ABSTRACT In order to address the most important Earth science questions, field scientists must incorporate new cyberinfrastructure (CI) technologies into their workflow and replace some of the traditional, analog methodologies that still prevail today (e.g., notebook, pen, and transit compass). Geologic field data collected via analog methods are far less likely to be fully digitized and integrated with other datasets. Cyberinfrastructure allows data longevity beyond the original investigator. Digital platforms that facilitate data sharing will help break down the artificial barriers between subfields within the Earth sciences and allow researchers to ask new types of questions and provide the means to contend with those that were previously unanswerable. Close communication and coordination between field-based geologists and computer scientists will facilitate the best cyberinfrastructure and data management for the future. Through a National Science Foundation (NSF)/EarthCube–funded project, discussions between these two groups of scientists were undertaken in a field setting so that computer scientists could better understand the type of data geologists collect and how those geoscientists desire to integrate various types of data into their workflow. Similarly, geologists gained a better understanding of how computer scientists can represent, manipulate, and archive complex data in data management systems, with potential solutions to field data challenges. These discussions centered on the unique issues faced by the geological community regarding the collection, storage, manipulation, representation, and integration of field-based data.

ABSTRACT In the recent decade, knowledge graph has been a key technique under quick development in artificial intelligence. Due to its great potential for tackling big data and solving complex scientific questions in the geosciences, it has attracted the attention of both computer scientists and geoscientists. In this paper, we review concepts and technologies relevant to the knowledge graph, the workflow of geoscience knowledge graph construction, and state-of-the-art examples from several geoscience disciplines. There are two general strategies for constructing geoscience knowledge graphs: top-down and bottom-up. The detailed technologies include geoscience domain knowledge modeling, data collection, knowledge extraction, knowledge cleaning and fusion, knowledge storage, and knowledge service and discovery. A few recent studies have shown that knowledge graph is a useful tool for improving our understanding of the evolution of the Earth and can assist in data-intensive geoscience studies. At the end of the paper, we discuss the best practices from the studies reviewed and propose research topics for future work. Both knowledge and rules in existing human-curated databases and text mining from the literature should be leveraged in constructing geoscience knowledge graphs. Moreover, development of a higher level schema for existing ontology models and a comparable training corpus should be considered.

ABSTRACT The techniques and approaches within geoinformatics and data science rely on the effective coupling of supporting infrastructures and institutions. Without underlying infrastructures for data discovery, analysis, management, distribution, and preservation, new computational techniques wither on the vine for lack of input or remain isolated as niche tools that miss broader potential audiences. Likewise, without supporting institutions that enable governance of policies and finances, coordination of stakeholders, and validation of new knowledge and tools, technological advances become detached from the people and organizations that operate and use them. This paper centers on a case study of work within the National Center for Atmospheric Research (NCAR) and University Corporation for Atmospheric Research (UCAR) to develop effective systems and processes for research data curation, access, discovery, and preservation. By emphasizing iterative alignment of institutional work (policies, intermediaries, governance processes, routines, and financial instruments) and infrastructural work (data storage systems, repositories, tools, and interfaces), balanced progress has been made toward developing solutions to gaps in organizational data services.

ABSTRACT The increased use of complex programmatic workflows and open data within the Earth sciences has led to an increase in the need to find and reuse code, whether as examples, templates, or code snippets that can be used across projects. The “Throughput Graph Database” project offers a platform for discovery that links research objects by using structured annotations. Throughput was initially populated by scraping GitHub for code repositories that reference the names or URLs of data archives listed on the Registry of Research Data Repositories ( https://re3data.org ). Throughput annotations link the research data archives to public code repositories, which makes data-relevant code repositories easier to find. Linking code repositories in a queryable, machine-readable way is only the first step to improving discoverability. A better understanding of the ways in which data is used and reused in code repositories is needed to better support code reuse. In this paper, we examine the data practices of Earth science data reusers through a classification of GitHub repositories that reference geology and paleontology data archives. A typology of seven reuse classes was developed to describe how data were used within a code repository, and it was applied to a subset of 129 public code repositories on GitHub. Code repositories could have multiple typology assignments. Data use for Software Development dominated ( n = 44), followed by Miscellaneous Links to Data Archives ( n = 41), Analysis ( n = 22), and Educational ( n = 20) uses. GitHub repository features show some relationships to the assigned typologies, which indicates that these characteristics may be leveraged to systematically predict a code repository’s category or discover potentially useful code repositories for certain data archives.

Abstract Geological surveys have long filled the role of providing Earth system science data and knowledge. These functions are increasingly complicated by accelerating environmental and societal change. Here we describe the US Geological Survey (USGS) response to these evolving conditions. Underpinning the USGS approach is the recognition that many of the issues facing the USA and the world involve interaction among geological, hydrological and biological processes, and how these interactions in turn affect society. Therefore, a goal of USGS planning is fostering interdisciplinary science. This focus is occurring in part through implementation of the recommendations of strategic planning teams. The USGS has also put in place groups building a broad information technology infrastructure as well as identifying and disseminating new Earth science research tools. In addition, the USGS has established an analysis and synthesis centre that brings together groups of scientists who address interdisciplinary Earth system science issues. The goal is for these building blocks to evolve towards a comprehensive USGS data and knowledge platform – EarthMAP (Earth Monitoring, Assessment, and Projection). We also recognize that the modern geological survey must be a member of a community of geological surveys contributing data to a global database of three-dimensional biogeophysical observations and interpretations.

Abstract During the period 28 February 2000–31 December 2013, the MODVOLC system ( http://modis.higp.hawaii.edu ) autonomously analysed almost 9 trillion (i.e. 9×10 12 ) pixels contained within almost 3 million MODIS images, searching for evidence of high-temperature thermal signatures associated with volcanic eruptions. Thermal unrest, mainly associated with active lava, be it in the form of flows, domes, lakes or confined to vents, was detected at 93 volcanoes during this period of time. The first part of this paper describes the physical basis and operational implementation of the MODVOLC algorithm. The second part presents data to detail the nature of the thermal emission from these 93 volcanoes over the past 14 years.

Abstract The Volcano Sensor Web (VSW) is a globe-spanning net of sensors and applications for detecting volcanic activity. Alerts from the VSW are used to trigger observations from space using the Earth Observing-1 ( EO-1 ) spacecraft. Onboard EO-1 is the Autonomous Sciencecraft Experiment (ASE) advanced autonomy software. Using ASE has streamlined spacecraft operations and has enabled the rapid delivery of high-level products to end-users. The entire process, from initial alert to product delivery, is autonomous. This facility is of great value as a rapid response is vital during a volcanic crisis. ASE consists of three parts: (1) Science Data Classifiers, which process EO-1 Hyperion data to identify anomalous thermal signals; (2) a Spacecraft Command Language; and (3) the Continuous Activity Scheduling Planning Execution and Replanning (CASPER) software that plans and replans activities, including downlinks, based on available resources and operational constraints. For each eruption detected, thermal emission maps and estimates of eruption parameters are posted to a website at the Jet Propulsion Laboratory, California Institute of Technology, in Pasadena, CA. Selected products are emailed to end-users. The VSW uses software agents to detect volcanic activity alerts generated from a wide variety of sources on the ground and in space, and can also be easily triggered manually.

Abstract We describe a new volcanic hotspot detection system, named Middle InfraRed Observation of Volcanic Activity (MIROVA), based on the analysis of infrared data acquired by the Moderate Resolution Imaging Spectroradiometer sensor (MODIS). MIROVA uses the middle infrared radiation (MIR), measured by MODIS, in order to detect and measure the heat radiation deriving from volcanic activity. The algorithm combines spectral and spatial principles, allowing the detection of heat sources from 1 megawatt (MW) to more than 10 gigawatt (GW). This provides a unique opportunity to: (i) recognize small-scale variations in thermal output that may precede the onset of effusive activity; (ii) track the advance of large lava flows; (iii) estimate lava discharge rates; (iv) identify distinct effusive trends; and, lastly, (v) follow the cooling process of voluminous lava bodies for several months. Here we show the results obtained from data sets spanning 14 years recorded at the Stromboli and Mt Etna volcanoes, Italy, and we investigate the above aspects at these two persistently active volcanoes. Finally, we describe how the algorithm has been implemented within an operational near-real-time processing chain that enables the MIROVA system to provide data and infrared maps within 1–4 h of the satellite overpass.