Project

Spatial Discovery of Research Data

Goal: In the larger context of a project on using spatial information and GIS for information search at libraries, we are now focusing on how to make research data discoverable and accessible. The two central ideas are (1) to complement search by author and theme with search by location(s) a data set is about, and (2) to link publications to the data and vice versa.

http://spatial.ucsb.edu/research/spatial-discovery

Date: 31 August 2015 - 31 August 2020

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Project log

Sara Lafia
added 4 research items
Academic libraries have always supported research across disciplines by integrating access to diverse contents and resources. They now have the opportunity to reinvent their role in facilitating interdisciplinary work by offering researchers new ways of sharing, curating, discovering, and linking research data. Spatial data and metadata support this process because location often integrates disciplinary perspectives, enabling researchers to make their own research data more discoverable, to discover data of other researchers, and to integrate data from multiple sources. The Center for Spatial Studies at the University of California, Santa Barbara (UCSB) and the UCSB Library are undertaking joint research to better enable the discovery of research data and publications. The research addresses the question of how to spatially enable data discovery in a setting that allows for mapping and analysis in a GIS while connecting the data to publications about them. It suggests a framework for an integrated data discovery mechanism and shows how publications may be linked to associated data sets exposed either directly or through metadata on Esri's Open Data platform. The results demonstrate a simple form of linking data to publications through spatially referenced metadata and persistent identifiers. This linking adds value to research products and increases their discoverability across disciplinary boundaries.
We describe a method and system design for improved data discovery in an integrated network of open geospatial data that supports collaborative policy development between governments and local constituents. Metadata about civic data (such as thematic categories, user-generated tags, geo-references, or attribute schemata) primarily rely on technical vocabularies that reflect scientific or organizational hierarchies. By contrast, public consumers of data often search for information using colloquial terminology that does not align with official metadata vocabularies. For example, citizens searching for data about bicycle collisions in an area are unlikely to use the search terms with which organizations like Departments of Transportation describe relevant data. Users may also search with broad terms, such as “traffic safety”, and will then not discover data tagged with narrower official terms, such as “vehicular crash”. This mismatch raises the question of how to bridge the users’ ways of talking and searching with the language of technical metadata. In similar situations, it has been beneficial to augment official metadata with semantic annotations that expand the discoverability and relevance recommendations of data, supporting more inclusive access. Adopting this strategy, we develop a method for automated semantic annotation, which aggregates similar thematic and geographic information. A novelty of our approach is the development and application of a crosscutting base vocabulary that supports the description of geospatial themes. The resulting annotation method is integrated into a novel open access collaboration platform (Esri’s ArcGIS Hub) that supports public dissemination of civic data and is in use by thousands of government agencies. Our semantic annotation method improves data discovery for users across organizational repositories and has the potential to facilitate the coordination of community and organizational work, improving the transparency and efficacy of government policies.
It is challenging for scholars to discover thematically related research in a multidisciplinary setting, such as that of a university library. In this work, we use spatialization techniques to convey the relatedness of research themes without requiring scholars to have specific knowledge of disciplinary search terminology. We approach this task conceptually by revisiting existing spatialization techniques and reframing them in terms of core concepts of spatial information, highlighting their different capacities. To apply our design, we spatialize masters and doctoral theses (two kinds of research objects available through a university library repository) using topic modeling to assign a relatively small number of research topics to the objects. We discuss and implement two distinct spaces for exploration: a field view of research topics and a network view of research objects. We find that each space enables distinct visual perceptions and questions about the relatedness of research themes. A field view enables questions about the distribution of research objects in the topic space, while a network view enables questions about connections between research objects or about their centrality. Our work contributes to spatialization theory a systematic choice of spaces informed by core concepts of spatial information. Its application to the design of library discovery tools offers two distinct and intuitive ways to gain insights into the thematic relatedness of research objects, regardless of the disciplinary terms used to describe them.
Werner Kuhn
added 2 project references
Werner Kuhn
added a project goal
In the larger context of a project on using spatial information and GIS for information search at libraries, we are now focusing on how to make research data discoverable and accessible. The two central ideas are (1) to complement search by author and theme with search by location(s) a data set is about, and (2) to link publications to the data and vice versa.