The huge volume of neuroscience data and the wide variety of data formats generated across different neuroscience communities has posed a challenge to traditional methods of data management, data sharing and data mining. Mandates on data sharing and the demand for using open data has driven the development of advanced methodologies and tools to effectively explore, mine and integrate data. However, the growing number of resources make it harder for researchers to navigate this landscape. Awareness of these tools and resources is vital for effective data mining and unlocking new discoveries. The goal of this research collection is to provide an overview of available resources, centred around making data findable, accessible, interoperable and reusable (FAIR).
The current "Research Topic" thus welcomes articles on topics that play a key role in accelerating data sharing, and facilitate the findability and reusability of Open Data. This topic will cover a broad range of neuroscience research areas, including experimental, computational, and clinical neuroscience research.
Topics may include (but are not limited to):
-Online neuroscience repositories: benefit/disadvantages, comparisons;
-Data management tools;
-Data and metadata standards and structures;
-Developments in metadata annotation and ontology development;
-Curation, automation and analysis tools/workflows for sharing or reusing open data;
-Opportunities and challenges in data mining.
The articles well suited to this topic include original research articles, reviews and perspectives.
The huge volume of neuroscience data and the wide variety of data formats generated across different neuroscience communities has posed a challenge to traditional methods of data management, data sharing and data mining. Mandates on data sharing and the demand for using open data has driven the development of advanced methodologies and tools to effectively explore, mine and integrate data. However, the growing number of resources make it harder for researchers to navigate this landscape. Awareness of these tools and resources is vital for effective data mining and unlocking new discoveries. The goal of this research collection is to provide an overview of available resources, centred around making data findable, accessible, interoperable and reusable (FAIR).
The current "Research Topic" thus welcomes articles on topics that play a key role in accelerating data sharing, and facilitate the findability and reusability of Open Data. This topic will cover a broad range of neuroscience research areas, including experimental, computational, and clinical neuroscience research.
Topics may include (but are not limited to):
-Online neuroscience repositories: benefit/disadvantages, comparisons;
-Data management tools;
-Data and metadata standards and structures;
-Developments in metadata annotation and ontology development;
-Curation, automation and analysis tools/workflows for sharing or reusing open data;
-Opportunities and challenges in data mining.
The articles well suited to this topic include original research articles, reviews and perspectives.