Free, Libre, and Open Source Software and Open Data

posted by Roy Schestowitz on Feb 11, 2026

• Hister ☛ How I Cut My Google Search Dependence in Half

  TL;DR: I built Hister, a self-hosted web history search tool that indexes visited pages locally. In just 1.5 months, I reduced my reliance on Google Search by 50%.

• Web Browsers/Web Servers

  • Ian Duncan ☛ TIL: HTTP/3 Is Not Always Faster Than HTTP/2

    In my parental leave time, I’ve been noodling on a new HTTP client library, and as one does, I wrote benchmarks. I expected HTTP/3 to be faster, as a matter of course. I mean, that’s why you make new network protocols right? Everyone says it’s faster, the RFCs imply it should be faster, the conference talks promise it. On my local network, HTTP/3 was consistently and measurably slower, by 50-100x.

• Content Management Systems (CMS) / Static Site Generators (SSG)

  • Iterative Wonders ☛ Three Ways phpMyAdmin Saves Your WordPress Site (And Your Sanity)

    I am serious. Before you touch the database, export a copy. If you accidentally delete the users table instead of editing it, I cannot help you. I am a blog post, not a magician.

  • Christian Hofstede-Kuhn ☛ Adding Fediverse Comments to a Pelican Blog

    Every static site eventually faces the comment question. Disqus tracks your readers. Self-hosted solutions like Commento or Isso need a server-side component and a database. Most options either compromise privacy or add operational complexity that feels disproportionate for a personal blog.

    Then I came across Jan Wildeboer’s approach for his Jekyll blog: use Mastodon as the comment backend. Every blog post gets a corresponding Mastodon post. Replies to that post become the article’s comments, fetched client-side through the public Mastodon API. No tracking, no database, no server-side logic. Just the Fediverse doing what it already does.

    I liked the idea enough to port it to Pelican. This article explains how it works, how it’s integrated, and how you could do the same.

• Openness/Sharing/Collaboration

  • Open Data

    • Rlang ☛ Simplifying Research Data Sharing with R

      Working with Water, Sanitation, and Hygiene (WASH) researchers across multiple resource-limited countries, we observed that valuable datasets often remain underutilized. This is frequently due to limited familiarity with FAIR (Findable, Accessible, Interoperable, Reusable) data practices (Wilkinson et al. 2016). As part of the academic community, we recognize that research extends beyond traditional metrics like citations and publications. The demanding work of generating, collecting, and cleaning data frequently goes unrecognized, leaving many contributors unacknowledged. As part of GHE’s Open Science project openwashdata we conducted surveys with participants from our network of collaborators who were interested in participating in a Data Science for Open WASH Data course. The collected data reveals suboptimal data storage practices among WASH researchers, with many still relying on methods that hinder portability and interoperability (see @plot-storage).