Tag: dataset

First scientific publication using digital specimen DOIs is out

Now, scientists will be in a much better position to really exchange and link data across institutions.

“The genera Chrysilla and Phintelloides revisited with the description of a new species (Araneae, Salticidae) using digital specimen DOIs and nanopublications” is the first scientific publication that uses digital specimen DOIs.

Linking data across collections

It is nothing new that our planet is facing a number of serious threats: climate change, biodiversity loss, pandemics… If you have been watching the news, all this is probably familiar to you. The wealth of data hosted in Natural history collections can contribute to finding a response to these challenges.Alas, today’s practices of working with collected bio- and geodiversity specimens lack some efficiency, thus limiting what our scientists can achieve.

In particular, there is a rather serious absence of linkages between specimen data. Sure, each specimen in a collection usually has its own catalogue ID that is unique within that collection, but the moment collections attempt to work with other collections -as they should in the face of planetary threats- problems start to arise because usually, each collection has its own way of identifying their data, thus leading to confusion.

Persistent identifiers: the DOIs

To avoid this problem, several initiatives have been launched in recent years to establish a globally accepted system of persistent identifiers (PIDs) that guarantee the “uniqueness” of collection specimens—physical or digital—over time. 

Digital specimen DOIs can point to individual specimens in a collections.

You can think of a PID as a marker, an identifier that points at a single individual object and only one, differentiating it from any other in the world. You must have heard of acronyms such as ISBN or ORCID. Those are PIDs for books and individual scholars, respectively. For digital research content, the most widely used PID is the DOI (Digital Object Identifier), proposed by the DOI Foundation. 

A DOI is an alphanumeric code that looks like this: 10.prefix/sufix 

For example, if you type https://doi.org/10.15468/w6ubjx in your browser, you will reach the Royal Belgian Institute of Natural Sciences’s mollusk collection database, accessed through GBIF. This specific DOI will never point at anything else, and the identifier will remain the same in the future, even if changes occur in the content of this particular database.

DiSSCo and the DOIs

The Distributed System of Scientific Collections (DiSSCo) aims to provide a DOI for all individual digital specimens in European natural history collections. The point is not only to accurately identify specimens. That is, of course, crucial, but the DOI of a digital specimen provides a number of other advantages that are extremely interesting for DiSSCo and natural history collections in general. Among them, two are simply revolutionary. 

The digital specimen DOI stores quick-access, basic metadata about the specimen.

Firstly, using DOIs allows linking the digital specimen to all other relevant information about the same specimen that might be hosted in other repositories (e.g. ecological data, genomic data, etc.). In creating this extended digital specimen that links different data types, digital specimen DOIs make a huge contribution to inter-institutional scientific work, filling the gap that is described at the beginning of this piece. Now scientists will be in a much better position to really exchange and link data across institutions.

Second, in contrast to most other persistent identifiers, the DOI of a digital specimen stores additional metadata (e.g. name, catalogue number) beyond the URL to which it redirects. This allows access to some information about the specimen without having to retrieve the full data object, i.e. without having to be redirected to the specimen HTML page. This metadata  facilitates AI systems to quickly navigate billions of digital specimens and perform different automated work on them, saving us (humans) precious time.

Use of DOIs in publications

With all this in mind, it is easier to understand why being able to cite digital specimens in scholarly publications using DOIs is an important step. So far, the only DOIs that we could use in publications were those at the dataset level, not at the individual specimen level. In the example above, if a scientist were to publish an article about a specific type of bivalve in the Belgian collection, the only DOI that she or he would have available for citation in the article would be that of the entire mollusk database -containing hundreds or thousands of specimens- not the one of the specific oyster or scallop that might be the focus of the publication. 

Main page of DiSSCo’s sandbox, the future DiSSCover service.

The publication in Biodiversity Data Journal about the Chrysilla and Phintelloides genera is the first of its kind and opens the door to citing not only dataset-level objects but also individual specimens in publications using DOIs. You can try it yourself: Hover over the DOIs that are cited in the publication and you will get some basic information that might save you the time of visiting the page of the institution where the specimen is. Click on it and you will be taken to DiSSCo’s sandbox -the future DiSSCover service- where you will find all the information about the digital specimen. There you will also be able to comment, annotate the specimen, and more, thus making science in a more dynamic and efficient way than until now.

A note about Christa Deeleman-Reinhold

At 94 years old, the Dutch arachnologist Christa Deeleman-Reinhold is not only one of the authors of the Chrysilla and Phintelloides article but also one of the most important arachnologists in the world. Born in 1930 on the island of Java -then part of the Dutch East Indies- Christa gained her PhD from Leiden University in 1978. Since then, she has developed a one-of-a-kind scientific career, mainly focused on spider species from South Asia. In her Forest Spiders of South East Asia (2001), Dr. Deeleman-Reinhold revised six spider families, describing 18 new genera and 115 new species. The Naturalis Biodiversity Center hosts the Christa Laetitia Deeleman-Reinhold collection, with more than 20,000 specimens.

Text and images provided by DiSSCo RI.

Research article:

Deeleman-Reinhold CL, Addink W, Miller JA (2024) The genera Chrysilla and Phintelloides revisited with the description of a new species (Araneae, Salticidae) using digital specimen DOIs and nanopublications. Biodiversity Data Journal 12: e129438. https://doi.org/10.3897/BDJ.12.e129438

Entangled “her”stories – How to create an open multi-linked dynamic dataset of plant genera named for women

Which plant genera do you know that are named for women? Who were/are they?

Guest blog post by  Siobhan Leachman, Sabine von Mering, Heather Lindon & Carmen Ulloa Ulloa

How it all began

A post on social media asked about plant genera named for women and sparked a lively discussion with many contributors. This simple question was not as easily answered as initially thought. The resulting informal working group tackled this topic remotely during the COVID-19 pandemic and beyond. The team was motivated by the desire to amplify the contribution of women to botany through eponymy. The work of this team has so far resulted in a paper in Biodiversity Data Journal, presentations at several conferences, and a linked open dataset.

Prior to our international collaboration, no dataset was available to answer these simple questions and the required information was scattered in many different data sources. We set out to bring these data together and in doing so developed and refined our workflow. Our data paper documents this innovative workflow bringing together the various data elements needed to answer our research questions. Ultimately we created a Linked Open Data (LOD) dataset that amplified the names of women and female mythological beings celebrated through generic names of flowering plants.

Linking the Data

During our research process we focused on pulling data from a wide variety of sources while at the same time proactively sharing the data generated as widely as possible. This was done by adding and linking it to multiple public databases and sources (push-pull) including the International Plant Name Index (hereafter IPNI), Tropicos®, Wikidata, Bionomia and the Biodiversity Heritage Library (hereafter BHL).

Visualisation of our workflow to create a working list of flowering plant genera named for women. 

For our list of genera, each of the protologues were reviewed to confirm the etymology or eponymy. To find the generic prologues, we searched botanical databases such as IPNI and Tropicos, openly accessible providers of digital publications and other digital libraries and websites that provide free access to such publications. Here the BHL was invaluable as the majority of protologues and many other relevant publications were openly accessible through this digital library. Where no digital publication was available we accessed scientific literature through our affiliated institutions.

For the women, our starting point was the “Index of Eponymic Plant Names – Extended Edition” by Lotte Burkhardt (2018). We manually extracted all genera honouring women.  This dataset was supplemented with other sources including IPNI (2023), Mari Mut (2017-2021), a 2022 updated version of Burkhardt’s document (Burkhardt 2022), as well as suggestions received from colleagues and generated from our own research.

We collected the following information as structured data: information on the woman honoured, the genera named in honour of the woman, the year and place of the protologue or original publication (the nomenclatural reference), the author(s) of the genus name, and the link to the protologue or original publication if available online.

Wikidata

Wikidata was the central data repository and linking mechanism for this project as it provided structured data that can be read and edited by humans and machines and it acts as a hub for other identifiers. As such Wikidata played a central role in semantic linking and enriching of our data.

Wikidata items for the plant genera were created or enriched with information about the name, the author(s) of the genus and the year of publication. Those statements were referenced using the original publication. If the protologue was available on BHL, the BHL bibliographic or page number was added to that reference, thus creating a digital link improving access to the protologue. While undertaking this work we also collated a list of all those public domain publications that appeared to be absent from BHL. We passed on this list to BHL and requested these texts be scanned and added to BHL for the benefit of everyone.

We then added a named after statement to the Wikidata item for the appropriate plant genera linking that item to the Wikidata item for the woman honoured. Wikidata items for the women honoured were newly created or enriched. We researched each person and her contributions, plus information on mythological figures where necessary, and added this information to Wikidata items. Our work also included disambiguating the woman from other people with identical or similar names. 

To amplify the women’s contributions to science and to enrich the wider (biodiversity) data ecosystem, we linked to other Wikidata items and websites or databases by adding other relevant identifiers. For example if the women were botanists, botanical collectors or other naturalists, we used the author property to link the women to publications written by them. In addition, we added the women to Bionomia and attributed specimens collected or identified by them to their profiles.

Our work also included enriching Wikidata items of taxon authors. IPNI and Tropicos were searched for these author names, and websites such as BHL, the Global Biodiversity Information Facility (GBIF) or other specialist databases were consulted. Corrections or newly researched information on taxon authors was placed not just in Wikidata but was also sent together with the corresponding references to IPNI and Tropicos. This information was then used by those organizations to update these databases accordingly. 

As a result of our data being placed in Wikidata it is available to be queried via the Wikidata Query Service.  

Our Goal Achieved

As a result of our project, we published a dataset of 728 genera honouring women or female beings. This was a nearly twenty-fold increase in the number of genera linked to women in Wikidata. Our analysis paper on this data is forthcoming.

Notable Women 

Monsonia L.

All of us came away from this research with a favourite story. One that stood out was Ann Monson, for whom Linnaeus named Monsonia. Linnaeus wrote a delightful letter to her about their creating, platonically of course, a kind of plant love-child between them, in the form of this new genus.

Translated from Latin : “….Lock these [seeds] in a pot, and place them in the window of the chamber towards the sun, when it bursts forth in February, and in the first summer the sun blooms and lasts the most beautiful Alstromeria, which no one has seen in England, and you bring forth no flowers. If it should come to pass, as I wish, if you offer our flames, I would only wish to beget with you an only child, as a pledge of my love, little Monsonia, by which you may perpetuate the fame of Lady in the kingdom of Flora, who was the Queen of Women.”

Fittonia Coem.

Two eponymous women with an interesting story are Sarah Mary Fitton and her sister Elizabeth. They wrote Conversations on Botany in 1817 accompanied by colour engravings of flowers which popularised botany with women. The genus Fittonia was named in their honour.

Chanekia Lundell

Another woman honoured in a plant genus was Mercedes Chanek, a Mayan plant collector who worked in the 1930’s for Cyrus Longworth Lundell and collected for the University of Michigan in British Honduras, today Belize. Very little is known about her life and work. However, her collections are detailed in Tropicos and Bionomia, and you can see the genus named for her by Lundell in IPNI under Chanekia.

Medusa Lour. and other genera

Medusa (c. 1597), by Caravaggio

An example of a mythological female being honoured in several plant names is that of Medusa, who has the most genera named after her, six, more than any real woman!

We hope that our data paper inspires others to use the methodology and workflow described to create other linked open datasets, e.g. celebrating and amplifying the contributions of underrepresented or marginalised groups in science.

Data paper: 

von Mering S, Gardiner LM, Knapp S, Lindon H, Leachman S, Ulloa Ulloa C, Vincent S, Vorontsova MS (2023) Creating a multi-linked dynamic dataset: a case study of plant genera named for women. Biodiversity Data Journal 11: e114408. https://doi.org/10.3897/BDJ.11.e114408

Unraveling nature’s chorus: AI detects bird sounds in Taiwan’s montane forests

Researchers developed an AI tool which identifies 169 species native to Taiwan from the sound of their calls.

Spectacular subtropical montane forest scenery in Yushan National Park. Credit: Ms. Wen-Ling Tsai

Montane forests, known as biodiversity hotspots, are among the ecosystems facing threats from climate change. To comprehend potential impacts of climate change on birds in these forests, researchers set up automatic recorders in Yushan National Park, Taiwan, and developed an AI tool for species identification using bird sounds. Their goal is to analyze status and trends in animal activity through acoustic data.

Prof. Hsueh-Wen Chang and Ph.D. Candidate Shih-Hung Wu from National Sun Yat-Sen University, Taiwan, Dr. Ruey-Shing Lin, Assistant Researcher Jerome Chie-Jen Ko from the Endemic Species Research Institute, and Ms. Wen-Ling Tsai from Yushan National Park Headquarters have published a paper in the open access journal Biodiversity Data Journal, detailing their use of AI to detect 6 million bird songs.

Compared to traditional observation-based methods, passive acoustic monitoring using automatic recorders to capture wildlife sounds provides cost-effective, long-term, and systematic alternative for long-term biodiversity monitoring.

The authors deployed six recorders in Yushan National Park, Taiwan, a subtropical montane forest habitat with elevations ranging from 1,200 to 2,800 meters. From 2020 to 2021, they recorded nearly 30,000 hours of audio files with abundant biological information.

An automatic recorder was installed on a tree to capture the surrounding soundscape. Credit: Ph.D. Candidate Shih-Hung Wu

However, analyzing this vast dataset is challenging and requires more than human effort alone.

To tackle this challenge, the authors utilized deep learning technology to develop an AI tool called SILIC that can identify species by sound.

SILIC can quickly pinpoint the precise timing of each animal call within the audio files. After several optimizations, the tool is now capable of recognizing 169 species of wildlife native to Taiwan, including 137 bird species, as well as frogs, mammals, and reptiles.

In this study, authors used SILIC to extract 6,243,820 vocalizations from seven montane forest bird species with a high precision of 95%, creating the first open-access AI-analyzed species occurrence dataset available on the Global Biodiversity Information Facility. This is the first open-access dataset with species occurrence data extracted from sounds in soundscape recordings by artificial intelligence.

The Gray-chinned Minivet (left) displays a secondary non-breeding season peak (right) which is possibly related to flocking behavior. Credit: Shih-Hung Wu, Ph.D. Candidate

The dataset unveils detailed acoustic activity patterns of wildlife across both short and long temporal scales. For instance, in diel patterns, the authors identify a morning vocalization peak for all species. On an annual basis, most species exhibit a single breeding season peak; however, some, like the Gray-chinned Minivet, display a secondary non-breeding season peak, possibly related to flocking behavior.

As the monitoring projects continue, the acoustic data may help to understand changes and trends in animal behavior and population across years in a cost-effective and automated manner.

The sound of Gray-chinned Minivet. Credit: Ph.D. Candidate Shih-Hung Wu

The authors anticipate that this extensive wildlife vocalization dataset will not be valuable only for the National Park’s headquarters in decision-making.

“We expect our dataset will be able to help fill the data gaps of fine-scale avian temporal activity patterns in montane forests and contribute to studies concerning the impacts of climate change on montane forest ecosystems,”

they say.

Original source:

Wu S-H, Ko JC-J, Lin R-S, Tsai W-L, Chang H-W (2023) An acoustic detection dataset of birds (Aves) in montane forests using a deep learning approach. Biodiversity Data Journal 11: e97811. https://doi.org/10.3897/BDJ.11.e97811

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Tag team: a tale of two Antarctic blue whales

For the first time, the satellite tracks of two Antarctic blue whales, tagged a decade ago, have been published in the open-access Biodiversity Data Journal.

Ten years ago, Dr Virginia Andrews-Goff was riding the bowsprit of a six-metre boat, as a 30-metre, 120-tonne Antarctic blue whale surfaced alongside.

That day in the Southern Ocean, she became the first and, so far, the only person, to deploy satellite tags on two of these critically endangered and rarely sighted giants.

Scientists approach a 30 metre blue whale in their six metre boat. ©Kylie Owens/Australian Antarctic Division

At the time, her success added weight to a case in the United Nations International Court of Justice, demonstrating that scientific research on whales could be conducted without killing them.

Dr Andrews-Goff and her colleagues at the Australian Antarctic Division have now published the two satellite tracks generated by that 2013 work, in the open-access Biodiversity Data Journal.

This is a unique data set that was incredibly challenging to get.

Dr Virginia Andrews-Goff

The tracks give an insight into the animals’ movement and behaviour on their feeding grounds, and illustrate the significant logistical challenges needed to successfully locate, tag, and track Antarctic blue whales.

“This is a unique data set that was incredibly challenging to get, and, unfortunately, for 10 years no-one has been able to generate more data,” Dr Andrews-Goff said.

“We know very little about the movement and distribution of Antarctic blue whales, where they migrate, where they forage and breed, and we don’t understand the threats they might face as they recover from whaling.”

Two satellite tagged Antarctic blue whales have provided the first insights into the movement and behaviour of these critically endangered ocean giants on their feeding grounds. ©Australian Antarctic Division

Part of the issue is that the animals are incredibly difficult to find. Commercial whaling in the 1960s and ‘70s killed about 290,000 Antarctic blue whales, accounting for 90% of the population. By the late 1990s, the world’s population of Antarctic blue whales was estimated at 2280 animals.

Back in 2013, the research team used novel acoustic tracking techniques to detect blue whale calls and hone in on their location from up to 1000 kilometres away. Once the whales were in sight (in two separate locations), an expert crew manoeuvred close to their fast-moving targets.

The satellite tags showed that the whales travelled 1390 kilometres in 13 days and 5550 kilometres in 74 days, with an average distance of more than 100 kilometres per day.

“The two whales did entirely different things, but what became obvious is that these animals can travel really quickly,” Dr Andrews-Goff said.

“If you consider how far and fast these animals moved, protecting the broader population against potential threats will be tricky because they could potentially circumnavigate Antarctica within a single feeding season.”

his map shows the movement of two satellite tagged Antarctic blue whales. The track on the bottom right are the movements of one whale over 13 days. The other three tracks capture segments of movement by the second whale over 74 days. The tag for this second whale did not transmit data consistently, resulting in data gaps throughout the tracking period.
The blue portions of track show where the whales were moving quickly and directly, suggesting they were in transit, while the orange locations show where they slowed down and appeared to be searching or foraging.    ©Australian Antarctic Division

Since the tracks were obtained, new analytical methods have added some behavioural context to the data.

Two movement rates were observed – a faster ‘in transit’ speed averaging 4.2 km/hr and a slower speed of 2.5 km/hr, thought to correspond with searching or foraging.

“It looks like the whales might hang around in one area to feed and then move quickly to another area and hang around there for another feed,” Dr Andrews-Goff said.

“There may be certain areas that are better feeding grounds than others. From a management perspective, it would be good to understand what is it that makes these areas important?”

Even at a sample size of two, Dr Andrews-Goff said the satellite tracks will assist the International Whaling Commission’s management of Antarctic blue whales, by providing initial insights into blue whale foraging ecology, habitat preferences, distribution, movement rates, and feeding. These will inform an in-depth assessment of Antarctic blue whales due to begin in 2024.

Original source:

Andrews-Goff V, Bell EM, Miller BS, Wotherspoon SJ, Double MC (2022). Satellite tag derived data from two Antarctic blue whales (Balaenoptera musculus intermedia) tagged in the east Antarctic sector of the Southern Ocean. Biodviersity Data Journal 10: e94228 https://doi.org/10.3897/BDJ.10.e94228

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Call for data papers describing datasets from Russia to be published in Biodiversity Data Journal

GBIF partners with FinBIF and Pensoft to support publication of new datasets about biodiversity from across Russia

Original post via GBIF

In collaboration with the Finnish Biodiversity Information Facility (FinBIF) and Pensoft Publishers, GBIF has announced a new call for authors to submit and publish data papers on Russia in a special collection of Biodiversity Data Journal (BDJ). The call extends and expands upon a successful effort in 2020 to mobilize data from European Russia.

Between now and 15 September 2021, the article processing fee (normally €550) will be waived for the first 36 papers, provided that the publications are accepted and meet the following criteria that the data paper describes a dataset:

The manuscript must be prepared in English and is submitted in accordance with BDJ’s instructions to authors by 15 September 2021. Late submissions will not be eligible for APC waivers.

Sponsorship is limited to the first 36 accepted submissions meeting these criteria on a first-come, first-served basis. The call for submissions can therefore close prior to the stated deadline of 15 September 2021. Authors may contribute to more than one manuscript, but artificial division of the logically uniform data and data stories, or “salami publishing”, is not allowed.

BDJ will publish a special issue including the selected papers by the end of 2021. The journal is indexed by Web of Science (Impact Factor 1.331), Scopus (CiteScore: 2.1) and listed in РИНЦ / eLibrary.ru.

For non-native speakers, please ensure that your English is checked either by native speakers or by professional English-language editors prior to submission. You may credit these individuals as a “Contributor” through the AWT interface. Contributors are not listed as co-authors but can help you improve your manuscripts.

In addition to the BDJ instruction to authors, it is required that datasets referenced from the data paper a) cite the dataset’s DOI, b) appear in the paper’s list of references, and c) has “Russia 2021” in Project Data: Title and “N-Eurasia-Russia2021“ in Project Data: Identifier in the dataset’s metadata.

Authors should explore the GBIF.org section on data papers and Strategies and guidelines for scholarly publishing of biodiversity data. Manuscripts and datasets will go through a standard peer-review process. When submitting a manuscript to BDJ, authors are requested to select the Biota of Russia collection.

To see an example, view this dataset on GBIF.org and the corresponding data paper published by BDJ.

Questions may be directed either to Dmitry Schigel, GBIF scientific officer, or Yasen Mutafchiev, managing editor of Biodiversity Data Journal.

The 2021 extension of the collection of data papers will be edited by Vladimir Blagoderov, Pedro Cardoso, Ivan Chadin, Nina Filippova, Alexander Sennikov, Alexey Seregin, and Dmitry Schigel.

This project is a continuation of the successful call for data papers from European Russia in 2020. The funded papers are available in the Biota of Russia special collection and the datasets are shown on the project page.

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Definition of terms

Datasets with more than 5,000 records that are new to GBIF.org

Datasets should contain at a minimum 5,000 new records that are new to GBIF.org. While the focus is on additional records for the region, records already published in GBIF may meet the criteria of ‘new’ if they are substantially improved, particularly through the addition of georeferenced locations.” Artificial reduction of records from otherwise uniform datasets to the necessary minimum (“salami publishing”) is discouraged and may result in rejection of the manuscript. New submissions describing updates of datasets, already presented in earlier published data papers will not be sponsored.

Justification for publishing datasets with fewer records (e.g. sampling-event datasets, sequence-based data, checklists with endemics etc.) will be considered on a case-by-case basis.

Datasets with high-quality data and metadata

Authors should start by publishing a dataset comprised of data and metadata that meets GBIF’s stated data quality requirement. This effort will involve work on an installation of the GBIF Integrated Publishing Toolkit.

Only when the dataset is prepared should authors then turn to working on the manuscript text. The extended metadata you enter in the IPT while describing your dataset can be converted into manuscript with a single-click of a button in the ARPHA Writing Tool (see also Creation and Publication of Data Papers from Ecological Metadata Language (EML) Metadata. Authors can then complete, edit and submit manuscripts to BDJ for review.

Datasets with geographic coverage in Russia

In correspondence with the funding priorities of this programme, at least 80% of the records in a dataset should have coordinates that fall within the priority area of Russia. However, authors of the paper may be affiliated with institutions anywhere in the world.

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Check out the Biota of Russia dynamic data paper collection so far.

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