APro, a process-based total system performance assessment (TSPA) tool for a geological disposal system, has a framework for simulating the radionuclide transport affected by thermal, hydraulic, mechanical or geochemical changes occurred in the disposal system. APro aims to be applied for the TSPA to long-term (> 100,000) evolution scenarios in real-world repository having more than 10,000 boreholes. In this large-scale TSPA, it is important not only to develop a high-performance numerical approach, but also to apply an efficient post-processing approach to massive spatiotemporal data. The post-processing refers to validating numerical analysis results, analyzing and evaluating target systems through data processing or visualization. Since APro uses COMSOL interface, the postprocessing function in COMSOL can be used. However, when the data size increases due to largescale numerical analysis, the time for the COMSOL post-processing increases, resulting in a problem that the analysis and evaluation are not performed effectively. In this case, it is possible to extract necessary data using the COMSOL exporting function and importing it into an external postprocessing program for the analysis and evaluation. In this study, the efficiency of external post-processing with extracted data from COMSOL was reviewed. And, we derived a proper data extraction approach (format and structure) that can increase efficiency of external post-processing.

Collection of ecological data such as the temporo-spatial distribution of a species is very difficult, due to broad distribution over large areas, phenology, and lack of resources for field survey. Citizen science, which is a cooperative scientific endeavor between researchers and interested citizens, is ideal for collecting large-scale ecological data. However, lack of proper equipment, species identification, and/or communication between researchers and participants are hindrance for a successful citizen science project. Here, we introduce the concept and methods of large-scale ecological data collection using smartphone apps. Most of the ecological data typically consist of sound or video recording, picture, geographic coordinate, and notes. There are several apps that can collect some or all of these ecological data. Furthermore, the result of a survey can be reported to researchers using Google Docs. The data collected by non-specialists can be validated by cross-checking of the survey report by Google Docs and the ecological data sent by apps. Finally, we report the results of a citizen science project in which temporo-spatial distributions of cicada species in Korea were studied via smartphone apps and Google Docs.