John W. C. McNabb

TL;DR: This chapter uses LIGO as an application case study in workflow design and implementation and outlines a few directions for future development and provides some long-term vision for applications related to gravitational wave data analysis.

TL;DR: The BlockNormal method as mentioned in this paper identifies candidate events by searching for places in the data stream where the characteristic statistics of the data change, which can then be investigated by a more computationally demanding multi-detector analysis.

TL;DR: In this paper, the plans for a joint search for unmodelled gravitational wave bursts being carried out by the LIGO and TAMA Collaborations using data collected during February-April 2003 are described.

TL;DR: In the search for unmodeled gravitational wave bursts, there are a variety of methods that have been proposed to generate candidate events from time series data as discussed by the authors, such as Block Normal, which is a method of identifying candidate events by searching for places in the data stream where the characteristic statistics of the data change.

TL;DR: In this article , a succession extension of the LANDIS-II forest landscape model was developed, which integrates a vegetation dynamics model (NECN) with a soil carbon model (DAMM-McNiP), a hydrologic model (SHAW), and a deep soil profile permafrost model (GIPL) in a spatially-explicit framework.