New web services that help authors choose journals

Journal selection behavior among early-career academicians in Iran: how they choose the most appropriate journal for their publications

Purpose

Journals are the essential tools of researchers, especially academicians, to present their scientific findings. So, choosing the right journal helps not only science development but also their academic promotion. The purpose of this study is to examine the factors that Iranian medical researchers consider when selecting scholarly journals in which to submit their work.

Design/methodology/approach

A self-administered online questionnaire was emailed in May 2021, with 101 responses received. The sample included all the faculty members with the role of “lecturer” in Iranian medical universities and who have 1–5 articles in the Scopus database as early-career Iranian medical researchers. The questionnaire consisted of 36 items, divided into five sections: basic information, attitudes and beliefs, ways to choose a journal, problems and familiarity with the components of scientometrics/validity metrics related to journals.

Findings

The findings indicate that these researchers value the expertise of experienced researchers and professionals, like librarians, when selecting publication venues. They often use journal indexes to guide journal selection. They also consider factors like the length of typical peer review and the complexity of submission guidelines when making decisions.

Research limitations/implications

The study of one country, though detecting requirements of journal selection behavior, cannot be generalized to the entire region.

Practical implications

The current study has academic implications as far as decisions on journal selection are concerned. University policymakers in Iran may consider re-examining their emphasis on academicians’ promotion policies at Iranian universities of medical sciences.

Originality/value

These findings may support the work of early-career researchers and those individuals (e.g., librarians) that serve them, as well as publishers and editors of scholarly journals.

Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

Selecting a text similarity measure for a content-based recommender system

Purpose

The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance.

Design/methodology/approach

Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain.

Findings

The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25.

Originality/value

This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.