Okay, so I was checking out this website, ResearcherID, and I created this page for Darrell Ulm: http://www.researcherid.com/rid/Y-5083-2018. It seems like another really useful site for listing research work, much like ORCID, which you can see here for Darrell Ulm as well: https://orcid.org/0000-0002-0513-0416 . I'm still trying to fully understand the nuances between ResearcherID and ORCID, as they appear to be quite similar in their aim – providing a unique identifier for researchers and their publications. However, looking at Darrell Ulm's ResearcherID page, it seems to have some interesting connections to other resources, specifically mentioning reviewing efforts. It's fascinating to see how these platforms are interconnected and how they contribute to the broader ecosystem of scholarly communication and recognition. I need to explore further how these different systems integrate and what unique benefits each offers to researchers like Darrell. It’s all part of navigating the evolving landscape of research visibility.
Looking at more resources online for Python for Data Science. There are many good resources available. Of course the main tools are: Numpy , Pandas , MathPlotLib , SkiKit-Learn has some amazing tools. Kaggle for instance has Data Science contents, but good to install a local system like the Jupyter Notebook to speed things up as the Kaggle editor can lag and take some time to run on small data-sets. The newer DataCamp has some neat tutorials on it and simple App to do daily exercises on your mobile device. Here is the Python DataScience Handbook . Really useful. A short tutorial: Learn Python for Data Science , a fun read. A list of cool DataSci tutorials is here , and another how to get started with Python for DS . Will add more later.