Tuesday, 26 November 2013

Frozen Datasets are Useful, So are Active ones

Frozen Raspberry are Tasty
"Frozen Raspberry are Tasty" by epSos.de

I think there's a crucial distinction we need to draw between data that is "active" or "working" and data that is  "finished" or "frozen"*, i.e. suitable for publication/consumption by others.

There's a lot of parallels that can be drawn between writing a novel (or a text book, or an article, or a blog post) and creating a dataset. When I sit down to write a blog post, sometimes I start at the beginning and write until I reach the end. In which case, if I was doing it interactively, then it might be useful for a reader to watch me type, and get access to the post as I'm adding to it. I'm not that disciplined a writer however - I reread and rewrite things. I go back, I shuffle text around, and to be honest, it'd get very confusing for someone watching the whole process. (Not to mention the fact that I don't really want people to watch while I'm writing - it'd feel a bit uncomfortable and odd.)

In fact, this post has just been created as a separate entity in its own right - it was originally part of the next post on citing dynamic data  - so if the reader wanted to cite the above paragraph and was only accessing the working draft of the dynamic data post, well, when they came back to the dynamic data post, that paragraph wouldn't be there anymore.

It's only when the blog post is what I consider to be finished, and is spell-checked and proofread, that I hit the publish button.

Now, sometimes I write collaboratively. I recently put in a grant proposal which involved coordinating people from all around the world, and I wrote the proposal text openly on a Google document with the help of a lot of other people. That text was constantly in flux, with additions and changes being made all the time. But it was only finally nailed down and finished just before I hit the submit button and sent it in to the funders. Now that that's done, the text is frozen, and is the official version of record, as (if it gets funded) it will become part of the official project documentation.

The process of creating a dataset can be a lot like that. Researchers understandably want to check their data before making it available to other people, in case of others finding errors. They work collaboratively in group workspaces, where a dataset may be changed lots very quickly, without proper version control, and that's ok. There has to be a process that says "this dataset is now suitable for use by other people and is a version of record" - i.e. hitting the submit, or the publish button.

But at the same time, creating datasets can be more like writing a multi-volume epic than a blog post. They take time, and need to be released in stages (or versions, or volumes, if you'd prefer). But each of those volumes/versions is a "finished" thing in its own right.

I'm a firm believer that if you cite something, you're using it to support your argument. In that case, any reader who reads your argument needs to be able to get to the thing you've used to support it. If that thing doesn't exist anymore, or has changed since you cited it, then your argument immediately falls flat. And that is why it's dangerous to cite active datasets. If you're using data to support your argument, that data needs to be part of the record, and it needs to be frozen. Yes, it can be superseded, or flat out wrong, but the data still has to be there.

You don't have this issue when citing articles - an article is always frozen before it is published. The closest analogy in the text world for active data is things like wiki pages, but they're generally not accepted in scholarly publishing to be suitable citation sources, because they change.

But if you're not looking to use data to support your argument, you're just doing the equivalent of saying "the dataset can be found at blah", well, that's when a link to a working dataset might be more appropriate.

My main point here is that you need to know whether the dataset is active or frozen before you link/cite it, as that can determine how you do the linking/citing. The user of the link/citation needs to know whether the dataset is active or not as well.

In the text world, a reader can tell from the citation (usually the publisher info) whether the cited text is active or frozen. For example, a paper from the Journal of Really Important Stuff (probably linked with a DOI), will be frozen, whereas a Wikipedia page (linked with a URL) won't be. For datasets, the publishers are likely to be the same (the host repository) whether the data is frozen or not - hence ideally we need a method of determining the "frozen-ness" of the data from the citation string text.

In the NERC data centres, it's easy. If the text after the "Please cite this dataset as:" bit on the dataset catalogue page has a DOI in it, then the dataset is frozen, and won't be changed. If it's got a URL, the dataset is still active. Users can still cite it, but the caveat there is that it will change over time.

We'll always have active datasets and we'll want to link to them (and potentially even freeze bits of them to cite). We (and others) are still trying to figure out the best ways to do this, and we haven't figured it out completely yet, but we're getting there! Stay tuned for the next blog post, all about citing dynamic (i.e. active) data.

In the meantime, when you're thinking of citing data, just take a moment to think about whether it's active or not, and how that will affect your citing method. Active versus frozen is an important distinction!

* I love analogies and terminology. Even in this situation, calling something frozen implies that you can de-frost it and refreeze it (but once that's done, is it still the same thing?) More to ponder...

1 comment:

  1. A good point, which BTW also applies to research software. Sharing work in progress and publishing results are different and the distinction should be made clear.