A preliminary investigation into the relationship between plant health and branch labelling technique at the Royal Botanic Garden Edinburgh

Anecdotal evidence exists at the Royal Botanic Garden Edinburgh (RBGE) to suggest that branches bearing plant labels are more prone to die-back than those without labels. During 2010–2011 a preliminary study was undertaken in order to assess the accuracy of this hypothesis and to investigate the possible causes and viable alternatives. The study focused on whether there were patterns of damage with respect to label material and wire, plant species or the location of plantings. The study involved a survey of the Living Collection in the four RBGE Gardens, a web-based questionnaire sent out to Botanic Gardens Conservation International member gardens and analysis of branch material and labelling wire. This report provides the information obtained when the hypothesis was investigated and makes recommendations. An extended version, along with the data gathered, is available in the Library at RBGE (Bradley, 2011)

A New Approach to Targeting Verifications at the Royal Botanic Garden Edinburgh

Verification is the process of identifying and accurately naming the plants in the Living Collections. The Royal Botanic Garden Edinburgh (RBGE) has had a well organised system for verifying plants in place for many years but, despite this, only 26% of the Living Collections has been verified. The process is slow and time consuming and is biased towards groups and geographical areas in which Garden staff have a research interest. In the last two years, however, a new, more targeted approach to verification, to run in tandem with the existing system, has been developed that is more timeefficient. With this approach herbarium material is collected for each accession and the whole group is verified in one intensive session. Trial runs have been conducted on Alnus and Acer to great effect and further tests are being conducted on Mahonia and Spiraea

Landscape Analysis for the Specimen Data Refinery

This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We consider the potential for repurposing existing tools, including workflow management systems; and areas where more development is required. This paper was written as part of the SYNTHESYS+ project for software development teams and informatics teams working on new software-based approaches to improve mass digitisation of natural history specimens

D3.2 DiSSCo Digitisation Guides Website - Consolidating Knowledge on Collections Mobilisation

In order to support the digitisation activities of DiSSCo, we have considered how best to prepare collections for digitisation, digitise them, curate their associated data, publish those data, and measure the outputs of projects and programmes. We have examined options and approaches for different types and sizes of collections, when outsourcing should be considered, and what different project management approaches are most appropriate in this range of circumstances. This report describes the approach we have taken to developing an online community-edited manual, our guidelines, other relevant resources and platforms, and a set of recommendations on how to develop and this work to enhance future digitisation capacity across DiSSCo collectionholding organisations.info:eu-repo/semantics/publishedVersio

Specimen Data Refinery: A landscape analysis on machine learning, computer vision and automated approaches to capture specimen metadata

Capturing data from specimen images is the most viable way of enriching specimen metadata cheaply and quickly compared to traditional digitisation. Advances in machine learning and computer vision-based tools, and their increasing accessibility and affordability, are greatly increasing the potential to take automated measurements and capture other data from specimens themselves, as well as to transcribe label data. More sophisticated segmentation of images allows us to find parts of interest: particular labels; individual specimens on a slide; or barcodes. Following segmentation, there is the potential to use colour analysis of specimens to perform conditional checking, such as looking for bad cases of verdigris in pinned insects or discoloration of gum-chloral mountant. Automating measurements and landmark analysis of specimens can be used to create trait datasets, all of which will enrich our knowledge of specimens. Segmentation of labels can allow us to cluster similar labels based on their visual properties including colour, shape and patterns—this in turn can be used to make optical character recognition, handwriting recognition and manual transcription much more efficient. Atomising, validating and resolving label data will create structured label data that can be more easily stored, searched and linked to other datasets. We present a landscape analysis on the approaches, summarising previous work, and outline our plan to build future tools and systems in the SYNTHESYS+ Project as part of the Specimen Data Refinery. This will cover the sharing of tools, reducing barriers to access, integrating workflow engines into a software architecture that allows the components to be re-used and re-purposed with provenance data for repeatability, and conforms with the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles (Wilkinson et al. 2016)

Label Transcript is Done – Now what do we do with that Data?

The transcription of natural history collection labels is occurring via a variety of different methods – in-house curators, commercial operations, citizen scientists, visiting researchers, linked data, optical character recognition (OCR), handwritten text recognition (HTR), etc., but what can a collections data manager do with this flood of data? There are a whole raft of questions around this incoming data stream - who values it, who needs it, where is it stored, where is it displayed, who has access to it, etc. This talk plans to address these topics with reference to the Royal Botanic Garden Edinburgh herbarium dataset

The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels

At the Royal Botanic Garden Edinburgh (RBGE) the use of Optical Character Recognition (OCR) to aid the digitisation process has been investigated. This was tested using a herbarium specimen digitisation process with two stages of data entry. Records were initially batch-processed to add data extracted from the OCR text prior to being sorted based on Collector and/or Country. Using images of the specimens, a team of six digitisers then added data to the specimen records. To investigate whether the data from OCR aid the digitisation process, they completed a series of trials which compared the efficiency of data entry between sorted and unsorted batches of specimens. A survey was carried out to explore the opinion of the digitisation staff to the different sorting options. In total 7,200 specimens were processed.When compared to an unsorted, random set of specimens, those which were sorted based on data added from the OCR were quicker to digitise. Of the methods tested here, the most successful in terms of efficiency used a protocol which required entering data into a limited set of fields and where the records were filtered by Collector and Country. The survey and subsequent discussions with the digitisation staff highlighted their preference for working with sorted specimens, in which label layout, locations and handwriting are likely to be similar, and so a familiarity with the Collector or Country is rapidly established

Birth and death dates for individuals of twelve Rhododendron species

Birth (planting) date, depart date, and depart type (C = censored (not dead), D = dead), for individuals of twelve Rhododendron species planted at the Royal Botanic Garden Edinburgh.Funding provided by: Danmarks Frie ForskningsfondCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100011958Award Number

Describing Living Collections and Specimens

Many institutions harbor living collections in the form of living plants, animals, microrganisms or seeds. In the framework of the TDWG collections and specimen descriptions standards, it has become important to align exisiting standards for living collections and specimens or to identify where concepts or controlled vocabularies would be needed in the current TDWG standards. In September 2021 a workshop was organized in the framework of the COST Action Mobilise (https://www.mobilise-action.eu/) to get a better common understanding of the different types of living collections to consider and set the scene for further work on standards alignments. The EU COST Action CA17106 on “Mobilising Data, Experts and Policies in Scientific Collections”. Invited experts to these workshop were representatives of the TDWG Collection Description Group, the GGBN and TDWG molecular collections group, living plants collections and seed banks (Botanic Gardens Conservation International: BGCI, https://www.bgci.org/), living animal and biobanks (European Association of Zoos and Aquaria: EAZA, https://www.eaza.net/) and the culture collections (World Federation for Culture Collections: WFCC, http://www.wfcc.info/), who gave presentations on their currently used standards and challenges.The second day was devoted to break out sessions to brainstorm the specific needs for the different living collections with the aim to check and update the controlled vocabularies and concepts as needed.Identified topics were : Session 1: Voucher specimens of living accessions.Session 2: Living collections and GBIF.Session 3: How do we compare botanical gardens with herbaria?Session 4: How do we compare zoos and aquaria with natural history collections?Session 5: Culture collections: best practices and guidelines.The goal of this presentation is to address the outcome of these sessions and recommend future steps in collaboration with TDWG and the different identified stakeholders