University of Glasgow

Roderic Page

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Over a decade ago [RSS](https://en.wikipedia.org/wiki/RSS) (RDF Site
Summary or Really Simple Syndication) was attracting a lot of interest
as a way to integrate data across various websites. Many science
publishers would provide a list of their latest articles in XML in one
of three flavours of RSS (RDF, RSS, Atom). This led to tools such as
[uBioRSS](http://ubio.org/rss/) \[[1](#Leary2007)\] and my own
[e-Biosphere Challenge: visualising biodiversity digitisation in real
time](https://iphylo.blogspot.com/2009/05/e-biosphere-challenge-visualising.html).
It was a time of enthusiasm for aggregating lots of data, such as the
[ill-fated](https://iphylo.blogspot.com/2013/07/the-demise-of-plos-biodiversity-hub.html)
PLoS Biodiversity Hub \[[2](#Mindell2011)\].

Since I seem to be condemned to revisit old ideas rather than come up
with anything new, I\'ve been looking at providing a tool like the now
defunct uBioRSS. The idea is to harvest RSS feeds from journals (with an
emphasis on taxonomic and systematic journals), aggregate the results,
and make them browsable by taxon and geography. Here\'s a sneak peak:

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What seems like a straightforward task quickly became a bit of a
challenge. Not all journals have RSS feeds (they seem to have become
less widely supported over time) so I need to think of alternative ways
to get lists of recent articles. These lists also need to be processed
in various ways. There are three versions of RSS, each with their own
idiosyncracies, so I need to standardise things like dates. I also want
to augment them with things like DOIs (often missing from RSS feeds) and
thumbnails for the articles (often available on publisher websites but
not the feeds). Then I need to index the content by taxon and geography.
For taxa I use a version of Patrick Leary\'s \"taxonfinder\" (see
<https://right-frill.glitch.me>) to find names, then the [Global Names
Index](https://index.globalnames.org) to assign names found to the GBIF
taxonomic hierarchy.

Indexing by geography proved harder. Typically
[geoparsing](https://en.wikipedia.org/wiki/Toponym_resolution#Geoparsing)
involves taking a body of text and doing the following:

- Using named-entity recognition
  [NER](https://en.wikipedia.org/wiki/Named-entity_recognition) to
  identity named entities in the text (e.g., place names, people names,
  etc.).
- Using a gazetteer of geographic names
  [GeoNames](http://www.geonames.org) to try and match the place names
  found by NER.

An example of such a parser is the [Edinburgh
Geoparser](https://www.ltg.ed.ac.uk/software/geoparser/). Typically
geoparsing software can be large and tricky to install, especially if
you are looking to make your installation publicly accessible.
Geoparsing services seem to have a short half-life (e.g.,
[Geoparser.io](https://geoparser.io)), perhaps because they are so
useful they quickly get swamped by users.

Bearing this in mind, the approach I've taken here is to create a very
simple geoparser that is focussed on fairly large areas, especially
those relevant to biodiversity, and is aimed at geoparsing text such as
abstracts of scientific papers. I\'ve created a small database of places
by harvesting data from Wikidata, then I use the \"flash text\"
algorithm \[[3](#Singh2017)\] to find geographic places. This approach
uses a [trie](https://en.wikipedia.org/wiki/Trie) to store the place
names. All I do is walk through the text seeing whether the current word
matches a place name (or the start of one) in the trie, then moving on.
This is very quick and seems to work quite well.

Given that I need to aggregate data from a lot of sources, apply various
transformations to that data, then merge it, there are a lot of moving
parts. I started playing with a \"NoCode\" platform for creating
workflows, in this case [n8n](https://n8n.io) (in many ways reminiscent
of the now defunct [Yahoo
Pipes](https://en.wikipedia.org/wiki/Yahoo!_Pipes)). This was quite fun
for a while, but after lots of experimentation I moved back to writing
code to aggregate the data into a CouchDB database. CouchDB is one of
the NoSQL databases that I really like as it has a great interface, and
makes queries very easy to do once you get your head around how it
works.

So the end result of this is \"BioRSS\" <https://biorss.herokuapp.com>.
The interface comprises a stream of articles listed from newest to
oldest, with a treemap and a geographic map on the left. You can use
these to filter the articles by taxonomic group and/or country. For
example the screen shot is showing arthropods from China (in this case
from a month or two ago in the journal *ZooKeys*). As much fun as the
interface has been to construct, in many ways I don\'t really want to
spend time making an interface. For each combination of taxon and
country I provide a RSS feed so if you have a favour feed reader you can
grab the feed and view it there. As BioRSS updates the data your feed
reader should automatically update the feed. This means that you can
have a feed that monitors, say, new papers on spiders in China.

In the spirit of \"release early and release often\" this is an early
version of this app. I need to add a lot more feeds, back date them to
bring in older content, and I also want to make use of aggregators such
as PubMed, CrossRef, and Google Scholar. The existence of these tools
is, I suspect, one reason why RSS feeds are less common than they used
to be.

So, if this sounds useful please take it for a spin at
<https://biorss.herokuapp.com>. Feedback is welcome, especially
suggestions for journals to harvest and add to the news feed. Ultimately
I\'d like to have sufficient coverage of the taxonomic literature so
that BioRSS becomes a place where we can go to find the latest papers on
any taxon of interest.

## References

> [1.]{#Leary2007} Patrick R. Leary, David P. Remsen, Catherine N.
> Norton, David J. Patterson, Indra Neil Sarkar, uBioRSS: Tracking
> taxonomic literature using RSS, Bioinformatics, Volume 23, Issue 11,
> June 2007, Pages 1434--1436,
> <https://doi.org/10.1093/bioinformatics/btm109>

> [2.]{#Mindell2011} Mindell, D. P., Fisher, B. L., Roopnarine, P.,
> Eisen, J., Mace, G. M., Page, R. D. M., & Pyle, R. L. (2011).
> Aggregating, Tagging and Integrating Biodiversity Research. PLoS ONE,
> 6(8), e19491.
> [doi:10.1371/journal.pone.0019491](https://doi.org/10.1371/journal.pone.0019491)

> [3.]{#Singh2017} Singh, V. (2017). Replace or Retrieve Keywords In
> Documents at Scale. CoRR, abs/1711.00046.
> <http://arxiv.org/abs/1711.00046>


uBioRSS: Tracking taxonomic literature using RSS

Aggregating, Tagging and Integrating Biodiversity Research

Over a decade ago RSS (RDF Site Summary or Really Simple Syndication) was attracting a lot of interest as a way to integrate data across various websites. Many science publishers would provide a list of their latest articles in XML in one of three flavours of RSS (RDF, RSS, Atom). This led to tools such as uBioRSS [1] and my own e-Biosphere Challenge: visualising biodiversity digitisation in real time.

Publicaciones de Rogue Scholar

Revisiting RSS to monitor the latest taxonomic research