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  • The effect of national and international multiple affiliations on citation impact
    arXiv.cs.DL Pub Date : 2020-01-19
    Sichao Tong; Ting Yue; Zhesi Shen; Liying Yang

    Researchers affiliated with multiple institutions are increasingly seen in current scientific environment. In this paper we systematically analyze the multi-affiliated authorship and its effect on citation impact, with focus on the scientific output of research collaboration. By considering the nationality of each institutions, we further differentiate the national multi-affiliated authorship and international multi-affiliated authorship and reveal their different patterns across disciplines and countries. We observe a large share of publications with multi-affiliated authorship (45.6%) in research collaboration, with a larger share of publications containing national multi-affiliated authorship in medicine related and biology related disciplines, and a larger share of publications containing international type in Space Science, Physics and Geosciences. To a country-based view, we distinguish between domestic and foreign multi-affiliated authorship to a specific country. Taking G7 and BRICS countries as samples from different S&T level, we find that the domestic national multi-affiliated authorship relate to more on citation impact for most disciplines of G7 countries, while domestic international multi-affiliated authorships are more positively influential for most BRICS countries.

    更新日期:2020-01-22
  • Measuring Diversity of Artificial Intelligence Conferences
    arXiv.cs.DL Pub Date : 2020-01-20
    Ana Freire; Lorenzo Porcaro; Emilia Gómez

    The lack of diversity of the Artificial Intelligence (AI) field is nowadays a concern, and several initiatives such as funding schemes and mentoring programs have been designed to fight against it. However, there is no indication on how these initiatives actually impact AI diversity in the short and long term. This work studies the concept of diversity in this particular context and proposes a small set of diversity indicators (i.e. indexes) of AI scientific events. These indicators are designed to quantify the lack of diversity of the AI field and monitor its evolution. We consider diversity in terms of gender, geographical location and business (understood as the presence of academia versus industry). We compute these indicators for the different communities of a conference: authors, keynote speakers and organizing committee. From these components we compute a summarized diversity indicator for each AI event. We evaluate the proposed indexes for a set of recent major AI conferences and we discuss their values and limitations.

    更新日期:2020-01-22
  • The evolution of knowledge within and across fields in modern physics
    arXiv.cs.DL Pub Date : 2020-01-20
    Ye Sun; Vito Latora

    The exchange of knowledge across different areas and disciplines plays a key role in the process of knowledge creation, and can stimulate innovation and the emergence of new fields. We develop here a quantitative framework to extract significant dependencies among scientific disciplines and turn them into a time-varying network whose nodes are the different fields, while the weighted links represent the flow of knowledge from one field to another at a given period of time. Drawing on a comprehensive data set on scientific production in modern physics and on the patterns of citations between articles published in the various fields in the last thirty years, we are then able to map, over time, how the ideas developed in a given field in a certain time period have influenced later discoveries in the same field or in other fields. The analysis of knowledge flows internal to each field displays a remarkable variety of temporal behaviours, with some fields of physics showing to be more self-referential than others. The temporal networks of knowledge exchanges across fields reveal cases of one field continuously absorbing knowledge from another field in the entire observed period, pairs of fields mutually influencing each other, but also cases of evolution from absorbing to mutual or even to back-nurture behaviors.

    更新日期:2020-01-22
  • The stability of Twitter metrics: A study on unavailable Twitter mentions of scientific publications
    arXiv.cs.DL Pub Date : 2020-01-21
    Zhichao Fang; Jonathan Dudek; Rodrigo Costas

    This paper investigates the stability of Twitter counts of scientific publications over time. For this, we conducted an analysis of the availability statuses of over 2.6 million Twitter mentions received by the 1,154 most tweeted scientific publications recorded by Altmetric.com up to October 2017. Results show that of the Twitter mentions for these highly tweeted publications, about 14.3% have become unavailable by April 2019. Deletion of tweets by users is the main reason for unavailability, followed by suspension and protection of Twitter user accounts. This study proposes two measures for describing the Twitter dissemination structures of publications: Degree of Originality (i.e., the proportion of original tweets received by a paper) and Degree of Concentration (i.e., the degree to which retweets concentrate on a single original tweet). Twitter metrics of publications with relatively low Degree of Originality and relatively high Degree of Concentration are observed to be at greater risk of becoming unstable due to the potential disappearance of their Twitter mentions. In light of these results, we emphasize the importance of paying attention to the potential risk of unstable Twitter counts, and the significance of identifying the different Twitter dissemination structures when studying the Twitter metrics of scientific publications.

    更新日期:2020-01-22
  • Discovering seminal works with marker papers
    arXiv.cs.DL Pub Date : 2019-01-22
    Robin Haunschild; Werner Marx

    Bibliometric information retrieval in databases can employ different strategies. Com-monly, queries are performed by searching in title, abstract and/or author keywords (author vocabulary). More advanced queries employ database keywords to search in a controlled vo-cabulary. Queries based on search terms can be augmented with their citing papers if a re-search field cannot be curtailed by the search query alone. Here, we present another strategy to discover the most important papers of a research field. A marker paper is used to reveal the most important works for the relevant community. All papers co-cited with the marker paper are analyzed using reference publication year spectroscopy (RPYS). For demonstration of the marker paper approach, density functional theory (DFT) is used as a research field. Compari-sons between a prior RPYS on a publication set compiled using a keyword-based search in a controlled vocabulary and three different co-citation RPYS (RPYS-CO) analyses show very similar results. Similarities and differences are discussed.

    更新日期:2020-01-22
  • Paper-Patent Citation Linkages as Early Signs for Predicting Delayed Recognized Knowledge: Macro and Micro Evidence
    arXiv.cs.DL Pub Date : 2019-06-19
    Jian Du; Peixin Li; Robin Haunschild; Yinan Sun; Xiaoli Tang

    In this study, we investigate the extent to which patent citations to papers can serve as early signs for predicting delayed recognized knowledge in science using a comparative study with a control group, i.e., instant recognition papers. We identify the two opposite groups of papers by the Bcp measure, a parameter-free index for identifying papers which were recognized with delay. We provide a macro (Science/Nature papers dataset) and micro (a case chosen from the dataset) evidence on paper-patent citation linkages as early signs for predicting delayed recognized knowledge in science. It appears that papers with delayed recognition show a stronger and longer technical impact than instant recognition papers. We provide indication that in the more recent years papers with delayed recognition are awakened more often and earlier by a patent rather than by a scientific paper (also called "prince"). We also found that patent citations seem to play an important role to avoid instant recognition papers to level off or to become a so called "flash in the pan", i.e., instant recognition. It also appears that the sleeping beauties may firstly encounter negative citations and then patent citations and finally get widely recognized. In contrast to the two focused fields (biology and chemistry) for instant recognition papers, delayed recognition papers are rather evenly distributed in biology, chemistry, psychology, geology, materials science, and physics. We discovered several pairs of "science sleeping"-"technology [...]. We propose in further research to discover the potential ahead of time and transformative research by using citation delay analysis, patent & NPL analysis, and citation context analysis.

    更新日期:2020-01-22
  • Science and Technology Advance through Surprise
    arXiv.cs.DL Pub Date : 2019-10-18
    Feng Shi; James Evans

    Breakthrough discoveries and inventions involve unexpected combinations of contents including problems, methods, and natural entities, and also diverse contexts such as journals, subfields, and conferences. Drawing on data from tens of millions of research papers, patents, and researchers, we construct models that predict next year's content and context combinations with an AUC of 95% based on embeddings constructed from high-dimensional stochastic block models, where the improbability of new combinations itself predicts up to 50% of the likelihood that they will gain outsized citations and major awards. Most of these breakthroughs occur when problems in one field are unexpectedly solved by researchers from a distant other. These findings demonstrate the critical role of surprise in advance, and enable evaluation of scientific institutions ranging from education and peer review to awards in supporting it.

    更新日期:2020-01-17
  • The Archives Unleashed Project: Technology, Process, and Community to Improve Scholarly Access to Web Archives
    arXiv.cs.DL Pub Date : 2020-01-15
    Nick Ruest; Jimmy Lin; Ian Milligan; Samantha Fritz

    The Archives Unleashed project aims to improve scholarly access to web archives through a multi-pronged strategy involving tool creation, process modeling, and community building - all proceeding concurrently in mutually-reinforcing efforts. As we near the end of our initially-conceived three-year project, we report on our progress and share lessons learned along the way. The main contribution articulated in this paper is a process model that decomposes scholarly inquiries into four main activities: filter, extract, aggregate, and visualize. Based on the insight that these activities can be disaggregated across time, space, and tools, it is possible to generate "derivative products", using our Archives Unleashed Toolkit, that serve as useful starting points for scholarly inquiry. Scholars can download these products from the Archives Unleashed Cloud and manipulate them just like any other dataset, thus providing access to web archives without requiring any specialized knowledge. Over the past few years, our platform has processed over a thousand different collections from about two hundred users, totaling over 280 terabytes of web archives.

    更新日期:2020-01-16
  • Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data
    arXiv.cs.DL Pub Date : 2020-01-15
    Shuqi Xu; Manuel Sebastian Mariani; Linyuan Lü; Matúš Medo

    Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent. We assess 17 network-based metrics by their ability to identify milestone papers and patents in three large citation datasets. We find that traditional information-retrieval evaluation metrics are strongly affected by the interplay between the age distribution of the milestone items and age biases of the evaluated metrics. Outcomes of these metrics are therefore not representative of the metrics' ranking ability. We argue in favor of a modified evaluation procedure that explicitly penalizes biased metrics and allows us to reveal metrics' performance patterns that are consistent across the datasets. PageRank and LeaderRank turn out to be the best-performing ranking metrics when their age bias is suppressed by a simple transformation of the scores that they produce, whereas other popular metrics, including citation count, HITS and Collective Influence, produce significantly worse ranking results.

    更新日期:2020-01-16
  • A Longitudinal Analysis of University Rankings
    arXiv.cs.DL Pub Date : 2019-08-28
    Friso Selten; Cameron Neylon; Chun-Kai Huang; Paul Groth

    Pressured by globalization and the increasing demand for public organisations to be accountable, efficient and transparent, university rankings have become an important tool for assessing the quality of higher education institutions. It is therefore important to carefully assess exactly what these rankings measure. In this paper, the three major global university rankings, The Academic Ranking of World Universities, The Times Higher Education and the Quacquarelli Symonds World University Rankings, are studied. After a description of the ranking methodologies, it is shown that university rankings are stable over time but that there is variation between the three rankings. Furthermore, using Principal Component Analysis and Exploratory Factor Analysis, we show that the variables used to construct the rankings primarily measure two underlying factors: a universities reputation and its research performance. By correlating these factors and plotting regional aggregates of universities on the two factors, differences between the rankings are made visible. Last, we elaborate how the results from these analysis can be viewed in light of often voiced critiques of the ranking process. This indicates that the variables used by the rankings might not capture the concepts they claim to measure. Doing so the study provides evidence of the ambiguous nature of university ranking's quantification of university performance.

    更新日期:2020-01-16
  • Funding information in Web of Science: An updated overview
    arXiv.cs.DL Pub Date : 2020-01-14
    Weishu Liu; Li Tang; Guangyuan Hu

    Despite the limitations of funding acknowledgment (FA) data in Web of Science (WoS), studies using FA information have increased rapidly over the last several years. Considering this WoS'recent practice of updating funding data, this paper further investigates the characteristics and distribution of FA data in four WoS journal citation indexes. The research reveals that FA information coverage variances persist cross all four citation indexes by time coverage, language and document type. Our evidence suggests an improvement in FA information collection in humanity and social science research. Departing from previous studies, we argue that FA text (FT) alone no longer seems an appropriate field to retrieve and analyze funding information, since a substantial number of documents only report funding agency or grant number information in respective fields. Articles written in Chinese have a higher FA presence rate than other non-English WoS publications. This updated study concludes with a discussion of new findings and practical guidance for the future retrieval and analysis of funded research.

    更新日期:2020-01-15
  • On the challenges ahead of spatial scientometrics focusing on the city level
    arXiv.cs.DL Pub Date : 2020-01-12
    Gyorgy Csomos

    Since the mid-1970s, it has become highly acknowledged to measure and evaluate changes in international research collaborations and the scientific performance of institutions and countries through the prism of bibliometric and scientometric data. Spatial bibliometrics and scientometrics (henceforward spatial scientometrics) have traditionally focused on examining both country and regional levels; however, in recent years, numerous spatial analyses on the city level have been carried out. While city-level scientometric analyses have gained popularity among policymakers and statistical/economic research organizations, researchers in the field of bibliometrics are divided regarding whether it is possible to observe the spatial unit 'city' through bibliometric and scientometric tools. After systematically scrutinizing relevant studies in the field, three major problems have been identified: 1) there is no standardized method of how cities should be defined and how metropolitan areas should be delineated, 2) there is no standardized method of how bibliometric and scientometric data on the city level should be collected and processed and 3) it is not clearly defined how cities can profit from the results of bibliometric and scientometric analysis focusing on them. This paper investigates major challenges ahead of spatial scientometrics, focusing on the city level and presents some possible solutions.

    更新日期:2020-01-14
  • Possibility and prevention of inappropriate data manipulation in Polar Data Journal
    arXiv.cs.DL Pub Date : 2020-01-13
    Takeshi Terui; Yasuyuki Minamiyama; Kazutsuna Yamaji

    Stakeholders in the scientific field must always maintain transparency in the process of publishing research results in journals. Unfortunately, although research misconduct has stopped, certain forms of manipulation continue to appear in other forms. As new techniques of scientific publishing develop, science stakeholders need to examine the possibility of inappropriate activity in these new platforms. The National Institute of Polar Research in Japan launched a new data journal Polar Data Journal (PDJ) in 2017 to review the quality of data obtained in the polar region. To maintain transparency in this new data journal, we investigated the possibility of inappropriate data manipulation in peer reviews before the inception of this journal. We clarified inappropriate activity for the data in the peer review and considered preventive measures. We designed a specific workflow for PDJ. This included two measures: (i) the comparison of hash values in the review process and (ii) open peer review report publishing. Using the hash value comparison, we detected two instances of inappropriate data manipulation after the start of the journal. This research will help improve workflow in data journals and data repositories.

    更新日期:2020-01-14
  • Comparing the impact of subfields in scientific journals
    arXiv.cs.DL Pub Date : 2020-01-13
    Xiomara S. Q. Chacón; Thiago C. Silva; Diego R. Amancio

    The impact factor has been extensively used in the last years to assess journals visibility and prestige. While the impact factor is useful to compare journals, the specificities of subfields visibility in journals are overlooked whenever visibility is measured only at the journal level. In this paper, we analyze the subfields visibility in a subset of over 450,000 Physics papers. We show that the visibility of subfields is not regular in the considered dataset. In particular years, the variability in subfields impact factor in a journal reached 75% of the average subfields impact factor. We also found that the difference of subfields visibility in the same journal can be even higher than the difference of visibility between different journals. Our results shows that subfields impact is an important factor accounting for journals visibility.

    更新日期:2020-01-14
  • An Evaluation of Percentile Measures of Citation Impact, and a Proposal for Making Them Better
    arXiv.cs.DL Pub Date : 2020-01-13
    Lutz Bornmann; Richard Williams

    Percentiles are statistics pointing to the standing of a paper's citation impact relative to other papers in a given citation distribution. Percentile Ranks (PRs) often play an important role in evaluating the impact of scholars, institutions, and lines of study. Because PRs are so important for the assessment of scholarly impact, and because citation practices differ greatly across time and fields, various percentile approaches have been proposed to time- and field-normalize citations. Unfortunately, current popular methods often face significant problems in time- and field-normalization, including when papers are assigned to multiple fields or have been published by more than one unit (e.g., researchers or countries). They also face problems for estimating citation counts for pre-defined PRs (e.g., the 90th PR). We offer a series of guidelines and procedures that, we argue, address these problems and others and provide a superior means to make the use of percentile methods more accurate and informative. In particular, we argue that two approaches, CP-IN and CP-EX, should be preferred in bibliometric studies because they consider the complete citation distribution. Both approaches are based on cumulative frequencies in percentages (CPs). The paper further shows how bar graphs and beamplots can present PRs in a more meaningful and accurate manner.

    更新日期:2020-01-14
  • Two Decades of Network Science as seen through the co-authorship network of network scientists
    arXiv.cs.DL Pub Date : 2019-08-22
    Roland Molontay; Marcell Nagy

    Complex networks have attracted a great deal of research interest in the last two decades since Watts & Strogatz, Barab\'asi & Albert and Girvan & Newman published their highly-cited seminal papers on small-world networks, on scale-free networks and on the community structure of complex networks, respectively. These fundamental papers initiated a new era of research establishing an interdisciplinary field called network science. Due to the multidisciplinary nature of the field, a diverse but not divided network science community has emerged in the past 20 years. This paper honors the contributions of network science by exploring the evolution of this community as seen through the growing co-authorship network of network scientists (here the notion refers to a scholar with at least one paper citing at least one of the three aforementioned milestone papers). After investigating various characteristics of 29,528 network science papers, we construct the co-authorship network of 52,406 network scientists and we analyze its topology and dynamics. We shed light on the collaboration patterns of the last 20 years of network science by investigating numerous structural properties of the co-authorship network and by using enhanced data visualization techniques. We also identify the most central authors, the largest communities, investigate the spatiotemporal changes, and compare the properties of the network to scientometric indicators.

    更新日期:2020-01-13
  • Practical method to reclassify Web of Science articles into unique subject categories and broad disciplines
    arXiv.cs.DL Pub Date : 2020-01-08
    Staša Milojević

    Classification of bibliographic items into subjects and disciplines in large databases is essential for many quantitative science studies. The Web of Science classification of journals into ~250 subject categories, which has served as a basis for many studies, is known to have some fundamental problems and several practical limitations that may affect the results from such studies. Here we present an easily reproducible method to perform reclassification of the Web of Science into existing subject categories and into 14 broad areas. Our reclassification is at a level of articles, so it preserves disciplinary differences that may exist among individual articles published in the same journal. Reclassification also eliminates ambiguous (multiple) categories that are found for 50% of items, and assigns a discipline/field category to all articles that come from broad-coverage journals such as Nature and Science. The correctness of the assigned subject categories is evaluated manually and is found to be ~95%.

    更新日期:2020-01-10
  • Domain-independent Extraction of Scientific Concepts from Research Articles
    arXiv.cs.DL Pub Date : 2020-01-09
    Arthur Brack; Jennifer D'Souza; Anett Hoppe; Sören Auer; Ralph Ewerth

    We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present two deep learning systems as baselines. In particular, we propose active learning to deal with different domains in our task. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.

    更新日期:2020-01-10
  • A Network-Level View of Author Influence
    arXiv.cs.DL Pub Date : 2019-11-27
    Henry Blanchette

    I compare several network-level measures of centrality to common measures of author reputation and influence (e.g. hindex, i10index), all taken over the data set of papers published in 2017 at major computer systems conferences and some controls. I hypothesize that centrality measures will correlate strongly with the reputation and influence measures. My results confirm several expected correlations and exhibit a few surprising absences of correlation. In particular, there was an absence of statistically significant correlation between degree centrality and hindex,

    更新日期:2020-01-09
  • Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics
    arXiv.cs.DL Pub Date : 2019-06-17
    Mario Krenn; Anton Zeilinger

    The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow sub-disciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus access to structured knowledge from a large corpus of publications could help pushing the frontiers of science. Here we demonstrate a method to build a semantic network from published scientific literature, which we call SemNet. We use SemNet to predict future trends in research and to inspire new, personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In SemNet, scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two physical concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SemNet thus confirm that it stores useful semantic knowledge. We train a deep neural network using states of SemNet of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data. With the neural network and theoretical network tools we are able to suggest new, personalized, out-of-the-box ideas, by identifying pairs of concepts which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings.

    更新日期:2020-01-09
  • Perspectives on urban theories
    arXiv.cs.DL Pub Date : 2019-11-07
    Denise Pumain; Juste Raimbault

    At the end of the five years of work in our GeoDiverCity program, we brought together a diversity of authors from different disciplines. Each person was invited to present an important question about the theories and models of urbanization. They are representative of a variety of currents in urban research. Rather than repeat here the contents of all chapters, we propose two ways to synthesize the scientific contributions of this book. In a first part we replace them in relation to a few principles that were experimented in our program, and in a second part we situate them with respect to a broader view of international literature on these topics.

    更新日期:2020-01-09
  • With Registered Reports Towards Large Scale Data Curation
    arXiv.cs.DL Pub Date : 2020-01-07
    Steffen Herbold

    The scale of manually validated data is currently limited by the effort that small groups of researchers can invest for the curation of such data. Within this paper, we propose the use of registered reports to scale the curation of manually validated data. The idea is inspired by the mechanical turk and replaces monetary payment with authorship of data set publication.

    更新日期:2020-01-08
  • The extent and drivers of gender imbalance in neuroscience reference lists
    arXiv.cs.DL Pub Date : 2020-01-03
    Jordan D. Dworkin; Kristin A. Linn; Erin G. Teich; Perry Zurn; Russell T. Shinohara; Danielle S. Bassett

    Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research in other fields has called attention to the prevalence of gender bias in citation practices. Because of the downstream effects that citations can have on visibility and career advancement, understanding and eliminating gender bias in citation practices is vital for addressing inequity in a scientific community. In this study, we sought to determine whether there is evidence of gender bias in the citation practices of neuroscientists. Utilizing data from five top neuroscience journals, we indeed find that reference lists tend to include more papers with men as first and last author than would be expected if gender was not a factor in referencing. Importantly, we show that this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing with time despite greater diversity in the academy. We develop a co-authorship network to determine the degree to which homophily in researchers' social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative of the field. We discuss possible mechanisms and consider how individual researchers might incorporate these findings into their own referencing practices.

    更新日期:2020-01-07
  • Identifying Historical Travelogues in Large Text Corpora Using Machine Learning
    arXiv.cs.DL Pub Date : 2020-01-06
    Jan RördenAIT Austrian Insitute of Technology; Doris GruberAustrian Academy of Sciences; Martin KricklAustrian National Library; Bernhard HaslhoferAIT Austrian Insitute of Technology

    Travelogues represent an important and intensively studied source for scholars in the humanities, as they provide insights into people, cultures, and places of the past. However, existing studies rarely utilize more than a dozen primary sources, since the human capacities of working with a large number of historical sources are naturally limited. In this paper, we define the notion of travelogue and report upon an interdisciplinary method that, using machine learning as well as domain knowledge, can effectively identify German travelogues in the digitized inventory of the Austrian National Library with F1 scores between 0.94 and 1.00. We applied our method on a corpus of 161,522 German volumes and identified 345 travelogues that could not be identified using traditional search methods, resulting in the most extensive collection of early modern German travelogues ever created. To our knowledge, this is the first time such a method was implemented for the bibliographic indexing of a text corpus on this scale, improving and extending the traditional methods in the humanities. Overall, we consider our technique to be an important first step in a broader effort of developing a novel mixed-method approach for the large-scale serial analysis of travelogues.

    更新日期:2020-01-07
  • The Demise of Single-Authored Publications in Computer Science: A Citation Network Analysis
    arXiv.cs.DL Pub Date : 2020-01-02
    Brian K. Ryu

    In this study, I analyze the DBLP bibliographic database to study role of single author publications in the computer science literature between 1940 and 2019. I examine the demographics and reception by computing the population fraction, citation statistics, and PageRank scores of single author publications over the years. Both the population fraction and reception have been continuously declining since the 1940s. The overall decaying trend of single author publications is qualitatively consistent with those observed in other scientific disciplines, though the diminution is taking place several decades later than those in the natural sciences. Additionally, I analyze the scope and volume of single author publications, using page length and reference count as first-order approximations of the scope of publications. Although both metrics on average show positive correlations with citation count, single author papers show no significant difference in page or reference counts compared to the rest of the publications, suggesting that there exist other factors that impact the citations of single author publications.

    更新日期:2020-01-04
  • Publishing computational research -- A review of infrastructures for reproducible and transparent scholarly communication
    arXiv.cs.DL Pub Date : 2020-01-02
    Markus Konkol; Daniel Nüst; Laura Goulier

    Funding agencies increasingly ask applicants to include data and software management plans into proposals. In addition, the author guidelines of scientific journals and conferences more often include a statement on data availability, and some reviewers reject unreproducible submissions. This trend towards open science increases the pressure on authors to provide access to the source code and data underlying the computational results in their scientific papers. Still, publishing reproducible articles is a demanding task and not achieved simply by providing access to code scripts and data files. Consequently, several projects develop solutions to support the publication of executable analyses alongside articles considering the needs of the aforementioned stakeholders. The key contribution of this paper is a review of applications addressing the issue of publishing executable computational research results. We compare the approaches across properties relevant for the involved stakeholders, e.g., provided features and deployment options, and also critically discuss trends and limitations. The review can support publishers to decide which system to integrate into their submission process, editors to recommend tools for researchers, and authors of scientific papers to adhere to reproducibility principles.

    更新日期:2020-01-04
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