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Reliable data sharing and long-term data archiving and reuse are becoming very important in global cooperative research. Concomitantly, many types of global databased research have been conducted on the Long-Term Ecological Research (LTER) network, with the objective of getting it to respond effectively to future changes in ecology, environment, and climate, by monitoring long-term ecological and environmental data. Korean National Long-Term Ecological Research (KNLTER), however, lacks a system for performing data collection, management, curation, and publication, and therefore global cooperative research through global data sharing is difficult in Korea. In this paper, we analyze one of the best practices link models, the TERN network, and the global data-sharing trend in the LTER area. Further, we propose a link model and necessary technologies for KNLTER and suggest a possible future direction for KNLTER.

Objective of the long-term ecological research is to monitor environmental changes due to climate change and track the consequences, to cope with the environmental issues that may arise now and in the future. Long-term ecological research addresses the temporal gap between short-term studies by studying processes on time scales from months to decades. For long-term ecological researches, a system (Cyber Infrastructure) is essential, which can continuously harvest and manage the data about ecosystem changes. Requirements of a long-term ecological research system are very diverse; standardized interface, sharing and data integration, flexibility, standardized vocabulary, and so on. To deal with these issues, countries around the world have been building platforms that can harvest and manage the data about ecosystem changes for long-term ecological researches. This paper presents requirements and technologies for the long-term ecological research platform, and examines current technological trends in order to meet various requirements of the long-term ecological research.

K-ecohub, a pilot repository of national long-term ecological data in Korea, has been developed to solve data fragmentation issues in the KNLTER project and utilize long-term ecological data in an integrated manner. One essential element in the long-term ecological research is data quality assurance because data which do not go through quality control are far less reliable. The paper presents data processing and quality assurance plans in K-ecohub.

We have developed a metadata service for the Belle experiment which provides a mechanism to locate files using descriptive information. However, for the Belle II experiment, we will have 50–60 times more data. This metadata service may have problems with performance, scalability, and durability when employed at Belle II. These issues are compounded when metadata searches are extended to the event-level. Accordingly, we have designed a new metadata scheme for Belle II which significantly reduces disk space and propose a new metadata service which provides good performance and scalability based on AMGA (Arda Metadata catalog for Grid Application). We find that the use of event-level metadata provides an efficient scheme for processing events with many tracks.

The Belle II experiment is expected to produce 50 times more data than the existing Belle experiment. Such huge data production requires not only scalability with respect to the storage service but also scalability regarding the metadata service. There has already been a metadata service at the Belle experiment, but it is not proper for the Belle II experiment because it has scalability problems and it is not intended to be used in a distributed grid environment. To deal with these issues, we designed an advanced metadata service system based on AMGA, which provides efficient and scalable metadata searching. We have built testbed sites to test the correctness, performance and scalability of the advanced metadata service system, and it has been proved to be able to provide efficient metadata searching for the Belle II experiment.

The KNLTER project had contributed to accumulating ecological knowledge on environmental change and to providing a basis for future ecosystem management planning and research on ecosystem preservation strategy. This project collected ecological data for 10 years, but currently researchers hardly take advantage of them because of data fragmentation, absence of quality assurance framework and lack of integrated data repository. To address the problem, we are building a system called K-ecoHub, an integrated repository for the long-term ecological research data in Korea. This paper describes requirements, key features and conceptual design for K-ecoHub.

Environmental factors play an important role in functions and distribution of organisms. Basic objective of the long-term ecological research is to monitor environmental changes due to climate change and track the consequences, to cope with the environmental issues that may arise now and in the future. For long-term ecological researches, a system (Cyber Infrastructure) is essential, which can continuously harvest and manage the data about ecosystem changes. Countries around the world have been building a platform that can harvest and manage the data about ecosystem changes for long-term ecological researches. This paper examines the trend of the long-term ecological research platform technology in order to meet various requirements of the long-term ecological research.

GEANT4-MT is the multi-threaded version of the GEANT4 particle transport code.(1, 2) The key goals for the design of GEANT4-MT have been a) the need to reduce the memory footprint of the multi-threaded application compared to the use of separate jobs and processes; b) to create an easy migration of the existing applications; and c) to use efficiently many threads or cores, by scaling up to tens and potentially hundreds of workers. The first public release of a GEANT4-MT prototype was made in 2011. We report on the revision of GEANT4-MT for inclusion in the production-level release scheduled for end of 2013. This has involved significant re-engineering of the prototype in order to incorporate it into the main GEANT4 development line, and the porting of GEANT4-MT threading code to additional platforms. In order to make the porting of applications as simple as possible, refinements addressed the needs of standalone applications. Further adaptations were created to improve the fit with the frameworks of High Energy Physics (HEP) experiments. We report on performances measurements on Intel Xeon™, AMD Opteron™ the first trials of GEANT4-MT on the Intel Many Integrated Cores (MIC) architecture, in the form of the Xeon Phi™ co-processor.(3) These indicate near-linear scaling through about 200 threads on 60 cores, when holding fixed the number of events per thread.