Retraction of global scientific publications from 2001 to 2010

Retraction is a self-cleaning activity done in the global science community. In this study, the retraction of global scientific publications from 2001 to 2010 was quantitatively analyzed by using the Science Citation Index Expanded. The results indicated that the number of retractions increased faster compared to the number of global scientific publications. Three very different patterns of retraction existed in each field. In the multi-disciplinary category and in the life sciences, retraction was relatively active. The impact factor strongly correlated with the number of retractions, but did not significantly correlate with the rate of retraction. Although the increases in the number of publications in China, India, and South Korea were faster, their retraction activities were higher than the worldwide average level.     

Citation regression analysis of computer science publications in different ranking categories and subfields

A number of bibliometric studies point out that the role of conference publications in computer science differs from that in other traditional fields. Thus, it is interesting to identify the relative status of journal and conference publications in different subfields of computer science based on the citation rates categorised by the China Computer Federation (CCF) classifications and venue types. In this research, we construct a dataset containing over 100,000 papers recommended by the CCF catalogue and their citation information. We also investigate some other factors that often influence a paper’s citation rate. An experimental study shows that the relative status of journals and conferences varies greatly in different subfields of computer science, and the impact of different publication levels varies according to the citation rate. We also verify that the classification of a publication, number of authors, maximum h-index of all authors of a paper, and average number of papers published by a publication have different effects on the citation rate, although the citation rate may have a different degree of correlation with these factors.     

Redistributed random sampling method for categorizing materials research publications from SCI database: Metallurgy and polymer subfields

This article introduced two sampling methods, including Directly Random Sampling (DRS) and Redistributed Random Sampling (RRS) methods for categorization of a large number of research articles retrieved from metallurgy and polymer subfields from the Science Citation Index (SCI) database. The accuracy of the proposed sampling methods was considered in association by comparing with reference results previously obtained by Fully Retrieving Sampling (FRS) method, which involved analyzing the contents and categories of all articles from the database. The results suggested that RRS and DRS methods were appropriate, efficient and reasonably accurate for categorization of relatively large volume of research articles. RRS method was highly recommended, especially when the contents of sample articles was unevenly distributed. By DRS and RRS methods, only about 6.3% of total articles were required for obtaining similar results as those given by FRS method. The percentage Expected Worst Errors (EWE) from DRS and RRS methods were observed to range from 1.0 to 5.5%. The EWE value could be reduced by increasing the sample size.     

A refinement strategy for identification of scientific software from bioinformatics publications

In the field of bioinformatics, a large number of classical software becomes a necessary research tool. To measure the influence of scientific software as one kind of important intellectual products, a few strategies have been proposed to identify the software names from full texts of papers to collect the usage data of packages in bioinformatics research. However, the performance of these strategies is limited because of the highly imbalance of data in the full texts. This study proposes EnsembleSVMs-CRF, a two-step refinement strategy based on ensemble learning that gradually increases the sentences that contain software mentions to improve the performance of named entity recognition. The experiment on the bioinformatics corpus shows that the performance of EnsembleSVMs-CRF, in terms of the local F1 (78.81%) and the global F1-A (73.49%), is superior to the rule-based bootstrapping method and direct CRF. Application of this strategy to the articles published between 2013 and 2017 in 27 bioinformatics journals extracted 8,239 unique packages. The most popular 50 packages thus identified demonstrate that most of them are professional software which generally requires inter-discipline knowledge, rather than programming skill. Meanwhile, we found that researchers in bioinformatics tend to use free scientific software, and the application of general software is increasing compared with professional software.

     

Quantifying the higher-order influence of scientific publications

Scientometrics - Citation impact is commonly assessed using direct, first-order citation relations. We consider here instead the indirect influence of publications on new publications via...     

Bibliometric features of some scientific subfields and the scientometric consequences therefrom

In the present work an attempt is made to select journal bases for some subfields in chemistry. Through the modification of the Hirst's discioline impact factor concept, theprimary information base for the selected subfields is detenained. Relating impact factors of citing and cited journals,citation strategy indicators are suggested. Determination of the mean impact factors for subfields enables the introduction ofsubfields factors that bring the impact factors of journals of various subfields on a comparavle level.     

A nonlinear collective credit allocation in scientific publications

Scientometrics - Collaboration among researchers plays an important role in scientific discoveries, especially in multidisciplinary research. How to allocate credit reasonably to coauthors of a...     

Participation of Mexican Civil Society Organizations in scientific publications

Scientometrics - We present a bibliometric study, from the perspective of the mode 2 knowledge production, from a transdisciplinary point of view (Gibbons et al. in The new production of knowledge:...     

Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references

Indicators based on non-patent references (NPRs) are increasingly being used for measuring and assessing science–technology interactions. But NPRs in patent documents contain noise, as not all of them can be considered ‘scientific’. In this article, we introduce the results of a machine-learning algorithm that allows identifying scientific references in an automated manner. Using the obtained results, we analyze indicators based on NPRs, with a focus on the difference between NPR- and scientific non-patent references-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological domain. These results signal the relevancy of delineating scientific references when using NPRs to assess the occurrence and impact of science–technology interactions.     

Collaboration patterns of Taiwanese scientific publications in various research areas

This paper employs bibliometric methods to observe collaboration patterns of scientific publications in biotechnology, information and computer technology, future energy, and nanotechnology among different institutions in Taiwan. The results show primary domestic and international collaborative patterns, the effect of collaborative papers on the world-wide average, collaborative networks, and the distribution of institutions on global map. The findings suggest that domestic collaboration in each area is higher in proportion than international collaboration. Biotechnology leads in both domestic and international collaborative percentage. Among cooperative benchmarking countries, the US and China are the main partners. Collaboration among research institutes and universities is the most frequent collaborative pattern in each area except biotechnology, which tends to occur between hospitals and universities. On average, international collaborative papers tend to have greater effect, except in nanotechnology. Academia Sinica collaborated frequently with foreign institutes in each research field. A further analysis on how each collaborative group forms is recommended, especially collaboration among the Triple-Helix relationships.     

Ranking scientific publications considering the aging characteristics of citations

Ranking the significance of scientific publications has been a challenging topic for a long time. So far, many ranking methods have been proposed, one of which is the well-known PageRank algorithm. In this paper, we introduce aging characteristics to the PageRank algorithm via considering only the first 10 year citations when aggregating resource from different nodes. The validation of our new method is performed on the data of American Physical Society journals. The results indicate that taking into account aging characteristics improves the performance of the PageRank algorithm in terms of ranking accuracy for both papers and authors. Though our method is only applied to citation networks in this paper, it can be naturally used in many other real systems and similar improvements are expected.

     

Citation histories of scientific publications. The data sources

Some 160 data-containing studies on the citation aging of scientific literature are reviewed. The hitherto proposed time-distribution models are examined for relevant parameter values.     

Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents

Knowledge memes are the cultural equivalent of genes that play an important role in the evolution of knowledge. In this paper, we are trying to identify and tracking scientific and technological knowledge memes, and infer the relationship between science and technology at micro-level. A new carbon nanomaterial—graphene is taken as an example, and publications and patents are used as data sources for the representation of science and technology. Citation networks of publications and patents are constructed, on which a knowledge meme discovery algorithm is used, in order to identify memes that play a key role in the evolution of scientific and technological knowledge. Then the diffusion and co-occurrence of knowledge memes are shown, and a word embedding model is used to track the semantic change of the memes. The research could provide guidance for promoting knowledge innovation and making research policy.     

Evaluation of some methods for the relative assessment of scientific publications

Some bibliometric methods for the assessment of the publication activity of research units are discussed on the basis of impact factors and citations of papers. Average subfield impact factor of periodicals representing subfields in chemistry is suggested. This indicator characterizes the average citedness of a paper in a given subfield. Comparing the total sum of impact factors of corresponding periodicals divided by the number of papers published by a research team to the average subfield impact factor a publication strategy indicator can be derived. A new bibliometric indicator, relative subfield impact, is introduced which compares the number of citations received by papers of a research unit to the average subfield impact factor.     

Evaluating scientific impact of publications: combining citation polarity and purpose

Citation counts are commonly used to evaluate the scientific impact of a publication on the general premise that more citations probably mean more endorsements. However, two questionable assumptions underpin this idea: a) that all authors contributed equally to the paper; and b) that the endorsement is positive. Obviously, neither of these assumptions hold true. Hence, with this study, we examine two components of citations—their purpose, i.e., the reason for the citation, and polarity, being the author’s attitude toward the cited work. Our findings provide a new perspective on the scientific impact of highly-cited publications. Our methodology consists of three steps. Firstly, a pre-trained model composed of a Word2Vec—a well-known word embedding approach—and a convolutional neural network (CNN) is used to identify citation polarity and purpose. Secondly, in a set of highly-cited papers, we compare eight categories of purpose from foundational to critical and three categories of polarity: positive, negative, and neutral. We further explore how different types of papers—those discussing discoveries or those discussing utilitarian topics—influence the evaluation of scientific impact of papers. Finally, we mine and discover the knowledge (e.g. method, concept, tool or data) to explain the actual scientific impact of a highly-cited paper. To demonstrate how combining citation polarity with purpose can provide far greater details of a paper’s scientific impact, we undertake a case study with 370 highly-cited journal articles spanning “Biochemistry & Molecular Biology” and “Genetics & Heredity”. The results yield valuable insights into the assumption about citation counts as a metric for evaluating scientific impact.

     

English as lingua franca in geological scientific publications. A bibliometric analysis

The examination of three samples of geological scientific publications: (A) 9 journals from Western Europe and USA; (B) 10 up-to-date review books, and (C) 3 sections of Volume 127 (1990–1991) of theZoological Record, shows that the statement that English is now the lingua franca in geological sciences is only in part true, but reflects a desire by many people in the scientific community, a desire which may not yet have been fulfilled     

Citation analysis using scientific publications on the Web as data source: A case study in the XML research area

With the primary goal of exploring whether citation analysis using scientific papers found on the Web as a data source is a worthwhile means of studying scholarly communication in the new digital environment, the present case study examines the scholarly communication patterns in XML research revealed by citation analysis of ResearchIndex data and SCI data. Results suggest that citation analysis using scientific papers found on the Web as a data source has both advantages and disadvantages when compared with citation analysis of SCI data, but is nonetheless a valid method for evaluating scholarly contributions and for studying the intellectual structure in XML research. This revised version was published online in August 2006 with corrections to the Cover Date.     

The diffusion of scientific publications: The case of Econometrica, 1987

This paper documents that salient features of (time series of annual) citations to scientific publications might be captured by a Bass type diffusion model. This is particularly useful as it allows for a comparison of these features across journals, across disciplines and over time. For the illustrative case of Econometrica 1987, it is found that the peak in citations occurs at 6.5 years, on average. Also, it is found that after 14 years there is only a little gap between cumulative citations and the estimated total cumulative amount, suggesting that on average the impact of these articles lasts for about 15 years or so. Finally, it appears that these features can partly be explained by the size of the articles, as it is found that longer papers get more citations and peak later.     

Statistical reliability of comparisons based on the citation impact of scientific publications

A method for estimating the standard error of mean citation rates per publication is proposed and examplified on journal impact factors. The use of the standard error values in statistical tests is also illustrated.