2021, Huber, Robert, D'Onofrio, Claudio, Devaraju, Anusuriya, Klump, Jens, Loescher, Henry W., Kindermann, Stephan, Guru, Siddeswara, Grant, Mark, Morris, Beryl, Wyborn, Lesley, Evans, Ben, Goldfarb, Doron, Genazzio, Melissa A., Ren, Xiaoli, Magagna, Barbara, Thiemann, Hannes, Stocker, Markus

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

2022, Bettoni, Manuele, Maerker, Michael, Sacchi, Roberto, Bosino, Alberto, Conedera, Marco, Simoncelli, Laura, Vogel, Sebastian

Landscape sensitivity is a concept referring to the likelihood that changes in land use may affect in an irreversible way physical and chemical soil properties of the concerned landscape. The objective of this study is to quantitatively assess the sensitivity of the southern Alpine soil landscape regarding land use change-induced perturbations. Alpine soil landscapes can be considered as particularly sensitive to land use changes because their effects tend to be enhanced by frequent extreme climatic and topographic conditions as well as intense geomorphologic activity. In detail, the following soil key properties for soil vulnerability were analysed: (i) soil texture, (ii) bulk density, (iii) soil organic carbon (SOC), (iv) saturated hydraulic conductivity (Ksat), (v) aggregate stability and (vi) soil water repellency (SWR). The study area is characterized by a steep, east-west oriented valley, strongly anthropized in the last centuries followed by a progressive abandonment. This area is particularly suitable due to constant lithological conditions, extreme topographic and climatic conditions as well as historic land use changes. The analysis of land use change effects on soil properties were performed through a linear mixed model approach due to the nested structure of the data. Our results show a generally high stability of the assessed soils in terms of aggregate stability and noteworthy thick soils. The former is remarkable, since aggregate stability, which is commonly used for detecting land use-induced changes in soil erosion susceptibility, was always comparably high irrespective of land use. The stability of the soils is mainly related to a high amount of soil organic matter favouring the formation of stable soil aggregates, decreasing soil erodibility and hence, reducing soil loss by erosion. However, the most sensitive soil property to land use change was SWR that is partly influenced by the amount of soil organic carbon and probably by the quality and composition of SOM.