Envisaging a global infrastructure to exploit the potential of digitised collections.

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.

Improved standardization of transcribed digital specimen data.

There are more than 1.2 billion biological specimens in the world's museums and herbaria. These objects are particularly important forms of biological sample and observation. They underpin biological taxonomy but the data they contain have many other uses in the biological and environmental sciences. Nevertheless, from their conception they are almost entirely documented on paper, either as labels attached to the specimens or in catalogues linked with catalogue numbers. In order to make the best use of these data and to improve the findability of these specimens, these data must be transcribed digitally and made to conform to standards, so that these data are also interoperable and reusable. Through various digitization projects, the authors have experimented with transcription by volunteers, expert technicians, scientists, commercial transcription services and automated systems. We have also been consumers of specimen data for taxonomical, biogeographical and ecological research. In this paper, we draw from our experiences to make specific recommendations to improve transcription data. The paper is split into two sections. We first address issues related to database implementation with relevance to data transcription, namely versioning, annotation, unknown and incomplete data and issues related to language. We then focus on particular data types that are relevant to biological collection specimens, namely nomenclature, dates, geography, collector numbers and uniquely identifying people. We make recommendations to standards organizations, software developers, data scientists and transcribers to improve these data with the specific aim of improving interoperability between collection datasets.

The Natural History Museum Data Portal.

The Natural History Museum, London (NHM), generates and holds some of the largest global data sets relating to the biological and geological diversity of the natural world. A majority of these data were, until 2015, not widely accessible, and, even when published, were typically hard to find, poorly documented and in formats that impede discovery and integration. To better serve the bespoke needs of user communities outside and within the NHM, a dedicated data portal was developed to surface these data sets and provide a sustainable platform to encourage their citation and reuse. This paper describes the technical development of the data portal, from its inception to beta launch in December 2015, its first 2 years of operation, and future plans for the project. It outlines the development principles adopted for this prototypical project, which subsequently informed new digital project management methodologies at the NHM. The process of developing the data portal acted as a driver to implement policies necessary to encourage a culture of data sharing at the NHM.