Quantifying long-term scientific impact

Signs of how the Sydney Declaration article is received in the forensic science literature

The Sydney Declaration is an initiative led by an informal group of forensic scientists with diverse backgrounds. It offers a vision of forensic science based on the trace, as a vestige of a past event related to security or a possible law violation. An article published in Forensic Science International (FSI) introduces to this view [1]. Our investigation delves into how the forensic science literature has received this article (the SD article), nearly two years after its publication. One of the main challenges of this exploratory study was to define the appropriate scope of forensic scientific literature, within which the SD article must be located. In general, the publishing domain is quickly evolving, with many competing players, while still being structured around standard academic disciplines. The forensic literature, meanwhile, is scattered and poorly connected. This reflects the fragmentation of practice and research in forensic science, and the difficulty of situating a scientific activity in such a way as to bring out its forensic substance. Nonetheless, the SD article fills a gap. By deciphering the critical concept of trace, it highlights how pivotal forensic science is in addressing societal challenges. Scholarly literature expresses clear quantitative interest in the SD article. It has received significant qualitative citations on multiple levels and dimensions, in a highly relevant manner and in accordance with its aim of providing a forensic foundation for various debates that have been conducted separately, notably over the last fifteen years.

Learning the mechanisms of network growth

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.

Using full-text content to characterize and identify best seller books: A study of early 20th-century literature

Artistic pieces can be studied from several perspectives, one example being their reception among readers over time. In the present work, we approach this interesting topic from the standpoint of literary works, particularly assessing the task of predicting whether a book will become a best seller. Unlike previous approaches, we focused on the full content of books and considered visualization and classification tasks. We employed visualization for the preliminary exploration of the data structure and properties, involving SemAxis and linear discriminant analyses. To obtain quantitative and more objective results, we employed various classifiers. Such approaches were used along with a dataset containing (i) books published from 1895 to 1923 and consecrated as best sellers by the Publishers Weekly Bestseller Lists and (ii) literary works published in the same period but not being mentioned in that list. Our comparison of methods revealed that the best-achieved result-combining a bag-of-words representation with a logistic regression classifier-led to an average accuracy of 0.75 both for the leave-one-out and 10-fold cross-validations. Such an outcome enhances the difficulty in predicting the success of books with high accuracy, even using the full content of the texts. Nevertheless, our findings provide insights into the factors leading to the relative success of a literary work.

Coronavirus disease-2019 and orthopedics: A bibliometric analysis of the literature

BACKGROUND

The coronavirus disease-2019 (COVID-19) pandemic has had a dramatic impact on global health, with orthopedics among the most affected specialties. An increasing number of COVID-19-related orthopedic studies have been published. The purpose of this study was to analyze the orthopedic literature published during the COVID-19 pandemic to guide future research.

METHODS

The Scopus database was searched for relevant literature published between 2020 and 2022. The keywords used in the retrieval process were ("COVID-19" OR "Coronavirus" OR "2019-nCoV" OR "SARS-CoV-2" OR "Betacoronavirus" OR "novel coronavirus 2019" OR "novel coronavirus" OR "coronavirus-19" OR "COVID 19" OR "nCOV" OR "COVID-2019" OR "COVID 2019") and ("orthopedic" OR "orthopedics" OR "orthopedic" OR "orthopedical" OR "orthopedical" OR "orthopedics"). Spreadsheet software (Excel, Microsoft Corp., Redmond, WA) was used to analyze the top 10 cited authors, countries, journals, and articles. The top 5 publication types were also analyzed. VOSviewer (Center for Science and Technology Studies, Leiden, Netherlands) was used to network and visualize the literature.

RESULTS

A total of 1619 publications relevant to COVID-19 and orthopedics were reviewed. Among these publications, the most active country, author, and publication type included the United States, Vaishya R, and original articles, respectively. The most frequently used keywords were human, coronavirus disease-2019, pandemic, and orthopedics. The Journal of Bone and Joint Surgery American Volume was the most cited journal, whereas the greatest number of articles was published in the Journal of Clinical Orthopedics and Trauma.

CONCLUSIONS

This study provides a perspective on the development of orthopedic publications during the COVID-19 pandemic and evidence for researchers worldwide to strengthen global cooperation in fighting the epidemic.

Persistence of collective memory of corporate bankruptcy events discussed on X (Twitter) is influenced by pre-bankruptcy public attention

Collective attention and memory involving significant events can be quantitatively studied via social media data. Previous studies analyzed user attention to discrete events that do not change post-event, and assume universal public attention patterns. However, dynamic events with ongoing updates are common, yielding varied individual attention patterns. We explore memory of U.S. companies filing Chapter 11 bankruptcy and being mentioned on X (formerly Twitter). Unlike discrete events, Chapter 11 entails ongoing financial changes as the company typically remains operational, influencing post-event attention dynamics. We collected 248,936 X mentions for 74 companies before and after each bankruptcy. Attention surged after bankruptcy, with distinct Low and High persistence levels compared with pre-bankruptcy attention. The two tweeting patterns were modeled using biexponential models, successfully predicting (F1-score: 0.81) post-bankruptcy attention persistence. Studying bankruptcy events on social media reveals diverse attention patterns, demonstrates how pre-bankruptcy attention affects post-bankruptcy recollection, and provides insights into memory of dynamic events.

Structural-topic aware deep neural networks for information cascade prediction

It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the structural information in cascade prediction task, we propose a structural-topic aware deep neural networks (STDNN), which firstly learns the structure topic distribution of each node in the cascade, feeds it to a sequential neural network, and finally predicts the future popularity of the cascades. It can inherit the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades by capturing indicative graph structures. We evaluate our model through quantitative experiments, where our model exhibits promising performance, efficiency higher than the baselines.

Idea engines: Unifying innovation & obsolescence from markets & genetic evolution to science

Innovation and obsolescence describe dynamics of ever-churning and adapting social and biological systems, concepts that encompass field-specific formulations. We formalize the connection with a reduced model of the dynamics of the "space of the possible" (e.g., technologies, mutations, theories) to which agents (e.g., firms, organisms, scientists) couple as they grow, die, and replicate. We predict three regimes: The space is finite, ever growing, or a Schumpeterian dystopia in which obsolescence drives the system to collapse. We reveal a critical boundary at which the space of the possible fluctuates dramatically in size, displaying recurrent periods of minimal and of veritable diversity. When the space is finite, corresponding to physically realizable systems, we find surprising structure. This structure predicts a taxonomy for the density of agents near and away from the innovative frontier that we compare with distributions of firm productivity, COVID diversity, and citation rates for scientific publications. Our minimal model derived from first principles aligns with empirical examples, implying a follow-the-leader dynamic in firm cost efficiency and biological evolution, whereas scientific progress reflects consensus that waits on old ideas to go obsolete. Our theory introduces a fresh and empirically testable framework for unifying innovation and obsolescence across fields.

A simulation-based analysis of the impact of rhetorical citations in science

Authors of scientific papers are usually encouraged to cite works that meaningfully influenced their research (substantive citations) and avoid citing works that had no meaningful influence (rhetorical citations). Rhetorical citations are assumed to degrade incentives for good work and benefit prominent papers and researchers. Here, we explore if rhetorical citations have some plausibly positive effects for science and disproportionately benefit the less prominent papers and researchers. We developed a set of agent-based models where agents can cite substantively and rhetorically. Agents first choose papers to read based on their expected quality, become influenced by those that are sufficiently good, and substantively cite them. Next, agents fill any remaining slots in their reference lists with rhetorical citations that support their narrative, regardless of whether they were actually influential. We then turned agents' ability to cite rhetorically on-and-off to measure its effects. Enabling rhetorical citing increased the correlation between paper quality and citations, increased citation churn, and reduced citation inequality. This occurred because rhetorical citing redistributed some citations from a stable set of elite-quality papers to a more dynamic set with high-to-moderate quality and high rhetorical value. Increasing the size of reference lists, often seen as an undesirable trend, amplified the effects. Overall, rhetorical citing may help deconcentrate attention and make it easier to displace established ideas.

Early-career factors largely determine the future impact of prominent researchers: evidence across eight scientific fields

Can we help predict the future impact of researchers using early-career factors? We analyze early-career factors of the world's 100 most prominent researchers across 8 scientific fields and identify four key drivers in researchers' initial career: working at a top 25 ranked university, publishing a paper in a top 5 ranked journal, publishing most papers in top quartile (high-impact) journals and co-authoring with other prominent researchers in their field. We find that over 95% of prominent researchers across multiple fields had at least one of these four features in the first 5 years of their career. We find that the most prominent scientists who had an early career advantage in terms of citations and h-index are more likely to have had all four features, and that this advantage persists throughout their career after 10, 15 and 20 years. Our findings show that these few early-career factors help predict researchers' impact later in their careers. Our research thus points to the need to enhance fairness and career mobility among scientists who have not had a jump start early on.

The evolution of scientific literature as metastable knowledge states

The problem of identifying common concepts in the sciences and deciding when new ideas have emerged is an open one. Metascience researchers have sought to formalize principles underlying stages in the life cycle of scientific research, understand how knowledge is transferred between scientists and stakeholders, and explain how new ideas are generated and take hold. Here, we model the state of scientific knowledge immediately preceding new directions of research as a metastable state and the creation of new concepts as combinatorial innovation. Through a novel approach combining natural language clustering and citation graph analysis, we predict the evolution of ideas over time and thus connect a single scientific article to past and future concepts in a way that goes beyond traditional citation and reference connections.

Network Analysis and Natural Language Processing to Obtain a Landscape of the Scientific Literature on Materials Applications

Recent progress in natural language processing (NLP) enables mining the literature in various tasks akin to knowledge discovery. Obtaining an updated birds-eye view of key research topics and their evolution in a vast, dynamic field such as materials science is challenging even for experienced researchers. In this Perspective paper, we present a landscape of the area of applied materials in selected representative journals based on a combination of methods from network science and simple NLP strategies. We found a predominance of energy-related materials, e.g., for batteries and catalysis, organic electronics, which include flexible sensors and flexible electronics, and nanomedicine with various topics of materials used in diagnosis and therapy. As for the impact calculated through standard metrics of impact factor, energy-related materials and organic electronics are again top of the list across different journals, while work in nanomedicine has been found to have a lower impact in the journals analyzed. The adequacy of the approach to identify key research topics in materials applications was verified indirectly by comparing the topics identified in journals with diverse scopes, including journals that are not specific to materials. The approach can be employed to obtain a fast overview of a given field from the papers published in related scientific journals, which can be adapted or extended to any research area.

SciSciNet: A large-scale open data lake for the science of science research

The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field.

Data, measurement and empirical methods in the science of science

The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.

Modeling Event Propagation via Graph Biased Temporal Point Process

Temporal point process is widely used for sequential data modeling. In this article, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users and news transmitting between websites. Given a collection of event propagation sequences, the conventional point process model considers only the event history, i.e., embed event history into a vector, not the latent graph structure. We propose a graph biased temporal point process (GBTPP) leveraging the structural information from graph representation learning, where the direct influence between nodes and indirect influence from event history is modeled. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic data set and two real-world data sets show the efficacy of our model compared with conventional methods and state-of-the-art ones.

Effect of the topic-combination novelty on the disruption and impact of scientific articles: Evidence from PubMed

Novelty, disruption and impact are essential concepts for understanding the originality and importance of scientific discoveries. By drawing on a large-scale corpus consisting of nearly 0.9 million PubMed papers published between 1970 and 2009 and their citations before 2018 in the Web of Science, we found that the topic-combination novelty has different effects on the impact and disruption of scientific papers, that is, an inverted U-shaped effect on the impact and a positive effect on disruption. One of our contributions is that we have significantly improved the reliability of topic-combination novelty by applying MeSH terms of PubMed to the measurement of novelty. Another contribution is that we have explained how a novel combination of MeSH terms of an article contributes to citations and citation networks, that is, the middle-level novelty is more likely to achieve large citation counts. In contrast, high topic-combination novelty relates to the discontinuity in the focal paper’s citation network.

Predictable by publication: discovery of early highly cited academic papers based on their own features

Purpose

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

Design/methodology/approach

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Findings

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Originality/value

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

Practical operation and theoretical basis of difference-in-difference regression in science of science: The comparative trial on the scientific performance of Nobel laureates versus their coauthors

Purpose

In recent decades, with the availability of large-scale scientific corpus datasets, difference-in-difference (DID) is increasingly used in the science of science and bibliometrics studies. DID method outputs the unbiased estimation on condition that several hypotheses hold, especially the common trend assumption. In this paper, we gave a systematic demonstration of DID in the science of science, and the potential ways to improve the accuracy of DID method.

Design/methodology/approach

At first, we reviewed the statistical assumptions, the model specification, and the application procedures of DID method. Second, to improve the necessary assumptions before conducting DID regression and the accuracy of estimation, we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention. Lastly, we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates, by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.

Findings

We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results. As a case study, we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.

Research limitations

This study ignored the rigorous mathematical deduction parts of DID, while focused on the practical parts.

Practical implications

This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.

Originality/value

This study gains insights into the usage of econometric tools in science of science.

Papers and patents are becoming less disruptive over time

Theories of scientific and technological change view discovery and invention as endogenous processes^1,2, wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton's words, 'stand on the shoulders of giants'^3-7. Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances^8,9. Yet contrary to this view, studies suggest that progress is slowing in several major fields^10,11. Here, we analyse these claims at scale across six decades, using data on 45 million papers and 3.9 million patents from six large-scale datasets, together with a new quantitative metric-the CD index¹²-that characterizes how papers and patents change networks of citations in science and technology. We find that papers and patents are increasingly less likely to break with the past in ways that push science and technology in new directions. This pattern holds universally across fields and is robust across multiple different citation- and text-based metrics^1,13-17. Subsequently, we link this decline in disruptiveness to a narrowing in the use of previous knowledge, allowing us to reconcile the patterns we observe with the 'shoulders of giants' view. We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices or field-specific factors. Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of science and technology.

Analyzing and predicting success of professional musicians

The emergence of streaming services, e.g., Spotify, has changed the way people listen to music and the way professional musicians achieve fame and success. Classical music has been the backbone of Western media for a long time, but Spotify has introduced the public to a much wider variety of music, also opening a new venue for professional musicians to gain exposure. In this paper, we use open-source data from Spotify and Musicbrainz databases to construct collaboration-based and genre-based networks. We call genres defined in these databases primary genres. Our goal is to find the correlation between various features of each professional musician, the current stage of their career, and the level of their success in the music field. We build regression models using XGBoost to first analyze correlation between features provided by Spotify. We then analyze the correlation between the digital music world of Spotify and the more traditional world of Billboard charts. We find that within certain bounds, machine learning techniques such as decision tree classifiers and Q-based models perform quite well on predicting success of professional musicians from the data on their early careers. We also find features that are highly predictive of their success. The most prominent among them are the musicians' collaboration counts and the span of their career. Our findings also show that classical musicians are still very centrally placed in the general, genre-agnostic network of musicians. Using these models and success metrics, aspiring professional musicians can check if their chances for career success could be improved by increasing their specific success measures in both Spotify and Billboard charts.

The 50 most cited studies on posterior tibial slope in joint preserving knee surgery

PURPOSE

To determine the 50 most cited studies on posterior tibial slope (PTS) in joint preserving knee surgery and assess their level of evidence, objective study quality scores as well as to examine whether the study quality correlated with the citation count and citation density in the top 50 list.

METHODS

A literature search on Web of Science was performed to determine the 50 most cited studies on the topic of PTS in joint preserving knee surgery between 1990 and 2022. The studies were evaluated for their bibliographic parameters, level of evidence rating (LOE), citation counts, the Modified Coleman Methodological Score (MCMS), the Methodological Index for Non-Randomized Studies (MINORS), and the Radiologic Methodology and Quality Scale (MQCSRE).

RESULTS

Of the top 50 list, 16 studies were published in the American Journal of Sports Medicine. A total of 23 studies were produced in the United States (46%). Of 10 different study types, case control studies (n = 16, 32%) and cadaveric studies (n = 10, 20%) were most common. 15 studies (30%) were purely radiological studies. 6 studies were level II (12%), 23 level III (46%), 15 level IV (30%), and 6 level V studies (12%), respectively. The number of citations ranged from 42 to 447 (mean 105.6 ± 79.2 citations) and showed a mean citation density of 10.3 ± 5.2, composed of the decades 1994 - 2000 (8.3 ± 4.1), 2001 - 2010 (11.1 ± 5.9), 2011 - 2019 (10.1 ± 5.1). Mean quality scores were 55.9 ± 13.0 for MCMS (n = 18), 14.5 ± 3.2 for MINORS (n = 18) and 18.1 ± 3.7 for MQCSRE (n = 20), respectively. High citation counts did not correlate with higher study quality scores (p > 0.05). Radiological studies were not significantly cited more often than non-radiological studies (mean 116.9 ± 88.3 vs. 100.8 ± 75.8 citations; p > 0.05).

CONCLUSION

In joint preserving knee surgery, the 50 most cited studies on PTS did not represent a ranking of the highest methodological quality scores. Citation counts and citation density over the past three decades did not significantly differ, even though the number of articles in the presented list multiplied over the same period. This list can serve as a reference tool for orthopedic surgeons aiming to review PTS literature.

Peer review in funding-by-lottery: A systematic overview and expansion

Despite the surging interest in introducing lottery mechanisms into decision-making procedures for science funding bodies, the discourse on funding-by-lottery remains underdeveloped and, at times, misleading. Funding-by-lottery is sometimes presented as if it were a single mechanism when, in reality, there are many funding-by-lottery mechanisms with important distinguishing features. Moreover, funding-by-lottery is sometimes portrayed as an alternative to traditional methods of peer review when peer review is still used within funding-by-lottery approaches. This obscures a proper analysis of the (hypothetical and actual) variants of funding-by-lottery and important differences amongst them. The goal of this article is to provide a preliminary taxonomy of funding-by-lottery variants and evaluate how the existing evidence on peer review might lend differentiated support for variants of funding-by-lottery. Moreover, I point to gaps in the literature on peer review that must be addressed in future research. I conclude by building off of the work of Avin in moving toward a more holistic evaluation of funding-by-lottery. Specifically, I consider implications funding-by-lottery variants may have regarding trust and social responsibility.

Public use and public funding of science

Knowledge of how science is consumed in public domains is essential for understanding the role of science in human society. Here we examine public use and public funding of science by linking tens of millions of scientific publications from all scientific fields to their upstream funding support and downstream public uses across three public domains-government documents, news media and marketplace invention. We find that different public domains draw from various scientific fields in specialized ways, showing diverse patterns of use. Yet, amidst these differences, we find two important forms of alignment. First, we find universal alignment between what the public consumes and what is highly impactful within science. Second, a field's public funding is strikingly aligned with the field's collective public use. Overall, public uses of science present a rich landscape of specialized consumption, yet, collectively, science and society interface with remarkable alignment between scientific use, public use and funding.

Connecting intercity mobility with urban welfare

While significant effort has been devoted to understand the role of intraurban characteristics on sustainability and growth, much remains to be understood about the effect of interurban interactions and the role cities have in determining each other's urban welfare. Here we consider a global mobility network of population flows between cities as a proxy for the communication between these regions, and analyze how it correlates with socioeconomic indicators. We use several measures of centrality to rank cities according to their importance in the mobility network, finding PageRank to be the most effective measure for reflecting these prosperity indicators. Our analysis reveals that the characterization of the welfare of cities based on mobility information hinges on their corresponding development stage. Namely, while network-based predictions of welfare correlate well with economic indicators in mature cities, for developing urban areas additional information about the prosperity of their mobility neighborhood is needed. We develop a simple generative model for the allocation of population flows out of a city that balances the costs and benefits of interaction with other cities that are successful, finding that it provides a strong fit to the flows observed in the global mobility network and highlights the differences in flow patterns between developed and developing urban regions. Our results hint towards the importance of leveraging interurban connections in service of urban development and welfare.

Combining informetrics and trend analysis to understand past and current directions in electronic design automation

There has been increasing interest in the study of research communities with the goal of optimizing their outcomes and impact. While current methods can predict future trends, they offer little insight about the causes of the trends. However, causal insight is important for strategic decision making to improve a community. This paper presents a new method to predict the possible causes for inefficiencies in a community by relating them to disconnections between trends, like trends in the number of publications, patents, citations, and so on. The method combines traditional scientometric and webometric metrics and metric predictions with a recent model for trend analysis in a community. The proposed method was used to analyze electronic design automation (EDA) domain. The analysis showed intriguing disconnections between the trends of the number of papers, number of granted patents, and impact of its main publications. The analysis suggests a slightly decreasing impact and visibility of EDA, while having less novel, commonly-accepted knowledge in the area. The gained insight suggests three possible strategic decisions to improve EDA community: avoiding to ignore new ideas, reducing the complexity of framed problems, and keeping a minimal gap between real-life needs and academic solutions.

Frontiers of ferroptosis research: An analysis from the top 100 most influential articles in the field

In recent years, ferroptosis has become a research hotspot in programmed cell death. Since the concept of ferroptosis was proposed, a growing number of articles have been published on this topic. Nevertheless, to our knowledge, these ferroptosis-related publications that have received a great deal of attention have not been quantitatively evaluated. In this study, we analyzed the top 100 most influential articles over the past decade through a bibliometric method to characterize the research status and trends in this field. Web of Science Core Collection was searched to identify relevant studies. After being manually screened, the top 100 most cited studies with original data were identified and analyzed. Bibliometric software including VOSviewer and R-Bibliometrix were used to perform visualization analysis. The citation frequency for the top 100 selected articles ranged from 135 to 3603 (326.6 citations on average). These articles originated from 25 countries/regions, with more than half originating from the United States and China. The most frequently nominated author was Stockwell BR from the Columbia University, and of the top 100 articles, 19 listed his name. Three core journals were Nature, Cell and Proceedings of the National Academy of Sciences of the United States of America. In addition to term of ferroptosis, these terms or phrases including cell death, cancer cell, GPX4, pathway, inhibitor, mechanism, iron, lipid peroxidation, resistance, erastin, sorafenib, P53, reactive oxygen species, necroptosis, apoptosis, glutathione peroxidase, ACSL4, autophagy, and SLC7A11 appeared more frequently in the top 100 articles. Overall, although much progress has been made, the research on ferroptosis is still at an early stage. The current attention in this field mainly focuses on potential regulatory mechanism and pathways including key ferroptosis-related genes/molecules, oxidant and antioxidant system, ferroptosis-inducing agents or nanomedicine for cancer therapy, as well as the role of ferroptosis in non-neoplastic disorders. Meanwhile, combination therapeutic strategies targeting ferroptosis in radiotherapy or immunotherapy also deserve further attention.

A modified LSTM network to predict the citation counts of papers

Quantifiable predictability in the citation counts of articles is significant in scientometrics and informetrics. Many metrics based on the citation counts can evaluate the scientific impact of research articles and journals. Utilising time series models, an article’s citation counts up to the yth year after publication can be predicted by those up to the previous years. However, the typically used models cannot predict the fat tail of the actual citation distributions. Thus, based on cumulative advantage of the citation behaviour, we propose a method to predict the accumulated citation counts, by using a random number sampled from a power-law distribution to modify the results given by a recurrent neural network (RNN), long short-term memory. Extensive experiments on the data set including 17 journals in information science verified the effectiveness of our method by the good fittings on distributions and evolutionary trends of the citation counts of articles. Our method has the potential to be extended to predict other popular assessment measures such as impact factor and h-index for journals.

Identifying Key Information on Life Cycle of Engineering Data by Graph Convolutional Networks and Data Mining

Engineering data, including product data-conversion networks and software dependency networks, are very important for the long-term preservation of product information. With the explosive growth of data in recent years, product information has become increasingly diversified and complex, which poses new challenges to the long-term preservation of product data. A better understanding of the functions of complex networks can help us take more effective measures to maintain and control such complex systems, and then adopt more effective methods to achieve life cycle management. It is currently difficult for traditional heuristic methods to deal with such large-scale complex systems. In recent years, however, the use of graph neural networks to identify key nodes attracted widespread attention, but this requires a large amount of training data. It is difficult to obtain large-scale relational data and establish identification models in engineering fields. Combining a graph convolution network with a data-mining method, a key node identification method in a graph convolution network based on data mining is proposed. The method first determines the type of complex network according to the power-law distribution and centrality of the network and then uses the corresponding evolutionary model to generate a large-scale synthetic network to effectively train the model. The experimental results from two real networks show that this method improves the identification performance of key nodes by using synthetic data with the same characteristics as the real network, and provides a new perspective for product life cycle management.

Breakthrough potential of emerging research topics based on citation diffusion features

This article uses the characteristics of citation curves in emerging research topics (ERTs) and combines them with the ERTs’ knowledge bases to draw conclusions by comparing their development patterns. The goal of this study is to enrich the toolset for predicting breakthroughs in scientific research. A set of multidimensional and practical bibliometric indicators is used to identify ERTs, to further identify the knowledge bases of ERTs and construct citation curves for both ERTs and their knowledge bases. The development trends of the citation curves of ERTs and their knowledge bases in different time periods are compared and analysed from two dimensions: knowledge transition and continuous growth. We use the field of stem cell research to test our method. Based on the outcome of the analysis, we can assess the breakthrough potential of ERTs. The stratification, transition and recent changes of the citation curve can be used as a basis for analysing and assessing the ERTs’ breakthrough potential. The combination of different citation diffusion patterns of ERTs and their knowledge bases can improve the effectiveness of identifying ERTs that can become breakthrough innovations.

Popularity of Video Games and Collective Memory

Describing the permanence of cultural objects is an important step in understanding societal trends. A relatively novel cultural object is the video game, which is an interactive media, that is, the player is an active contributor to the overall experience. This article aims to investigate video game permanence in collective memory using their popularity as a proxy, employing data based on the Steam platform from July 2012 to December 2020. The objectives include characterizing the database; studying the growth of players, games, and game categories; providing a model for the relative popularity distribution; and applying this model in three strata, global, major categories, and among categories. We detected linear growth trends in the number of players and the number of categories, and an exponential trend in the number of games released. Furthermore, we verified that lognormal distributions, emerging from multiplicative processes, provide a first approximation for the popularity in all strata. In addition, we proposed an improvement via Box–Cox transformations with similar parameters (from −0.12 (95% CI: −0.18, −0.07) to −0.04 (95% CI: −0.08, 0)). We were able to justify this improved model by interpreting the magnitude of each Box–Cox parameter as a measure of memory effects.

Co-citation Percentile Rank and JYUcite: a new network-standardized output-level citation influence metric and its implementation using Dimensions API

Judging value of scholarly outputs quantitatively remains a difficult but unavoidable challenge. Most of the proposed solutions suffer from three fundamental shortcomings: they involve (i) the concept of journal, in one way or another, (ii) calculating arithmetic averages from extremely skewed distributions, and (iii) binning data by calendar year. Here, we introduce a new metric Co-citation Percentile Rank (CPR), that relates the current citation rate of the target output taken at resolution of days since first citable, to the distribution of current citation rates of outputs in its co-citation set, as its percentile rank in that set. We explore some of its properties with an example dataset of all scholarly outputs from University of Jyväskylä spanning multiple years and disciplines. We also demonstrate how CPR can be efficiently implemented with Dimensions database API, and provide a publicly available web resource JYUcite, allowing anyone to retrieve CPR value for any output that has a DOI and is indexed in the Dimensions database. Finally, we discuss how CPR remedies failures of the Relative Citation Ratio (RCR), and remaining issues in situations where CPR too could potentially lead to biased judgement of value.

Synchronized bursts of productivity and success in individual careers

Notwithstanding a significant understanding of epidemic processes in biological, social, financial, and geophysical systems, little is known about contagion behavior in individual productivity and success. We introduce an epidemic model to study the contagion of scholarly productivity and YouTube success. Our analysis reveals the existence of synchronized bursts in individual productivity and success, which are likely mediated by sustained flows of information within the networks.