Abstract Knowledge management plays an important role in scientific research and provides a basis for technical development in the era of Big Data. Studies of Cenozoic volcanoes in China have been undertaken for more than half a century, generating plentiful relevant literature and data. However, these data have stayed scattered between different authors and libraries, and this hampers management and access. Based on theories, knowledge bases and related technologies, we developed the Knowledge Base of Cenozoic Volcanoes (KBCV) to collect such volcanic data in China. The directory tree of the KBCV is structured based on five levels according to the volcano distribution, magma origin, data type and file format. The KBCV supports querying, searching and browsing. It can provide well-managed Cenozoic volcanic data and technical support for scientific research and public communication. The KBCV is still in its early stages and is imperfect with respect to data completeness and universalization of the system, and efforts are being made to continuously develop and popularize the system.

Abstract Mudrocks are highly heterogeneous in a range of physical and chemical properties, including: porosity and permeability, fissility, colour, particle composition, size, orientation, carbon loading, degree of compaction, and diagenetic overprint. It is therefore important that the maximum information be extracted as efficiently and completely as possible. This can be accomplished through high-resolution analysis of polished thin sections by scanning electron microscopy (SEM), with the collection of large-area images and X-ray elemental map montages, and the application of targeted particle analysis. A workflow model, based on these techniques, for the digitization of mudrocks is presented herein. A range of the data that can be collected and the variety of analyses that can be achieved are also illustrated. Data collection is discussed in terms of inherent problems with acquisition, storage, transfer and manipulation, which can be time-consuming and non-trivial. Similar information and resolutions can be achieved through other techniques, such as QEMSCAN and infra-red (IR)/Raman spectroscopic mapping. These can be seen as complementary to the workflow described herein.

Abstract Infrared (IR) satellite-based sensors allow the detection and quantification of volcanic hot spots. Sensors flown on geostationary satellites are particularly helpful in the early warning and continuous tracking of effusive activity. Development of operational monitoring and dissemination systems is essential to achieve the real-time ingestion and processing of IR data for a timely response during volcanic crises. HOTVOLC is a web-based satellite-data-driven monitoring system developed at the Observatoire de Physique du Globe de Clermont-Ferrand (Clermont-Ferrand), designed to achieve near-real-time monitoring of volcanic activity using on-site ingestion of geostationary satellite data (e.g. MSG-SEVIRI, MTSAT, GOES-Imager). Here we present the characteristics of the HOTVOLC system for the monitoring of effusive activity. The system comprises two acquisition stations and secure databases (i.e. mirrored archives). The detection of volcanic hot spots uses a contextual algorithm that is based on a modified form of the Normalized Thermal Index (NTI*) and VAST. Raster images and numerical data are available to open-access on a Web-GIS interface. Tests are carried out and presented here, particularly for the 12–13 January 2011 eruption of Mount Etna, to show the capability of the system to provide quantitative information such as lava volume and time-averaged discharge rate. Examples of operational application reveal the ability of the HOTVOLC system to provide timely thermal information about volcanic hot spot activity.

Abstract Using the NEODAAS-Dundee AVHRR receiving station (Scotland), NEODAAS-Plymouth can provide calibrated brightness temperature data to end users or interim users in near-real time. Between 2000 and 2009 these data were used to undertake volcano hot spot detection, reporting and time-average discharge rate dissemination during effusive crises at Mount Etna and Stromboli (Italy). Data were passed via FTP, within an hour of image generation, to the hot spot detection system maintained at Hawaii Institute of Geophysics and Planetology (HIGP, University of Hawaii at Manoa, Honolulu, USA). Final product generation and quality control were completed manually at HIGP once a day, so as to provide information to onsite monitoring agencies for their incorporation into daily reporting duties to Italian Civil Protection. We here describe the processing and dissemination chain, which was designed so as to provide timely, useable, quality-controlled and relevant information for ‘one voice’ reporting by the responsible monitoring agencies.

The first step in the preservation of digital scientific data is gathering enough information “about” a scientific outcome or data collection so that it can be discovered and used a decade later as easily as it is used at the time. Data provenance, or lineage of a collection, can capture the way in which a particular scientific collection was created, when, and by whom. Tools that automate the collection of provenance can reduce the burden on the researcher, and provenance data can be stored in ways that make the data more amenable to long-term preservation. We discuss the various dimensions of data provenance in data-driven geospatial science with the goal of conveying a good grasp of provenance collection, representation, and use. Our research in data cyberinfrastructure utilizes real-time observational data in on-demand weather forecasts, and we discuss this aspect as well.