Data, data where art thou?

We all curse it, love it, analyse it, (try to) understand it and ultimately, we all need it….data.

It’s true, as conservationists we can’t do anything without data. How can we make a decision without justification through data? How can we be legitimate scientists without having data to analyse and present? There is an interesting irony in that we seem to have too much data to possibly analyse and yet we don’t have enough to answer those important, large-scale questions that could save the planet.

With seemingly unsurmountable problems piling up ahead like climate change, mass extinctions and habitat loss, effective policies founded in reliable data are key to instigating worldwide, transformative change. Yet, to fully influence policy-making, the data needs to be collected, analysed and presented in ways that break the boundaries of communication between all the sectors involved. Science is a vital component to solving environmental problems and over the past few years, the process of scientific enquiry continues to evolve. From instances like Wikileaks and Climategate, the scientific community has been striving to make data collection and analysis more transparent than ever before. It was only recently after all, that we began submitting our R scripts alongside our publications. Although we may grumble about the extra work involved, in the grand scheme of things, this development has made the scientific community much more open, accessible and inclusive.

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Data collection

Picture this: an intelligent scientist, carrying high-tech equipment, trouping through an exotic rainforest recording delicate, rarely-detected organisms. Classic image of data collection, right? Although such trips do still happen, it is just as common (albeit slightly less exciting) to carry out this stage in the comfort of your office with some steaming tea and a biscuit at hand. All you need is a reliable internet connection and you have access to an incredible array of freely available data. Often updated in real-time, these massive datasets contain detailed information that can be useful in so many ways. Yet, I feel this privileged access is undervalued and there remains a push to collect more and more data without fully exploring the potential of what we already have at our fingertips (literally). Our time might be better spent improving these existing datasets than collecting more. After all, how can we prove impact and effectiveness of projects without using long-term, large-scale data? Perhaps we should invest more time merging smaller snippets of data and digitising findings gathering dust on shelves, wedged in-between long-forgotten MSc theses. I know it is against human nature to stop, think and value what we have. We so often want more but don’t realise that the dress we had at the back of our closet, is basically the exact same dress as the one we just bought. In the context of data, taking the time to combine what we already have would be incredibly valuable.

As these large-scale datasets become more important, I think ensuring the recording is standardised across the various projects feeding into the larger remit, is truly vital. Without a uniform approach, the final stage of combining and presenting the data would be unnecessarily difficult. This may be more challenging than it initially sounds. Although datasets contain information on the same topic, countries may have contrasting approaches to their collection and presentation. Until that is addressed, being able to fairly combine or compare these will remain a challenge. I discovered this first-hand during data collection on privately protected areas (PPAs) for my master’s thesis. You would think that well-established platforms like the EU, would have some sort of standardisation regarding datasets of protected areas. However, even across western Europe, the way countries approach their initial defining of PPAs, their enforcement and the collection of their information differs so considerably that no one is 100% sure how many potential PPAs exist and where the sources of this data may be!

Luckily, I think that through large citizen science projects, we are beginning to tackle some of these problems. By having to standardise data collection across different contexts and collectors, we are ensuring the final datasets can be merged and will be valuable for a broad range of uses. Also, involving the public in the collection phase brings a whole other wealth of benefits. Not only are people part of the research process, they also feel connected to the projects. In building the public’s trust regarding the scientific process, this is incredibly valuable.

Data analysis

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Ah, now we come to the glorious world of statistics and modelling, that most of us dread when we walk into class. I am always in awe of my wonderful friends that look at a problem and with a few strikes of the keyboard have crafted a beautiful code that makes perfect sense of the confusion I was seeing before. This is an art of its own and I admit I am not a master. I do respect the process though and willingly struggle through. It is key to transforming raw data into something relatable to real-world processes, to map out details over time and space and to summarise what has been recorded for decades. Then again, because these complex analyses scare mere mortals like me, data analysis has often been an estranging concept to people and has ultimately fostered mistrust in scientific rigour. Especially when it comes to the application of intricate models with numerous inputs, attempting to simplify real-world processes; people lose their minds. It can be hard to understand and believe what we are seeing. This has been a barrier in policy-making because without understanding the process that generated the results, how can we defend them and ultimately gather support?

Luckily, there has been more focus recently on making data analysis less overwhelming. With statistics and analytical processes being fostered earlier in school and interdisciplinary courses evolving (conservation science!), I think inclusivity is being fostered.

Presenting…..data!

Without presenting the data the entire process has been for nothing. Someone needs to hear about it and act on it after all. We all aim to do something useful and by delivering our findings to an audience, that potential is there. Misinterpretation is a scary thing and communicating our work effectively and clearly to different sectors, stakeholders and age groups can be daunting. Especially nowadays where the problems we face involve anyone under the sun, translating our work so that it can be understood is unbelievably important. Not only do we aim for understanding, we aim for attachment. Our work has been our entire world for the past few months, years even, and we want people to feel connected to what we present. Data is the key ingredient sweetening the cake because everything is built around it. Without the data we would have nothing to present after all.

Ultimately, our approach to scientific enquiry is evolving and I am looking forward to the future of data. We are transforming the way we relate to data and how to involve the public more intimately in the process. Not only are we directly involving citizens in the collection phase, we are being as open about our data as never before. We are prepared to be vulnerable and this vulnerability has begun to forge trust with the wider society. Where there is trust, there is belief and ultimately positive actions will follow.

Lisa Kopsieker