mozhgan oroji

Families, as crucial social institutions, play a vital role in providing emotional and physical support to their members and teaching life skills. The rapid evolution of communication technologies and media has significantly influenced this institution, leading to major changes within it. This research aims to categorize issues and offer suggestions for addressing gaps in research or media policy-making. The research question is: How have studies in the field of family and the media been classified, and what topics have been discussed under each classification? The theoretical framework of this article is based on Dependency Theory, Manuel Castells' Theory, and Cultivation Theory, with data analysis conducted using meta-analysis and thematic analysis methods. In this study, after reviewing 93 extracted articles on the subject of media and family, and analyzing descriptive statistics and themes in areas such as social networks, television, and cinema, the strengths and weaknesses of the research were identified. The results indicate that the effects of modernity and media on the family institution in Iran have been profound and multifaceted. These value changes include the removal of traditions, weakening of kinship relations, empowerment of women, increased importance of individualism, and changes in lifestyles. These transformations represent a period of cultural and social transition, emphasizing the need to focus on the Iranian-Islamic lifestyle, the identity of men and women, strengthening family cohesion and relationships, media literacy, and attention to the role and position of children and adolescents within the family.

Introduction There are many data collections in decision-making and every day a large number of these data are collected in research projects by humans or by devices and in this data, to better understand the issues related to data, we need to first understand the data and the literacy related to them. Data literacy is defined as information by reading, creating and communicating with data: that we can find data, make information about it, learn the tools to work with data, have less management of it. We can have, analyze and refine data, learn to share data and make simple decisions. Research data management includes; production, access, tools, storage and reuse of research data with sufficient and easy-to-use help in virtual research infrastructures that form the main part of the monitoring cycle, which itself includes ideation. It is to create or receive, evaluate, select, ingest, preserve, store, access, reuse (Cox and Verban, 2014).Studies on research data management are now common, while there is a global ease of research data, but it continues to be difficult to keep data easily accessible. Session, we know more than yesterday about the role of research data in the design and implementation of new research, but the trends and infrastructure to support researchers in research data management, need. (Varana, 2024).Considering the research that has been conducted on research data management literacy, the aim of this study is to determine the components and indicators of management literacy. ) and to provide a suitable model for research data management literacy.

Methods and Materoal:

The present study was conducted with a quantitative and survey method and aimed at evaluating and validating the tool built using the proposed research model. The statistical population of the National Institute of Higher Education Research and Planning was 112 academic centers affiliated with the Ministry of Science and the total number of faculty members of the humanities and social sciences of the country's public universities was 8,441. Due to the large volume of data, 360 people were selected using cluster sampling. Then, the questionnaire was completed and descriptive statistical methods (mean, deviation indices, frequency table) and inferential statistics (structural equation modeling and exploratory factor analysis) and SPSS and Smart Pls software were used to analyze the data.

The findings indicate that the six factors of stakeholders, services, policy, types of literacy, data cycle, and financial issues are critical together, explaining 60 percent of the total variance of changes. Also, the highest level of the level is related to the stakeholders factor with a mean of 4.09 and a standard deviation of 0.57, followed by the factors of services, policy, data life cycle, types of literacy, and financial issues, respectively. Using the Pearson correlation coefficient test, it was shown that all components of research data management literacy have a positive and significant correlation with the set at the 0.01 error level. The coefficients of the factor loadings of the subscales of research data management literacy also have a good understanding of the concept of their analysis and have a strong and significant correlation with their belief.

Research data management contributes to scientific integrity at different levels. When research data management literacy is sufficient, research data are accurate, complete, valid, and reliable. The risk of losing or damaging data, as well as the risk of unauthorized access, is minimized. In addition, research data can be shared with others with minimal effort and individuals can easily confirm the results. The relationships between the components and indicators of research data management literacy from the perspective of faculty members in the humanities and social sciences of Iranian public universities show that higher than any of these components in improving the quality and efficiency of research, research data management literacy has a positive effect. The search for understanding the methods and infrastructures related to data management is a research for individuals to achieve better research results and valuable results. The results of this study show that different levels of research data management literacy among university professors know, and also need to have literacy skills in research data management that they do and create. Collecting, processing, validating, publishing, sharing, and archiving data are involved, and this is a characteristic of good research data management.

 Research data management is a set of activities related to the storage, access, and preservation of generated data which includes benefits such as maintaining the integrity and replication of research, increasing data security leading to risk of data loss, and enhancing the implementation of quality research. Acquiring knowledge and skills related to research data management creates many job opportunities for information professionals and helps researchers to fulfill their demands. In this regard, this research aimed to identify the state of research in the field of research data of management literacy.

 The current research was a systematic review with qualitative approach, which is devoted to collecting the studies conducted on a specific subject, examined and revised the resulting information according to certain systematic principles, and presented the results for analysis. More concretely a comprehensive view of related studies and providing researchers with a specific subject area for this purpose, researches related to several stages of searching in international and domestic databases, considering the time range, foreign books and articles between 2012 and 2022 AD and Persian between 1391 and 1401 AD. Fifty works related to the topic of research data of management literacy were searched from research articles of publications, conferences, and dissertations.

 Research data management is one of the emerging branches of science and the main goal of this new field is to help researchers, professors, and students in planning from start to finish. A review of the studies showed that 50 domestic and foreign researchers have addressed the issue of research data of management literacy and related elements. Research has been in 5 main axes, which include: 1. Research data management infrastructure, 2. Research data sharing, 3. Data literacy and research data management literacy, 4. Research data management training. Research, and 5. Research management skills in this field.

 The results of this research showed that research in the field of research data segmentation has been investigated more than other related topics, but the research related to research data management literacy is very limited, especially in Iran. Increasing the level of research data of management literacy requires a lot of effort from universities and higher education institutions. In addition, according to the research conducted outside of Iran, business skills related to research data of management literacy will help people to work harder in advancing the goals of this valuable topic and start to transform the management mechanism. Having their data, they also benefit from teaching and improving students' literacy from the beginning of their studies in the curriculum of different levels.

The present research identifies the components and indicators of research data management literacy. In this research, a qualitative approach has been used using the meta-composite method and in the next step, the Delphi method. After preparing a comprehensive list of available documents in the field of research data management literacy, relevant keywords were searched in scientific databases inside and outside of Iran, and then the components of research data management were identified. The second stage was refined and validated using a researcher-made questionnaire in two rounds of Delphi among Delphi panel members. Six main categories were found in the process of identifying the components of research data management: 1- beneficiaries, 2- services, 3- politics, 4- types of literacy, 5- data, 6- budget and after implementation. The opinion of experts added 2 other categories: 1- types of participation and 2- monitoring. Also, 10 new concepts were identified, which were approved by experts. The identified codes were also 50 codes related to research data management literacy. The results showed that the six main identified categories explain 60% of the total variance of the variables. Each of which includes related concepts and codes and appropriate criteria for measurement. It is important to know how practical the development, acceptance and application of these categories can be in conducting research data management methods.