This post is an update of a previous analysis on quantixed. We have covered publication lag times – the time it takes for a paper go from submitted (received) to accepted and published – a lot on here. It is possible to look at lag times, in R using data from PubMed. Previously my code performed these calculations using an XML file downloaded from the PubMed website.

As the COVID-19 pandemic continues, different countries are experiencing various restrictions including lockdowns. Some of these restrictions alter our ability to do science: by hindering lab access or taking time away from researchers for homeschooling. So, what impact has the pandemic had on scientific output? One way to look at this – for biology – is to look at newly deposited papers on bioRxiv.

The scientific response to the COVID-19 pandemic has been astounding. Aside from efforts to generate vaccines, the genomic surveillance of the virus has been truly remarkable. For example, the nextstrain project has sequence many SARS-CoV-2 genomes. In fact, the rapid identification of multiple new strains and mutations by diverse groups of scientists has resulted in a nomenclature crisis.

The database clinicaltrials.gov is a web resource of clinical trials around the world. It has a REST API that gives access to clinical trial data. There are some resources available to interact with this resource using R, such as rclinicaltrials and ClinicalTrialsAPI. We were interested at looking clinical trials for rare diseases and particularly whether this year, COVID-19 had affected these trials.