Data

Scope & Guideline

Unlocking insights through open-access data exploration.

Introduction

Delve into the academic richness of Data 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
ISSN-
PublisherMDPI
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationDATA-BASEL / Data
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND

Aims and Scopes

The journal 'Data' is dedicated to the dissemination of high-quality datasets and associated methodologies across various fields, emphasizing the importance of data in scientific research and innovation. It serves as a platform for researchers to share datasets that enable reproducibility, foster collaboration, and enhance the understanding of complex phenomena.
  1. Data Collection and Sharing:
    The journal focuses on the systematic collection and sharing of diverse datasets, including but not limited to, environmental data, genomic sequences, and social media analytics. This promotes transparency and replicability in research.
  2. Methodological Innovation:
    It emphasizes the development and application of innovative methodologies for data analysis, including machine learning, statistical modeling, and data mining techniques, thereby enhancing the analytical capabilities of researchers.
  3. Interdisciplinary Applications:
    The journal covers interdisciplinary research applications, showcasing datasets that span multiple fields such as biology, environmental science, social science, and engineering, thereby broadening the impact of data-driven research.
  4. Open Data and Accessibility:
    Promoting open data principles, the journal encourages the publication of datasets that are accessible to the research community and the public, facilitating collaborative research and knowledge exchange.
  5. Data Quality and Standards:
    It addresses issues related to data quality, standardization, and validation, ensuring that published datasets meet rigorous scientific standards and can be reliably used by researchers.
The journal 'Data' has demonstrated a dynamic evolution in its thematic focus, with several emerging themes gaining traction in recent publications. These trends indicate the journal's responsiveness to the evolving landscape of data science and research needs.
  1. Machine Learning and Artificial Intelligence:
    There is a significant increase in datasets and studies utilizing machine learning and artificial intelligence techniques. This trend highlights the growing importance of these technologies in data analysis and predictive modeling across various domains.
  2. Environmental and Climate Data:
    Research related to environmental and climate data is on the rise, reflecting global concerns about climate change and the need for data-driven solutions to environmental challenges.
  3. Health Data and Bioinformatics:
    The journal is seeing a surge in health-related datasets, particularly those related to genomics, public health, and the impacts of diseases such as COVID-19, showcasing the critical role of data in health research.
  4. Multimodal Datasets:
    There is an emerging focus on multimodal datasets that integrate various types of data (e.g., audio, visual, and text), reflecting the complexity of real-world problems and the need for comprehensive analytical approaches.
  5. Data Privacy and Ethics:
    As data usage expands, there is a growing emphasis on datasets that address data privacy and ethical considerations, particularly in sensitive areas like health and social media.

Declining or Waning

While 'Data' has broadened its focus in several areas, certain themes have shown a decline in prominence over recent years. This may reflect shifts in research priorities or the maturation of specific fields.
  1. Traditional Statistical Analysis:
    There has been a noticeable decrease in papers solely focused on conventional statistical methods without incorporating modern data analytics techniques. This shift suggests a growing preference for advanced methodologies like machine learning and artificial intelligence.
  2. Small Scale Local Datasets:
    Research centered around small-scale or localized datasets is becoming less frequent, possibly due to the increasing emphasis on large-scale, global datasets that can provide broader insights and applicability.
  3. Descriptive Studies:
    There is a waning interest in purely descriptive studies that do not leverage advanced analytical techniques. Researchers are increasingly expected to provide predictive or inferential insights from their data.
  4. Single-Discipline Focus:
    The journal has seen a decline in datasets that focus exclusively on a single discipline, as interdisciplinary approaches are gaining more traction, reflecting the need for comprehensive understanding across various fields.

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