GigaScience 
Journal Title
- GigaScience
ISSN
- E 2047-217X | 2047-217X
Publisher
- BioMed Central
- Springer Nature
Listed on(Coverage)
| JCR | 2015-2019 |
|---|---|
| SJR | 2013-2019 |
| CiteScore | 2013-2019 |
| SCIE | 2016-2021 |
| CC | 2016-2021 |
| SCOPUS | 2017-2020 |
| MEDLINE | 2016-2021 |
| DOAJ | 2017-2021 |
| EMBASE | 2016-2020 |
OA Info.
| OA |
based on the information
|
|---|---|
| Keywords | open data, big data, life sciences, biomedicine |
| Review Process | Open peer review |
| Journal info. pages | |
| Licences | CC BY |
| Copyrights | No |
| DOAJ Coverage |
|
| Subject(s) | Medicine: Medicine (General): Computer applications to medicine. Medical informatics |
Active
- Active
based on the information
- SCOPUS:2020-10
Country
- ENGLAND
Aime & Scopes
- GigaScience aims to revolutionize publishing by promoting reproducibility of analyses and data dissemination, organization, understanding, and use. As an open access and open-data journal, we publish ALL research objects (data, software tools and workflows) from 'big data' studies across the entire spectrum of life and biomedical sciences. These resources are managed using the FAIR Principles for scientific data management and stewardship that state that research data should be Findable, Accessible, Interoperable and Reusable. To achieve our goals, the journal has a novel publication format: one that links standard manuscript publication with an extensive database that hosts all associated data and provides data analysis tools and cloud-computing resources. GigaDB provides a direct link between the published manuscript and the relevant supporting data. We have also built GigaGalaxy, a Galaxy-based data analysis platform to host computational methods and workflows, maximizing use of the data, tools and workflows in our papers in a more accessible and reproducible environment. Our scope covers not just 'omic' type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.