Publications and Presentations

Refereed Journal Papers

Takahiro Miura, Kimitaka Asatani and Ichiro Sakata, "Revisiting the uniformity and inconsistency of slow-cited papers in science," Journal of Informetrics, 17, 1, 2023. [paper]

Quantitative analyses on delayed recognition indicated by slow-cited papers, including delayed papers and durable papers, have long been discussed to reveal why outstanding discoveries remain unnoticed. However, these analyses include contradictory arguments, such as which combinations of knowledge, over-specialization, or transdisciplinary factors have led to undervaluation. We claim that this is because the indicators of delayed recognition are methodologically similar but capture conceptually different phenomena. Subsequently, this paper examined the overlap of 11 slow-cited measures to identify the uniformity and inconsistency of delayed recognition. Consequently, each measure practically obtained different papers as delayed recognition objectively classified into four groups by citation feature clustering, albeit based on similar concepts. Despite the ambiguity, we found that all delayed recognition measures extract papers that are more likely to be single-author projects that make disruptive contributions to more diverse fields without extremely novel nor conventional knowledge combinations that have been gradually awakened, compared to the null models. This result is robust when applying other hyperparameters, research topic-controlled null models, year-controlled null models, and other fields. This strongly indicates that delayed recognition leads to the reconstruction of a new direction of science and contributes to pioneering a revolutionary research topic. The source code for extracting slow-cited papers is available online.1

Takahiro Miura, Kimitaka Asatani and Ichiro Sakata, "Large-scale analysis of delayed recognition using sleeping beauty and the prince," Applied Network Science, 6, 48, 2021. [paper]

Delayed recognition in which innovative discoveries are re-evaluated after a long period has significant implications for scientific progress. The quantitative method to detect delayed recognition is described as the pair of Sleeping Beauty (SB) and its Prince (PR), where SB refers to citation bursts and its PR triggers SB’s awakeness calculated based on their citation history. This research provides the methods to extract valid and large SB–PR pairs from a comprehensive Scopus dataset and analyses how PR discovers SB. We prove that the proposed method can extract long-sleep and large-scale SB and its PR best covers the previous multi-disciplinary pairs, which enables to observe delayed recognition. Besides, we show that the high-impact SB–PR pairs extracted by the proposed method are more likely to be located in the same field. This indicates that a hidden SB that your research can awaken may exist closer than you think. On the other hand, although SB–PR pairs are fat-tailed in Beauty Coefficient and more likely to integrate separate fields compared to ordinary citations, it is not possible to predict which citation leads to awake SB using the rarity of citation. There is no easy way to limit the areas where SB–PR pairs occur or detect it early, suggesting that researchers and administrators need to focus on a variety of areas. This research provides comprehensive knowledge about the development of scientific findings that will be evaluated over time.

Refereed Conference Papers

Takahiro Miura, Kimitaka Asatani, Ichiro Sakata, "Temporal Dynamics of Research Field Integration on Slow-cited Papers and the Awakeners," ASIS&T SIG/MET workshop, 2022 [paper]

Understanding the long-term impact of scientific findings requires understanding the dynamic process of new research fields' formation. In new research fields, slow-cited papers (SCP) and the awakeners (AW) are more likely to exist, indicating explorers revisited underrated but significant past papers relocating the findings in the new paradigm. This study acquired SCP-AW pairs located in the integrated point of two different research fields using the inheritance of clusters. We found that research field integration, including SCP-AW pairs, was diverse but followed a similar pattern throughout history, generating an equal mix of SCP and AW fields. The recent trend toward more AW-centric disciplinary combinations supports the belief that field integration will become increasingly technology-driven in the coming years.

Takahiro Miura, Kimitaka Asatani Ichiro Sakata. "Measuring Career Growth Related to Organisational Movement for Junior and Senior Researchers," 2022 Portland International Conference on Management of Engineering and Technology (PICMET), 2022 [paper]

Global competition for talented researchers has intensified in recent years, and increasing organisations, such as Google and Microsoft, are growing rapidly by attracting talented researchers. In the computer science field in particular, more young researchers have been engaged in research. From the researcher perspective, they want to move to an organisation that will allow them to experience more career growth; however, existing organisational metrics equate organisational growth with individual growth and do not quantify whether a researcher can really grow after moving. In this study, we clarify researcher growth from organisational movement using 5.6 million articles published between 1970 and 2018 via Scopus. By analysing the characteristics of the influx of researchers to growing organisations in recent years by career stage, we identify the differences in the impact of research organisations on junior and senior researchers and consider the current strategies of various organisation. The findings show that, while they move to similar organisations, the organisational environments in which junior researchers and senior researchers can experience career growth differ. This analysis contributes to a better understanding of researchers’ career trajectories and organisational strategies for scientific innovation.

Takahiro Miura, Ichiro Sakata,  "Storyteller: The papers co-citing Sleeping Beauty and Prince before awakening",  ASIS&T SIG/MET workshop sponsored by Elsevier's ICSR, 2021. [paper]

This study proposes a“Storyteller” that focuses on the connection between Sleeping Beauty(SB) and Prince(PR) before SB gets citation burst by co-citation. PR is found to be the paper awakening SB in retrospect, but it is not easy to detect it as the trigger of SB’s awakeness at the time of PR submission. We named the papers which co-cites SB and PR before the citation burst of SB as “Storyteller”(ST) and analyze (1) how ST contributes to broadening the novelty of SB&PR connections and (2) how much ST leads the citation burst after awakening.

Takahiro Miura, Kimitaka Asatani and Ichiro Sakata,  "Classifying Sleeping Beauties and Princes Using Citation Rarity,"  2020 International Conference on Complex Networks and Their Applications. Springer, Cham, 2020. p. 308-318. [paper]

The scientific community sometimes resists important scientific findings initially. This is the so-called “delayed recognition.” A “sleeping beauty (SB),” a representative phenomenon of delayed recognition, is a paper reported by a Prince (PR) paper. The SB includes many key breakthrough concepts for resolving scientific problems. Although many PRs discover their SBs, it is still unknown how they do that because the citation culture differs depending on the category of the paper. This study classifies SBs and their PR pairs using citation rarity within clusters that represent a unique category of a paper. Results show that citation rarity corresponds to the types of contributions to PR papers. Rare citations explore methodological insights into PR fields. Meanwhile, common citations can lead to rediscovery of the core concepts of a sleeping beauty. Furthermore, informatics and materials sciences cover major studies that include citations for SBs, whereas biological subjects find key papers through rediscovery. Results indicate that different categories of citations yield different types of SBs.

Takahiro Miura, Kimitaka Asatani Ichiro Sakata. "Identifying Affiliation Effects on Innovation Enhancement, 2019 Portland International Conference on Management of Engineering and Technology (PICMET)", 2019 [paper]

Analysis of bibliographic information provides important evidence for identifying scientific innovation and future technological developments. The efficient operation of a research organization requires management of factors that enhance the future publishing of scientists' results. However, existing methodologies such as the use of the Times Higher Education University Rankings does not distinguish the reputation of affiliation from that of its members. Therefore, superior scientists do not perform well because of inferior research environments: so-called Brain Graveyards. As described herein, we propose the Research Productivity Enhancement (RPE) index to quantify affiliation effects on scientists' performance by tracking their scientific publications along with their movements among affiliations. Results show that scientists moving to state-of-the-art institutions do not always achieve enhanced productivity. Rather, some of them collect talented authors. Divided by nationality, many Chinese affiliations show high RPE. Conversely, Japan and Korea give less of a contribution to scientists' productivity. This analysis elucidates scientists' incentives and suggests means by which research organizations can enhance scientific innovation.

Non-Refereed Conference Papers

Hiroki Tahara, Takahiro Miura, Yuta Shimizu. "Tweet Trend Analysis of 2016 US Presidential Candidate Supporters Using Similarity Network," 14th Netecosympo, 2017

Twitter is one of the most popular microblogging service in the world even initiatively used at the 2016 US presidential election, which contained a controversial issue of SNS use. Usually, tweets are classified into two types. One is to state their own opinion and the other is to spread someone’s idea. According to the preceding study, these types are mentioned by only its mention network or sentimental-analysis. In this study, we use the similarity of each text to elucidate how different Trump side’s tweets trend and Clinton side’s tweets trend were. We also make a similarity network that shows information diffusion.

Invited Talk