Leveraging the GeoLink Knowledge Base for Cruise Information

Open Linked Data (LOD) is providing an excellent opportunity for repositories, libraries, and archives to expand the use of their holdings and advance the work of researchers. The implementation of the GeoLink Knowledgebase has created an exciting LOD framework for organizations specializing in Earth Sciences. As an NSF EarthCube Building Block, GeoLink brings together several powerful data sources, such as BCO-DMO, Rolling Deck to Repository (R2R), Data One, IEDA, IODP, and LTER, with publication providers such as the MBLWHOI Library’s Woods Hole Open Access Server (WHOAS), ESIP, and AGU. While publishing to the GeoLink knowledgebase offers a great way to make collections and metadata more findable and relevant, becoming a linked data publisher is not the only way to engage with linked data or the GeoLink project. Any repository can use simple, easily customizable code developed by members of the GeoLink team to add live GeoLink content to a page based on the item's metadata, leveraging GeoLink’s powerful framework for searching across repositories, organizations, and disciplines.GeoLink was funded by the National Science Foundation, EAGER: Collaborative Research: Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery EarthCube Building Blocks: Collaborative Proposal: GeoLink – Leveraging Semantics and Linked Data for Data Sharing and Discovery in the Geoscienc

The Frictionless Data Package : data containerization for addressing big data challenges [poster]

Presented at AGU Ocean Sciences, 11 - 16 February 2018, Portland, ORAt the Biological and Chemical Oceanography Data Management Office (BCO-DMO) Big Data challenges have been steadily increasing. The sizes of data submissions have grown as instrumentation improves. Complex data types can sometimes be stored across different repositories . This signals a paradigm shift where data and information that is meant to be tightly-coupled and has traditionally been stored under the same roof is now distributed across repositories and data stores. For domain-specific repositories like BCO-DMO, a new mechanism for assembling data, metadata and supporting documentation is needed. Traditionally, data repositories have relied on a human's involvement throughout discovery and access workflows. This human could assess fitness for purpose by reading loosely coupled, unstructured information from web pages and documentation. Distributed storage was something that could be communicated in text that a human could read and understand. However, as machines play larger roles in the process of discovery and access of data, distributed resources must be described and packaged in ways that fit into machine automated workflows of discovery and access for assessing fitness for purpose by the end-user. Once machines have recommended a data resource as relevant to an investigator's needs, the data should be easy to integrate into that investigator's toolkits for analysis and visualization. BCO-DMO is exploring the idea of data containerization, or packaging data and related information for easier transport, interpretation, and use. Data containerization reduces not only the friction data repositories experience trying to describe complex data resources, but also for end-users trying to access data with their own toolkits. In researching the landscape of data containerization, the Frictionlessdata Data Package (http://frictionlessdata.io/) provides a number of valuable advantages over similar solutions. This presentation will focus on these advantages and how the Frictionlessdata Data Package addresses a number of real-world use cases faced for data discovery, access, analysis and visualization in the age of Big Data.NSF #1435578, NSF #163971

The Frictionless Data Package : data containerization for automated scientific workflows [poster]

Presented at the Fall AGU Meeting, New Orleans, LA, 11-15 December 2017As cross-disciplinary geoscience research increasingly relies on machines to discover and access data, one of the critical questions facing data repositories is how data and supporting materials should be packaged for consumption. Traditionally, data repositories have relied on a human's involvement throughout discovery and access workflows. This human could assess fitness for purpose by reading loosely coupled, unstructured information from web pages and documentation. In attempts to shorten the time to science and access data resources across may disciplines, expectations for machines to mediate the process of discovery and access is challenging data repository infrastructure. This challenge is to find ways to deliver data and information in ways that enable machines to make better decisions by enabling them to understand the data and metadata of many data types. Additionally, once machines have recommended a data resource as relevant to an investigator's needs, the data resource should be easy to integrate into that investigator's toolkits for analysis and visualization. The Biological and Chemical Oceanography Data Management Office (BCO-DMO) supports NSF-funded OCE and PLR investigators with their project's data management needs. These needs involve a number of varying data types some of which require multiple files with differing formats. Presently, BCO-DMO has described these data types and the important relationships between the type's data files through human-readable documentation on web pages. For machines directly accessing data files from BCO-DMO, this documentation could be overlooked and lead to misinterpreting the data. Instead, BCO-DMO is exploring the idea of data containerization, or packaging data and related information for easier transport, interpretation, and use. In researching the landscape of data containerization, the Frictionlessdata Data Package (http://frictionlessdata.io/) provides a number of valuable advantages over similar solutions. This presentation will focus on these advantages and how the Frictionlessdata Data Package addresses a number of real-world use cases faced for data discovery, access, analysis and visualization.National Science Foundation Award #1435578, Award #163971

CSCL: Structuring the Past, Present and Future Through Virtual Portfolios

Web-based processes of learning and collaboration produce an enlarged structural opportunity at many levels. Careful structuring of the virtual space supports and adds quality to both collaborative learning between students, and to instruction. Such enhancement in quality may take place through use of individual and collaborative spaces for learning activities, overview of process and content, increased clarity of learning expectations, and facilitation of collaborative and individual processes of reflection and self-reflection. This chapter investigates the structuring potential of a virtual version of portfolios for supporting these aspects. It discusses the conceptual and structural complexity associated with design and use of virtual portfolios from the perspective of, both learners and instructors, and on the basis of the design and use of virtual portfolios in a web-based American course on global change

The advantages of machine aided co-reference resolution for research cruise metadata

Presented at Linking Environmental Data and Samples, Commonwealth Scientific and Industrial Research Organization (CSIRO), Canberra, Australia, 29 May - 2 June 2017One of the central incentives of deploying linked open data is the opportunity to leverage the linkages between source datasets to retrieve related information. The Biological and Chemical Oceanography Data Management Office (BCO-DMO) reaps these benefits by linking its cruise-level metadata to the Rolling Deck to Repository (R2R) – the trusted, authoritative source for cruises undertaken by the U.S. academic research fleet. Even though the process of identifying a link between these two repositories is easy for a human, this talk will explore the advantages of using a machine-aided process to suggest links to R2R cruises to a BCO-DMO data manager.NSF #143557

Lettre de Douglas Fils à Sa Tante

4 page

ESIP Labs PROV Report Out (Narock)

Tom Narock's slides from the ESIP Labs PROV Report Out

ESIP Labs PROV Report Out (Narock)

Tom Narock's slides from the ESIP Labs PROV Report Out

ESIP Labs PROV Report Out (Fils)

Doug Fils' slides from the ESIP Labs PROV Report Out