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The aim of this study is to identify the status of managing gray literature (GL) in medical science libraries in terms of three dimensions, collection development, organization and dissemination.

In this survey study, a structured questionnaire was used. The questionnaire consisted of 30 questions and consisted of six sections (demographic characteristics, the use of the term GL, types of GL, collection development, organization and dissemination). In total, 50 librarians from 15 medical science libraries participated in this study. The questionnaires were distributed manually to librarians by visiting libraries. All the librarians filled in the questionnaires. It should be noted that descriptive statistics and Excel and SPSS software were used for data analysis.

The results of using the term GL showed that 68 per cent of librarians use the source name itself. Most GL in libraries were theses (94 per cent). Moreover, a review of the status of GL collection showed that 60 per cent of libraries had written instructions for providing these resources. A total of 62 per cent of librarians stated that there is a GL selection committee in their library and the librarian is the most important member of the collection department. A total of 40 per cent of libraries were weeding GL. The most common way of obtaining GL was through deposition. The analysis of the status of GL organization indicated that 80 per cent of libraries had GL organization. A total of 90 per cent of libraries had digitized GL, and that librarians played a large role in organizing such resources. Evaluation of the dissemination of GL showed that all libraries have enabled users to access GL. In most libraries, users were only allowed to use GL in the library, and it was not possible to copy GL. Students and faculty members were the most important users of GL. Informing through the library website and the parent organization was the most important way of informing about these resources.

GL is one of the most important resources in medical and non-medical academic libraries. In this study, for the first time, the status of GL management in Iranian libraries of medical sciences was investigated. The results of this study can be useful for policymakers and managers of medical and non-medical libraries.

The purpose of this paper is to study the presence of highly cited papers of Nature in social media websites and tools. It also tries to examine the correlation between altmetric and bibliometric indicators.

This descriptive study was carried out using altmetric indicators. The research sample consisted of 1,000 most-cited articles in Nature. In February 2019, the bibliographic information of these articles was extracted from the Scopus database. Then, the titles of all articles were manually searched on Google, and by referring to the article in the journal website and altmetric institution, the data related to social media presence and altmetric score of articles were collected. The data were analyzed using Microsoft Excel and SPSS.

According to the results of the study, from 1,000 articles, 989 of them (98.9 per cent) were mentioned at least once in different social media websites and tools. The most used altmetric source in highly cited articles was Mendeley (98.9 per cent), followed by Citeulike (79.8 per cent) and Wikipedia (69.4 per cent). Most Tweets, blog posts, Facebook posts, news stories, readers in Mendeley, Citeulike and Connotea and Wikipedia citations belonged to the article titled “Mastering the game of Go with deep neural networks and tree search”. The highest altmetric score was 3,135 which belonged to this paper. Most tweeters and articles’ readers were from the USA. The membership type of the tweeters was public membership. In terms of fields of study, most readers were PhD students in Agricultural and Biological Sciences. Finally, the results of Spearman’s Correlation revealed positive significant statistical correlation between all altmetric indicators and received citations of highly cited articles (p-value = 0.0001).

The results of this study can help researchers, editors and editorial boards of journals better understand the importance and benefits of using social media and tools to publish articles.

Altmetrics is a relatively new field, and in particular, there are not many studies related to the presence of articles in various social media until now. Accordingly, in this study, a comprehensive altmetric analysis was carried out on 1000 most-cited articles of one of the world's most reliable journals.

This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques.

The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed.

The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty.

The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users.

The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.