IDBS Data Management Blog

Could it be the next breakthrough in scientific collaboration?

As a wise man once noted, science is the search for shared knowledge. And in my view it’s the word – share – that is the most important part of the process, especially in the fast moving world of R&D.

As Steven Johnson pointed out at TED, face-to-face lab meetings (maybe over a cup of your favorite brew) are where all the latest thinking can be opened up to be discussed, scrutinized, pulled apart, reassembled, added to other ideas, and then built on to create real innovation.

That’s not so surprising, given that at heart most scientists are particularly social people who are terrific at communicating at a personal and local level. But those skills start to show cracks when you bring groups together or they are forced to communicate over a distance – indeed I’ve even seen people have trouble communicating between different floors!

Paper… who uses paper anymore?

So are paper reports or scientific literature useful in bridging geographical gaps? They have their place, but where is the interaction to stimulate productive debate? There is the net of course, but despite its connectivity, more barriers seem to appear every week. So with the opportunities for real social interaction disappearing daily, we stand to lose a frightening amount of potential innovation.

Dynamic, rich content dialogue – perhaps fuelled with just a little caffeine to get the grey cells firing – is what is needed.

Collaboration is good. Good collaboration is better.

Today’s business mantra has it that collaboration is the way forward. I agree. However, it must produce data in a shared and open way. All too often data summaries are delayed by the reporting process. That leads to poor communication, with far less context, which can result in a lack of trust.

One answer would be to get everyone involved to share their experimental data over a coffee in the canteen. No easy feat if your organization happens to be spread across several sites or around the world!

So now more than ever we need to embrace new ways of socializing. The new virtual world of social media and instant messaging are great ways to communicate. If you add to that the new opportunities for tagging, commenting and sharing, a new more social scientific world starts to emerge.

Yes, it needs to be done in a controlled way. Yes, it needs to be secure. But, when you boil it down, all I’m arguing is that the only way to do this is to start managing our data better and then liberate it through multidisciplinary enterprise information systems.

The cup of coffee is, of course, optional. Tea would do just as well.

Check out Chris’ latest article at NewPharmaThinkers

Amongst the companies providing fodder for booth trophy hunters, national dancers, alphorns and buckets of beer, what was one of the big buzzes at BIO 2012? Bioprocessing. Last year this area was just a zone, but it has fermented into a healthy biomass of hundreds of large molecule therapeutics, antibody and vaccine CMOs. Not just the Lonza, DSM and Xcellerons but many, many more providers of bespoke biologics manufacturing were there. This was no Pittcon but the amount of stainless steel and bio manufacturing expertize was the largest ever.

So, how come this growth-based industry is growing so fast? The FDA has fed the growth by its Quality by Design (QbD) requirements, backed up by some recent high profile fines for major pharma batch failures. Coupled with the rise in large molecule therapeutics, vaccines and biosimilars the momentum is unstoppable.

But there is still a big problem brewing in fermenters around the world….data overload. Bioprocess development and QA produce mountains of process and scientific data. This data is vital in building recipes that provide consistency of production and to compare today’s yield against historical data, to take decisions and share them with others. Amazingly many of these processes are still paper driven and laborious, so the impact for a low margin CMO, seeking to attract business and keep the FDA happy,  is clear. Go digital and get efficient. This is not a transactional ‘BI-like’ aggregation of dispersed data but an integrated, managed data approach.

When developing a process and undertaking experimental design, each step needs to be captured, compared to historical data, and integrated with other data to secure IP and provide process insight.  Into this data-rich environment step GMP-compliant process ELNs such as IDBS’ E-WorkBook which is both a research ELN and Process Execution System, able to cross the boundary of R&D. Coupled with Design of Experiments systems such as uMetrics, and internal historian systems, these platforms are able to capture, compute, compare and secure process data; then integrate upstream to Manufacturing Execution Systems (MES) and Enterprise Requirements Planning Systems such as SAP.

The bioprocess development process has been very document-driven and this is hugely inefficient. Documents should be generated just in time rather than generating data from multiple documents. This is where data management takes over from legacy document/paper management. Fast-moving Bioprocess organizations’ process engineers  now inhabit a flexible, cheaper data-driven world with documents generated on demand, rather than living in a document heavy environment.

There was plenty more at BIO 2012 but for sheer biomass at BIO in Mass the Bioprocess team wins. With improved data management and associated process insight these companies will expand further to become major league players providing cheaper, better therapeutics in the decades to come.

How we use Web 2.0 methodology so you’ll never say this about IDBS

In his last blog post, Chris Molloy highlighted reasons why accurate content is important. So when it comes to product documentation, getting accurate content is all there is to it. Right? Wrong! There is little point in having accurate documentation if no one can find it, can’t understand it when they find it or have to wade through unnecessary detail. Even worse is being left with insufficient detail and no means of getting it. Grrrrrr!

By asking customers a few simple questions – knowing their existing knowledge level, the level of detail needed, where they’ve come from or are going to next – we can identify the best strategy for giving them what they want and guiding them through the large amount of information we have for them. The easier it is for them to find relevant information, the sooner they return to do what they do best with an increased level of effectiveness.

Delivering content in a Web 2.0 world

It’s all plain sailing then? Well it could be if it wasn’t for technology. These days the boundaries between different content formats are not so much blurred, but virtually transparent. Hardcopy can be embedded inside web based content and links to web based content can be displayed in printed documents. Add in the arrival of smartphones, iPads, Kindles and other tablets and you have a myriad of platforms all with their unique browser or rendering characteristics. Next throw in changes to the way people access and use the available information – just think how often we assume things have touch screens when most things do not – and you have a serious mix of variables to cope with. Finally consider social media, which has many questioning whether their tried and trusted documentation delivery mechanism still cuts the mustard.

Having your cake AND eating it

We’ve implemented an approach that uses a consistent style, look and feel throughout, yet provides targeted access to only the information an end user requires. Additional information is easily available and links are provided to lead them to the next workflow step. In short, there is detail if they need it but no-one has to wade through that detail to get to simple help and guidance. The E-WorkBook Suite help includes videos, printable quick start guides and embedded sample files. Customers can even download and import them into their system to try things out and get learning.

Improving content through feedback

We believe that you ignore the future at your peril. Customer demographics, usage patterns and new technology all conspire to make documentation seem like a lumbering dinosaur in no time. Our ability to keep things up to date and relevant is significantly enhanced by regular and rapid feedback. To ensure we get this, we use an analytics application designed specifically for Technical Writers. It allows us to interrogate how users consume our documentation. Armed with this we can identify any pain our customers experience and implement solutions – fast. The beauty of this is that a solution may only require a redesign of the user interface or workflow and no documentation changes.

Not changing a document? Now that’s something I bet you never thought you’d hear a Technical Writer saying!

Scientific research is changing. Our customers want to break down the silos between departments, improve communication, simplify the way they work and let scientists focus on doing science. Their biggest frustration, – especially in chemical research - is that they know they are repeating work that has been done countless times before. But there’s no way to know in advance that a particular chemical synthesis is doomed to failure. Unless you’re lucky enough to have a colleague who has tried it before, you have no way of finding out.

So you go ahead and run the synthesis. Then, for some unknown reason, it fails. You shrug and  – and this is the important part – you don’t tell anyone about it. And that, in my view, is a huge mistake.

Failure is good

Of course, you record the fact that the experiment was performed, exactly as you should, in your paper or electronic journal. Maybe someday one of your colleagues, trying to do the same thing will see your previous attempts, and learn from them. But scientists are cautious about publishing failure. Where is the benefit? Journals aren’t interested in science that doesn’t work. Funding bodies are unlikely to reward someone with a track record of failure. So this huge corpus of potential knowledge is lost. Some sources say only 5% of scientific work ever gets published, the rest of it (the less successful or more humdrum work) sits in paper or electronic repositories, never to see the light of day again.

However, people are beginning to recognise that hiding less than perfect results is a problem, so we, along with many others, are trying to address it.

Instant messaging – for chemists

The advent of free online chemistry resources like the RSC ChemSpider.com, now makes it easy for researchers to find information on compounds. Recent work done by the RSC, in collaboration with the Unilever Centre for Molecular Informatics at Cambridge University, allows researchers using our ELN, E-WorkBook, to look up chemical structures on Chemspider. Here you can see physicochemical properties, assay results and commercial availability for example, all from within your Notebook, by simply pressing a button. And you can share your knowledge more freely, by publishing your compounds to Chemspider. It only takes one click.

Work is well advanced on a way to publish synthetic reaction schemes too, so even those failed reactions that are no good to anyone (except, of course, they are) can go in there too.

I think this is a great thing. It lets chemists learn from each other’s mistakes. It makes more scientific research public knowledge. It breaks down barriers. And it lets chemists communicate their findings with each other openly – hopefully to the betterment of all researchers everywhere. Even better it’s now available free for our ELN users. Take a look at it in action here.

If you want to share your research data with the scientific community – whether it’s a breakthrough or a letdown – or you just want to find out fast information on compounds, download the plugin from our IDBS Labs support portal today.

The more you open up, the more you’ll be amazed at what you can learn.

Say what you’re not saying, don’t say it, say what you didn’t say

Last time I blogged that modelling is not limited to software engineering, play and simulation; but is universal in human endeavour. I mentioned that considering accuracy is important but not sufficient in assessing a model. What other considerations are there?

My favourite lens for looking at a model is abstraction. In philosophical terminology, abstraction is about grouping concepts together at decreasing levels of detail. So, a duck is a duck and no other thing is a duck (no matter how it looks or walks or sounds); but applying abstraction allows us to talk about birds and say useful things, which might be rather exasperating if we had to list every bird in the world to say them. This kind of classification is a particular feature of object-oriented programming languages (which may or may not be a good thing).

Leaving it out

A modeller, not saying

However another way of considering abstraction is to pause before asking what a model is saying, and ask: what is this model not saying?

The model of biological change that we call evolution has incredible empirical support, so that its application has great explanatory and predictive power (some would even say that we don’t apply it enough). Strangely, though, it seems to cause an awful lot of consternation to those who subscribe to another model called creationism.

Why strange? At first sight, both of these models deal with how the world came to be the way it is. But evolution models a process, and has nothing whatsoever to say about how that process began, or why it began, or who began it. Conversely, creationism says nothing about how its proposed agent went about his craft (well, usually). He just did it. Apples and oranges.

Putting it back in

Any critical analysis or use of a model has to carefully stick to assessing or building upon what it actually models. This might sound simple, but humans find it remarkably tricky. We are fond of making cultural and doctrinal assumptions and applying intuitions without knowing about it. (In a black alley, a black cat spies a black rat. How?*)  Unfortunately this is not only inevitable, it’s usually necessary.

Why so? Models almost always rely on background information. Of particular interest in computer science and artificial intelligence is the notion of semantics: the meaning of symbols. Tell a robot to fetch you a cuppa, and it may suffer the same semantic confusion as is now affecting US readers: a cuppa what?