Patrick West

Ontology-supported scientific data frameworks: The Virtual Solar-Terrestrial Observatory experience

We have developed a semantic data framework that supports interdisciplinary virtual observatory projects across the fields of solar physics, space physics and solar-terrestrial physics. This work required a formal, machine understandable representation for concepts, relations and attributes of physical quantities in the domains of interest as well as their underlying data representations. To fulfill this need, we developed a set of solar-terrestrial ontologies as formal encodings of the knowledge in the Ontology Web Language-Description Logic (OWL-DL) format. We present our knowledge representation and reasoning needs motivated by the context of Virtual Observatories, from fields spanning upper atmospheric terrestrial physics to solar physics, whose intent is to provide access to observational datasets. The resulting data framework is built upon semantic web methodologies and technologies and provides virtual access to distributed and heterogeneous sets of data as if all resources appear to be organized, stored and retrieved from a local environment. Our conclusion is that the combination of use case-driven, small and modular ontology development, coupled with free and open-source software tools and languages provides sufficient expressiveness and capabilities for an initial production implementation and sets the stage for a more complete semantic-enablement of future frameworks.

Ontology dynamics in a data life cycle: Challenges and recommendations from a Geoscience Perspective

Ontologies are increasingly deployed as a computer-accessible representation of key semantics in various parts of a data life cycle and, thus, ontology dynamics may pose challenges to data management and re-use. By using examples in the field of geosciences, we analyze challenges raised by ontology dynamics, such as heavy reworking of data, semantic heterogeneity among data providers and users, and error propagation in cross-discipline data discovery and re-use. We also make recommendations to address these challenges: (1) communities of practice on ontologies to reduce inconsistency and duplicated efforts; (2) use ontologies in the procedure of data collection and make them accessible to data users; and (3) seek methods to speed up the reworking of data in a Semantic Web context.

Enabling Scientific Research using an Interdisciplinary Virtual Observatory: The Virtual Solar-Terrestrial Observatory Example

Our work is aimed at enabling a new style of virtual, distributed scientific research. We have designed, built, and deployed an interdisciplinary virtual observatory—an online service providing access to what appears to be an integrated collection of scientific data. The Virtual Solar-Terrestrial Observatory (VSTO) is a production semantic web data framework providing access to observational data sets from fields spanning upper atmospheric terrestrial physics to solar physics. The observatory allows virtual access to a highly distributed and heterogeneous set of data that appears as if all resources are organized, stored, and retrieved or used in a common way. The end-user community includes scientists, students, and data providers. We will introduce interdisciplinary virtual observatories and their potential impact by describing our experiences with VSTO. We will also highlight some benefits of the embedded semantic web technology and also provide evaluation results after the first year of use.

Effective Tooling for Linked Data Publishing in Scientific Research

Challenges that make it difficult to find, share, and combine published data, such as data heterogeneity and resource discovery, have led to increased adoption of semantic data standards and data publishing technologies. To make data more accessible, interconnected and discoverable, some domains are being encouraged to publish their data as Linked Data. Consequently, this trend greatly increases the amount of data that semantic web tools are required to process, store, and interconnect. In attempting to process and manipulate large data sets, tools -- ranging from simple text editors to modern triplestores -- eventually breakdown upon reaching undefined thresholds. This paper shares our experiences in curating metadata, primarily to illustrate the challenges, and resulting limitations that data publishers and consumers have in the current technological environment. This paper also provides a Linked Data based solution to the research problem of resource discovery, and offers a systematic approach that the data publishers can take to select suitable tools to meet their data publishing needs. We present a real-world use case, the Resource Discovery for Extreme Scale Collaboration (RDESC), which features a scientific dataset(maximum size of 1.4 billion triples) used to evaluate a toolbox for data publishing in climate research. This paper also introduces a semantic data publishing software suite developed for the RDESC project.

Toward cyberinfrastructure to facilitate collaboration and reproducibility for marine integrated ecosystem assessments

There is a growing need for cyberinfrastructure to support science-based decision making in management of natural resources. In particular, our motivation was to aid the development of cyberinfrastructure for Integrated Ecosystem Assessments (IEAs) for marine ecosystems. The IEA process involves analysis of natural and socio-economic information based on diverse and disparate sources of data, requiring collaboration among scientists of many disciplines and communication with other stakeholders. Here we describe our bottom-up approach to developing cyberinfrastructure through a collaborative process engaging a small group of domain and computer scientists and software engineers. We report on a use case evaluated for an Ecosystem Status Report, a multi-disciplinary report inclusive of Earth, life, and social sciences, for the Northeast U.S. Continental Shelf Large Marine Ecosystem. Ultimately, we focused on sharing workflows as a component of the cyberinfrastructure to facilitate collaboration and reproducibility. We developed and deployed a software environment to generate a portion of the Report, retaining traceability of derived datasets including indicators of climate forcing, physical pressures, and ecosystem states. Our solution for sharing workflows and delivering reproducible documents includes IPython (now Jupyter) Notebooks. We describe technical and social challenges that we encountered in the use case and the importance of training to aid the adoption of best practices and new technologies by domain scientists. We consider the larger challenges for developing end-to-end cyberinfrastructure that engages other participants and stakeholders in the IEA process.

Weaving a Knowledge Network for Deep Carbon Science

Geoscience researchers are increasingly dependent on informatics and the Web to conduct their research. Geoscience is one of the first domains that take lead in initiatives such as open data, open code, open access, and open collections, which comprise key topics of Open Science in academia. The meaning of being open can be understood at two levels. The lower level is to make data, code, sample collections and publications, etc. freely accessible online and allow reuse, modification and sharing. The higher level is the annotation and connection between those resources to establish a network for collaborative scientific research. In the data science component of the Deep Carbon Observatory (DCO), we have leveraged state-of-the-art information technologies and existing online resources to deploy a web portal for the over 1000 researchers in the DCO community. An initial aim of the portal is to keep track of all research and outputs related to the DCO community. Further, we intend for the portal to establish a knowledge network, which supports various stages of an open scientific process within and beyond the DCO community. Annotation and linking are the key characteristics of the knowledge network. Not only are key assets, including DCO data and methods, published in an open and inter-linked fashion, but the people, organizations, groups, grants, projects, samples, field sites, instruments, software programs, activities, meetings, etc. are recorded and connected to each other through relationships based on well-defined, formal conceptual models. The network promotes collaboration among DCO participants, improves the openness and reproducibility of carbon-related research, facilitates accreditation to resource contributors, and eventually stimulates new ideas and findings in deep carbon-related studies.

Enhancing the impact of science data toward data discovery and reuse

The a mount of data produced in support of scientific research continues to grow rapidly. Despite the accumulation and demand for scientific data, relatively little data are actually made available for the broader scientific community. We surmise that one root of this problem is the perceived difficulty of electronically publishing scientific data and associated metadata in a way that makes it discoverable. We propose exploiting Semantic Web technologies and best practices to make metadata both discoverable and easy to publish. We share experiences in curating metadata to illustrate the cumbersome nature of data reuse in the current research environment. We also make recommendations with a real-world example of how data publishers can provide their metadata by adding limited additional markup to HTML pages on the Web. With little additional effort from data publishers, the difficulty of data discovery, access, and sharing can be greatly reduced and the impact of research data greatly enhanced.

Experiences of a “semantics smackdown”

Within the field of ocean science there is a long history of using controlled vocabularies and other Semantic Web techniques to provide a common and easily exchanged description of datasets. As an activity within the European Union, United States, Australian-funded project “Ocean Data Interoperability Platform”, a workshop took place in June 2014 at Rensselaer Polytechnic Institute to further the use of these Semantic Web techniques with the aim of producing a set Linked Data publication patterns which describe many parts of a marine science dataset. During the workshop, a Semantic Web development methodology was followed which promoted the use of a team with mixed skills (computer, data and marine science experts) to rapidly prototype a model ontology which could be iterated in the future. In this paper we outline the methodology employed in the workshop, and examine both the technical and sociological outcomes of a workshop of this kind.