A few thoughts:
Publication bias
While making data available would help address some problems in publication bias, merely having data sitting on the internet somewhere isn't even close to enough to solve it if people still use journals as content curators.
Reproducibility problem
Available data = / = Reproducible data
I need to run another experiment
This is, in many ways, desirable. "I took the same data and got the same answer" is a fairly low form of reducibility. Yes, it catches statistical errors, and enables you to try new methods, but at least in my field, before something can really be thought of as "reproduced" it needs to be obtained via an entirely different experiment, preferably in a different population. That enables the understanding if there is a consistent effect that occurs in a variety of contexts, or if it was a fleeting result that was either noise, or (more philosophically) just the tail of an effect that is randomly but not perfectly distributed around 1.
Or if I want to conduct a meta-analysis, maybe having other
researcher's raw data is better than just the mean/CI they report in
journals.
It depends on the analysis you want to do, but this is not inherently true. Also note that it is often the case that, if this was what you're doing, an email to the researchers may provide what you need. Both times in my career where I have genuinely needed someone's raw data, I've been able to get it.
If scientists' mission is for public good and for the advancement of
knowledge
You are making a massive assumption here: That the mission of scientists is the public good. A few notes:
- Even for idealistic scientists, the actually doing of science doesn't occur in a vacuum. In order for you to continue to do your science, keep your people paid and the lights on, etc. you have to compete with other labs. Collecting data is often a long and laborious process, and there is a very real temptation to continue to mine that data past the initial publication. It is a competitive advantage, and science is competitive.
- It is not axiomatically true that the public good from the release of data > the public good that comes from a lab being otherwise successful. An idealistic lab sacrificing themselves on the altar of data access doesn't necessarily help.
why don't they publish their results in raw (of course they need to
remove research participants' privacy information).
This is a considerable hurdle. For example, there are a number of studies I've worked on where identifiable information is essential to the finding in question. This might be a special case, but it's not an uncommon one. There may also be agreements in place preventing this - many minority groups, for example, are very justifiably skeptical of "And then we can do anything with your data we want".
They shouldn't be afraid of others' criticising their work. Only truth
can endure the testing of time.
Very often, the concern is much more about preserving the ability of their data to generate new publications over time.
Nowadays, with the prevalence (and low price) of online storage
platform and sophisticated database management, why don't they do it
for the public's good?
Because the public's good doesn't pay my postdoc's salary.
Now this all sounds jaded and bitter and horrible. Which is ironic, because I actually do try to make as much data as possible available to the public. But there are very real constraints, both on the nature of the data themselves and in the doing of science, that stand in the way of automatically making data available. One must be able to acknowledge these when thinking about data accessibility and reproducibility.
In my case, for example, what "the data" is is often a somewhat murky concept, and I find the tendency to view "I downloaded your code and data and ran it" as what people sometimes view as reproducibility to be...troubling.
