Due to international commitments to reduce emissions in the shipping sector, new fuels and drivetrains are being explored. However, the potential role of batteries is often overlooked in strategic studies. We fill this gap through a broad literature study of grey and academic literature, complemented with three deep dives into Systems Engineering, Sustainable Business Models, and Transition approaches. Battery electric systems are currently the most frequently applied among alternative fuel-drivetrains, although they account for a low percentage of energy use. They are the preferred technology for zero-emission vessels. However, they mostly find application in small to medium hybrid vessels and support functions. Key drivers are regulation and policies, the increase in energy density, and the decrease in costs. Sectoral barriers include infrastructure, the capital intensity of vessels, cost-driven performance, weak governance, and international operations. Challenges for battery applications include integration in a ship's energy system, battery safety, charging, decision support on feasible applications, and establishing viable renewables-based port energy communities that integrate services to and from battery systems on berthing ships. Due to their diversity, versatility, and current application, batteries are likely to become more broadly applied on small to medium-sized vessels, and as enabling technology in hybrid applications and support functions. They thereby have the potential to influence the transition in the sector. Considering the diversity in batteries, shipping segments, and contexts, this will result in many small steps forward. Fast development requires strong policy support. Inherent uncertainty regarding fuels and drivetrains is best countered by robust decision-making in the sector, and can include battery usage.

Management of large hydrologic datasets including storage, structuring, indexing and query is one of the crucial challenges in the era of big data. This research originates from a specific data query problem: time series extraction at specific locations takes a long time when a large multidimensional dataset is stored in non-chunked NetCDF classic or 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file based solutions and a multidimensional (MD) array database management system (DBMS) applying chunked storage structure are benchmarked to determine the best solution for storing and querying large hydrologic datasets. To achieve this, expert consultancy was conducted to establish benchmark sets. To guarantee a fair benchmark test environment, HydroNET-4 system was utilized and adapters for NetCDF files and SciDB were developed to manage and query data. In final benchmark tests, effect of data storage configurations such as chunk size and compression on query performance is also explored. Results indicate that SciDB arrays utilizing small chunk sizes show favorable performance. However with current implementation of SciDB, large numbers of small chunks cause huge overload of main memory which constraints SciDB's scalability. Compression of SciDB can either have negative or no effect on query performance, while it causes significant query degradation to NetCDF-4 solution. The research illustrates that for big hydrologic array data management, the properly chunked NetCDF-4 solution without compression is in general more efficient than the SciDB DBMS. So under current big data environment, traditionally adopted file-based hydroinformatic solutions can still be applicable after proper updating.