This is a side-bar more directed at the unfortunate consequences that we suffer from caused by the pressure on researchers to produce positive results.
The main issue with that right now seems to be the lack of real incentives: those badges are nice to have, but in the end you're evaluated by publications in highly regarded journals and conferences, citations, and funding brought in. Those things of course tend to let people focus on novel, fancy, exciting work, instead of the time-consuming process of making things easy to replicate by others, or even replication of other people's experiments. Incentive-wise, the publication business follows the principle of fire-and-forget.
What's needed, but I believe very difficult to introduce, is the strict expectation of independent replication. That would mean replication as part of the core review process, which would A. create a lot of work for reviewers B. initially reduce the attractiveness of whichever conference introduces it first.
One thing that would be easy to introduce in computer science would be to make it mandatory to share all code used in experiments - if you cannot/won't do that, you should not able to take part in this game.
It has been a long time since I’ve read a paper where I actually wanted to use whatever they were selling, let alone reproduce whatever results they were claiming. We don’t even have “novel, exciting, fancy” work!
When something comes out of a paper that really changes the game (like say MapReduce), it gets replicated a lot.
However, I do think there is a lot of incremental, piecemeal type of work that over the years could amount to a decent step forward. It's just that currently, from looking at the code behind quite a few published papers, the statements often just cannot be trusted enough to actually build on these works. It is my view that some subsubfields in CS sustain themselves by avoiding the most pertinent questions, because their answers would reveal that the entire subsubfield has been superseded, or was never that promising to begin with. Unfortunately, that type of noise generation is actually more profitable than a single paper saying "nope", although the value of the latter in terms of knowledge generation is enormously high.
Pharma and biotech startups only make money if a drug works (for the most part), so they tend to have a much higher bar for the robustness of findings than academic researchers and journal editors do.
Of course, this only covers a tiny subset of research