The current situation of your data
Data tables in PDF (example from your forestry statistics page) are a great example of 1-star data: The data is on the web, but it's pretty much unusable for computer programs without some heavy-duty data extraction. After all, PDF is where data goes to die. My advice: Just don't do it.
Your example Excel file is actually a good start. It's 2-star data, since it's still in a proprietary file format, but it's structured and machine-readable. It could easily be turned into 3-star data ("Save as CSV"). In other words, the difference between 2-star and 3-star data is mainly ideological and much less technical.
Getting to 3-star data is not a problem. Turning it into 4-star and 5-star data is where things start to get interesting.
- Your data has three dimensions: County (expressed as number, code, and abbreviation), land use class, and year.
- For each combination of these three dimensions, there is one data value, which is the area in hectare.
Expressing two dimensions in tabular form — such as an Excel spreadsheet — is trivial: On dimension (e.g. county) goes on the vertical axis, the other dimension (e.g. land use class) on the horizontal axis. However once the dimensionality is larger than 2, it becomes tricky to represent the data in two dimensions (e.g. in a table on a computer screen).
There are standard solutions to deal with multi-dimensional data, such das Pivot Tables and OLAP Cubes. In the context of Linked Data, the RDF Data Cube Vocabulary is the current best practice for expressing multi-dimensional data.
What needs to be done to move to 4- or 5-star data?
4-star data means that you "use open standards from W3C" (such as RDF) and/or "use URIs to denote things" so that people can point at your stuff. 5-star data means that you link your data to other people's data.
What does that mean in case of your statistical data? Well, basically it means that you would have to turn your statistical data into RDF Data Cubes. With the current state of tool support, this still requires a lot of effort and expertise. While RDF Data Cubes are a great thing in theory, so far they are not really relevant in practice because, outside of the Semantic Web research community, not that many people use this technology.
Other things you could do
If you do not want to go for RDF Data Cubes — and unless you have strong reasons in favor, I would not recommend it — there are other things you could do to make your data more machine-readable:
Publish your data as (compressed) flat CSV tables
This makes it trivial to
- import the data into a spreadsheet application and create a pivot table
- import the data into OpenRefine and perform advanced operations
- turn the data into RDF Data Cubes, if someone wants to do it and already has the necessary skills
You could even add another column containing URIs representing the counties (e.g. https://www.wikidata.org/entity/Q104926 for Uppsala County). This would fulfil the requirements for 5-star data (although we skipped the requirements for 4-star data).
So far it's only a W3C Working Draft, but it sounds promising:
Validation, conversion, display and search of tabular data on the web requires additional metadata that describes how the data should be interpreted. This document defines a vocabulary for metadata that annotates tabular data. This can be used to provide metadata at various levels, from collections of data and how they relate to each other down to individual cells within a table.
