To optimize space weather research and information services, it is important to establish a comprehensive system that enables us to analyze observation and simulation data in an integrated manner. For this, we recently constructed a new computing environment called the "Space Weather Cloud Computing System" of the National Institute of Information and Communications Technology (NICT). Currently, the Space Weather Cloud contains a high performance computer, a distributed mass storage system using the Grid Data Farm (Gfarm) technology, servers for analysis and visualization of data, a job service based on the RCM (R&D Chain Management) system, servers for Solar-Terrestrial data Analysis, and the Reference System (STARS).

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From 1926 to 1969, a long term solar full disk observation was done by Kyoto University. Daily, Ca II K (393.4 nm) spectroheliographic images and white light images were recorded on photographic plates. In this report, we will give the current status of our project to digitize all these images and to construct a database of these images for public use through the IUGONET system. In addition, we will discuss our perspective on the scientific analysis of the database by taking the solar CaII K brightness as a proxy measure of the solar UV irradiance on the terrestrial upper atmosphere.

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The Earth's surface is under the continuous influence of a variety of natural forces and human induced sources. Strain data are good examples of such disturbed signals. To determine the geodetic strain behavior before and after the 2003 Tokachi-oki earthquake (M8.0), we decomposed the disturbed strain data into trend, air pressure, earth tide, and precipitation responses components. The decomposition of the disturbed strain data and the interpolation of the missing observations are performed very effectively by using state-space modeling and the Kalman filter/smoother. The validity of the data processing is confirmed by the fact that the model derived to fit the strain data matches the GPS data extremely well.
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¹The following contribution was presented at the WDS conference and presents a more complete explanation of the data processing and its implementation. Subsequent to the Conference, the authors completed their research and have published a comprehensive paper presenting their final results in [Modeling of the post-seismic slip of the 2003 Tokachi-oki earthquake M8 off Hokkaido: constraints from volumetric strain, Earth, Planets and Space, doi: 10.5047/eps.2012.12.003, in press]. That paper includes not only a preliminary data processing presented here, but also contains many additional results concerned with a geophysical phenomenon. The CODATA Data Science Journal regrets the delay in publishing this conference paper.

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The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) provides users of its data with comprehensive search services that enable users to find data in JAMSTEC's various data dissemination sites. These are the "JAMSTEC Data Search Portal" that helps users to search for observational data on a map and the "JAMSTEC Data Catalog" that enables users to find data sites by selecting science keywords. The "Data Search Portal" and the "Data Catalog" have been developed and operated as dedicated metadata publication and search services that collaborate with data sites in JAMSTEC.

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Cross-sectional studies have become important for an improved understanding of various Solar-Terrestrial Physics (STP) fields, given the great variety and different types of observations from the Sun to the Earth. In order to better combine, compare, and analyze different types of data together, a system named STARS (Solar-Terrestrial data Analysis and Reference System) has been developed. Cross-sectional study requires cooperative work. STARS has two functions for cooperative work, the "Stars Project List (SPL)" and the "Event Listing". The SPL is used for exchanges of plotting information by cooperating persons. The event list database provides all users of STARS hints for recognizing typical occurrences of STP phenomena.

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The World Ocean Database (WOD) is the most comprehensive global ocean profile-plankton database available internationally without restriction. All data are in one well-documented format and are available both on DVDs for a minimal charge and on-line without charge. The latest DVD version of the WOD is the World Ocean Database 2009 (WOD09). All data in the WOD are associated with as much metadata as possible, and every ocean data value has a quality control flag associated with it. The WOD is a product of the U.S. National Oceanographic Data Center and its co-located World Data Center for Oceanography. However, the WOD exists because of the international oceanographic data exchange that has occurred under the auspices of the Intergovernmental Oceanographic Commission (IOC) and the International Council of Science (ICSU) World Data Center (WDC) system. World Data Centers are part of the ICSU World Data System.

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Researchers across disciplines are increasingly utilizing electronic tools to collect, analyze, and organize data. However, when it comes to publishing their work, there are no common, well-established standards on how to make that data available to other researchers. Consequently, data are often not stored in a consistent manner, making it hard or impossible to find data sets associated with an article - even though such data might be essential to reproduce results or to perform further analysis. Data repositories can play an important role in improving this situation, offering increased visibility, domain-specific coordination, and expert knowledge on data management. As a leading STM publisher, Elsevier is actively pursuing opportunities to establish links between the online scholarly article and data repositories. This helps to increase usage and visibility for both articles and data sets and also adds valuable context to the data. These data-linking efforts tie in with other initiatives at Elsevier to enhance the online article in order to connect with current researchers’ workflows and to provide an optimal platform for the communication of science in the digital era.

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The Japanese Antarctic Research Expedition has conducted geomagnetic observations at Syowa Station, Antarctica, since 1966. Geomagnetic variation data measured with a fluxgate magnetometer are not absolute but are relative to a baseline and show drift. To enhance the importance of the geomagnetic data at Syowa Station, therefore, it is necessary to correct the continuous variation data by using absolute baseline values acquired by a magnetic theodolite and proton magnetometer. However, the database of baseline values contains outliers. We detected outliers in the database and then converted the geomagnetic variation data to absolute values by using the reliable baseline values.

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Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models in the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index. Moving differences between sequential fitted parameters are then extracted, and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity, and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance.

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Many long-term geomagnetic observation results recorded on photographic bromide paper have not yet been fully digitized. To that end, we developed a method to automatically convert photographic records to one-minute digital data. We applied our method to the observation records of Kakioka Magnetic Observatory and confirmed that the resolution of time and amplitude could be greatly improved by numerical conversion compared with conventional data conversion by hand scaling. Our results suggest that highly precise digitization of analog magnetograms is possible.

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The Geospatial Information Authority of Japan (GSI) has been conducting geomagnetic surveys in Japan since 1949 to clarify the geographical distribution of direction, intensity of the geomagnetic field, and their secular variations. Recently, we have carried out continuous observations at geodetic observatories and continuous geomagnetic stations and repetitive observations at several first-order geomagnetic stations. The results of the surveys, “geomagnetic charts”, have been published every 10 years. In 2011, GSI released the newest geomagnetic charts for the epoch 2010.0 created by adopting a new spatial-temporal model. This model needs continuous data of good quality. In addition to the GSI geodetic observatories, the Kakioka Magnetic Observatory, which has been conducting high-quality and stable observations for 100 years, makes a large contribution.

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Monitoring systems consisting of several magnetometers and cameras were installed to detect artificial magnetic disturbances at the Kakioka, Memambetsu, and Kanoya Magnetic Observatories in Japan. These systems calculate the magnetic dipole moment and source position of artificial magnetic disturbances as a routine matter. For sources of disturbance that are not magnetic dipole moments, each event must be tackled individually. For a non-dipole disturbance at Kanoya from 16 to 22 June 2011, the position and intensity of two lines of direct currents were calculated, and the baseline value affected by these currents was corrected appropriately.

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There are six landmark architectures in the Kakioka Magnetic Observatory. The characteristics of these buildings are as follows:(1) The variation building is a semi-underground granite building that has two advantages: one is stability at room temperature in the basement, and the other is low humidity in the room above ground.(2) The thick walls of the magnetic laboratory, the new variation building, and the new absolute building are made of unusual white brick. (3) The ornamentation found on the facade of the magnetic laboratory, the office building, and the electrometer hut show influence from the building design trends of the era, and the office building is one of the first examples of Spanish detail implementation in modern Japan.

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The Japan Meteorological Agency (JMA) is operating four geomagnetic observatories in Japan. Kakioka Magnetic Observatory (KMO), commissioned in 1913, is the oldest. The hourly records at KMO cover over almost 100 years. KMO is JMA's headquarters for geomagnetic and geoelectric observations. Almost all data are available at the KMO website free of charge for researchers. KMO and two other observatories have been certified as INTERMAGNET observatories, and quasi-real-time geomagnetic data from them are available at the INTERMAGNET website.

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A general-purpose Global Research Data Infrastructure (GRDI) for all sciences and research purposes is not conceivable for the next decade as there are too many discipline-specific modalities that currently prevail for such generalisation efforts to be effective. On the other hand, a more pragmatic approach is to start from what currently exists, identify best practices and key issues, and promote effective inter-domain collaboration among different components forming an ecosystem. This will promote interoperability, data exchange, data preservation, and distributed access (among others). This ecosystem of interoperable research data infrastructures will be composed of regional, disciplinary, and multidisciplinary components, such as libraries, archives, and data centres, offering data services for both primary datasets and publications. The ecosystem will support data-intensive science and research and stimulate the interaction among all its elements, thus promoting multidisciplinary and interdisciplinary science. This special issue includes a set of independent papers from renowned experts on organisational and technological issues related to GRDIs. These documents feed into and compliment the GRDI2020 roadmap, which supports a Global Research Data Infrastructure ecosystem.

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In the vision of Global Research Data Infrastructures (GRDIs), data storage and management plays a crucial role. A successful GRDI will require a common globally interoperable distributed data system, formed out of data centres, that incorporates emerging technologies and new scientific data activities. The main challenge is to define common certification and auditing frameworks that will allow storage providers and data communities to build a viable partnership based on trust. To achieve this, it is necessary to find a long-term commitment model that will give financial, legal, and organisational guarantees of digital information preservation. In this article we discuss the state of the art in data storage and management for GRDIs and point out future research directions that need to be tackled to implement GRDIs.

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Virtual Research Environments are innovative, web-based, community-oriented, comprehensive, flexible, and secure working environments conceived to serve the needs of modern science. We overview the existing initiatives developing these environments by highlighting the major distinguishing features. We envisage a future where regardless of geographical location, scientists will be able to use their Web browsers to seamlessly access data, software, and processing resources that are managed by diverse systems in separate administration domains via Virtual Research Environments. We identify and discuss the major challenges that should be resolved to fully achieve the proposed vision, i.e., large-scale integration and interoperability, sustainability, and adoption.

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The use of published digital data, like the use of digitally published literature, depends upon the ability to identify, authenticate, locate, access, and interpret them. Data citations provide necessary support for these functions, as well as other functions such as attribution of credit and establishment of provenance. References to data, however, present challenges not encountered in references to literature. For example, how can one specify a particular subset of data in the absence of familiar conventions such as page numbers or chapters? The traditions and good practices for maintaining the scholarly record by proper references to a work are well established and understood in regard to journal articles and other literature, but attributing credit by bibliographic references to data are not yet so broadly implemented. This report discusses the current state of data citation practices, its supporting infrastructure, a set of guiding principles for implementing data citation, challenges to implementation of good data citation practices, and open research questions.

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