Quantifying the impact of scholarly papers based on higher-order weighted citations.
Ontology highlight
ABSTRACT:
SUBMITTER: Bai X
PROVIDER: S-EPMC5875758 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Publications
Quantifying the impact of scholarly papers based on higher-order weighted citations.
Bai Xiaomei X Zhang Fuli F Hou Jie J Lee Ivan I Kong Xiangjie X Tolba Amr A Xia Feng F
PloS one 20180329 3
Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address ...[more]
Similar Datasets
Project description:Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by co-authors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity.
| S-EPMC5015995 | biostudies-literature
Project description:Research in computer systems often involves the engineering, implementation, and measurement of complex systems software and data. The availability of these artifacts is critical to the reproducibility and replicability of the research results, because system software often embodies numerous implicit assumptions and parameters that are not fully documented in the research article itself. Artifact availability has also been previously associated with higher paper impact, as measured by citations counts. And yet, the sharing of research artifacts is still not as common as warranted by its importance. The primary goal of this study is to provide an exploratory statistical analysis of the artifact-sharing rates and associated factors in the research field of computer systems. To this end, we explore a cross-sectional dataset of papers from 56 contemporaneous systems conferences. In addition to extensive data on the conferences, papers, and authors, this analyze dataset includes data on the release, ongoing availability, badging, and locations of research artifacts. We combine this manually curated dataset with citation counts to evaluate the relationships between different artifact properties and citation metrics. Additionally, we revisit previous observations from other fields on the relationships between artifact properties and various other characteristics of papers, authors, and venue and apply them to this field. The overall rate of artifact sharing we find in this dataset is approximately 30%, although it varies significantly with paper, author, and conference factors, and it is closer to 43% for conferences that actively evaluated artifact sharing. Approximately 20% of all shared artifacts are no longer accessible four years after publications, predominately when hosted on personal and academic websites. Our main finding is that papers with shared artifacts averaged approximately 75% more citations than papers with none. Even after controlling for numerous confounding covariates, the release of an artifact appears to increase the citations of a systems paper by some 34%. This metric is further boosted by the open availability of the paper's text.
| S-EPMC9044204 | biostudies-literature
Project description:Words are the building blocks of communicating science. As our understanding of the world progresses, scientific disciplines naturally enrich their specialized vocabulary (jargon). However, in the era of interdisciplinarity, the use of jargon may hinder effective communication among scientists that do not share a common scientific background. The question of how jargon limits the transmission of scientific knowledge has long been debated but rarely addressed quantitatively. We explored the relationship between the use of jargon and citations, using 21 486 articles focusing on cave research, a multidisciplinary field particularly prone to terminological specialization, and where linguistic disagreement among peers is frequent. We demonstrate a significant negative relationship between the proportion of jargon words in the title and abstract and the number of citations a paper receives. Given that these elements are the hook to readers, we urge scientists to restrict jargon to sections of the paper where its use is unavoidable.
| S-EPMC8059506 | biostudies-literature
Project description:Citations are an important, but often overlooked, part of every scientific paper. They allow the reader to trace the flow of evidence, serving as a gateway to relevant literature. Most scientists are aware of citations' errors, but few appreciate the prevalence of these problems. The purpose of the present study was to examine how often frequently cited papers in biomedical scientific literature are cited inaccurately. The study included an active participation of the first authors of included papers; to first-hand verify the citations accuracy. Findings from feasibility study, where we reviewed 1540 articles containing 2526 citations of 14 most cited articles in which the authors were affiliated with the Faculty of Medicine University of Belgrade, were further evaluated for external confirmation in an independent verification set of articles. Verification set included 4912 citations identified in 2995 articles that cited 13 most cited articles published by authors affiliated with the Mayo Clinic Division of Nephrology and Hypertension. A citation was defined as being accurate if the cited article supported or was in accordance with the statement by citing authors. At least one inaccurate citation was found in 11 and 15% of articles in the feasibility study and verification set, respectively, suggesting that inaccurate citations are common in biomedical literature. The most common problem was the citation of nonexistent findings (38.4%), followed by an incorrect interpretation of findings (15.4%). One-fifth of inaccurate citations were due to chains of inaccurate citations. Based on these findings, several actions to reduce citation inaccuracies have been proposed.
| S-EPMC8048031 | biostudies-literature
Project description:BackgroundInaccurate citations are erroneous quotations or instances of paraphrasing of previously published material that mislead readers about the claims of the cited source. They are often unaddressed due to underreporting, the inability of peer reviewers and editors to detect them, and editors' reluctance to publish corrections about them. In this paper, we propose a new tool that could be used to tackle their circulation.MethodsWe provide a review of available data about inaccurate citations and analytically explore current ways of reporting and dealing with these inaccuracies. Consequently, we make a distinction between publication (i.e., first occurrence) and circulation (i.e., reuse) of inaccurate citations. Sloppy reading of published items, literature ambiguity and insufficient quality control in the editorial process are identified as factors that contribute to the publication of inaccurate citations. However, reiteration or copy-pasting without checking the validity of citations, paralleled with lack of resources/motivation to report/correct inaccurate citations contribute to their circulation.Results and discussionWe propose the development of an online annotation tool called "MyCites" as means with which to mark and map inaccurate citations. This tool allows ORCID users to annotate citations and alert authors (of the cited and citing articles) and also editors of journals where inaccurate citations are published. Each marked citation would travel with the digital version of the document (persistent identifiers) and be visible on websites that host peer-reviewed articles (journals' websites, Pubmed, etc.). In the future development of MyCites, challenges such as the conditions of correct/incorrect-ness and parties that should adjudicate that, and, the issue of dealing with incorrect reports need to be addressed.
| S-EPMC7500547 | biostudies-literature
Project description:The effective evaluation of the impact of a scholarly article is a significant endeavor; for this reason, it has garnered attention. From the perspective of knowledge flow, this paper extracted various knowledge flow patterns concealed in articles citation counts to describe the citation impact of the articles. First, the intensity characteristic of knowledge flow was investigated to distinguish the different citation vitality of articles. Second, the knowledge diffusion capacity was examined to differentiate the size of the scope of articles' influences on the academic environment. Finally, the knowledge transfer capacity was discussed to investigate the support degree of articles on the follow-up research. Experimental results show that articles got more citations recently have a higher knowledge flow intensity. The articles have various impacts on the academic environment and have different supporting effects on the follow-up research, representing the differences in their knowledge diffusion and knowledge transfer capabilities. Compared with the single quantitative index of citation frequency, these knowledge flow patterns can carefully explore the citation value of articles. By integrating the three knowledge flow patterns to examine the total citation impact of articles, we found that the articles exhibit distinct value of citation impact even if they were published in the same field, in the same year, and with similar citation frequencies.
| S-EPMC6872168 | biostudies-literature
Project description:The exponential increase in the number of scientific publications raises the question of whether the sciences are expanding into a fractured structure, making cross-field communication difficult. On the other hand, scientists may be motivated to learn extensively across fields to enhance their innovative capacity, and this may offset the negative effects of fragmentation. Through an investigation of the distances within and clustering of cross-sectional citation networks, this study presents evidence that fields of science become more integrated over time. The average citation distance between papers published in the same year decreased from ?5.33 to 3.18 steps between 1950 and 2018. This observation is attributed to the growth of cross-field communication throughout the entire period as well as the growing importance of high-impact papers to bridge networks in the same year. Three empirical findings support this conclusion. First, distances decreased between almost all disciplines throughout the time period. Second, inequality in the number of citations received by papers increased, and, as a consequence, the shortest paths in the network depend more on high-impact papers later in the period. Third, the dispersion of connections between fields increased continually. Moreover, these changes did not entail a lower level of clustering of citations. Both within- and cross-field citations show a similar rate of slowly growing clustering values in all years. The latter findings suggest that domain-spanning scholarly communication is partly enabled by new fields that connect disciplines.
| S-EPMC6825301 | biostudies-literature
Project description:Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using nine million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro- F1 measure of 0.76 with F1 of individual subject categories ranging from 0.50 to 0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers.
| S-EPMC8025978 | biostudies-literature
Project description:Synaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher order network structure, and suggests that the higher order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.
| S-EPMC7069066 | biostudies-literature
Project description:A number of new metrics based on social media platforms--grouped under the term "altmetrics"--have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters' Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators.
| S-EPMC4363625 | biostudies-literature