An analytics dashboard for webmention.io data. Contribute to maxboeck/webmention-analytics development by creating an account on GitHub.
I built a tool to analyze incoming webmentions. This new side project generates monthly reports to see how and where content is mentioned.
It is 160 days since I first noted that I would like to make use of Bise, Jason McIntosh's blog-readership reporter, 118 since I automated downloading the access logs. With half an eye on the project day at IndieWebCamp Austin, time to make good on my promise. Bise expects its log files to be named ...
A tool to analyze Twitter accounts.
Get a fully functioning Matomo in seconds with our new WordPress plugin.
Explore any URL featuring Hypothesis annotation. CROWDLAAERS provides learning analytics about active participants, temporal activity (active days), collaborative discourse (threads), and also Hypothesis tags. Groups of individual annotations may be sorted by date, contributor, annotation, tags, and level (or the position of an annotation reply in a thread). Select any annotation to read the full content within CROWDLAAERS or in context of the source document. Or explore how CROWDLAAERS has been applied to curated sets of online texts by selecting from Projects.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
PlumX gathers and brings together appropriate research metrics for all types of scholarly research output.
We categorize metrics into 5 separate categories: Usage, Captures, Mentions, Social Media, and Citations.
I’m going on the journey of building a simple, private, self-hosted, cookie-free analytics tool that I’m calling Kownter. I may fail. But it will be fun and interesting! Come along!
Hi, My name is Ross. I’ve been thinking a lot about GDPR lately and considering how I will become compliant with it as I run my business and projects, so I’m looking to slim down the data that I capture about people.
The topics of both analytics and server logs have come up several times. It’s not entirely clear to me that either fall into the category of personal data, but I’ve been considering my use of them anyway.
I use Google Analytics on most sites/projects that I create, but I’m not that sophisticated in my use of it. I’m mostly interested in:
and it occurred to me that I can collect this data without using cookies and without collecting anything that would personally identify someone.
- how many visitors I’m getting and when
- which pages are popular
- where people are coming from
I would also be happier if my analytics were stored on a server in the EU rather than in the US – I can’t find any guarantee that my Google Analytics data is and remains EU-based.
I’m aware that there are self-hosted, open-source analytics solutions like Matomo (previously Piwik) and Open Web Analytics. But they always seem very large and clunky. I’ve tried them and never got to grips with them.
So I wondered: how hard would it be to build my own, simple, high-privacy, cookie-free analytics tool?
I could see this being an interesting thing to study the recent #DeleteFacebook movement.
This is the transcript of my lightning talk from the beyond tellerrand Berlin pre-conference warm-up on 6 November 2017. It was a condensed version of my longer, work-in-progress and upcoming talk on privacy as a core pillar of ethical UX design. If you are interested in the final talk or know about a conference or event that might be, I’d be thrilled to hear from you.
I love the fact that people are working on solving these seemingly mundane issues. This is a great little presentation Sebastian!