The Glacial Pace of Scientific Publishing
1–10 of 35 posts
Re: The Glacial Pace of Scientific Publishing
#2Re: The Glacial Pace of Scientific Publishing
#3Date: 2012
Re: The Glacial Pace of Scientific Publishing
#4Re: The Glacial Pace of Scientific Publishing
#5It's like Mark Zuckerberg telling young programmers, don't start an internet company because society is suffering from all the evils of internet.
Re: The Glacial Pace of Scientific Publishing
#6Re: The Glacial Pace of Scientific Publishing
#7And really you can shorten those timelines by a couple of months because people make their papers available as soon as they're publicly accepted.
Re: The Glacial Pace of Scientific Publishing
#8Perhaps this problem is worse among biomedical journals? I've routinely had publications in ACS journals that go from submission to acceptance within two months, usually closer to one---I hear the same from colleagues.
Re: The Glacial Pace of Scientific Publishing
#9Perhaps this problem is worse among biomedical journals? I've routinely had publications in ACS journals that go from submission to acceptance within two months, usually closer to one---I hear the same from colleagues.
Re: The Glacial Pace of Scientific Publishing
#10Perhaps this problem is worse among biomedical journals? I've routinely had publications in ACS journals that go from submission to acceptance within two months, usually closer to one---I hear the same from colleagues.
For example, if a researcher chooses an algorithm with a "fairly recent" publication date of 2 years ago, that paper may have been first written 3 years prior to that, after a 1-year process of developing the algorithm and another 2-year process of generating and analyzing a dataset that showcases the algorithm so they can get it published. So that "2 year old" algorithm is actually 2 + 3 + 1 + 2 = 8 years old. If the algorithm is in a fast-moving field like next-generation sequencing analysis, it is probably obsolete several times over (i.e. the tool that obsoleted it is already itself obsolete), if it isn't completely useless now due to increases in dataset size during the past 8 years (e.g. it can only handle 1 million sequences, but today's datasets are 100 million sequences).
Any bioinformatics programmer knows this, of course, and only relies on the publication record as a last resort for information about bioinformatics algorithms. But a biologist who has a dataset they want to analyze and is looking for an algorithm to use probably won't know this, and will end up selecting a hopelessly outdated algorithm as a result.