BKH is a one-stop portal that allows users to access FAIR and interlinked biodiversity data and services in a few clicks. BKH was designed to support a new emerging community of users over time and across the entire biodiversity research cycle providing its services to anybody, anywhere and anytime.
In an age where we more than ever need to appreciate and preserve the magnificent biodiversity inhabiting the Earth, we decided to go for a lighter and fun take on the work of taxonomists that often goes unnoticed by the public.
From the ocean depths surrounding Indonesia to the foliage of the native forests of Príncipe Island and into the soils of Borneo, we started with 16 species described as new to science in journals published by Pensoft over the years.
Out of these most amazing creatures, over the past several weeks we sought to find who’s got the greatest fandom by holding a poll on Twitter (you can follow it further down here or via #NewSpeciesShowdown).
While we need to admit that we ourselves expected to crown an animal as the crowd-favourite, we take the opportunity to congratulate the botanists amongst our fans for the well-deserved win of Nepenthes pudica (see the species description)!
Find more about the curious one-of-a-kind pitcher plant in this blog post, where we announced its discovery following the new species description in PhytoKeys in June 2022:
What’s perhaps even more curious, is that there is only one species EVER described in a Pensoft-published journal that has so far triggered more tweets than the pitcher plant, and that species is the animal that has ended up in second place in the New Species Showdown: a tiny amphibian living in Peru, commonly known as the the Amazon Tapir Frog (Synapturanus danta).Which brings us once again to the influence of botanists in taxonomic research.
Read more about its discovery in the blog post from February 2022:
Another thing that struck us during the tournament was that there was only one species described in our flagship journal in systematic journal ZooKeys: the supergiant isopod Bathynomus raksasa, that managed to fight its way to the semi-finals, where it lost against S. danta.
This makes us especially proud with our diverse and competitive journal portfolio full of titles dedicated to biodiversity and taxonomic research!
Twice a week, @Pensoft would announce a match between two competing species on Twitter using the hashtag #NewSpeciesShowdown, where everyone could vote in the poll for their favourie.
This competition is for entertainment purposes only. As it was tremendously tough to narrow the list down to only sixteen species, we admit that we left out a lot of spectacular creatures.
To ensure fairness and transparency, we made the selection based on the yearly Altmetric data, which covers articles in our journals published from 2010 onwards and ranks the publications according to their online mentions from across the Web, including news media, blogs and social networks.
We did our best to diversify the list as much as possible in terms of taxonomic groups. However, due to the visual-centric nature of social media, we gave preference to immediately attractive species.
(in chronological order)
Round 2 – Quarter-finals
Round 3 – Semi-finals
But why did we hold the tournament right now?
If you have gone to the Pensoft website at any point in 2022, visited our booth at a conference, or received a newsletter from any of our journals, by this time, you must be well aware that in 2022 – more precisely, on 25 December – we turned 30. And we weren’t afraid to show it!
Indeed, 30 is not that big of a number, as many of us adult humans can confirm. Yet, we take pride in reminiscing about what we’ve done over the last three decades.
Long story short, we wanted to do something special and fun to wrap up our anniversary year. While we have been active in various areas, including development of publishing technology concerning open and FAIR access and linkage for research outcomes and underlying data; and multiple EU-supported scientific projects, we have always been associated with our biodiversity journal portfolio.
Besides, who doesn’t like to learn about the latest curious creature that has evaded scientific discovery throughout human history up until our days? 😉
In their Research Idea, published in Research Ideas and Outcomes (RIO Journal), Swiss-Dutch research team present a promising machine-learning ecosystem to unite experts around the world and make up for lacking expert staff
Guest blog post by Luc Willemse, Senior collection manager at Naturalis Biodiversity Centre (Leiden, Netherlands)
Imagine the workday of a curator in a national natural history museum. Having spent several decades learning about a specific subgroup of grasshoppers, that person is now busy working on the identification and organisation of the holdings of the institution. To do this, the curator needs to study in detail a huge number of undescribed grasshoppers collected from all sorts of habitats around the world.
The problem here, however, is that a curator at a smaller natural history institution – is usually responsible for all insects kept at the museum, ranging from butterflies to beetles, flies and so on. In total, we know of around 1 million described insect species worldwide. Meanwhile, another 3,000 are being added each year, while many more are redescribed, as a result of further study and new discoveries. Becoming a specialist for grasshoppers was already a laborious activity that took decades, how about knowing all insects of the world? That’s simply impossible.
Then, how could we expect from one person to sort and update all collections at a museum: an activity that is the cornerstone of biodiversity research? A part of the solution, hiring and training additional staff, is costly and time-consuming, especially when we know that experts on certain species groups are already scarce on a global scale.
We believe that automated image recognition holds the key to reliable and sustainable practises at natural history institutions.
Today, image recognition tools integrated in mobile apps are already being used even by citizen scientists to identify plants and animals in the field. Based on an image taken by a smartphone, those tools identify specimens on the fly and estimate the accuracy of their results. What’s more is the fact that those identifications have proven to be almost as accurate as those done by humans. This gives us hope that we could help curators at museums worldwide take better and more timely care of the collections they are responsible for.
However, specimen identification for the use of natural history institutions is still much more complex than the tools used in the field. After all, the information they store and should be able to provide is meant to serve as a knowledge hub for educational and reference purposes for present and future generations of researchers around the globe.
This is why we propose a sustainable system where images, knowledge, trained recognition models and tools are exchanged between institutes, and where an international collaboration between museums from all sizes is crucial. The aim is to have a system that will benefit the entire community of natural history collections in providing further access to their invaluable collections.
We propose four elements to this system:
A central library of already trained image recognition models (algorithms) needs to be created. It will be openly accessible, so any other institute can profit from models trained by others.
A central library of datasets accessing images of collection specimens that have recently been identified by experts. This will provide an indispensable source of images for training new algorithms.
A digital workbench that provides an easy-to-use interface for inexperienced users to customise the algorithms and datasets to the particular needs in their own collections.
As the entire system depends on international collaboration as well as sharing of algorithms and datasets, a user forum is essential to discuss issues, coordinate, evaluate, test or implement novel technologies.
How would this work on a daily basis for curators? We provide two examples of use cases.
First, let’s zoom in to a case where a curator needs to identify a box of insects, for example bush crickets, to a lower taxonomic level. Here, he/she would take an image of the box and split it into segments of individual specimens. Then, image recognition will identify the bush crickets to a lower taxonomic level. The result, which we present in the table below – will be used to update object-level registration or to physically rearrange specimens into more accurate boxes. This entire step can also be done by non-specialist staff.
Another example is to incorporate image recognition tools into digitisation processes that include imaging specimens. In this case, image recognition tools can be used on the fly to check or confirm the identifications and thus improve data quality.
Using image recognition tools to identify specimens in museum collections is likely to become common practice in the future. It is a technical tool that will enable the community to share available taxonomic expertise.
Using image recognition tools creates the possibility to identify species groups for which there is very limited to none in-house expertise. Such practises would substantially reduce costs and time spent per treated item.
Image recognition applications carry metadata like version numbers and/or datasets used for training. Additionally, such an approach would make identification more transparent than the one carried out by humans whose expertise is, by design, in no way standardised or transparent.
Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187