), Crossref metadata, and Essential Science Indicators categories, integrating them accordingly. We investigated (1) the statistics of DOI links and associated Wikipedia articles, (2) the difference and product sets from Wikipedias, (3) the top contents highly referenced on each Wikipedia, and (4) distributions of the DOI registration years/published years on DOI links newly added to English and Japanese Wikipedia.
Results. (1) The diversities of RAs for DOI links increased in both Wikipedia from 2015 to 2023. (2) From 2015 to 2023, DOI links referenced in both Wikipedias increased five times. Notably, unique DOI links referenced in Japanese Wikipedia (not overlapping with English Wikipedia) increased ten times. (3) DataCite DOIs have emerged among the top five contents in English and Japanese Wikipedia as of 2023. Three of the top five contents in Japanese Wikipedia were JaLC DOIs, all written in Japanese. (4) For DOI links added after 2015, most references in both Wikipedia point to scholarly articles published before 2015.
Contributions. We clarified the long-term progress of adding DOI links as scholarly references on English and Japanese Wikipedia from 2015 to 2023. The findings of this study contribute to understanding the characteristics of DOI links referenced on Wikipedia and the trend of activities related to scholarly content on Wikipedia communities, as well as applications to Altmetrics studies.

Along with the digitization of scholarly communication, numerous scholarly documents have been referenced and used on the Web. One of the changes arising from the development and dissemination of scholarly information infrastructures on the Web is the utilization of scholarly documents by various people and communities, including readers other than traditional ones such as researchers and specialists. As such an example, there are many references and access to scholarly documents via Wikipedia.
Considering this background, we have been engaged in the research project to identify and analyze scholarly bibliographic references on Wikipedia since 2014. To date, a method is built to identify the “first appearance of the scholarly reference”, i.e. the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable. The first appearances of the 1.47 million scholarly bibliographic references on the 310,000 articles on English Wikipedia as of October 2021 are identified by using the proposed method. Moreover, a time-series analysis of adding scholarly references to English Wikipedia by using this dataset is conducted. As a result, it was observed that tendencies to decrease over time in the time lag between creating Wikipedia articles and adding the first scholarly references. 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. In this talk, the findings of our recent studies will be introduced in detail.