Designing an Herbarium Digitisation Workflow with Built-In Image Quality Management

Abstract

Digitisation of natural history collections has evolved from creating databases for the recording of specimens' catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows. However, digital imaging of large numbers of fragile specimens is an expensive and time-consuming process that should be performed only once. To achieve this, the digital images produced need to be useful for the largest set of applications possible and have a potentially unlimited shelf life. The constraints on digitisation speed need to be balanced against the applicability and longevity of the images, which, in turn, depend directly on the quality of those images. As a result, the quality criteria that specimen images need to fulfil influence the design, implementation and execution of digitisation workflows. Different standards and guidelines for producing quality research images from specimens have been proposed; however, their actual adaptation to suit the needs of different types of specimens requires further analysis. This paper presents the digitisation workflow implemented by Meise Botanic Garden (MBG). This workflow is relevant because of its modular design, its strong focus on image quality assessment, its flexibility that allows combining in-house and outsourced digitisation, processing, preservation and publishing ‡ ‡ ‡ § § § § § § © Nieva de la Hidalga A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.facilities and its capacity to evolve for integrating alternative components from different sources. The design and operation of the digitisation workflow is provided to showcase how it was derived, with particular attention to the built-in audit trail within the workflow, which ensures the scalable production of high-quality specimen images and how this audit trail ensures that new modules do not affect either the speed of imaging or the quality of the images produced.

Colección Viva del Jardín Botánico de Bogotá, Colombia

“…Esto ha sido resultado del desarrollo de bases de datos asociadas a un SIG, el uso de plataformas en dispositivos móviles y el constante trabajo de campo para la identificación de las plantas. Esto es congruente con diversos trabajos que han resaltado la importancia de desarrollar procesos claros en la digitalización de colecciones botánicas (Morgan, 2011;de la Hidalga et al, 2020), así como fortalecer las herramientas de sistemas de información geográfica para tener datos actualizados en las áreas de un jardín botánico (Dobesova, 2012;Dobesova et al, 2013;Escuer & Uibo, 2019).…”

Identification of herbarium specimen sheet components from high‐resolution images using deep learning

“…The visibility of the specimen label data in the corresponding digital image ‘allows the data capture process to be undertaken remotely, both in distance and time’ (Haston et al, 2015 , p. 116). Digitising enables creation of a ‘digital specimen’ (Nieva de la Hidalga et al, 2020 ): generating a digital image of each specimen sheet, manually transcribing some or all of the data present on the specimen label into a searchable database, and then sharing that information for reuse via online biodiversity repositories such as the Atlas of Living Australia (ALA; https://www.ala.org.au/ ), Global Biodiversity Information Facility (GBIF; https://www.gbif.org/ ) and iDigBio ( https://www.idigbio.org/ ).…”

The effectiveness and limitations of digital images for taxonomic research

“…The highest‐quality specimen images available online tend to be from larger institutes (in this paper referred to as “institutional images”) and many follow the JSTOR Global Plants Initiative guidelines for the digitisation of herbarium specimens (JSTOR, 2018a,b). These recommend 600 pixels/dots per inch (dpi) as the ideal scanning resolution (although the online image is usually smaller; Nieva de la Hidalga & al., 2020), as well as the addition of scale bars and colour charts to facilitate analysis and measurement. Many institutes have also inserted barcodes and provided access to metadata.…”

“…Many papers covering digitisation have focused on process such as digitisation workflows and equipment (Blagoderov & al., 2012; Holovachov & al., 2014; Takano & al., 2019; Borsch & al., 2020; Nieva de la Hidalga & al., 2020; Davis & al., 2021), standards and best practice (Häuser & al., 2005; Baskauf & Kirchoff, 2008), novel ways of specimen data input such as Optical Character Recognition (OCR) (Drinkwater & al., 2014) and crowdsourcing (Zhou & al., 2018; King & al., 2019). Studies on the use of these images have concentrated on species identification or specific extractable traits, usually related to leaf morphology (Corney & al., 2012; Carranza‐Rojas & al., 2017; Borges & al., 2020; Pryer & al., 2020).…”