When it comes to contemporary chemistry, data management may be a daunting task. Scientists have to do several experiments to establish the correct circumstances for a reaction, which generates a large quantity of raw data. For example
Machine-learning algorithms may learn a great deal from both successful and unsuccessful trials, just as people can.
In reality, the most successful experiments are published since no human can meaningfully process the huge number of unsuccessful tests..
A significant shift has been brought about by AI; the technology can now accomplish exactly what machine-learning methods can, provided the data is saved in a machine-usable manner that can be utilised by everyone.
Due to the restricted number of pages in printed journal articles, the requirement to compress content has existed for a long time.
Reproducibility remains a problem for chemists even though many journals no longer provide printed copies; this is because journal papers sometimes omit important information.
The lack of publication of unprocessed raw data forces researchers to waste time and money replicating the authors’ unsuccessful experiments and to struggle to design on top of published results.
In reality, in addition to the sheer amount, there is a diversity of data to contend with. Electronic Lab Notebooks (ELNs) have been used by research organisations to store data in proprietary formats that are sometimes incompatible with one other. It is practically hard for research organisations to share data due to the lack of a standard approach.
There is an open platform for the full chemical workflow, from conception to publishing, which a group of researchers has published in Nature Chemistry.