Integrating data and analysis technologies within leading environmental research infrastructures: Challenges and approaches

When researchers analyze data, it typically requires significant effort in data preparation to make the analysis ready. This often involves cleaning, pre-processing, harmonizing, or integrating from one multiple sources and placing them into a computational environment form suitable for analysis. Research infrastructures their repositories host available researchers, but rarely offer Published are persistently identified, such identifiers resolve onto landing pages that must be (manually) navigated identify how accessed. navigation is challenging impossible machines. paper surveys existing approaches improving environmental access facilitate more rapid analyses environments, thus contribute seamless integration of By analysing current state-of-the-art solutions being implemented by world?leading research infrastructures, we highlight practices interface with environments challenges moving forward. We found while level standardization has improved during recent years, still machines discover based on persistent identifiers. problematic regard emerging requirements FAIR (Findable, Accessible, Interoperable, Reusable) general, analysis, particular. There number promising would improve state-of-the-art. A key approach presented here software libraries streamline reading metadata environments. describe this detail two infrastructures. argue development maintenance specialized each RI range programming languages used does not scale well. Based observation, propose set established standards web that, if will enable language independent much reduced required library implementation as well considerably lower learning users. To catalyse advancement, roadmap action points technology harmonization among RIs build foundation efficient effective