Human Information Interaction

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Background and Aim: The purpose of this paper is to study, identifying and discuss the foundation and concepts, models and frameworks, dimensions and challenges of research data curation and management in scientific and academic environments.
Method: This article is a review article and library method was used to collect scientific and research texts in this field. In this research, external and internal scientific databases, as well as web resources, were searched with the keywords "data curation", "digital curation", "research data management", "research data curation" and their equivalent in Persian. After removing duplicate sources and unrelated sources, a total of 132 sources were selected and their content evaluated and analyzed.
Results: The analysis of the literature revealed that the curation and management of research data is a emerging area with complex issues and different dimensions which included of a wide range of educational, organizational, cultural, technological, legal, and security issues. Since the 2000s,This area has been at the forefront of governments, funding organizations, universities and has been formed around it a strong research community of researchers, especially in the field of information and knowledge science, archives and information systems. Today, the curation and management of research data is considered as one of the main components of the large-scale science and technology policy and needs government support and legislative and policy-making institutions. Different models and frameworks have been drawn up at various levels of the national, institution or community for understand the dimensions of the curation and management of research data and its implementation. The study of literature also has shown that libraries and librarians have the ability and competence to take on the roles and responsibilities of curation and management of research data at universities and scientific institutes. However, the research dat curation and management in implementation and practice faces with challenges such as privacy, data ownership, intellectual property rights, lack of data sharing by researchers, lack of proper data management infrastructure, lack of awareness and cognition and knowledge of the process of research data curation and management, and so on.
Conclusion:Despite extensive research abroad, this area has not received much attention in Iran. Therefore, this article addressed relatively comprehensive the subject and dimensions of the research data curation and management, and it can be useful for researchers, policy makers of science and technology, librarians for research and implement research data curation and management services. 
Preservation and creat added value for data research throughout its lifecycle in order to reuse it for new purpose of research is an important application of data curation. Several models have been provided by various reserchers and organizations for data curation based on the life cycle approach.Among which Digital Curation Center curation model had important role in identification of data and curation practices as well as others models development.

This paper is an opinion paper based on library method.

This paper addresses the issue of Data Curation and its foundations, models and issues. So, it could be of interest to Information professionals, Archive and data management researchers, academic and scientific and educational organizations’ managers and other data-intensive Environments.

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Background and Aim: E-learning is an important topic  in the educational settings and students are  significant prerequisites of it,  who have an essential role for the acceptance and effective use of e-learning management systems so that knowing their attitudes and mental models is essential for the successful implementation of such a method. Therefore, the aim of this study was to investigate the Adoption Rate of students' mental model with the structure of the learning management system of the University of Tehran using the card sorting method.   
Methodology: Research had qualitative approach with card sorting and interview tools. Usabilitest software, descriptive statistics, distance matrix, and hierarchical clustering were used to analyze the data. Sample consisted of 15 postgraduate students at Tehran  University (second semester of the academic year 2019-2020) that were interacting with the learning management system (Moodle).
Findings: Findings indicate that out of 42 cards examined, the status and classification of 36 cards (85%) in the learning management system were fully consistent with the participants' mental model and only in some cases such as "Help" and" Recent lessons referred" according to their mental model, users expected these sections to be placed in other categories. As well as labeling; 66% of users found the "settings" tag more appropriate than their "preferences" and the function of some, such as "medal management", "medal preferences" was unclear to them. Also, the categories presented in the three sections: "User Profile", "Quick Access" and "My Lessons" were approved by users.
Conclusion: The results show that the degree of adaptation of students' mental model to the structure of the learning management system of the University of Tehran is at a desirable level.

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Purpose: This is an applied research, with the aim of a comparative study of the presence and participation of Iranian and international researchers affiliated with the top scientific centers (Times Ranking 2020), in the Research Gate research network. Altimetric indicators, such as "RG score", "Reads", "number of registrations" and "number of research items" were considered.
Methodology: Survey performed with altimetric approach and analytical method. Sample of top 10 universities in Iran and 10 scientific centers around the world by Times Ranking  performance index of education, research, knowledge transfer, and international perspective done. First, a comparative study of the activities of Iranian researchers with one another, then of international researchers completed separately. Lastly, an analysis of the differences in performance amongst these two groups was performed by "Libre Office Calc" software.
Findings: Among Times top 10 international scientific institutes, the indicators of "number of registrations," "RG-score per member," "number of publications per member" and "reads" the highest rates were observed in researchers at Cambridge University in London, the California research center, and the California research center at Oxford university, respectively. Among the top 10 Universities in Iran, the indicators of "number of registrations," "RPG score per media member," "number of research copies per member" and "reading rate of research copies, "among the researchers with organizational affiliation to the Amir Kabir University of technology, Tehran University of medical sciences, Iran university of medical sciences, Tehran university of medical sciences, the highest amount was observed. The total "average score per member" at the international level was 8.4 and at the Iranian level was 5.1. The "average reads" index for the top 10 universities or research institutes at the international level was 154990.2. The figure was obtained for the top 10 universities in Iran, 22736.1.
Conclusion: Researchers affiliated with top universities in Iran, compared to their international counterparts, have a stronger social interaction in terms of indicators of ResearchGate in activities such as "enquiring," "answering questions" and "suggestion." Although the difference between the number of research items shared internationally is more than 3 times that of Iran, but the average RG score is not seen 3 times that of Iran globally. So, the high RG score is not related to the number of research items on the ResearchGate. Also, comparative study on the presence and activities of researchers affiliated with Times top Universities in Iran and internationally could lead to better future.

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Background and Aim: The meaning of cross-domain recommendation is that instead of dealing with each domain independently, transfer knowledge gained in one domain (source) to another domain (target) and use it. The present article systematically reviews the research in this field in terms of foundations, applications and challenges.
Method: The Prisma guidelines had been used. Search in Persian and English scientific information sources with related keywords were conducted and 98 English language sources were found in the period 2007 to 2021. Applying the initial refinement, inclusion and exclusion criteria by experts, 28 English documents were selected to enter in the systematic review.
Findings: There are four levels of cross-domain recommendations: Attributes, types, items and systems. Machine learning algorithms are used to predict user rating in cross-domain recommendations, and three categories of:  Prediction, ranking, and classification criteria are used to evaluate predictions based on confusion matrix. Cross-domain recommendations can be used to increase the accuracy of recommendations, resolve cold start problems, cross-sell, and improve personalization by transferring knowledge between domains. The most challengeable recommendations of cross-domain is the differences between domains. These differences include the mismatch between the properties of the domains and/or unclear relationships between the domains. In addition, differences in domain size and poor performance of basic algorithms in predicting user rating are other challenges in cross-domain recommendations.
Conclusion: While this subject has been shaped in the last decade, but the keen attention of computer science and information researchers shows its importance. Items level are the main category of cross-domain recommendations. Due to the formation of e-business groups, in the future, cross-domain recommendations at the system level will be given more consideration. Cross-domain recommendations could be used to improve the performance of recommender systems, user modeling in human-computer interaction, and e-commerce.

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Background and Objective: The study aims to develop and validate a model for analyzing the textual feedback of users in social networks in the face of environmental events and actions with emphasis on identifying the factors affecting the presentation of text messages by users in social networks.
Research Methodology: Heuristic mixed method has been used. In the first stage, the meta-combined method was applied with a qualitative basis. In the second stage, to inspect, validate the identified factors and prepare the final research model, the survey method via questionnaire and forming conveyor group was combined. Population consisted of: 1) Selection and analysis of written documents related to the analysis of textual feedback and users' feelings, including 60 articles and works based on valid criteria from among 198 articles and works; 2) Experts in  information technology, sociology, behaviorism, etc., which 15 people were selected, but as a result and limitations of the corona pandemic comments and suggestions were remotely submitted.
Results: Using the seven steps of meta-combination, a conceptual pattern was obtained in six conceptual layers, categories and codes. In each layer, concepts and topics were included, and to end 27 components were identified. For qualitative validation, the obtained model was found based on the opinions of experts in the form of focus groups and the conceptual model was approved by the research experts.
Conclusion: The conceptual model - obtained from the hybrid stages and focus groups – which has been approved and accepted by experts could be used as a basis for future research to guide, and direct the behavior of users in social  networking in order to provide strategies and executive policies for officials and decision makers in relevant organizations and institutions.

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