Identification and characterization of information-networks in long-tail data collections

Abstract

Scientists' ability to synthesize and reuse long-tail scientific data lags far behind their ability to collect and produce these data. Many Earth Science Cyberinfrastructures enable sharing and publishing their data over the web using metadata standards. While profiling data attributes advances the Linked Data approach, it has become clear that building informationnetworks among distributed data silos is essential to increase their integration and reusability. In this research, we developed a Long-Tail Information-Network (LTIN) model, which uses a metadata-driven approach to build 20 semantic information-networks among datasets published over the web and aggregate them around environmental events. The model identifies and characterizes the spatial and temporal contextual association links and dependencies among datasets. This paper presents the design and application of the LTIN model, and an evaluation of its performance. The model capabilities were demonstrated by inferring the information-network of a stream

Clowder

“…The framework attempts to fulfill these requirements by leveraging stable open source software, supporting flexible metadata definitions and offloading expensive operations to a cloud based extraction bus. Clowder has been developed and leveraged in a wide variety of use cases, including material science research [24], cultural heritage [31], digital curation [23,26], clinical research informatics [36], geosciences [11], environmental monitoring [2,15], digital humanities [18,29], social science [28], as well as industry collaborations.…”

Environmental timeseries lifecycle in the Internet of Things era : Lowering e-science barriers

“…One key objective of the environmental data science is to narrow the data-to-knowledge latency (Elag et al, 2017). According to Foster et al (2012), scientific data doubles every twelve months, which is often faster than it can be converted into useful knowledge.…”

“…Syntactic heterogeneity is a factor which hinders the adoption of a universal strategy to acquire data originating from disparate information sources. It also obstructs the environmental data science core objective: to narrow the data-to-knowledge latency (Elag et al, 2017) by supporting environmental data discovery and access; and by enabling re-usability (Horsburgh et al, 2009;Ames et al, 2012;Athanasiadis, 2015;Holzworth et al, 2015;Granell et al, 2010). FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for scientific data management and stewardship highlight the importance of scientific data reusability and reproducibility (Wilkinson et al, 2016).…”

“…However, transforming a dataset to be compatible with a certain data specification is a laborious process (Horsburgh et al, 2009) and usually requires a human expert intervention (Athanasiadis, 2015). This process hinders environmental data reusability (Ames et al, 2012), facilitates the formation of data silos (Terrizzano et al, 2015) and ultimately widens the data-to-knowledge gap (Elag et al, 2017).…”