Databasing References: Difference between revisions

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== Assembling the Custom Components for Specimen Imaging, Consortium of Pacific Northwest Herbaria, WTU Herbarium, Burke Museum, Version 1.0, by Ben Legler.  ==

!scope="col" width="15%" | Pub Date || 7/9/2010

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!scope="col" | URL || http://www.pnwherbaria.org/documentation/custom-components-v1.pdf#

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!scope="col"; style="vertical-align:top";  | Description ||  

This document provides instructions for assembling the custom hardware components used for imaging specimens under the Consortium of Pacific Northwest Herbaria’s 2010-2013 collaborative NSF Grant. It is intended as a guide for similar projects elsewhere. However, the components described here are specific to our choice of imaging equipment and may not be suitable for use elsewhere. Also discussed here are the custom software scripts used for metadata capture, image processing, and image tiling. The tiling script creates a version of the image that can be viewed with the Gmap Image Viewer (http://www.rmh.uwyo.edu/gmapviewer/about.php), an online, open-source viewer created for use with herbarium specimens.

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== Australian Museum Data capture of specimen labels using volunteers, by John Tann & Paul Flemons. ==

!scope="col" width="15%" | Pub Date || December 2008

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!scope="col" | URL ||  http://australianmuseum.net.au/Uploads/Documents/23183/Data%20Capture%20of%20specimen%20labels%20using%20volunteers%20-%20Tann%20and%20Flemons%202008.pdf#

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!scope="col"; style="vertical-align:top";  | Description ||  

This is a report of an attempt to speed up the capture of information on the labels of specimens held by the Australian Museum. A trial was conducted using volunteers with a camera to photograph specimen labels and transcribe that data into a spreadsheet. Location information was georeferenced. The data in the resulting spreadsheet was then entered into EMu by a museum technician. Times and costs were compared to direct data entry, as well as with a previous trial using an off-shore data transcription service. The outcome of the trial was successful in clarifying the following. Importing data into EMu is not straightforward and is a specialist task. Having the data transcribed into a spreadsheet before import into EMu does not help. Errors, misspellings, and uncertainties on many of the labels meant that a spreadsheet of data became a clumsy and inefficient method of data entry. Photographing a label has advantages – a photograph becomes a verbatim record in the database of the label for later referral, and makes the data entry process quicker by about 20%, as well as easier and more convenient. Recommendations: The Australian museum could train and use a small team of volunteers to photograph specimen labels. These photographs would be saved on EMu as a record of the label, and subsequently used for data entry by AM technical staff. Investigate the Emu inline toolset as a possible route for engaging volunteers for accurate and reliable data entry.

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== Australian Museum Rapid Digitisation Project: A Guide to Handling and Digising Archival Material - Registers by L. Prater, R. Stephens, and P. Flemons, 19 pp. ==

!scope="col" width="15%" | Pub Date || August 2011

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!scope="col" | URL ||  http://australianmuseum.net.au/Uploads/Documents/22932/Archive%20Training%20Compressed.pdf#

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!scope="col"; style="vertical-align:top";  | Description ||  

This publications documents methods for ditigizing printed records associated with museum collections.

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== Automontage Imaging Guidelines, by AntWeb. ==

!scope="col" width="15%" | Pub Date || June, 2010

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!scope="col" | URL ||  http://www.antweb.org/homepage/AntWeb%20Imaging%20guidelines%20v01.pdf#

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!scope="col"; style="vertical-align:top";  | Description ||  

This presentation from AntWeb offers detailed information about imaging ant specimens using Automontage.

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!scope="col" | URL ||  http://www.ncbi.nlm.nih.gov/pubmed/20567932#

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!scope="col"; style="vertical-align:top";  | Description ||  

Digital imaging has provided scientists with new opportunities to acquire and manipulate data using techniques that were difficult or impossible to employ in the past. Because digital images are easier to manipulate than film images, new problems have emerged. One growing concern in the scientific community is that digital images are not being handled with sufficient care. The problem is twofold: (1) the very small, yet troubling, number of intentional falsifications that have been identified, and (2) the more common unintentional, inappropriate manipulation of images for publication. Journals and professional societies have begun to address the issue with specific digital imaging guidelines. Unfortunately, the guidelines provided often do not come with instructions to explain their importance. Thus they deal with what should or should not be done, but not the associated 'why' that is required for understanding the rules. This article proposes 12 guidelines for scientific digital image manipulation and discusses the technical reasons behind these guidelines. These guidelines can be incorporated into lab meetings and graduate student training in order to provoke discussion and begin to bring an end to the culture of "data beautification".

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