Progress in Open-World, Integrative, Transparent, Collaborative Science Data Platforms
published: Nov. 28, 2013, recorded: October 2013, views: 4198
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
As collaborative, or network science spreads into more science, engineering and medical fields, both the participants and their funders have expressed a very strong desire for highly functional data and information capabilities that are a) easy to use, b) integrated in a variety of ways, c) leverage prior investments and keep pace with rapid technical change, and d) are not expensive or time-consuming to build or maintain. In response, and based on our accumulated experience over the last decade and a maturing of several key semantic web approaches, we have adapted, extended, and integrated several open source applications and frameworks that handle major portions of functionality for these platforms. At minimum, these functions include: an object-type repository, collaboration tools, an ability to identify and manage all key entities in the platform, and an integrated portal to manage diverse content and applications, with varied access levels and privacy options. At the same time, there is increasing attention to how researchers present and explain results based on interpretation of increasingly diverse and heterogeneous data and information sources. With the renewed emphasis on good data practices, informatics practitioners have responded to this challenge with maturing informatics-based approaches. These approaches include, but are not limited to, use case development; information modeling and architectures; elaborating vocabularies; mediating interfaces to data and related services on the Web; and traceable provenance. The current era of data-intensive research presents numerous challenges to both individuals and research teams. In environmental science especially, sub-fields that were data-poor are becoming data-rich (volume, type and mode), while some that were largely model/ simulation driven are now dramatically shifting to data-driven or least to data-model assimilation approaches. These paradigm shifts make it very hard for researchers used to one mode to shift to another, let alone produce products of their work that are usable or understandable by non-specialists. However, it is exactly at these frontiers where much of the exciting environmental science needs to be performed and appreciated. XVIII Research networks (even small ones) need to deal with people, and many intellectual artifacts produced or consumed in research, organizational and/our outreach activities, as well as the relations among them. Increasingly these networks are modeled as knowledge networks, i.e. graphs with named and typed relations among the 'nodes'. Some important nodes are: people, organizations, datasets, events, presentations, publications, videos, meetings, reports, groups, and more. In this heterogeneous ecosystem, it is important to use a set of common informatics approaches to co-design and co-evolve the needed science data platforms based on what real people want to use them for. We present our methods and results for information modeling, adapting, integrating and evolving a networked data science and information architecture based on several open source technologies (e.g. Drupal, VIVO, the Comprehensive Knowledge Archive Network; CKAN, and the Global Handle System; GHS) and many semantic technologies. We discuss the results in the context of the Deep Carbon Virtual Observatory and the Global Change Information System, and conclude with musings on how the smart mediation among the components is modeled and managed, and its general applicability and ecacy.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !