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Proper data curation and uninterrupted access to various data sources are inseparable parts of the research process. Dealing with a continuous flow of high-quality data is the desired situation in all disciplines of science and technology. This ensures that reliable and valid research can be carried out uninterruptedly in a specified timeframe.
At the same time, the rapid growth of data volume may be overwhelming. We have long crossed the state where analyzing the collected research material could be processed manually. The data load is getting bigger, becoming far more complex for humans to comprehend, and the results are needed quicker than ever. Hence the need to equip researchers with a set of methods and techniques to adjust to new circumstances and constantly emerging challenges.
One of the possibilities to automate and enrich the research process is by using machine learning (ML).
The main advantage of applying machine learning to any activity is removing repetitive data processing activities. Some techniques that come to mind are classification, i.e., assigning a specific label to a particular observation and object detection. Classification of plant or animal species, recognition of land cover types, determining whether we are dealing with healthy tissue, assessing the material damage type, or simply counting the occurrence of some object or phenomena. All these non-trivial problems can be automated to some extent.
Of course, depending on the complexity of the task, the result of the machine learning model may be more or less accurate. Sometimes we can even accept a result worse than what could be obtained by the researcher's manual work. ML's scale effect can compensate for the shortcomings by treating data with a high temporal and spatial resolution. One should remember that even the best result is only useful if we can obtain it at the right time for it to be included in the research process.
Moreover, ML gains when working in a larger research team. There are cases in which we need to ensure that our results are uniform and standardized. A correctly implemented and trained machine learning model generalizes well, thanks to which it can deal with contentious cases by aiding researchers in the decision process.
The data stream is not only valuable because it is capable of producing new observations. Up to now, research teams worldwide have already stored and processed a lot of data. Experience shows that among various research projects, there are hidden gems among archived datasets that can be revisited and successively reprocessed. Previously acquired data can be frequently used as an ML training set, offering the possibility to build a valuable model and apply it during future research projects but on a larger scale.
Gain intuition into the methods used, basic workflow, and some possible pitfalls of preparing a data set for a ML project by reading this article.
ML solutions are meant to be more than just a passive element of a research data processing pipeline. The incredible complexity of modern neural networks and the possibility of partially explaining them with the help of explainable AI (XAI) builds a foundation for discovering new patterns and searching for new knowledge. Recognized relationships fixed in the hidden layers of the neural network can help researchers gain insight into the studied phenomena. Understanding how a machine learning model solves a given problem can be a set of valuable clues for a researcher.
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To a large extent, machine learning techniques force us to treat part of the research activity as a software engineering project. This is related to the need to formalize part of the research procedure and save it as a computer code. While maintaining good programming practices, we can preserve crucial parts of our research project as a maintainable, versioned, and tested part of our research project. This approach is worth the effort because it enables efficient reproduction of the results of scientific work, which is extremely important in any scientific discipline. It is also slowly becoming a standard practice, confirmed by the existence of such portals as paperswithcode.com. One should remember that the results of scientific work, which are persisted in code, are a bit closer to being implemented and used in commercial activity.
Involving machine learning in research in a given scientific discipline does not mean that each researcher must become a programmer or an expert in building models based on artificial neural networks. Working in interdisciplinary research teams is nowadays a standard and often one of the conditions for success.
Moreover, an increasing number of people dealing with machine learning also have competence in other disciplines, such as biotechnology, medicine, or earth sciences. Having the basics, a machine learning engineer can immediately start working on a given problem and support the research team from day one.
Machine learning can enrich the research process with new techniques and approaches to conducting a scientific project. Combining the features of a research project and an engineering project can meet the requirements of the most challenging research problems. ML models will never replace researchers, but they have a chance to support them in their work and pursuit of discovering more exciting secrets of the world around us.