The Open Data Scotland website provides an up-to-date list of the Open Data resources about Scotland. It is being developed by the volunteer-run OD_BODS project team, and the idea for it originated from Ian Watt’s Scottish Open Data audit.
The website has been built using the JKAN framework which provides to end users, a ready-made search-the-datasets feature (try the search box near to the top of this page). However, its search can sometimes excessively exclude because it returns only those datasets whose metadata contain all of the search words, consecutively.
For instance, say that we wanted to find all datasets related to waste management. We might think of entering the search words:
tip. With JKAN, we would fairly much have to search for each of those words individually then collate the results.
Search tuning is its own whole field of research/area of business but, we have built a simple alternative to the JKAN search, to better support exploratory searching. Click on the image below to try the demo.
On our requirements list is, to weave interest-based navigation maps through our data site. And feedback from the recent SODU 2021 conference, affirmed this:
I like the site’s tools and visualisations, but more needs to be done to help me navigate my path of interest through the prototype website.
In an exploratory step towards fulfilling that requirement, we have annotated some data points with explanations/narrative. The idea is that that these annotations could become waymarks in navigation maps, to guide users between the datapoints which underpin data-based stories. We might even imagine how clicking a ‘next’ button on a waymark would visually ‘fly’ the user to the next datapoint in the story (which is, perhaps, on a different graph or different page). But(!) back to our present, very simple proof-of-concept implementation…
Here’s how the annotations look in our present, proof-of-concept implementation:
Each annotation is depicted by an emoji which is plotted beside a datapoint (on a graph, or in a table). When the user hovers over (or clicks on) an annotation’s emoji, a pop-up will display some informative text.
We want to code annotations just as we would any other dataset – as a straighforward CSV file. So we have built a data-drive annotation mechanism. This has allowed us to specify annotations, as data, in a CSV file like this:
Each annotation record contains
datapoint coordinates which specify the datapoint against which the annotation is to be plotted. The
datapoint coordinates include a
record-type which specifies the dataset against which the annotation is to be plotted. (In this example, the specified dataset
household-waste-derivation-generation is a derived dataset, based on the
This proof-of-concept, data-driven, annotation mechanism has been useful because it has:
given us a model with moving parts to learn from,
provided hints about how annotations can be used to help users understand and navigate the data,
shown us that we need more structure around the naming and storage of derived datasets (and their annotations), and
uncovered the difficultlies of retro-fitting an annotations mechanism into our
prototype-6 website. (Annotations are displayed using off-the-shelf Vega-lite tooltips and Bulma CSS dropdowns, but these don’t provide a satisfactory level of placement/control/interactivity. More customised webpage components will be needed to provide a better user experience.)