When it comes to contemporary chemistry, data management may be a daunting task. If, for example, scientists are doing numerous rounds of tests to determine the proper conditions for the synthesis of a novel molecule, they will create huge volumes of raw data throughout this process.
Machine-learning algorithms may learn a great deal from both successful and unsuccessful trials, just as people can.
In practise, the most successful experiments are published since no human can meaningfully process the enormous number of unsuccessful tests.
Because of AI’s ability to accomplish exactly what machine learning methods can do, if the data is recorded in a machine-usable way, it can be utilised by anybody.
Because printed journal articles have a restricted page count, there has always been a necessity to condense material.
Although many journals are no longer printed, reproducibility is still a problem for scientists because of the lack of critical information in journal articles.
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. As a result, research teams are increasingly turning to technologies like Electronic Lab Notebook software, which stores data in proprietary formats that can be difficult to integrate. It is practically hard for research organisations to share data due to the lack of a standard approach.
As a result, a team of scientists published a viewpoint in Nature Chemical outlining an open platform for the whole chemistry workflow, from ideation through publishing of the project.