Open Access News

[D]ata volumes are doubling every year in most areas of modern science and the analysis is becoming more and more complex....With data correlated over many dimensions and millions of points, none of the old steps — do experiment, record results, analyse and publish — is straightforward. Many predict dramatic changes to the way science is done, and suspect that few traditional processes will survive in their current form by 2020....As data volumes grow, it is increasingly arduous to extract knowledge. Scientists must labour to organize, sort and reduce the data, with each analysis step producing smaller data sets that eventually lead to the big picture. Analysing terabytes of data (one terabyte is 1,000 gigabytes) is a challenge; but petabyte data sets (of more than 1,000 terabytes) are on the horizon. One petabyte is equivalent to the text in one billion books, yet many scientific instruments, including the Large Synoptic Survey Telescope, will soon be generating several petabytes annually....

Procedures already involve instruments and software with myriad parameters. It is difficult to capture all the model numbers, software revisions, parameter settings and process steps in an enduring format. For example, imagine a measurement taken using a DNA-sequencing machine. The output is cross-correlated with a sequence archive (GenBank) and the results are analysed with Matlab. Fully documenting these steps would be arduous, and there is little chance that someone could repeat the exact procedure 20 years from now; both Matlab and GenBank will change enormously in that time. As experiments yield more data, and analysis becomes more complex, data become increasingly difficult to document and reproduce. One might argue that complex biological experiments have always been difficult to reproduce, as there are so many variables. But we believe that with current trends it is nearly impossible to reproduce experiments. We do not have a solution for this problem, but it is important to recognize it as such....Standards are essential at several levels: in formatting, so that data written by one group can be easily read and understood by others; in semantics, so that a term used by one group can be translated (often automatically) by another without its meaning being distorted; and in workflows, so that analysis steps can be executed across the Internet and reproduced by others at a later date....

Many scientists no longer 'do' experiments the old-fashioned way. Instead they 'mine' available databases, looking for new patterns and discoveries, without ever picking up a pipette. But this data-rich approach to science faces challenges. The speed of the Internet has not kept pace with the growth of scientific data sets. And so large data archives are becoming increasingly 'isolated' in the network sense —one can copy gigabytes across the Internet today, but not petabytes. In the future, working with large data sets will typically mean sending computations to the data, rather than copying the data to your workstation. But the management of distributed computations raises new questions of security, free access to public data and cost....The publication process itself is increasingly electronic, with new ways to disseminate scientific information (such as the preprint repository arXiv.org). But there is, as yet, no standard for publishing large volumes of data. Paper appendices cannot hold all the data needed to reproduce the results. Some disciplines have created their own data archives, such as GenBank; others just let data show up, and then disappear, on individual scientists' websites. Astronomers created the International Virtual Observatory Alliance, integrating most of the world's medium and large astronomy archives. This required new standards for data exchange, and a semantic dictionary that offers a controlled vocabulary of astronomy terms. To encourage data sharing, it should be rewarded. Public data creators and publishers should be given credit, and archives must be able to automatically provide provenance details. Current databases have a long way to go to achieve this ideal.

PS: Also see other Nature articles from the same issue on 2020 Computing (all OA). The Szalay-Gray article above is based on a longer report from Microsoft Research.