Abstract

Big data launches a modern way of producing science and research around the world. Due to an explosion of data available in scientific databases, combined with recent advances in information technology, the researcher has at his disposal new methods and technologies that facilitate sci- entific development. Considering the challenges of producing science in a dynamic and complex scenario, the main objective of this article is to present a method aligned with tools recently devel- oped to support scientific production, based on steps and technologies that will help researchers to materialize their objectives efficiently and effectively. Applying this method, the researcher can apply science mapping and bibliometric techniques with agility, taking advantage of an easy-to- use solution with cloud computing capabilities. From the application of the “Scientific Mapping Process ”, the researcher will be able to generate strategic information for a result-oriented sci- entific production, assertively going through the main steps of research and boosting scientific discovery in the most diverse fields of investigation. 

Introduction

Scientific discoveries take place in a dynamic, ongoing, complex environment, with significant changes, marked by a constant overcoming of research topics. These challenges require new skills and competencies, so researchers must apply a method and technology to achieve their research goals. Also, there is a considerable increase in scientific publications in various areas of knowledge, so researchers’ choices need to be based on the analysis and interpretation of large amounts of data.

The “big data” context opens opportunities for the integration of new data analysis tools and methods to boost researchers´ productivity with speed, efficiency, and accuracy in their scientific production, expanding the science horizons. (Carrera-Rivera et al., 2022; Kwabena et al., 2023; Mengist et al., 2020; Pessin et al., 2022; Westphaln et al., 2021). It is rare for a researcher to start their studies without first applying bibliometric and scientometrics techniques to gain insights into the science landscape, identify knowledge gaps, and more accurately uncover the “state-of-the-art ” in a particular research field. (Chen et al., 2018; Favaretto et al., 2019; Sahoo, 2021; Sheng et al., 2019; Trieu, 2017).

Typically, the most well-known methods for scientific production recommend a preliminary analysis of the data available on official scientific portals, generating bibliometric information to guide the selection of theoretical repertoire. (Alcaide–Muñoz et al., 2017a, 2017b; Donthu et al., 2021; Pessin et al., 2022; Rodríguez-Bolívar et al., 2018; Seddon et al., 2017).

The data available on scientific portals are valuable inputs for generating strategic information related to the global scientific production landscape, which opens space for the development of new methods that bring together a management process of activities and technology applications to drive scientific advancement (Kolajo et al., 2019). Therefore, it is highly recommended that the researcher bases the scientific process on a consistent, managed method, starting from a series of well-directed, organized steps. A well-applied method has a high potential to guide scientific writing efficiently, at various steps of execution. It is also crucial for the researcher to choose a quality theoretical reference with good judgment from the beginning of their work, being aligned with the main discussions of the scientific community regarding a particular study object. In other words, it is highly advisable to choose a quality bibliographic repertoire from the beginning of the research, which increases the probability of publication success (Alcaide–Muñoz et al., 2017b; Donthu et al., 2021; Hallinger, 2020; Pallottino et al., 2018). 

According to the literature, Science Mapping (SM) and bibliometric analysis methods can be performed by combining different tools to analyze the evolution of the cognitive structure of a certain research topic, leading to the discovery of scientific boundaries (Alcaide–Muñoz et al., 2017a). Despite being more consolidated in the field of Medicine/Health (Liang et al., 2021), the use of bibliometrics and SM is spreading to various areas, as mapping science is complex and difficult to manage, involving many steps and often requiring numerous bibliometric tools, not always free (Aria and Cuccurullo, 2017).

Over the years, these bibliometric tools have evolved along with information systems, either providing SM analyses with a focus on visualizations (Aria and Cuccurullo, 2017; Hallinger and Kovačević, 2019) or providing bibliometric analyses based on classification and statistical measures (Carrera-Rivera et al., 2022; de Campos et al., 2018; Dervis, 2019). Today, the possibility of incorporating artificial intelligence (AI), with machine learning and natural language processing (NLP) resources into these processes is being discussed (Kwabena et al., 2023; Pessin et al., 2022; Weißer et al., 2020).

The first discussions on the mapping of scientific literature show the importance of methodological applications and systems that can perform bibliometric analyses facilitating the work of the researcher and promoting accuracy in scientific writing (Ahlgren and Jarneving, 2008; Chavalarias and Cointet, 2008; Noyons et al., 1999; Small, 1997; Soós, 2011; Soós and Kampis, 2012). These methodologies and systems have evolved significantly since scientific databases began to emerge, opening opportunities for the application of technological solutions with the capacity to process massive amounts of data (Akdur et al., 2018; Frezza et al., 2019).

In the business and academic environment, it is essential that research development must be based on data analysis, using methods and technologies that promote rationality to the scientific production process and that relates the knowledge boundaries under the prism of certain authors and journals around a certain issue, uncovering important nuances of science evolution through scientific data (Aria and Cuccurullo, 2017; Cobo et al., 2014; De Carvalho et al., 2020; Donthu et al., 2021; Galvão and Ricarte, 2019).

In this bibliographic review task, systematized research is a traditional procedure in the academic context to facilitate scientific writing (Alemdag and Cagiltay, 2018; Bak et al., 2020; Schreiber and Cramer, 2022), being recognized for following a transparent and replicable method of execution, so that it is possible to select the most relevant works and eliminate those that are least relevant for a specific search (Pagani et al., 2015).

Systematic literature reviews (SLR) have significantly contributed to the advancement of knowledge in various fields by examining existing studies with attention to theoretical boundaries, units of analysis, data sources, study contexts and definitions, and operationalization of constructs, as well as research methods, with the aim of refining or revising an existing theory (Durach et al., 2017). In other words, scientific production goes through a series of systematic steps and presupposes SM and bibliometric systems to assist the researcher in their decisions about which theoretical framework to use, giving rise to various methods (Carrera-Rivera et al., 2022; Kwabena et al., 2023; Lo et al., 2020; Mdingi and Ho, 2021; Mengist et al., 2020; Quan-Hoang et al., 2020; Scheuer et al., 2021; Weißer et al., 2020; Westphaln et al., 2021).

It was from these opportunities in the field of scientometrics that a system was developed to automate the processes of SM (visualizations) and bibliometric (statistical measurements) analysis embodied in the system named “Smart Bibliometrics” (Pessin et al., 2022), developed on a Business Intelligence (BI) platform and freely accessible. As a differential, the system has cloud computing resources to embody the selection of the theoretical framework and it is constantly evolving to incorporate new analysis functions in tune with the concept of open science.

Therefore, the main objective of this article is to present a method for scientific production, applying a systematic process, while providing the researcher with an information system that facilitates the attainment of their research objectives. It is expected that this method, called the “Scientific Mapping Process”, brings accuracy to the researcher in their creative process of producing knowledge and contributes to the advancement of the science world wild.

Conclusions

Staying updated has become a challenge for any knowledge professional era, especially in the innovation market and academic environment where the constant surpassing of research topics in a complex discussion environment is observed. Producing relevant scientific material requires a lot of dedication and time, and the systematic methods of SM and bibliometric are valuable to optimize scientific production with a focus on scientific results and publication. As a result of this movement, innovative technologies, and methods arise to support these complex processes, opening fields for new developments to overcome future challenges.

In the business environment, data analysis to support decision-making has been widely applied, as well as in the scientific environment where research is complex and requires total alignment with a well-defined problem. A solid theoretical framework is what provides support for a solid investigation, worthy of defense before the scientific community. The researcher in the knowledge era needs to act intelligently, selecting up-to-date and high-quality bibliographic repertoire to keep up with constant scientific publications.

Specialized journals have become increasingly demanding, so scientific production requires a consistent method based on consistent data analysis. Having a broad overview of the “state-of-the-art” of any topic, at the beginning of the research, is a predominant factor that may affect the decision of a publication to occur.

The scientific contributions of these authors, grouped by topic and target journal, in addition to revealing the discussions of the scientific community, have a high potential to bring clarity to the expectations of the various publication vehicles about their research field. It provides a logical sequence of the importance of articles based on assertive metrics that investigate the relationship among journals, authors, and research areas. Such analysis can direct the researcher’s work with great assertiveness, converting intelligence into results-oriented writing with scientific innovation and publication.

Therefore, the “Scientific Mapping Process” method presented has the potential to facilitate the researcher’s work, increasing the assertiveness in scientific production, and in a complementary way, contributing to the expansion of knowledge horizons, being the main contribution of this article.

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