EBSDinterp Demonstration Data 1 - Fractured Albite

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About this Collection

EBSDinterp Demonstration Data 1 - Fractured Albite

EBSDinterp is a GUI-based Matlab program to perform microstructurally constrained interpolation of electron backscatter diffraction (EBSD) datasets. The dataset in this collection comprises ASCII Channel Text Files (.ctf) of the raw and processed datasets used in the publication describing EBSDinterp. The dataset contains EBSD data of a large frac... more

Mineralogy and Crystallography Ore Deposit Petrology Structural Geology

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EBSD albite gold interpolation Hamlet St Ives

Data were collected using a Bruker eFlash detector and Bruker Esprit software on a Zeiss ULTRA PLUS field emission gun SEM. Acquisition conditions were a working distance of 23 mm, accelerating voltage of 20 keV, and beam current of 12.1 nA, with the stage tilted at 70° to the horizontal and a thin carbon coat of ~5 nm The data were exported from Esprit to a .ctf format, which can be read by EBSDinterp (Large_Albite.ctf). Interpolation was performed using EBSDinterp using the parameters described in the logfile (also included), and exported back to a .ctf format file following interpolation (Large_Albite_processed.ctf).

CSIRO Data Licence

CSIRO (Australia)

Pearce, Mark (2015): EBSDinterp Demonstration Data 1 - Fractured Albite. v1. CSIRO. Data Collection.

All Rights (including copyright) CSIRO Australia 2015.

The metadata and files (if any) are available to the public.

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About this Project

Gold Research

Examining the relationship between damage and chemical reaction fronts around gold deposits. The project combines microstructural and microchemical analysis techniques to examine in detail the relationship between deformation and alteration and how these relate to gold mineralisation

Rob Hough

EBSD data processing and analysis

Electron backscatter diffraction data give crystallographic orientation and phase information in crystalline samples. Raw data can be processed using a variety of methods to analyse patterns and trends in the data.


Mark Pearce

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