Data Management Plan
(odnośnik otworzy się w nowym oknie)
The data management plan aims to guarantee that research data and results adhere to the FAIR principles and are managed efficiently. Through the adoption of the strategies outlined below, the Data Management Plan for DEEPDIVERSE seeks to advance scientific knowledge by managing efficiently and securely the generated data. The data management plan for DEEPDIVERSE seeks to foster collaboration, maximize resource utilization, uphold data transparency, quality, and reproducibility, and mitigate potential risks.To enhance collaboration and promote efficiency, DEEPDIVERSE includes the use of online collaboration platforms, such as Slack or Microsoft Teams, to facilitate real-time communication, file sharing, and collaboration among project team members and external collaborators.Data transparency and reproducibility are allowed by generating public virtual protocols, and pipelines and making them accessible. Data including taxonomy, ecology, biology, geographical distribution, images, genomic data, etc., will be made available by depositing them in public databases: Muséum national d’histoire naturelle (MNHN) invertebrate collection database for all metadata associated with studied physical specimens (georeferences, natural history, habitat, etc); GBIF for geographic distribution; EMBL, BOLD, and NCBI, for newly generated genomic and barcode data; GigaScience and MorphoBank databases for all 3D models of μCT data and morphological data; WoRMS and Zoobank will include updates in taxonomy. Project findings will be published in open-access journals or preprint repositories to ensure unrestricted and free access to research outputs.All planning sampling will adhere to regulations and local laws, ensuring compliance with the Nagoya Protocol. This ensures ethical and legal standards are met in collecting materials.
