Journal of Data and Information Science

Scope & Guideline

Transforming data into actionable knowledge for all.

Introduction

Delve into the academic richness of Journal of Data and Information Science with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN2096-157x
PublisherSCIENDO
Support Open AccessNo
CountryPoland
TypeJournal
Convergefrom 2016 to 2024
AbbreviationJ DATA INFO SCI / J. Data Info. Sci.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressBOGUMILA ZUGA 32A, WARSAW, MAZOVIA 01-811, POLAND

Aims and Scopes

The Journal of Data and Information Science focuses on the intersection of data science and information science, emphasizing the analysis, management, and application of data across various domains. Its core aims include promoting innovative methodologies, advancing bibliometric studies, and exploring the implications of data in research and policy.
  1. Data Analysis and Methodology Development:
    The journal publishes research that develops new methods for data analysis, including machine learning, bibliometric analysis, and quantitative assessment techniques.
  2. Interdisciplinary Research and Collaboration:
    It emphasizes the importance of interdisciplinary approaches and collaborations, exploring how different fields can integrate data science methods for enhanced research outcomes.
  3. Impact Measurement and Research Evaluation:
    A key focus is on evaluating the impact of research through various metrics and indicators, including citation analysis and the exploration of journal impact factors.
  4. Research Integrity and Ethics:
    The journal addresses issues of research integrity, publication ethics, and the implications of open access publishing, fostering discussions on ethical practices in scientific communication.
  5. Emerging Technologies and Their Implications:
    Topics related to emerging technologies, such as artificial intelligence and large language models, are explored for their impact on scientific research and communication.
The Journal of Data and Information Science has identified several emerging themes that reflect current trends in data science and information research. These trends highlight the journal's responsiveness to contemporary challenges and innovations in the field.
  1. Application of AI and Machine Learning in Research:
    Recent publications show a growing interest in utilizing artificial intelligence and machine learning techniques for various applications in research, including automated analysis and data processing.
  2. Data and Information Ethics:
    There is an increasing focus on ethical considerations surrounding data usage, including privacy concerns, responsible data sharing, and the implications of AI in research practices.
  3. Quantitative Assessment of Research Impact:
    Emerging themes include sophisticated quantitative methods for assessing research impact, such as ego citation networks and new metrics for evaluating academic performance.
  4. Interdisciplinary Approaches to Problem Solving:
    The journal is increasingly showcasing studies that employ interdisciplinary methods to tackle complex scientific problems, reflecting the necessity for collaborative research in addressing global challenges.
  5. Emerging Research Trends in Technology and Policy:
    Papers analyzing the intersection of technology, policy, and research trends, particularly in areas like artificial intelligence and transformative research, are gaining traction, signaling a shift towards understanding the broader implications of scientific advancements.

Declining or Waning

While the Journal of Data and Information Science has maintained a diverse range of topics, certain themes have shown a declining focus in recent years. These waning scopes reflect shifts in research priorities and the evolving nature of data and information science.
  1. Traditional Bibliometric Analysis:
    There has been a noticeable decrease in publications focused solely on traditional bibliometric analysis without integration of modern methodologies or interdisciplinary approaches.
  2. General Discussions on Open Access:
    While discussions on open access remain relevant, the frequency of papers solely dedicated to its merits or challenges has diminished, as the conversation has shifted towards more nuanced discussions on the implications of open access in specific contexts.
  3. Single-Discipline Studies:
    Research that focuses exclusively on a single discipline without interdisciplinary connections has become less prominent, as the journal encourages broader, cross-disciplinary studies.
  4. Historical Analysis of Research Trends:
    Papers that provide purely historical perspectives on research trends without integrating current data analysis techniques or predictive modeling have seen a decline.

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