Hudson Lawrence N, Blagoderov Vladimir, Heaton Alice, Holtzhausen Pieter, Livermore Laurence, Price Benjamin W, van der Walt Stéfan, Smith Vincent S
Department of Life Sciences, Natural History Museum, Cromwell Road, London, SW7 5BD, United Kingdom.
Division of Applied Mathematics, Stellenbosch University, Stellenbosch 7600, South Africa.
PLoS One. 2015 Nov 23;10(11):e0143402. doi: 10.1371/journal.pone.0143402. eCollection 2015.
The world's natural history collections constitute an enormous evidence base for scientific research on the natural world. To facilitate these studies and improve access to collections, many organisations are embarking on major programmes of digitization. This requires automated approaches to mass-digitization that support rapid imaging of specimens and associated data capture, in order to process the tens of millions of specimens common to most natural history collections. In this paper we present Inselect-a modular, easy-to-use, cross-platform suite of open-source software tools that supports the semi-automated processing of specimen images generated by natural history digitization programmes. The software is made up of a Windows, Mac OS X, and Linux desktop application, together with command-line tools that are designed for unattended operation on batches of images. Blending image visualisation algorithms that automatically recognise specimens together with workflows to support post-processing tasks such as barcode reading, label transcription and metadata capture, Inselect fills a critical gap to increase the rate of specimen digitization.
世界自然历史藏品构成了关于自然界科学研究的巨大证据库。为推动这些研究并改善对藏品的获取，许多机构正在着手开展大规模数字化项目。这需要采用自动化方法进行大规模数字化，以支持标本的快速成像和相关数据采集，从而处理大多数自然历史藏品中常见的数千万件标本。在本文中，我们介绍了Inselect——一套模块化、易于使用的跨平台开源软件工具，它支持对自然历史数字化项目生成的标本图像进行半自动处理。该软件由一个适用于Windows、Mac OS X和Linux的桌面应用程序以及一些命令行工具组成，这些命令行工具专为对批量图像进行无人值守操作而设计。Inselect融合了能自动识别标本的图像可视化算法以及支持诸如条形码读取、标签转录和元数据捕获等后处理任务的工作流程，填补了提高标本数字化速度的关键空白。
