About this Research Topic
Neuroscience research generates vast amounts of data that require advanced computing resources for data storage, management, analysis, and simulation. Efficiently using high-performance compute architectures and processing these massive data sets pose significant challenges that require the development of new computational methods and algorithms. The use of advanced computational methods and algorithms is crucial in addressing these challenges.
The goal of this Research Topic is to explore current advances and promising directions for large-scale computing in neuroscience research. This Research Topic aims to provide a comprehensive overview of the current challenges in large-scale computing in neuroscience research and provide future directions to address those challenges.
We welcome contributions that address the key challenges in large-scale computing in neuroscience. Specific themes include:
(i) Interdisciplinary strategies and collaborations to address issues related to data sharing, integration, and analysis
(ii) Security and privacy challenges, such as ethical issues, regulation, and government policies, and the potential for harmful or accidental data breaches
(iii) Reproducibility and transparency concerns in large scale computing, including data sharing, standards, and best practices for data collection, analysis, and archival
(iv) Novel strategies and algorithms to exploit large-scale computing architectures and cloud technologies in neuroscience, e.g., for simulation, analysis, and data presentation
All the manuscripts studying the aspects of Large Scale Data Analysis in Neuroscience should be submitted to Emerging Trends in Large-Scale Data Analysis for Neuroscience Research.
We encourage authors to present original research articles, reviews, opinion pieces, perspectives, and innovative studies in this field.
Overall, this Research Topic aims to foster a deeper understanding of the challenges and opportunities in large-scale computing in neuroscience and to highlight new pathways for advanced research in this critical field.
The goal of this Research Topic is to explore current advances and promising directions for large-scale computing in neuroscience research. This Research Topic aims to provide a comprehensive overview of the current challenges in large-scale computing in neuroscience research and provide future directions to address those challenges.
We welcome contributions that address the key challenges in large-scale computing in neuroscience. Specific themes include:
(i) Interdisciplinary strategies and collaborations to address issues related to data sharing, integration, and analysis
(ii) Security and privacy challenges, such as ethical issues, regulation, and government policies, and the potential for harmful or accidental data breaches
(iii) Reproducibility and transparency concerns in large scale computing, including data sharing, standards, and best practices for data collection, analysis, and archival
(iv) Novel strategies and algorithms to exploit large-scale computing architectures and cloud technologies in neuroscience, e.g., for simulation, analysis, and data presentation
All the manuscripts studying the aspects of Large Scale Data Analysis in Neuroscience should be submitted to Emerging Trends in Large-Scale Data Analysis for Neuroscience Research.
We encourage authors to present original research articles, reviews, opinion pieces, perspectives, and innovative studies in this field.
Overall, this Research Topic aims to foster a deeper understanding of the challenges and opportunities in large-scale computing in neuroscience and to highlight new pathways for advanced research in this critical field.
Keywords: computing challenges, Data storage, neuroinformatics, Large-scale computing, computational methods
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
