This post is part of BHL at 20: Treasures from the Biodiversity Heritage Library, a series contributed by members of the BHL community, highlighting remarkable works from across the collection in celebration of its 20th anniversary.
One of the greatest challenges for a digital library, especially one as large as the Biodiversity Heritage Library, is simply finding the content you are after. Recently, I made a website to give a sense of this challenge. The website features about 200,000 pages of content from BHL Australia, a small fraction of what is in BHL overall, but already it’s something of a challenge to find specific items you might be after. If you were looking for a particular article, how would you find it?
Interactive browser of BHL Australia
Articles, articles, articles
For most scientists, the article is the fundamental unit of research, not the journal title, and not a journal volume. The article is what we download as a PDF, what we store in our reference managers, and what gets cited. At the outset BHL did not have articles, so over a decade ago I set about developing a tool to find those. This tool became BioStor, which was described in a paper in 2011 (Extracting scientific articles from a large digital archive: BioStor and the Biodiversity Heritage Library). The basic idea behind BioStor is to take information about an article, such as journal, volume, pages, and year, and then try and find that article in BHL.
Mapping journal, volume, and pagination from an article to BHL.
In principle this seems straightforward, but often the vagaries of metadata complicate the task. The image below shows some of the issues encountered with the journal Ibis. The source of metadata for the articles was CrossRef, via a commercial publisher (Wiley). You might expect this data to be high quality, but it contains errors such as bad character encoding. To further complicate things, Wiley decided to renumber all the volumes of the journal, so that the original volume information we see in BHL (such as series 2, volume 1) bears little relation to what is in CrossRef (volume 7, issue 1).
Matching CrossRef metadata for an article in Ibis to BHL.
The list of metadata messes like this is almost endless. There are journals that have more than one numbering system for the same volumes (e.g., Annali del Museo civico di storia naturale Giacomo Doria where the same item is both series 3, volume 7 and volume 47), there are multiple abbreviations for the same journal, and there are journals with multiple names (e.g., title 8097 is “Annuaire du Musée zoologique de l’Académie des sciences de St. Pétersbourg”, “ЕЖЕГОДНИКЬ ЗООЛОГИЧЕСКАГО МУЗЕЯ ИМПЕРАТОРСКОЙ АКАДЕМІЙ НАУКЬ”, and “Ezhegodnik Zoologicheskago muzeia …”).
A further complication is that a scanned item in BHL may contain several issues or volumes, each with its own set of overlapping page numbers, which means we have to decide which page “1” is the page 1 that matches the article we are searching for. Once we solve all that, we encounter further problems. Perhaps the most challenging is pagination. In most modern articles, the page range, e.g. 1–5, completely encompasses the article, including figures, charts, illustrations, etc. But for the older literature this is often not the case. Typesetting text and reproducing plates were different processes, and hence the plates might be disconnected from the article (often appearing at the end of a volume). This means that extracting, say pages 1–5, from BHL is no guarantee that you have the whole article.
A good deal of code in BioStor is trying to make sense of matching article metadata to BHL items, finding the correct page to match to, and extracting the set of pages that correspond to that article, as well as providing tools to manually correct metadata and add missing pages (for example, the plates mentioned above). Hence the process of finding articles is, at best, semiautomated.
BioStor old and new
The original BioStor website dates back to 2009, and looked something like this:
Original BioStor website.
This site could display individual articles, and you could edit metadata. For a variety of reasons, it was no longer feasible to host this at the university where I was based, so I split the website into two versions. The original site now runs only on my laptop, and I use it to process files and locate articles in BHL. The new version runs in the cloud and features a cleaner interface, along with much better search. Below is the same article in the current BioStor.
Current BioStor website.
Once articles are discovered using the old BioStor, they get pushed to the public version of BioStor at https://biostor.org. This website is also the point of contact between BioStor and BHL: each day BHL runs an automated process which asks BioStor whether it has any new articles, and, if the answer is yes, it fetches those and adds them to BHL (in BHL articles are referred to as either “parts” or “segments”). The end result is that articles defined in BioStor now become visible in the Table of Contents in BHL.
BioStor article displayed in BHL.
One advantage of having a separate project such as BioStor is that I can use it to experiment with different ways to view BHL content. For example, BioStor looks for geographic coordinates (latitude and longitude) in the OCR text for each article. Any pairs of coordinates that it finds get stored in a map, which you can browse. In the diagram below we have selected a small region in the centre of the map, on the right you can see a list of articles about that area.
Maps showing localities on the island of Sulawesi that are mentioned in BioStor articles.
Identifiers
BioStor has been running since 2009. In that time it has contributed over 260,000 articles to BHL, making it the single largest source of BHL “parts”. Having articles is nice, but even better is having articles with persistent, citable identifiers, such as DOIs. The Persistent Identifier Working Group has been working to add DOIs to BHL content, especially “parts”. This work has focused on two kinds of DOIs. The first are existing DOIs minted, for example, by commercial publishers. BioStor adds a lot of articles using CrossRef metadata, so we get these “for free” (there are other sources of DOIs that BioStor uses, but that is another story). Why does it matter to have external DOIs for BHL content? Well, many of these articles are free in BHL but behind a paywall on the publisher’s website. Services such as Unpaywall can link existing DOIs to free versions of the corresponding article, and BHL is one of Unpaywall’s providers.
But the more exciting (and onerous) task is minting new DOIs for articles in BHL, so that BHL is the version of record for that content. This has several implications. It means that BHL is effectively a publisher, and has the responsibility to maintain access to this content in perpetuity. It also changes the way we think about adding articles. For example, most of my work with BioStor has been opportunistic – I’m working on a taxonomic database, I see that there are some papers that should be in BHL, find them, then add them to BioStor so future BHL users can find those articles. But once we start creating DOIs, the goal is quite different: you want to get every article in the journal that is in BHL, and mint DOIs for all of them. While this appeals to a completionist mindset, it does mean getting metadata for every article before you can add DOIs.
Luckily, the hard work in minting DOIs has a striking payback, we can see how many times articles in BHL are cited in the scientific literature. The last time the results were analyzed, BHL articles had been cited some 74,446 times! Without BHL these publications would appear as simple text strings in the literature cited, now they are first-class digital citizens with clickable DOI links.
Metadata matters
By now it is obvious that the way BioStor finds articles depends on having good quality metadata for articles (or chapters), which it then attempts to locate in BHL. The lack of freely accessible metadata is a major impediment to increasing the rate at which articles are added. In the past I have made extensive use of taxonomic databases as a source of bibliographic data (see my BioNames project, for example). Yet the quality of citations in these databases is often poor. I have also made extensive use of sources such as CrossRef, which covers articles that have been assigned DOIs by that agency, and also data provided by volunteers, such as those working with Nicole Kearney (thank you Bob Griffith and Heidi Griffith!). Another major source of data has come from scraping the web, a time-consuming process that is becoming increasingly difficult to do as the web becomes increasingly closed under the onslaught of AI bots (see also Joel Richard’s blog post A Brief Bit on BHL Battling a Barrage of Bots).
There is a clear need for a free and open bibliographic database. The nearest we have is OpenAlex, whose tagline is “All the world’s research, connected and open.” Sadly this is still more of an aspirational goal rather than a fact: a lot of taxonomic literature is not in OpenAlex. Perhaps it is time, therefore, to revive “CiteBank”, which was an early BHL project to collect bibliographic metadata. If we had a comprehensive database of the taxonomic and related literature, locating articles in BHL would be a much easier task.
Machines reading
BioStor’s method of finding articles works, but it is not the only way we could locate articles. Instead of relying on external sources of metadata, what if we could simply have a computer read the volume and extract the articles automatically? Early attempts to do this for BHL content were not particularly successful, see for example A metadata generation system for scanned scientific volumes. But the advent of large language models (LLMs) and AI chatbots has dramatically changed the way we can tackle finding articles in BHL. In my own work I routinely use AI to extract articles in bulk from a scanned volume. Typically the approach involves finding tables of contents in the scanned volume, using AI to parse that into structured data, then finding the corresponding pages in the volume, checking that they match the table of contents, and then using AI to extract bibliographic data (e.g., article title, authors, etc.). The result of this process is a data file that gets fed into BioStor, so that articles get found and added to BHL in the usual way. It is not bulletproof, and AI can quite happily make mistakes, but in my experience it works well.
But the holy grail would be to simply point an AI at a volume and it would identify and extract all the articles, find any stray plates, and present the results to BHL. Given the spectacular advances in OCR text and understanding document layout in recent years, perhaps there will be a point where BioStor can gracefully retire from the scene. Its hundreds upon hundreds of lines of regular expressions and special-case hacks quietly gathering dust in a GitHub repo while machines of loving grace read BHL for us.
From the Biodiversity Heritage Library
As BHL celebrates twenty years of open biodiversity knowledge, this post reminds us that access depends not only on digitised pages, but on the tools, metadata, identifiers, and infrastructure that make them discoverable and citable. With your support, BHL can continue strengthening the systems that connect biodiversity literature to the researchers, communities, and future discoveries that depend on it.
