Executing Streamlined and Cost-Effective Investigations Across Disparate Data Sources

2021 ◽  
pp. 144-159
Author(s):  
Daniel S. Meyers ◽  
Al-Karim Markhani ◽  
Joseph Pochron
10.29007/2dsg ◽  
2018 ◽  
Author(s):  
Matthew Stenson ◽  
Ashley Sommer ◽  
Ross Searle ◽  
David Freebairn

As with many industries, digital disruption will play a major role in shaping agriculture over the coming years as decisions become increasingly data driven. A significant proportion of this data will come from on-farm sensors that are becoming easier to source and deploy. While access to sensors is becoming increasingly cost effective, accessing and integrating the data they provide is still a major issue for many, due to the use of different standards for describing and sharing the data. The Soil sensing - new technology for tracking soil water availability, managing risk and improving management decisions project has developed a distributed system that addresses the technical challenge of federating disparate data sources through the use of a software mediation layer and a semantically enabled metadata harvest, search and discovery tool. These web services, the O&M Translator and the Data Brokering Layer, allow a unified and federated view of the data, enabling integrated search and discovery and provide access through a SOS compliant API, delivering the data to client using the O&M data model and a TimeseriesML representation. The resulting Data Stream Integrator is already being tested in applications such as SoilWaterApp.


Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.


Author(s):  
Qiu Xiao ◽  
Ning Zhang ◽  
Jiawei Luo ◽  
Jianhua Dai ◽  
Xiwei Tang

Abstract Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.


2016 ◽  
Vol 60 (3) ◽  
pp. 356-368 ◽  
Author(s):  
Mehdi Aminipouri ◽  
Anders Knudby ◽  
Hung Chak Ho

2010 ◽  
Author(s):  
Christian P. Minor ◽  
Mark H. Hammond ◽  
Kevin J. Johnson ◽  
Susan L. Rose-Pehrsson

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