Dear subscriber, welcome to the second edition of the newsletter and also a warm welcome to all new subscribers. It has been an interesting week. In this edition I will talk about some work updates, a one-day workshop I’ve attended last week and I have a video recommendation.

Published today in GigaScience is a Data Note describing the National COVID-19 Chest Imaging Database (NCCID), a centralised database containing chest X-rays, Computed Tomography (CT) and MRI scans from patients across the UK. Utilising the UK National Health Service’s unique position as the world’s single largest integrated healthcare system, the benefits of collecting chest imaging data this large are extensive and already being used

A new article published today in GigaScience demonstrates that machine learning can yield “proxy measures” for brain-related health issues from large population data, without the need for a specialist’s assessment.

Quick note on a tool I've been working on to parse citations, that is to take a series of strings such as: Möllendorff O (1894) On a collection of land-shells from the Samui Islands, Gulf of Siam. Proceedings of the Zoological Society of London, 1894: 146–156. de Morgan J (1885) Mollusques terrestres & fluviatiles du royaume de Pérak et des pays voisins (Presqúile Malaise). Bulletin de la Société Zoologique de France, 10: 353–249.

GigaScience has always had a focus on reproducibility rather than subjective impact, and it can be challenging for our reviewers to judge this, especially now that more and more tools are being created – bringing data science to the masses.  This also means more efficiency and ease is required especially when multiple collaborators and contributors on a specific project are involved.

Note to self. The challenge of finding specimen citations in papers keeps coming around. It seems that this is basically the same problem as finding citations to papers, and can be approached in much the same way. If you want to build a database of reference from scratch, one way is to scrape citations from papers (e.g., from the "literature cited" section), convert those strings into structured data, and add those to your database.

Note to self about a possible project. This PLoS ONE paper: describes a method for inferring a hierarchy from a set of tags (and cites related work that is of interest). I've grabbed the code and data from http://hiertags-beta.elte.hu/home/ and put it on GitHub. Possible project Use Tibély et al. method (or others) on taxonomic names extracted from BHL text (or other) and see if we can reconstruct taxonomic classifications.