Digitising the Natural History Museum London’s entire collection could contribute over £2 billion to the global economy

In a world first, the Natural History Museum, London, has collaborated with economic consultants, Frontier Economics Ltd, to explore the economic and societal value of digitising natural history collections and concluded that digitisation has the potential to see a seven to tenfold return on investment. Whilst significant progress is already being made at the Museum, additional investment is needed in order to unlock the full potential of the Museum’s vast collections – more than 80 million objects. The project’s report is published in the open science scientific journal Research Ideas and Outcomes (RIO Journal).

One of the Museum’s digitisers imaging a butterfly to join the 4.93 million specimens already available online. 
© The Trustees of the Natural History Museum, London

The societal benefits of digitising natural history collections extends to global advancements in food security, biodiversity conservation, medicine discovery, minerals exploration, and beyond. Brand new, rigorous economic report predicts investing in digitising natural history museum collections could also result in a tenfold return. The Natural History Museum, London, has so far made over 4.9 million digitised specimens available freely online – over 28 billion records have been downloaded over 429,000 download events over the past six years. 

Digitisation at the Natural History Museum, London 

Digitisation is the process of creating and sharing the data associated with Museum specimens. To digitise a specimen, all its related information is added to an online database. This typically includes where and when it was collected and who found it, and can include photographs, scans and other molecular data if available. Natural history collections are a unique record of biodiversity dating back hundreds of years, and geodiversity dating back millennia. Creating and sharing data this way enables science that would have otherwise been impossible, and we accelerate the rate at which important discoveries are made from our collections.  

The Natural History Museum’s collection of 80 million items is one of the largest and most historically and geographically diverse in the world. By unlocking the collection online, the Museum provides free and open access for global researchers, scientists, artists and more. Since 2015, the Museum has made 4.9 million specimens available on the Museum’s Data Portal, which have seen more than 28 billion downloads over 427,000 download events. 

This means the Museum has digitised  about 6% of its collections to date. Because digitisation is expensive, costing tens of millions of pounds, it is difficult to make a case for further investment without better understanding the value of this digitisation and its benefits. 

In 2021, the Museum decided to explore the economic impacts of collections data in more depth, and commissioned Frontier Economics to undertake modelling, resulting in this project report, now made publicly available in the open-science journal Research Ideas and Outcomes (RIO Journal), and confirming benefits in excess of £2 billion over 30 years. While the methods in this report are relevant to collections globally, this modelling focuses on benefits to the UK, and is intended to support the Museum’s own digitisation work, as well as a current scoping study funded by the Arts & Humanities Research Council about the case for digitising all UK natural science collections as a research infrastructure.

Sharing data from our collections can transform scientific research and help find solutions for nature and from nature. Our digitised collections have helped establish the baseline plant biodiversity in the Amazon, find wheat crops that are more resilient to climate change and support research into potential zoonotic origins of Covid-19. The research that comes from sharing our specimens has immense potential to transform our world and help both people and the planet thrive,

says Helen Hardy, Science Digital Programme Manager at the Natural History Museum.

How digitisation impacts scientific research?

The data from museum collections accelerates scientific research, which in turn creates benefits for society and the economy across a wide range of sectors. Frontier Economics Ltd have looked at the impact of collections data in five of these sectors: biodiversity conservation, invasive species, medicines discovery, agricultural research and development and mineral exploration. 

The Natural History Museum’s collection is a real treasure trove which, if made easily accessible to scientists all over the world through digitisation, has the potential to unlock ground-breaking research in any number of areas. Predicting exactly how the data will be used in future is clearly very uncertain. We have looked at the potential value that new research could create in just five areas focussing on a relatively narrow set of outcomes. We find that the value at stake is extremely large, running into billions,”

says Dan Popov, Economist at Frontier Economics Ltd.

The new analyses attempt to estimate the economic value of these benefits using a range of approaches, with the results in broad agreement that the benefits of digitisation are at least ten times greater than the costs. This represents a compelling case for investment in museum digital infrastructure without which the many benefits will not be realised.

This new analysis shows that the data locked up in our collections has significant societal and economic value, but we need investment to help us release it,

adds Professor Ken Norris, Head of the Life Sciences Department at the Natural History Museum.

Other benefits could include improvements to the resilience of agricultural crops by better understanding their wild relatives, research into invasive species which can cause significant damage to ecosystems and crops, and improving the accuracy of mining.  

Finally, there are other impacts that such work could have on how science is conducted itself. The very act of digitising specimens means that researchers anywhere on the planet can access these collections, saving time and money that may have been spent as scientists travelled to see specific objects.

The value of research enabled by digitisation of natural history collections can be estimated by looking at specific areas where the Museum’s collections contribute towards scientific research and subsequently impact the wider economy. 
© Frontier Economics Ltd.

Original source: 

Popov D, Roychoudhury P, Hardy H, Livermore L, Norris K (2021) The Value of Digitising Natural History Collections. Research Ideas and Outcomes 7: e78844. https://doi.org/10.3897/rio.7.e78844

Artificial neural networks could power up curation of natural history collections

Deep learning techniques manage to differentiate between similar plant families with up to 99 percent accuracy, Smithsonian researchers reveal

Millions, if not billions, of specimens reside in the world’s natural history collections, but most of these have not been carefully studied, or even looked at, in decades. While containing critical data for many scientific endeavors, most objects are quietly sitting in their own little cabinets of curiosity.

Thus, mass digitization of natural history collections has become a major goal at museums around the world. Having brought together numerous biologists, curators, volunteers and citizens scientists, such initiatives have already generated large datasets from these collections and provided unprecedented insight.

Now, a study, recently published in the open access Biodiversity Data Journal, suggests that the latest advances in both digitization and machine learning might together be able to assist museum curators in their efforts to care for and learn from this incredible global resource.

A team of researchers from the Smithsonian Department of BotanyData Science Lab, and Digitization Program Office recently collaborated with NVIDIA to carry out a pilot project using deep learning approaches to dig into digitized herbarium specimens.

Smithsonian researchers classifying digitized herbarium sheets.
Smithsonian researchers classifying digitized herbarium sheets.

Their study is among the first to describe the use of deep learning methods to enhance our understanding of digitized collection samples. It is also the first to demonstrate that a deep convolutional neural network–a computing system modelled after the neuron activity in animal brains that can basically learn on its own–can effectively differentiate between similar plants with an amazing accuracy of nearly 100%.

In the paper, the scientists describe two different neural networks that they trained to perform tasks on the digitized portion (currently 1.2 million specimens) of the United States National Herbarium.

The team first trained a net to automatically recognize herbarium sheets that had been stained with mercury crystals, since mercury was commonly used by some early collectors to protect the plant collections from insect damage. The second net was trained to discriminate between two families of plants that share a strikingly similar superficial appearance.

Sample herbarium specimen image of stained clubmoss
Sample herbarium specimen image of stained clubmoss.

The trained neural nets performed with 90% and 96% accuracy respectively (or 94% and 99% if the most challenging specimens were discarded), confirming that deep learning is a useful and important technology for the future analysis of digitized museum collections.

“The results can be leveraged both to improve curation and unlock new avenues of research,” conclude the scientists.

“This research paper is a wonderful proof of concept. We now know that we can apply machine learning to digitized natural history specimens to solve curatorial and identification problems. The future will be using these tools combined with large shared data sets to test fundamental hypotheses about the evolution and distribution of plants and animals,” says Dr. Laurence J. Dorr, Chair of the Smithsonian Department of Botany.

 

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Original source:

Schuettpelz E, Frandsen P, Dikow R, Brown A, Orli S, Peters M, Metallo A, Funk V, Dorr L (2017) Applications of deep convolutional neural networks to digitized natural history collections. Biodiversity Data Journal 5: e21139. https://doi.org/10.3897/BDJ.5.e21139