What is metadata?
Metadata is data about data – but not necessarily the actual content of the data.
Examples of metadata include information about who authored the data, when, and what sort of keywords best describe the data and its collection process.
Good quality metadata raises the quality of a dataset, because metadata helps make data FAIR – findable, accessible, interoperable, and reusable. This also helps end-users evaluate if the dataset, and even particular records, are fit for their purpose or require manipulation or data cleaning.
For open-sourced data (like on ALA), good-quality metadata becomes even more critical. For example, since data arrive from multiple sources, good metadata can indicate how data were collected, such as if a survey was conducted systematically or opportunistically.
If you’re a data provider you can nominate how you would like to be acknowledged. Typical attributions include:
- your name, date
- your company name, date
- your website URL as a hyperlink
- department name, section, contact details
To make it easier we recommend you use the below template to write your metadata and include it with your data submission to the ALA.
Even if you’re only providing us with a few records it’s still important to also include metadata. The more information you can provide the better, however a few sentences can go a long way into helping a user identify if the data is fit for their purpose. This first example demonstrates this, while not lengthy, it provides the who, what, when, where, and why.
This second example is much longer with much more detail. This is partly to do with the fact that the data being provided is different. This survey included chemical analysis and morphometric measurements, rather than purely observations. This type of data therefore has more in-depth methodologies which can be useful for the user to know. Not all metadata will look like this, simply because this level of detail won’t exist and that’s ok!