It's 2018 and perhaps you are looking for interesting new tools to add to your repertoire. Here are some interesting ones relating to cleaning, analysis and modelling of data that I have encountered in the last year. JASP - Open Source statistical software similar to SPSS with support of COS

In my final blog post of the year, I'm going to talk about some of the developments in librarianship and the related domains that caught my eye.

One of the major issues with institutional repositories is that it is difficult to get researchers to self-deposit their work. Assuming one could wave a magic wand and solve that, institutional repositories still have another barrier to overcome - the discovery barrier. With content scattered across thousands of sites, one would need an aggregator site to provide a one-search across all of them.

CC BY-SA 3.0 Nick Youngson When I first started in academic libraries, one of the most popular questions I got was "what database should I use to find articles on X". Today ten years later, I still get such questions but to a far lesser degree. This is due to a combination of better discovery from all in one tools like Google Scholar, various library discovery services and just more digital material out there that is much more findable.

Edit : For a fuller guide on authenication methods for electronic resources refer to "Access to Online Resources - A Guide for the Modern Librarian" by Kristina Botyriute who is at OpenAthens at the time of this post. Debates on privacy in libraries are not new though they have recently become more heated over two issues. One issue resolves around learning analytics and this has been brewing for a while.

I recently attended the Crossref Live17 event in Singapore. I discovered that these events often have a heavy publisher presence, who make up most of Crossref's membership.

As a generalist with hands in many pies, I'm prone to throw around terms I barely understand. API or Application Programming Interface might be one of them.  But learning about APIs are important as we are using it whether we know it or not. Recently, I have started to get comfortable with them , at least in terms of pulling out the data and parsing the JSON output.

I'm actually a pretty big fan of Google Scholar, which in some ways is better than our library discovery service ,but even if you aren't a fan, given it's popularity it's important for librarians to keep up with the latest developments. In any case, I'm happy to see that Google continues to enhance Google Scholar with new features. These are some of the new features and things I've learnt about Google Scholar lately. 1.

In honor of this year's Open Access Week, here's a personal reflection of my engagement with open access over the 10 years of my career in academic libraries.

The concept of ORCID (Open Researcher Contributor ID)  appears to be simple. The promise is to have a unique identifier for each researcher "the same way books have ISBN" . As a librarian who has helped researchers pull out citation counts from Scopus, Google Scholar in the past, the idea of ORCID seems elegant and logical.

We can all agree that Google Scholar has many strengths , but no matter how complete or deep it's indexing, how much better it is at finding free articles or it's presumed better relevancy ranking , we librarians have always had one weakness of Google Scholar to point at. We often say "Despite it's strengths, still we have to be careful, after all we don't know what Google Scholar actually includes, as they refuse to provide lists of