The following is a guest post by Bob Mesibov. The Atlas of Living Australia (ALA) adds "assertions" to Darwin Core occurrence records. "Assertions" are indicators of particular data errors, omissions and questionable entries, such as "Coordinates are transposed", "Geodetic datum assumed WGS84" and "First [day] of the century".
Today (8 August 2020) I looked at assertions attached to records in ALA for non-fossil animals in the Australian State museums. There were 62 occurrence record collections from the seven museums (I lumped the two Tasmanian museums together), with 45 different assertions. I then calculated assertions per record for each collection. The worst performer was the Queensland Museum Porifera collection (3.84 ass/rec), and tied for best were the Museums Victoria Herpetology and Ichthyology collections (1.09 ass/rec).
I also aggregated museum collections to build a kind of league table by State:
The clear winner is Museums Victoria.
But how well do ALA's assertions measure the quality of data records? Not all that well, actually.
- The tests used to make the assertions generate false positives and false negatives, although at a low rate
- The tests aren't independent, so that a single data error can "smear" across several assertions
- The tests ignore errors and omissions in DwC fields that many data users would consider important
ALA's assertions also have a strong spatial/geographical bias, with 23 of the 45 assertions in my sample dataset saying something about the "where" of the occurrence. Looking just at those 23 "where" assertions, the museums league table again shows Museums Victoria ahead, this time by a wide margin:
ALA is currently working on better ways for users to filter out records with selected assertions, in what's misleadingly called a "Data Quality Project". The title is misleading because the overall quality of ALA's holdings doesn't improve one bit. Getting data providers to fix their data issues would be a more productive way to upgrade data quality, but I haven't seen any evidence that Australian museums (for example) pay much attention to ALA's assertions. (There are no or minimal changes in assertion totals between data updates.)
It's been pointed out to me that that museum and herbarium records amount to only a small fraction of ALA's ca 90 million records, and that citizen scientists are growing the stock of occurrence records far faster than institutions do. True, and those citizen science records are often of excellent quality (see https://www.datafix.com.au/BASHing/2020-02-05.html). However, citizen science observations are strongly biased towards widespread and common species. ALA's records for just six common Australian birds (5,072,599 as of 8 August 2020; https://dashboard.ala.org.au/) outnumber all the museum animal records I looked at in the assertion analysis (4,669,508).
In my humble view, the longer ALA's institutional data providers put off fixing their mistakes, the less valuable ALA becomes as a bridge between biodiversity informatics and biodiversity science.
