Quick Guide

The following is a summary of how the Open Data Technical Publishing Guidelines can be put into practice, highlighting some of the common challenges that potential publishers face.

To get started, publishers will want to identify what datasets to publish as Open Data.

To help understand what a high-value dataset is, check-out Guideline 1: Publish High-Value Datasets. For guidance on creating a roadmap for your organisation, see Guideline 3: Put in Place an Open Data Publishing Roadmap.

This is a common challenge, as datasets can be created and managed in any of the units across an organisation. One person will seldom have a complete view on all datasets. Also, while technical teams may process the data, it is often business teams who use and understand the data. Therefore, in order to get a better understanding of your organisation’s data holdings, data audits can be carried out. This can begin be a simple list of what datasets a unit manages, or a detailed description of all datasets in every unit. To find out more about how to carry out a data audit, check-out Guideline 2: Perform Data Audits.

For more information on data formats, go to Guideline 11: Publish Data in Open Formats. If you would like to learn more about APIs, providing access to dynamic data, and publishing historical data, have a look at Guideline 12: Publish Historical Datasets, Guideline 13: Provide Access to Dynamic Data, and Guideline 14: Publish Data via APIs. Associating data with an open licence is also essential to clearly let users know what they are allowed to do with the data. For more information on licencing, go to Guideline 4: Associate Data with an Open Data Licence.

Inevitably, domain experts working with data will be best placed to understand this data. However this doesn’t mean that this data cannot have value outside of this use-case. Open Data can have numerous applications outside of those that were foreseen when it was initially collected. To help potential users fully understand the data, it is important to provide as much contextual and descriptive information about the data as possible. To find out more, see Guidelines 5: Provide Comprehensive Metadata, Guideline 6: Provide Accurate Timeframe Metadata, Guideline 8: Enable the Geolocation of Data, Guideline 9: Provide Granular Data, and Guideline 10: Co-Locate Documentation for the Dataset.

Interoperability stems from using data standards, i.e. common ways of describing concepts in your dataset. For more guidance on improving the interoperability of your data, check out Guideline 15: Use Data Standards, Guideline 16: Use Trusted Identifiers to Link Datasets, and Guideline 18: Identify Related Datasets

One of the most common issues with Open Data is that it is outdated. To build trust with users, datasets need to be kept current and all links should be working. To learn more about maintaining Open Data, read Guideline 7: Ensure Data is Up-to-Date and Guideline 17: Ensure Dataset URLs are Operational.

There are many ways to help get an understanding of user needs, and also to help measure data use. If you know who your users are, you can work directly with them at face-to-face events, or online via social media or user forums. There are also a number of features on data.gov.ie that can help measure user needs and dataset access. To learn more about engaging with users and understanding demand, see Guideline 19: Support Non-Technical Users in Understanding the Data, Guideline 20: Collaborate with User Community, Guideline 21: Provide Examples of Data in Use, and Guideline 22: Measure Data Use.