Tag: Scholarly communication

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Driving UK Research – Is copyright a help or a hindrance?

© is the copyright symbol
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The following is my contribution to a collection prepared by the British Library and released today at the Wellcome Trust, called “Driving UK Research. Is copyright a help or a hindrance?”  - Press Release – Document[pdf] – which is being released under a CC-BY-NC license. The British Library kindly allowed authors to retain copyright on their contributions so I am here releasing the text into the public domain via a CCZero waiver. I would also like to acknowledge the contribution of Chris Morrison in editing and improving the piece.

If I want to be confident that this text will be used to its full extent  I am going to have to republish it separately to this collection. Not because the collection uses  restrictive rights management or licences, it actually uses a relatively liberal copyright licence. No, the problem is copyright itself and the way it interacts with how we create knowledge in the 21st century.

Until recently we would use texts or data by reading, taking notes, making photocopies, and then writing down new insights. We would refer to the originals by citing them. A person making limited copies or taking notes (perhaps quoting the text) does not breach copyright because of the notion of “fair dealing”. Making copies of reasonable portions of a work is explicitly not a violation of copyright. If it were we wouldn’t be able to do any useful work at all.

Today, scholarship and research cannot effectively proceed via manual human processes. There is simply too much for us to handle. On the other hand we have excellent computer systems that can, to some extent at least, take these notes for us. Automated assistants that can read the text for us, that can do text mining, data aggregation and indexing allowing us to cope with the volume of information. As these tools improve we have an opportunity to radically increase the speed of the innovation cycle, using the human brain for what it is best at: insight and creative thinking; and using machines for what they are best at: indexing, checking, collecting.

The problem is that to do this those machines need to take a copy of the whole of the text and in doing so they trigger copyright. Even though the collection you are reading is released under a Creative Commons licence that allows non-commercial use, no-one can take a copy, find an interesting sentence, and then index it if they are going to make money. Google are not allowed to check what is here and index it for us.

Or perhaps they are. Perhaps this does come under “fair use” in the US. Or maybe it does, but not in the UK. What about Australia? Or Brazil? All with slightly different copyright law and a slightly different relationship between copyright and contract law. Even if current legal opinion says it is allowed a future court case could change that. The only way I can be sure that my text is available into the future is to give up the copyright altogether.

To build effectively on the scientific and cultural data being generated today we need computers. If a human were doing the job it would clearly be covered by fair dealing. What we need is a clear and explicit statement that machine based analysis for the purpose of indexing, mining, or collecting references is a fair dealing exception, even where a full copy is taken. There clearly need to be boundaries. The entire work should not be kept or distributed. As with existing fair dealing we could have guidelines on amounts kept or quoted: perhaps no more than 5% of a work. These could easily be developed and be compatible with existing fair dealing guidance.

We risk stifling the development of new tools, both commercial and academic, and new knowledge under the weight of a legal regime that was designed to cope with the printing press. At the same time a simple statement that this kind of analysis is fair dealing will provide certainty without damaging the interests of copyright holders or complicating copyright law. These new uses will ultimately bring more traffic, and perhaps more customers, to the primary documents. By taking the simple and easy step of making automated analysis an allowable fair dealing exception everyone wins.

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It’s not information overload, nor is it filter failure: It’s a discovery deficit

Clay Shirky
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Clay Shirky’s famous soundbite has helped to focus on minds on the way information on the web needs to be tackled and a move towards managing the process of selecting and prioritising information. But in the research space I’m getting a sense that it is fuelling a focus on preventing publication in a way that is analogous to the conventional filtering process involved in peer reviewed publication.

Most recently this surfaced at Chronicle of Higher Education, to which there were many responses, Derek Lowe’s being one of the most thought out. But this is not isolated.

@JISC_RSC_YH: How can we provide access to online resources and maintain quality of content?  #rscrc10 [twitter via@branwenhide]

Me: @branwenhide @JISC_RSC_YH isn’t the point of the web that we can decouple the issues of access and quality from each other? [twitter]

There is a widely held assumption that putting more research onto the web makes it harder to find the research you are looking for. Publishing more makes discovery easier.

The great strength of the web is that you can allow publication of anything at very low marginal cost without limiting the ability of people to find what they are interested in, at least in principle. Discovery mechanisms are good enough, while being a long way from perfect, to make it possible to mostly find what you’re looking for while avoiding what you’re not looking for.  Search acts as a remarkable filter over the whole web through making discovery possible for large classes of problem. And high quality search algorithms depend on having a lot of data.

It is very easy to say there is too much academic literature – and I do. But the solution which seems to be becoming popular is to argue for an expansion of the traditional peer review process. To prevent stuff getting onto the web in the first place. This is misguided for two important reasons. Firstly it takes the highly inefficient and expensive process of manual curation and attempts to apply it to every piece of research output created. This doesn’t work today and won’t scale as the diversity and sheer number of research outputs increases tomorrow. Secondly it doesn’t take advantage of the nature of the web. They way to do this efficiently is to publish everything at the lowest cost possible, and then enhance the discoverability of work that you think is important. We don’t need publication filters, we need enhanced discovery engines. Publishing is cheap, curation is expensive whether it is applied to filtering or to markup and search enhancement.

Filtering before publication worked and was probably the most efficient place to apply the curation effort when the major bottleneck was publication. Value was extracted from the curation process of peer review by using it reduce the costs of layout, editing, and printing through simple printing less.  But it created new costs, and invisible opportunity costs where a key piece of information was not made available. Today the major bottleneck is discovery. Of the 500 papers a week I could read, which ones should I read, and which ones just contain a single nugget of information which is all I need? In the Research Information Network study of costs of scholarly communication the largest component of publication creation and use cycle was peer review, followed by the cost of finding the articles to read which represented some 30% of total costs. On the web, the place to put in the curation effort is in enhancing discoverability, in providing me the tools that will identify what I need to read in detail, what I just need to scrape for data, and what I need to bookmark for my methods folder.

The problem we have in scholarly publishing is an insistence on applying this print paradigm publication filtering to the web alongside an unhealthy obsession with a publication form, the paper, which is almost designed to make discovery difficult. If I want to understand the whole argument of a paper I need to read it. But if I just want one figure, one number, the details of the methodology then I don’t need to read it, but I still need to be able to find it, and to do so efficiently, and at the right time.

Currently scholarly publishers vie for the position of biggest barrier to communication. The stronger the filter the higher the notional quality. But being a pure filter play doesn’t add value because the costs of publication are now low. The value lies in presenting, enhancing, curating the material that is published. If publishers instead vied to identify, markup, and make it easy for the right people to find the right information they would be working with the natural flow of the web. Make it easy for me to find the piece of information, feature work that is particularly interesting or important, re-intepret it so I can understand it coming from a different field, preserve it so that when a technique becomes useful in 20 years the right people can find it. The brand differentiator then becomes which articles you choose to enhance, what kind of markup you do, and how well you do it.

All of these are things that publishers already do. And they are services that authors and readers will be willing to pay for. But at the moment the whole business and marketing model is built around filtering, and selling that filter. By impressing people with how much you are throwing away. Trying to stop stuff getting onto the web is futile, inefficient, and expensive. Saving people time and money by helping them find stuff on the web is an established and successful business model both at scale, and in niche areas. Providing credible and respected quality measures is a viable business model.

We don’t need more filters or better filters in scholarly communications – we don’t need to block publication at all. Ever. What we need are tools for curation and annotation and re-integration of what is published. And a framework that enables discovery of the right thing at the right time. And the data that will help us to build these. The more data, the more reseach published, the better. Which is actually what Shirky was saying all along…

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