Zenodo, an Archive and Publishing Repository: A tale of two herbarium specimen pilot projects

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Abstract

Zenodo (https://zenodo.org) is an open-access repository operated by CERN (European Organization for Nuclear Research), which provides researchers with an easy and stable platform to archive and publish their data and other output, such as software tools, manuals and project reports. In the context of the ICEDIG (Innovation and Consolidation for Large scale Digitisation of Natural Heritage) project, Zenodo was investigated for its usability as a platform where digitized images of collection specimens could be archived and published. In a production digitization pipeline, we foresee the automated archiving of daily image production. If Zenodo could be used for this purpose, such a process would also immediately mean that data and images are published FAIR-ly (Findable, Accessible, Interoperable and Reusable) within hours of their creation. To evaluate performance of the system, we first used a test dataset of 1800 herbarium specimen images, which was uploaded using Zenodo's API (Application Programming Interface) (Dillen et al. 2019). This dataset includes lossless TIFF images, label-segmented overlays and JSON-LD (JavaScript Object Notation for Linked Data) metadata using DwC (Darwin Core) terminology, constituting over 208 gigabytes of data. In addition, for all individual digital specimens the data about the specimen (in DwC) as well as metadata about its deposition on Zenodo (in Zenodo's internal data model) were available in multiple machine-readable formats. All data in DwC were provided as linked data with their DwC identifiers (e.g. http://rs.tdwg.org/dwc/terms/basisOfRecord). All individual specimens received minted DOIs (Digital Object Identifiers). A second upload of 280,000 herbarium JPEG images from a single institution (ca. 1 terabyte of data) with limited metadata (but using the same approach) was launched as well. In this presentation, the workflow for proper usage of the API will be described as well as some performance metrics, flexibilities and functionalities of the platform. Some issues and potential developments to tackle them will be discussed. Currently, the rate of ingestion into Zenodo seems only fast enough for small scale digitization pipelines. However, a modest improvement in transfer rate would make this a realistic proposition for large volume usage.

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A benchmark dataset of herbarium specimen images with label data

Mathias Dillen, Quentin J Groom, Simon Chagnoux … (2019)

Abstract Background More and more herbaria are digitising their collections. Images of specimens are made available online to facilitate access to them and allow extraction of information from them. Transcription of the data written on specimens is critical for general discoverability and enables incorporation into large aggregated research datasets. Different methods, such as crowdsourcing and artificial intelligence, are being developed to optimise transcription, but herbarium specimens pose difficulties in data extraction for many reasons. New information To provide developers of transcription methods with a means of optimisation, we have compiled a benchmark dataset of 1,800 herbarium specimen images with corresponding transcribed data. These images originate from nine different collections and include specimens that reflect the multiple potential obstacles that transcription methods may encounter, such as differences in language, text format (printed or handwritten), specimen age and nomenclatural type status. We are making these specimens available with a Creative Commons Zero licence waiver and with permanent online storage of the data. By doing this, we are minimising the obstacles to the use of these images for transcription training. This benchmark dataset of images may also be used where a defined and documented set of herbarium specimens is needed, such as for the extraction of morphological traits, handwriting recognition and colour analysis of specimens.