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Data papers, as one of the channels to encourage researchers to open up research data under the open science movement, are expected to provide strong incentives through formal citations. However, few studies have investigated the drivers of this emerging type of publication. This study examines researchers' motivations, and considerations for data paper submission, as well as their perspectives on this scholarly publication. Through an in‐depth interview approach with ten data paper authors, our preliminary results found that, researchers are often driven by extrinsic factors to increase their publications, and data papers are sometimes viewed as territory claims before further research. Although the academic community widely recognizes the benefits of publishing data papers, some still cast a doubtful eye on its academic value and impact. We anticipate such insights on the driving forces and point of views of data papers could provide opportunities for stakeholders to fill gaps and strengthen the open science ecosystem. [ABSTRACT FROM AUTHOR]

Citations between papers and patents reflect transfer of knowledge between science and technology. Patents commonly cite papers but papers rarely cite patents. Here, we identified 6,033 paper‐to‐patent citations in a collection of 1.5 million PubMed Central open access articles. These citing papers and cited patents contained 132,536 paper‐to‐paper, 200,339 patent‐to‐patent, and 36,342 patent‐to‐paper citations. These four citation datasets were used to model the temporal patterns of knowledge transfer within and across patents and papers. We found that the cited patents are generally much older than the cited papers, regardless of whether they are cited by papers or patents. Discipline, affiliation type, and self‐citation also affect the age of the cited papers and patents. The recency of the citations partly explains the asymmetry in citations between papers and patents. [ABSTRACT FROM AUTHOR]

Citations between papers and patents indicate knowledge transfer between science and technology. This study discusses the function of paper‐to‐patent citations through citation contexts. 7,223 articles having at least one paper‐to‐patent citation are identified from a collection of 2.4 million PubMed Central open access articles. Within 429,698 references cited by the 7,223 articles, 11,165 (2.6%) are paper‐to‐patent citations, while 418,533 (97.4%) are paper‐to‐paper citations. In comparison to paper‐to‐paper citations, paper‐to‐patent citations were more concentrated at the beginning of articles. Higher proportions of patents are cited in the introduction and methods sections and are only cited once. The results indicate that patents are likely to be cited for providing background knowledge. The appearance of paper‐to‐patent citations in the method sections implies that patents might also contribute to the development of research methods. [ABSTRACT FROM AUTHOR]

During the COVID‐19 epidemic, the characteristics of rapid sharing of research findings were fully reflected on the bioRxiv platform, which received considerable attention from scholars. bioRxiv currently collaborates with 242 journals, addressing that it can save authors time in submitting papers to journals or peer review services by transmitting their manuscript files and metadata directly from bioRxiv. To see if preprints are altering the publishing process, we compared the duration of the peer review process between bioRxiv and journal papers. This study investigated papers' submission and publication times from bioRxiv to journals (B2J) and authors to journals (A2J) in 119 academic journals from November 2013 to February 2020. Findings reveal that 33 journals exhibited significant differences in the duration time between B2J and A2J papers. B2J papers were published faster than A2J papers in 29 journals, indicating that sharing on the preprint platform could shorten the peer review time. [ABSTRACT FROM AUTHOR]

Geography is one of the defining factors in scientific collaboration. Despite the voluminous evidence for how geographical proximity shapes the formation of collaboration in research articles, it has been rarely examined in the emerging genre of data papers, one that describes research data and has enjoyed growing attention in the data‐driven paradigm of research. This poster presents preliminary findings from our project that aims to evaluate the geographical dynamics behind the production of data papers. We analyze how researchers from different countries collaborate with one another using 6,821 data papers published in Scientific Data and Data in Brief between 2014 and 2020. We found that data papers rely heavily upon domestic collaboration and the collaboration pattern largely mirrors that of research articles, although some distinctiveness was also observed. We discuss future work in conclusion, with the ultimate goal of opening a more meaningful conversation about the relationship between the data‐driven paradigm and knowledge production. [ABSTRACT FROM AUTHOR]

Data sharing and reuse promise many benefits to science, but many researchers are reluctant to share and reuse data. Data papers, published as peer‐reviewed articles that provide descriptive information about specific datasets, are a potential solution as they may incentivize sharing by providing a mechanism for data producers to get citation credit and support reuse by providing contextual information about dataset production. Data papers can receive many citations. However, does citation of a data paper mean reuse of the underlying dataset? This paper presents preliminary findings from a content‐based citation analysis of data papers (n = 103) published in two specialized data journals, one in earth sciences and one in physical and chemical sciences. We conclude that while the genre of data papers facilitates some data sharing and reuse, they fail to live up to their full potential. Further, practices of reuse of datasets from data papers vary considerably between disciplines. We propose measures for academic publishers to enhance the data paper's role in scholarly communication to attract more attention from researchers and to inform discipline‐specific policy and practices related to data publication. [ABSTRACT FROM AUTHOR]

To reveal whether there exists a correlation between the number of references and the number of citations in a paper of the Artificial Intelligence‐related conference, we present an experiment based on a total number of 9,458 papers published in CVPR, ICML, and AAAI from the year 2014 to 2018. Spearman's rank correlation tests show that the number of references and that of the citations are correlated with a correlation coefficient of 0.435, 0.464, and 0.180 respectively, in each p <.01. This means an increasing number of references in a paper may be contributing to the increase in citations. However, citing more references does not guarantee that a paper will be a highly cited paper. We hope this finding can provide a basis for the prediction of the citation impact of the Artificial Intelligence conference papers in the future. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates the effect of diffusing scientific articles on Twitter on their citation impact. For a set of 1.3 million papers covered by the Web of Science and published in 2012, normalized citation rates are compared between tweeted and non-tweeted articles published in the same journal. The results indicate that tweeted papers, published in the same year and in the same journal received around 30% higher citation rates than papers which were not diffused on Twitter. This type of citation advantage appeared across disciplines and countries of authors, but varied in extent. [ABSTRACT FROM AUTHOR]

The present study investigates the disciplinarity of papers describing scientific software (i.e., "software papers") as the first step to gain a deeper understanding of how science is operated in its material and practice bases. The preliminary analysis mapped 768 software papers in the Journal of Statistical Software and about 57,000 papers that cited them to the five knowledge domains in the CWTS classification system and traced how these papers are cited within and across disciplinary boundaries. [ABSTRACT FROM AUTHOR]

This article investigates the lengths of time that publications with different numbers of policy document mentions take to receive their first mention (the beginning stage), and then compares the lengths of time to receive two or more mentions after receiving the first mention (the accumulative stage) based on complete policy document dataset from Altmetric database. We find that in response time distribution, i.e., from zero to one policy document mention, highly and mediumly mentioned papers exhibit obviously different lengths of time compared with lowly mentioned papers. In accumulative time distribution, i.e., from one to N policy document mentions, highly mentioned papers begin to receive mentions much more rapidly than medium‐ and low‐mentioned papers. However, as N increases, the difference in receiving new mentions among high‐, medium‐, and low‐mentioned publications does not increase quite significantly. [ABSTRACT FROM AUTHOR]

A paradigm shift is currently underway with the emergence of the Collections as Data movement, which advocates the creation and dissemination of cultural heritage collections that are amenable to large‐scale computation to empower both collection managers and users. Although this discourse is beginning to gain some traction in the literature, critical evidence‐based assessments of the opportunities and risks of this process are underexplored. This paper presents the results of a content analysis of the official position statements (n = 83) produced in the Collections as Data forums and written by international professionals working with digital collections. Although preliminary, the analysis presented and discussed here sheds light on the initial reception of the idea of Collections as Data and its articulation in practice. The study represents the first systematic attempt to explore the complexities of the intersection of ethics and artificial intelligence in the context of cultural heritage, aiming at providing a valuable precedent for further elaboration and discussion. [ABSTRACT FROM AUTHOR]

Recently, open access has gradually become an important publishing model in scientific research, while the academic purification effect of open access on flawed publications needs to be further explored. In this study, we use the methods of entropy balancing matching (EBM) and difference in difference (DID) in causal inference to examine the academic purification effect of different open access levels. The results show that in the retraction speed, limited open access papers had the slowest retraction speed, non‐open access had the second and full open access had the fastest, reflecting the best performance of full open access in the detection of problematic papers. As for post‐retraction citation changes, the citation of limited open access papers declined slightly slower than that of non‐open access papers after retraction, while the citation of full open access papers showed an increase rather than a decrease. The next step of the research is to discover what factors may cause the above results. [ABSTRACT FROM AUTHOR]

Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant prior work and causes researchers to spend a considerable amount of time building up their literature list. Citations play a significant role in discovering relevant literature. However, not all citations are created equal. A majority of the citations that a paper receives are for providing contextual, and background information to the citing papers and are not central to the theme of those papers. However, some papers are pivotal to the citing paper and inspire or stem up the research in the citing paper. Hence the nature of citation the former receives from the latter is significant. In this work in progress paper, we discuss our preliminary idea towards establishing a lineage for a given research via identifying significant citations. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for at least a certain category of papers. The distal goal of this work is to identify the real impact of research work or a facility beyond direct citation counts. [ABSTRACT FROM AUTHOR]

ABSTRACT There have been many studies on the factors influencing paper citation counts. A number of studies have focused on the citing papers, and corresponding methods were proposed to measure the prestige of citations based on the journal impact factors, the total citation counts and the PageRank algorithm values. However, there are drawbacks to these methods. In this paper, we propose a novel method to identify prestigious citations from the affiliation of the citing paper. Specifically, if the authors of the citing paper are affiliated with a prestigious university, the citing paper could be counted as a prestigious citation. As a pilot, we used the top 200 universities on the QS World University Rankings 2015 to identify the prestigious universities so that the prestigious citations, named as QS citations, were identified accordingly. Experimental results validated that QS citations have more important impact on the cited papers than other citations. Papers with QS citations have better performance across most disciplines not only in total citation counts, but also in altmetrics such as the Altmetric Attension Score and Mendeley reader counts. [ABSTRACT FROM AUTHOR]

Scientific breakthroughs have the potential to revolutionize the course of research and shape the trajectory of scientific knowledge. This study investigates the characteristics of Disruptive Citing Papers (DCP) and Consolidating Citing Papers (CCP) associated with Nobel‐winning scientific breakthroughs, aiming to provide insights into the mechanisms of knowledge creation and dissemination. By analyzing a dataset of Nobel‐winning papers and their citation networks, we find that Nobel‐winning papers tend to attract a higher proportion of DCP compared to CCP. However, CCP exhibit a higher impact, as evidenced by their citation counts and likelihood of becoming hit papers. Furthermore, DCP are associated with larger research teams, highlighting the collaborative nature of disruptive research, while CCP employ a higher degree of professional language style characterized by shorter titles and specialized jargon. These findings deepen our understanding of the role played by disruptive and consolidating impact in scientific breakthroughs, shedding light on the dynamics of knowledge creation and dissemination in the scientific community. This research contributes to the broader understanding of scientific progress and provides valuable insights for researchers, policymakers, and stakeholders in the scientific ecosystem. [ABSTRACT FROM AUTHOR]

New media platforms have enhanced the efficiency and diversity of information dissemination, providing new possibilities for the dissemination and promotion of academic papers. Currently, a large number of Chinese academic journals from different disciplines have established WeChat official accounts to promote their papers. This study examines WeChat official accounts from three disciplines: social sciences, natural sciences, and medicine. We analyze the existing paper promotion methods employed by these academic journal official accounts from four dimensions: content presentation format, number of papers promoted in a single post, interactive forms, and publishing time. The findings reveal that the current promotion methods for academic papers on WeChat official accounts are relatively limited, with low utilization of multimedia content. Therefore, there is a need for further improvement in new media promotion for academic papers. [ABSTRACT FROM AUTHOR]

Data papers, a new class of scholarly publication emerging from the open‐science movement, foster data discovery and reuse by offering comprehensive descriptions of research data. Yet, despite their promising growth, the role of data papers in scholarly communication remains underexplored. This work therefore investigates the perceived contributions and functions of data papers to scholarly communication by interviewing 14 data‐paper authors operating in the field of natural science. Using conceptual frameworks adopted from Borgman (2007) and Van de Sompel et al. (2004), we identify four general functions of scholarly communication (i.e., legitimization; dissemination; access, preservation, and curation; and rewarding). Additionally, our data lead us to propose that verification is a distinct scholarly communication, underscoring the importance of data papers in validating research findings in the context of ensuring research transparency. By elucidating the crucial role that data papers now play within the scholarly communication ecosystem, this study seeks to raise the academic community's awareness of their fundamental position, as well as their co‐existence with other forms of data publication, in advancing scientific research. [ABSTRACT FROM AUTHOR]

The data paper is a new type of scientific publication aiming to describe data objects per se and bridge the gaps between scientific writing and data‐driven scientific practice. Despite its importance, we are still largely in the dark as to the narrative structure of this emerging genre of scientific writing. In this poster, we presented and discussed some preliminary empirical evidence of the narrative structure of data papers and the distribution of cited sources in relation to this structure, based on a manual examination of 40 data papers from the journal of Scientific Data. Our findings suggest that even though data papers have a different structure from research papers, their paper sections bear distinct narrative functions as illustrated by the pattern of citation distribution. Moreover, some of these sections have similar functions to their counterparts in research papers. The implications of our initial findings and the next step of our project were explained in conclusion. [ABSTRACT FROM AUTHOR]

This study investigated papers posted on bioRXiv. In 2013–19, 17,818 papers were published in academic journals, accounting for 40.67% of the total publication. Among them, the Web of Science (WoS) Core Collection recorded journals of 16,525 published papers, accounting for 92.74% of the total. The following conclusions can be drawn. (1) Results indicate scientists' attention to the preprint system. (2) A large number of papers still use traditional publishing as the ultimate goal. (3) Because the posted papers scrutinized in this study were published in journals with a high impact factor, they have attracted the attention of many scientists. Nevertheless, we believe that the preprint platform will play a greater role from the academic exchange perspective. [ABSTRACT FROM AUTHOR]

This paper studied whether opening up review reports benefits science in terms of citations, by taking Nature Communications as an example. To address these questions, we extracted 3,500 papers published in Nature Communications in 2016 and 4,326 papers in 2017 and retrieved their three‐year citations since publication in the Web of Science database. By applying the Matching method, we constructed an observation group including 1,726 open peer review (OPR) papers and a control group including 1,726 non‐OPR counterparts. The results of the paired sample t‐test showed no significant difference between the OPR and non‐OPR papers. We conclude that opening up peer review reports did not benefit papers in Nature Communications in terms of citations. We further examined whether the length, the rounds or the lexical diversity of the review report contributed to the paper's citations, through regression analysis. As a result, we found that longer comments, more rounds of review and the more diversified words did not contribute to the citations of the OPR papers in Nature Communications. Further research covering more OPR journals is required to justify the benefits of OPR. [ABSTRACT FROM AUTHOR]

Argumentation structure within the content of scientific paper is the fundamental for proving scientific findings. Constructing the argumentation graph is an effective way to realize the visualization of argumentation structure and help users quickly understand the logical of papers. This study presents the method for constructing argumentation graph of scientific papers, includes an ontology for describing the scientific papers' argumentation structure, a process for deep semantic annotation and a mapping protocols for transforming the annotation results into graph structure by using Neo4J. The method was applied to construct an argumentation graph and the argumentation process retrieval was also realized. The result suggest that the argumentation graph could be benefit for the argumentation visualization and strategic reading of scientific papers. [ABSTRACT FROM AUTHOR]

Online bibliographic databases have enabled new research through which bibliographic records are analyzed as data about science. Within these records, the acknowledgements sections of papers are often used to draw conclusions about funding support for published research. While acknowledgements and funding statements can be informative for research and policy development, this poster adds to a body of literature that highlights limitations of funding data for scientometric and policy research, using evidence gathered from a questionnaire of authors of astronomy journal articles. The study shows that only 71.4% of a sample of authors of papers tied to NSF grants through acknowledgements reported in the survey that NSF funded the research presented in the respective papers. A brief analysis of the questionnaire followed by recommendations and considerations for further research are presented. The discrepancy in reporting appears to indicate that funding streams can be fluid and not always apparent to authors, overall raising the question of what sorts of research should be addressed with funding statements, where conceptually tying a paper directly to a grant is not straightforward. [ABSTRACT FROM AUTHOR]

Citation content Analysis is very useful to reveal the nature of citations among scientific papers. However, most of previous studies had been done only from the side of citing papers and ignored the side of cited papers. We proposed a similarity‐based unsupervised citation text identification method and a ranking‐based supervised cited text identification method. Based on the identified citation and cited texts, we performed a fine‐grained citation analysis and found there was a difference in the cited texts between the citing papers in different disciplines. [ABSTRACT FROM AUTHOR]

ABSTRACT Citation is an important measurement in science which provides valuable clues to the historical development and trend forecast of science. However, the evolution of citation structure remains poorly understood despite long-period and frequent use of citation counts for assessment purposes within scientific community. We observe citation structure from a cascade perspective and construct a large and long-range citation graph based on real data. Two metrics are used to quantify the structural virality and cascade size. Preliminary results show that (1) age plays an important role for the evolution of citation cascades; (2) average depth tends to be influenced by both lifespan and the whole volume of scientific literature and grows much slower than cascade size; (3) there exists a stable positive relationship between average depth and cascade size, and it seems that an ultimate status exists where most papers eventually have large values for both metrics. [ABSTRACT FROM AUTHOR]

This study examines the relation between acceptance times in preprint publishing and journal publishing. Specifically, we investigated the association between a paper's posting time to bioRχiv, a preprints server, and journal articles' peer‐review and acceptance time for PLOS ONE. So far, of the total papers published in 1,626 academic journals, the average publication rate of those posted in bioRχiv is 40.67%. Meanwhile, PLOS ONE was the journal that published more papers. Analysis of peer‐review and acceptance time of papers published in journals via preprints showed the time these papers are posted in relation to these intervals. The median of the peer‐review and acceptance time of the journal submission date that was later than the date of first posting to bioRχiv was 110.00 days, and in the reverse case, it was 139.50 days. Posting to the preprint server before journal submission shows a better order than vice versa. This study provides us a good understanding of the peer‐review process. It also gives us good insights into optimizing this process, which would then facilitate paper publication and knowledge dissemination. [ABSTRACT FROM AUTHOR]

Peer review can evaluate the quality of academic articles involving the evaluation of some aspects, e.g., methodology, experiment, and aspects are usually the critical sections, substance and properties of the article concerned by reviewers. Previous research on content mining of peer review did not distinguish the round of reviews. Detecting differences peer review comments among different rounds can help us to understand the focus change of the reviewers in the different rounds. This paper takes the Nature Communications as an example to build the corpora of peer reviews in multiple rounds. We correlate review rounds and citations and extract aspects from the corpora to analyze the round characteristics of review comments. We find that there is no significant correlation between the review rounds and the citations frequency, but when the number of rounds is within 1–3, there is a weak positive correlation between the review rounds and citations. Additionally, the reviewers tend to pay more attention to the results analysis and the significance of the work firstly, and then they will focus on the details of the article, such as diagrams. This study can provide new ideas for peer review mining and the application of bibliometrics. [ABSTRACT FROM AUTHOR]

Research trend detection is an important topic for scientific researchers. Future work sentences (FWS), as direct descriptions of future research, aren't fully utilized in research trend detection. Therefore, this article uses FWS to investigate research trends of different tasks in a particular domain. Taking the conference papers in the natural language processing (NLP) field as our research objects, we obtain the FWS in each paper to build the corpus and classified them into 6 main types. After that, the task of each paper is annotated, and a task system with 29 categories is constructed to compare the FWS in different tasks. The results show that the proportion of method mentioned in FWS is the highest, and different tasks focus on different FWS types: emerging tasks need more resources, while mature tasks prefer method and application. This study provides researchers a reference to understand the research trend of specific tasks and is helpful to compare different tasks. [ABSTRACT FROM AUTHOR]

Retraction removes seriously flawed papers from the scientific literature. However, even papers retracted for scientific fraud continue to be cited and used as valid after their retraction. Retracted papers are inadequately identified on publisher pages and in scholarly databases, and scholars' personal libraries frequently contain retracted papers. To address this, we are developing a tool called ReTracker (https://github.com/nikolausn/ReTrackers) that automatically checks a user's Zotero library for retracted articles, and adds retraction status as a new metadata field directly in the library. In this paper, we present the current version of ReTracker, which automatically flags retracted articles from PubMed. We describe how we have iteratively improved ReTracker's matching performance through its initial two versions. Our tests show that the current version of ReTracker is able to flag retracted articles from PubMed with high precision and recall, and to distinguish retracted articles from articles about retraction. In its current state, ReTracker can actively and automatically bring retraction metadata into Zotero, and in future work we will test its usability with scholars. [ABSTRACT FROM AUTHOR]

As data becomes omnipresent in the scientific system, a new academic genre aiming to describe data objects (data papers) and the venue to publish these articles (data journals) gradually emerged from the end of the 2000s. However, it is largely unknown how much these scientific outputs are indexed in scientific databases, which has greatly prevented them from being thoroughly studied in large‐scale, quantitative studies. This poster presents our preliminary efforts to address this gap, by compiling a list of data journals that primarily accept data papers (i.e., exclusively data journals) and examining their presence in four major scientific databases. Our results indicate that exclusively data journals are comprehensively indexed in Crossref and Dimensions, two relatively new scientific databases, which can be used to conduct future studies on data papers and journals. The next steps of our project are also discussed in this poster. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates a collaborative attribution strategy based on six authorship attribution schemes representing the two main paradigms used in authorship studies. Based on very frequent words as features, the classical paradigm (or similarity-based methods) proposes to compute an intertextual distance between the disputed text and the different author profiles (concatenation of their writings). As a second paradigm, we can apply different machine learning schemes such as the naïve Bayes, and the support vector machines (SVM). As an evaluation corpus, we have used The Federalist Papers, a wellknown collection in authorship attribution. During our evaluation, we have tried to follow the recommendations and the best practices known to assess the various attribution schemes. The evaluation shows that, in the two paradigms, we can find effective attribution schemes. But when combining these individual results using a vote aggregation method, the final collaborative decision is always correct and robust. Moreover, to indicate the degree of belief attached to the combined attribution, we can consider the percentage of votes obtained by each possible assignment. When analyzing the output given by the individual attribution schemes, we also found that the provided information is difficult to interpret, at least, for the end-user. [ABSTRACT FROM AUTHOR]