jabref VS NumPy

JabRef might work for you. Website link and GitHub link.

This issue https://github.com/JabRef/jabref/issues/5867 hints at JavaFX issue with the potential workaround of running with the environment variable GDK_DISPLAY=1.

I use JabRef for managing references, which allows you to generate entries via a DOI and saves to a local .bib file.

JabRef

If the town is "online reference managers," you are probably right, but I would argue that reference managers are one of those areas where you really want something offline:

- Offline ensures that you do not suffer an externally caused downtime just before a deadline

- Offline ensures that you have a path for keeping your database throughout your research career, and to do system updates when _you_ want to.

- Offline ensures that if you leave academia, you will always have access to local copies of the academic papers you have referenced.

My favorite offline/local reference manager is `jabRef` [0] which stores all metadata directly in a bibtex-file. The GUI has an excellent pdf-integration, and everything is local and super fast.

Case in point: after a decade in industry, I am looking to get back into my academic fief. All the papers I ever read are in my Dropbox, and all I had to do to pick up where I left was download a current version jabRef and point it to my database which it read without any issues.

[0]: https://www.jabref.org/

I used JabRef throughout my work. It's indeed too late for my PhD work, but Zotero does indeed look very nice.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]