Bioinformatics and IT Infrastructure
Data science has become omnipresent in the natural sciences: not only does it feature in the analysis of genomic, proteomic and metabolomic data, but also has importance in the context of environmental monitoring, where imaging technology is making an increasingly significant contribution.
To meet this demand, IPK has put in place a comprehensive, powerful and expanding IT infrastructure, which includes a strong internal network well connected to the German National Research and Education Network (DFN), adequate data storage capability, computational resources powerful enough to analyse large data sets, a centralised archiving facility, a range of database, a central laboratory information management system and a substantial number of web servers.
Several web-based databases are accessible to users through our website, as are various analytical tools. The goal of transforming IPK into a bio digital Resource Centre is being addressed by an ongoing effort to develop these databases and tools.
One focus of the bioinformatics and information technology activities is the transformation of the IPK into a bio-digital resource center. In order to achieve this, the databases and "tools" offered are constantly being further developed. In addition, information and research results are to be curated, made uniquely identifiable and links implemented in a data stewardship process.
In order to keep the generated research results permanently, structured and freely accessible, participation in national and international initiatives is also essential. Here, the IPK actively participates in initiatives such as the German Network for Bioinformatics Infrastructure "de.NBI", the European Infrastructure Network for the Life Sciences "ELIXIR" and the consortia of the National Research Data Infrastructures (NFDI) FAIRagro and NFDI4Biodiversity. Furthermore, processes and infrastructure services are aligned with international standards, such as the DataCite consortium as well as in the GO-FAIR consortium. FAIR (stands for keeping scientific data findable, accessible, interoperable and reusable). The main goal of the FAIR Data Principles is the optimal preparation of research data for researchers around the world and for machine analysis.
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