Sci-Hub statistics and database

Yeah, I have heard this reasoning, but it seems muddled. How is keeping the site online so old articles are available but no new articles are added acting in "good faith"? It's not like the old articles are any less copyrighted than the new articles, so this doesn't make sense to me.

The court case has also been delayed for over a year now, so if it is delayed indefinitely, like it seems to be, then we will also not get access to new articles, also indefinitely? That's ridiculous. The last update from the court proceedings claimed that there would be a new update over a month ago, which in turn got delayed yet again to a few days ago, and there's been nothing [1].

[1] https://delhihighcourt.nic.in/dhc_case_status_oj_list.asp?pn...

> why are our scientists made to rely on elsevier et al to sift through the junk and find for them the perfect paper instead of doing it themselves?

Scientists do do that themselves. That‘s why it is called peer review. Journals take scientists work for free, they just pre-select papers, but don‘t do the review.

> Because Sci-Hub has a good chance of winning the case.

I understand that this is the party line that is parroted whenever this issue comes up, but it does not make any sense as a rationale for keeping new articles off the site. How is not adding any new articles (but, for example, keeping old articles accessible) assisting the possible winning of the case? And more to the point, why does it matter at all if it wins or loses the case? As stated, neither the owner or the infrastructure is Indian, so of what relevancy is this jurisdiction?

And further still, the case appears to have been delayed indefinitely. That last update claims that there was going to be an update a few days ago, but there was not. The proceedings are just now a list of one postponement after another [1]. Given that new articles are being held hostage, it thus very obviously benefits the legal system and the prosecution to continue to delay the case indefinitely.

[1] https://delhihighcourt.nic.in/dhc_case_status_oj_list.asp?pn...

> 100 TB is pretty small. I wonder if she will start torrenting it so people can back it up and share the load

This has been ongoing for a while now:

Rescue Mission for Sci-Hub and Open Science: We are the library https://www.reddit.com/r/DataHoarder/comments/nc27fv/rescue_...

An older comment of mine https://news.ycombinator.com/item?id=30049522 fits well here. I'll adapt it to your question ;)

Basically: medicine as a whole is already some sort of expert system.

- Data collection and cleanup: Researchers conduct experiments to produce meaningful data and extract conclusions from that data.

This part isn't more automated because we have strict rules that prevent medical data collection and analysis without a clear purpose. Otherwise we'd be able to collect a lot more information to try and extract results from it using more inference-oriented techniques (deep learning and the like).

- Modeling & training: Expert panels produce guidelines from the results of that research. These panels are the "training part" of the system.

As a sibling comment said, replacing these panels with ML-based techniques isn't trivial because the data produced in the previous step is fairly noisy (p-value hacking, difficulty of capturing all the variables, etc.). Furthermore, the techniques that yield best results nowadays also produce them without clear explanations on why they hold, which is not something we are prepare to accept in medicine.

- Execution: Doctors diagnose and treat following said guidelines. In fact, they use decision flows that they themselves call... algorithms!

The main reason why execution is not automated is that we do not have the technology for machines to capture the contextual and communication nuances that doctors pick up on. There can be a world of difference between the exact same statement given by two different patients or even the same patient in two different situations. Likewise, the effect of a doctors' statement can be quite literally the opposite depending on who the patient is and their state of mind. One of the most important aspects of the GP's job is to handle these differences to achieve the best possible outcomes for their patients.

All that being said, there are companies trying to produce expert systems to help doctors diagnose. See https://infermedica.com/product/infermedica-api for instance.