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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]

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, 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]

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]

Up to this point, keyword extraction task typically relies solely on textual data. Neglecting visual details and audio features from image and audio modalities leads to deficiencies in information richness and overlooks potential correlations, thereby constraining the model's ability to learn representations of the data and the accuracy of model predictions. Furthermore, the currently available multimodal datasets for keyword extraction task are particularly scarce, further hindering the progress of research on multimodal keyword extraction task. Therefore, this study constructs a multimodal dataset of academic paper consisting of 1,000 samples, with each sample containing paper text, images, audios and keywords. Based on unsupervised and supervised methods of keyword extraction, experiments are conducted using textual data from papers, as well as text extracted from images and audio. The aim is to investigate the differences in performance in keyword extraction task with respect to different modal information and the fusion of multimodal information. The experimental results indicate that text from different modalities exhibits distinct characteristics in the model. The concatenation of paper text, image text and audio text can effectively enhance the keyword extraction performance of academic papers. [ABSTRACT FROM AUTHOR]

The academic community may make research findings public by mentioning Chinese scholarly papers on WeChat. However, why and how Chinese research papers were cited in WeChat articles remains unknown. This work conducted a content analysis of the motivations, emotions, forms, and elements of WeChat mentions. Data was collected from CNKI and WeChat official accounts, resulting in a dataset of 7,970 core journal papers with 21,134 WeChat mentions. Key findings include that: (1) Most of the mentions in the WeChat articles present Chinese academic publications for sharing and introduction. These mentions account for about 64%. (2) Mention sentiments are mainly neutral (around 64%). Negative emotions are almost absent. (3) In the mention forms, most WeChat articles mentioning scholarly papers do not include reference lists. They mention the papers directly in the body of the WeChat articles with recognizable elements of academic papers. (4) Concerning the mentioned elements, academic papers consist of essential content parts. They include titles (78%), abstracts (12%), and full texts (9%), and that gets the most WeChat mentions. This would give a better base and reference for advancing altmetrics in China. And it would be helpful to know the cause and how the academic papers spread on Chinese social media. [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]

The cross‐domain knowledge diffusion from science to policy is a prevalent phenomenon that demands academic attention. To investigate the characteristics of cross‐domain knowledge diffusion from science to policy, this study suggests using the citation of policies to scientific articles as a basis for quantifying the diffusion strength, breadth, and speed. The study reveals that the strength and breadth of cross‐domain knowledge diffusion from scientific papers to policies conform to a power‐law distribution, while the speed follows a logarithmic normal distribution. Moreover, the papers with the highest diffusion strength, breadth, and fastest diffusion speed are predominantly from world‐renowned universities, scholars, and top journals. The papers with the highest diffusion strength and breadth are mostly from social sciences, especially economics, those with the fastest diffusion speed are mainly from medical and life sciences, followed by social sciences. The findings indicate that cross‐domain knowledge diffusion from science to policy follows the Matthew effect, whereby individuals or institutions with high academic achievements are more likely to achieve successful cross‐domain knowledge diffusion. Furthermore, papers in the field of economics tend to have the higher cross‐domain knowledge diffusion strength and breadth, while those in medical and life sciences have the faster cross‐domain knowledge diffusion speed. [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]

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]

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]

The citation of scientific papers is considered a simple and direct indicator of papers' impact. This paper predicts papers' citations through team‐related variables, team composition, and team structure. Team composition includes team size, male/female dominance, academia/industry collaboration, unique race number, and unique country number. Team structures are made up of team power level and team power hierarchy. Team members' previous citation number, H‐index, previous collaborators, career age, and previous paper numbers are a proxy of team power. We calculated the mean value and Gini coefficient to represent team power level (the collective team capability) and team power hierarchy (the vertical difference of power distribution within a team). Taking 1,675,035 CS teams in the DBLP dataset, we trained the XGBoost model to predict high/low citation. Our model has reached 0.71 in AUC and 70.45% in accuracy rate. Utilizing Explainable AI method SHAP to evaluate features' relative importance in predicting team citation categories, we found that team structure plays a more critical role than team composition in predicting team citation. High team power level, flat team power structure, diverse race background, large team, collaboration with industry, and male‐dominated teams can bring higher team citations. Our project can provide insights into how to form the best scientific teams and maximize team impact from team composition and team structure. [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]

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]

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]

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]

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]

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]

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]

The digitization, curation, and utilization of Chinese ancient books are crucial to the digital humanities. Despite progress in these areas, issues with data interoperability, data sharing, and data linkage persist due to a lack of standardized annotated ancient corpus and a general description framework for ancient books. To overcome these challenges, this paper proposes an ontology‐based description framework that integrates catalogs of Chinese ancient books from various institutions, creating a standardized, interpretable, and researchable knowledge base. The framework combines general standards with unique ancient book characteristics, revealing complex relationships between books and books, books and people, and books and times, providing a more comprehensive understanding of the knowledge contained within ancient books. Additionally, this paper applied the framework to The National Rare Ancient Book Directory, a catalog containing 13,026 books from over 400 institutes, to develop an interactive system. The system is available at https://rarebib.pkudh.org/. Our results demonstrate that the framework standardizes data and provides a sophisticated and nuanced understanding of the knowledge within ancient books. This has noteworthy implications for individuals engaged in research, scholarship, and reading in the digital age. [ABSTRACT FROM AUTHOR]

Researchers in academia and industry face a deluge of data in our digital world. In this paper, we investigate a novel problem, query-centric scientific topic evolution. Using heterogeneous graph mining techniques we construct a topic evolution tree (TET) from massive collections of scientific publications, enabling students and researchers to explore the foundation of research topics outside their specialization. Prior research has focused mainly on citation relationships; in this study we employed multiple types of relationships, including authorship, citation, publishing venue, and the contributions authors, papers, and venues have made to a specific topic. We examine multiple restricted meta-paths in constructing a TET covering topics from the MeSH vocabulary for biomedical research. [ABSTRACT FROM AUTHOR]

This paper is based on the Web of Science database and takes advantage of CiteSpace, a scientometric software, to conduct visualization analyses. It is found that privacy issues in data ethics have reached the research peak in the past two years. The findings of this paper are that the current global research in this field is characterized by three different periods and multidisciplinary perspectives. And the hotspots of this field are relatively concentrated and gradually deepened. Besides, the research in this field has moved from the theoretical stage to the practical application stage. This paper attempts to present the scientific knowledge structure, patterns and distribution of privacy issues in data ethics, exploring the global frontier hotspots, providing inspiration and experience for Chinese academic research and industry practice in data ethics. [ABSTRACT FROM AUTHOR]

ABSTRACT Scientific software is as important to scientific studies as raw data. Yet, attention to this genre of research data is limited in studies on data reuse, citation, and metadata standards. This paper presents results from an exploratory study that examined how scientific software's reuse information is presented in the current citation practice and natural language descriptions in research papers. We selected LAMMPS, popular simulation software used in material science, for this study. Both descriptive metadata elements and the types of reuse are examined from a sample of 400 research papers. The results indicate that both descriptive metadata elements and reuse types about LAMMPS are presented in incomplete and inconsistent ways, and this interferes with the values of scientific software, as a type of research data. Our findings necessitate future studies on the metadata standards to facilitate the identification of information related with scientific software reuse. [ABSTRACT FROM AUTHOR]

In recent years the number of individuals struggling with mental illness has increased, and traditional mental health services are now considered insufficient under the current circumstances which has prompted researchers to develop new approaches for mental healthcare. Social media usage is growing, and it is been utilized to help provide additional insight on mental health by using the information shared by individuals, as well as data taken from their social media activity. While this approach may provide a unique and effective perspective for mental health services, it is critical that privacy risks and protections are considered in the process. Social media services collect, process, and stores a substantial amount of information about its users and how that information is shared as well as what type of predictions are made may pose serious privacy concerns. This study aims to understand how privacy is addressed and emphasized during the process of using social media data for mental healthcare by constructing a systematic review on previous scholarly papers related to the topic. Solove's taxonomy of privacy is used to evaluate these publications privacy considerations and to demonstrate the privacy risks that may arise when social media data is used for mental health. [ABSTRACT FROM AUTHOR]

Publications represent the most tangible products of research and scholarly communication. Growing pressures placed on researchers to increase their publication counts may encourage them to publish shorter papers, so that more publications may be generated from the same project and program. Is this the case? Furthermore, do shorter papers garner the same impact as longer papers as measured by the citations received? This research examines the relationship between article length and citations received by articles published in three prominent top biomedical research journals over the lifetime of the journals. The study reveals that instead of getting shorter, articles in these journals are getting longer over time. Longer articles in these journals, on average, attract more citations, and are therefore considered more impactful. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a 'rough co-citation', which is a measure of relationship to expand co-citation networks so as to include new relevant documents. A rough co-citation relationship is a linkage between a pair of documents which are cited by two other documents in a similar citation context. The linkage strength of a rough co-citation relationship may be weaker than the original co-citation relationship, because a rough co-citation relationship is determined by citations in two separate documents. Rough co-citation linkages, however, may yield new relevant documents that are not identified by the original co-citation linkages. For example, the rough co-citation can identify relevant documents that are published after the citing document of the original co-citation becomes public. This study conducted IR experiments to evaluate the search performances of retrieval methods using the co-citation networks expanded by the rough co-citation relationships. Specifically, the random walk with restart, which is one of the latest graph search algorithms, is applied to the expanded and original co-citation networks. Scores of the normalized discounted cumulative gain (nDCG@K) are then compared. The results indicate that the search performance of the method using the expanded network outperforms a baseline method using the original network. [ABSTRACT FROM AUTHOR]

ABSTRACT In this poster, we describe partial findings from a larger study that explored the co-first and co-corresponding author phenomenon in two biomedical research areas, pharmacy and anesthesia. The top 10 journals in the two areas by the five-year impact factor as listed in 2013 JCR were used for the paper sources. A total of 17802 pharmacy papers and 35801 anesthesia papers were included in the analyses. Each paper was manually examined to identify the co-first and co-corresponding author papers. In this poster, we will show the distributions of the paper numbers and percentages of the co-first and co-corresponding papers over twenty years (1995-2014), the distributions of author numbers sharing the co-first and co-corresponding authorship, and the combinations and distributions of the authors' byline positions. [ABSTRACT FROM AUTHOR]

This short paper presents an analysis of the distribution of downloads in medical literature, with the aim of understanding the specific conditions unique to the medical research field that necessitate the use of black open access sources such as Sci‐Hub. The dataset used in this analysis was obtained by scraping the official Sci‐Hub domains during the summer of 2022, and it provides valuable insights into user behavior and interaction with the website. The results demonstrate that a significant portion of the downloads from Sci‐Hub during the period were related to medical journals, indicating the acute needs and time‐sensitive nature of medical research. The data further highlights that most of the downloaded papers were related to medicine or medical subjects, underscoring the importance of easy and immediate access to the latest research and literature. The study emphasizes the pressing need for universal access to essential health‐care information and the importance of continued efforts to democratize access to medical literature. The use of black open access sources like Sci‐Hub serves as a reminder of the urgency to find alternative solutions that ensure medical professionals have the necessary resources to provide the best possible care for their patients [ABSTRACT FROM AUTHOR]

Scientific retractions can be an indicator that misinformation is present in a research paper. Retractions are therefore an interesting research object to explore news coverage of misleading scientific information. This poster presents how problematic research (before and after retraction) is portrayed in news outlets, the impact of the retraction on these reports and the relationship of retracted scientific results and the spread of false information. Starting from a list of 270 retracted COVID‐19 papers from the Retraction Watch blog (https://retractionwatch.com), we analysed news articles of 16 retracted publications, that were immensely discussed in journalistic formats. By presenting three different use cases, we show how misinformation emanates from retractions, how problematic research is presented in media and what factors influence the message of the article. Our research contributes to a better understanding of how retractions are used and perceived in propagating scientific misinformation on one hand and in mitigating it on the other. [ABSTRACT FROM AUTHOR]

Text mining is a research method that uses interdisciplinary techniques to identify patterns and trends in unstructured data and can be used to detect research trends and developments in various fields. Thus, this study attempts to identify research trends in the issue of privacy concern (PC), focusing on the differences between the first two decades of the 21st century. This is done by applying an automated textual analysis tool (Voyant Tools) on a vast corpora of academic papers conducted on this field. We found that while the first examined decade of PC research focused on commerce and marketing issues, there was much more research emphasis on the social dimension of privacy in the second decade. This difference was explained by the technological developments that were prominent in the respective decades. The research provided support for the use of textual analysis of academic literature as a tool to identify research trends in various fields, including technologically‐oriented fields. [ABSTRACT FROM AUTHOR]

We carried out an experiment to extract ecological‐restoration‐technology‐related entities (i.e., entities related to the fragile ecological remediation technology of desertification, rocky desertification, water and soil erosion control areas, place entities, and time entities related to the technology performed,) from full‐text documents published in the CNKI from 1978 to 2017. Based on the extraction of the time entities, place entities, and fragile ecological remediation technology entities, we analyzed the technologies with the greatest potential from among China's fragile ecological restoration technologies using the category clustering of fragile ecological restoration technologies with LDA and the place distribution and evolution of fragile ecological treatment technologies. This research shows that the proposed Bi‐LSTM+CRF combined with the feature‐based named entity knowledgebase can be used to perform geographical knowledge discovery from text, and it has has good prospects for applications in intelligence analysis based on text mining. [ABSTRACT FROM AUTHOR]

Research trends are the keys for researchers to decide their research agenda. However, only few works have tried to quantify how scholars follow the trends. This paper addresses this problem by proposing a novel measurement for quantifying how a scientific entity (paper or researcher) follows the hot topics in a research field. Specifically, the topic evolution and papers are vectorizing by dynamic topic modeling. Then the degree of hotness tracing is explored from three different perspectives: mainstream, short‐term direction, long‐term direction. Papers and researchers in the field of Computer Vision from 2006 to 2017 were selected to evaluate our method. Further study will show the results of topic evolution patterns and researchers' clusters. [ABSTRACT FROM AUTHOR]

Database maintenance and migration are critical but under‐supported activities in libraries, archives, museums (LAMs), and other scholarly spaces. Existing guidelines for digital curation rarely account for the maintenance needed to keep digital curation infrastructures functioning over time. Though many case studies have been published describing individual instances of migration, there has been little generalizable research done in this area. Thus, it is challenging to understand overall trends or best practices in this space. We bridge this gap by conducting an integrative literature review of papers describing database migrations and maintenance in LAMs and other scholarly contexts. By qualitatively coding 75 articles from 58 publication venues, we identify common motivations for database migrations and maintenance actions. We find that databases are migrated to support changing user needs as well as to ward off technological obsolescence; we also find that common challenges include schema crosswalking and a need for data cleaning. Practitioners describe community collaboration as key in surmounting these challenges. Through this integrative review, we build a base for further best practices development and identify a need to better model database curation as part of the digital curation lifecycle. [ABSTRACT FROM AUTHOR]

Publication is a vital part of the career of any researcher. There is an increasing awareness that the presented gender of authors can impact their acceptance at competitive venues, and also influence the support they receive to participate in conferences. Similarly, geography and the associated costs of travel to conferences also impact the likelihood a potential author will submit to a conference. Recent research has examined these impacts in the context of related disciplines within computing and considered the gender profile of reviewers and authors at the iConference. This paper examines the impact of gender and geography on the representation of authors in the ASIST Annual Meeting and the JASIST journal. Through an evaluation of the first authors of accepted papers or articles, we assess the relative representation of genders, and the degree to which conference location impedes or enables authors from different regions. Our findings provide a foundation for the information science community to work towards better representation of female and international authors. [ABSTRACT FROM AUTHOR]

This paper proposes an indicator for measuring the level of commitment to academic journals. The indicator is demonstrated on a sample of core LIS‐journals. By monitoring authorship patterns over a 20‐year period, it is shown that some journals have a higher frequency of returning authors than others, consequently showing a larger degree of community commitment. The paper discusses how the indicator may be applied when studying factors influencing researchers' journal selection decisions. [ABSTRACT FROM AUTHOR]

Manufacturing enterprises always generate, acquire, and transform large amount of information which flows across every link of their innovation process and value chain. Information flow is one of the basic conditions for information to be reproduced, utilized and value added. Unreasonable paths and transmission methods of information flow will lead to problems such as poor flow efficiency, low information sharing and even information silos, which will further restrict the R&D innovation and service upgrade. Optimizing information flow mechanisms and paths is a vital part for improving the innovation capability of manufacturing enterprises. In order to reflect the interactions, flow‐path differences and nonlinear laws among manufacturing enterprises' innovation units and to reveal relations between information‐flowing efficiency and innovation capabilities, the paper divides innovation activities of manufacturing enterprises into three phases, such as information acquisition, information transformation and information value addition based on the theory of innovation value chain, and puts forward a mathematical model of information absorption and transformation to ultimately reveal the mechanisms for information flow across inside and outside units. Further, the paper takes a non‐linear function for absorptive capacity based on level of experiential knowledge, continuous R&D intensity, and agent relation strength to quantify the information absorption and transformation process and takes index of innovation information amount and cumulative information amount to calculate innovation abilities and measure information value addition. Finally, the paper uses simulation tool to analyze the influencing factors of information absorption and transformation optimization. [ABSTRACT FROM AUTHOR]