Stephan Zednik

Provenance Representation for the National Climate Assessment in the Global Change Information System

The important topic of global climate change builds on a huge collection of scientific research. It is common for agencies releasing climate change information to be served with requests for all supporting materials resulting in a particular conclusion. Capturing and presenting global change provenance, linking to the research papers, data sets, models, analyses, observations, satellites, etc., that support the key research findings in this domain can increase understanding and aid in reproducibility of results and conclusions. The U.S. Global Change Research Program is now coordinating the production of a national climate assessment (NCA) that presents our best understanding of global change. We are now developing a global change information system that will present the content of that report and its provenance, including the scientific support for the findings of the assessment. We are using an approach that will present this information both through a human accessible Web site as well as a machine-readable interface for automated mining of the provenance graph. We plan to use the developing World Wide Web Consortium (W3C) PROV data model and ontology for this system. This paper will describe an overview of the process of developing the NCA and how the provenance trail of the report and each of the technical inputs can be captured and represented using the W3C PROV ontology. This will improve the visibility into the assessment process, increase understanding and possibility of reproducibility, and ultimately increase the credibility and trust of the resulting report.

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.

System Transparency, or How I Learned to Worry about Meaning and Love Provenance!

Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data without requiring the user be an expert in data processing. These tools simplify science analysis for all science users by reducing the data processing overhead for the user. The benefits of these tools come with a cost, the increased need for transparency in data processing. By providing a clear explanation of the science concepts and processing performed by the science analysis tool we can increase user trust, understanding, and accountability and reduce misinterpretation or generation of inconsistent results.

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.

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.

Integrating provenance into an operational data product information system

Knowledge of how a science data product has been generated is a critical component to determining its fitness-for-use for a given analysis. One objective of science information systems is to allow users to search for data products based on a wide range of criteria; spatial and temporal extent, observed parameter, research domain, and organizational project are common search criteria. Currently, science information systems are geared towards helping users find data, but not in helping users determine how the products were generated. An information system that exposes the provenance of available data products, that is what observations, assumptions, and science processing were involved in the generation of the data products, would contribute significant benefit to user fitness-for-use decision-making. In this work we discuss semantics-driven provenance extensions to the Virtual Solar Terrestrial Observatory (VSTO) information system. The VSTO semantic web portal uses an ontology to provide a unified search and product retrieval interface to data in the fields of solar, solar-terrestrial, and space physics. We have developed an extension to the VSTO ontology that allows it to express item-level data product records. We will show how the Open Provenance Model (OPM) and the Proof Markup Language (PML) can be used to express the provenance of data product records. Additionally, we will discuss ways in which domain semantics can aid in the formulation - and answering - of provenance queries. Our extension to the VSTO ontology has also been integrated with a solar-terrestrial profile of the Observation and Measurement (OM we utilize this integration to connect observation events to the data product record lineage. Our additions to the VSTO ontology will allow us to extend the VSTO web portal user interface with search criteria based on provenance and observation characteristics. More critically, provenance information will allow the VSTO portal to display important knowledge about selected data records; what science processes and assumptions were applied to generate the record, what observations the record derives from, and the results of quality processing that had been applied to the record and any records it derives from. We conclude by showing our interface for showing record provenance information and discuss how it aids users in determining fitness-for-use of the data.