I'll elaborate. When I use the word "research" I mean activity performed by accredited researchers. Researcher is a rank, or a title if you want. It's similar to "software engineer" -- if you are in an academic institution the word "researcher" could be part of your title / job description.
The activity has as its goal to publish the findings. Findings are expected to be meaningful and to be found in the domain of sciences. It is also possible to extend the scope to arts, but I'm hesitant about it, and the way I used the word doesn't truly relate to what happens in arts department (for this purpose, math is an art).
Accredited, here means association with academic institution by means of employment or similar.
On conceptual level, all discoveries in sciences must have a hypothesis whose validity and truthfulness is to be established through empirical evidence, i.e. experiment. To interpret experiments one needs to use statistics. This is the vehicle driving experiments. This is where the kind of programming I'm talking about comes in.
Note that researchers might use programming for other activities s.a. eg. automation of keeping their diaries, or automating their correspondence and so on. But I meant specifically the statistical aspect of doing research.
I think what OP means is that Julia has a number of features that work very well for the workflows and processes of programming for scientists.
1. Interactive workflows. One of the defining features of doing science is that you don't know what the right answer or right approach is. This makes interactive workflows (like REPLs) really valuable since you can load data once and do 100 different analyses on it. Notebooks are also really useful as a means of showing both code and results at the same time, and Pluto.jl is one of the best here since it removes the possibility of ending up with inconsistent state by tracking dependencies between cells.
2. Reproducibility. Another important feature for scientific code is that you want someone to be easily able to take your data and code, be easily able to install the code, run it and get the same answer. This is one of python's biggest shortcomings. Python has an incredibly rich package ecosystem, but is lacking a good unified system for reproducibly installing packages (Poetry is the closest but it has problems with binary dependencies). Julia (and Rust) have virtual environments and the idea of a manifest file that records the exact version of all your (transitive) package dependencies built in which make it trivial for someone else with no instructions get an exact clone of all the software needed to run your code.
3. Ease of use. Most scientists are scientists first and bad to mediocre at programming (there are obviously exceptions, some scientists are great programmers). Static type systems and manual memory management are major impediments to beginner use. C++ gets some scientific use for it's performance, but there's a reason Python R and Matlab are the languages of most scientists.
4. Performance. Lots of fields (bio, astronomy, high energy physics, chemistry) need a fast language to be able to get results in a reasonable amount of time. Julia is fast and is one of the easier languages to write GPU accelerated code in.
5. Open source. Closed source languages (Matlab) are a total pain to deal with.
The activity has as its goal to publish the findings. Findings are expected to be meaningful and to be found in the domain of sciences. It is also possible to extend the scope to arts, but I'm hesitant about it, and the way I used the word doesn't truly relate to what happens in arts department (for this purpose, math is an art).
Accredited, here means association with academic institution by means of employment or similar.
On conceptual level, all discoveries in sciences must have a hypothesis whose validity and truthfulness is to be established through empirical evidence, i.e. experiment. To interpret experiments one needs to use statistics. This is the vehicle driving experiments. This is where the kind of programming I'm talking about comes in.
Note that researchers might use programming for other activities s.a. eg. automation of keeping their diaries, or automating their correspondence and so on. But I meant specifically the statistical aspect of doing research.