As attempts to leverage AI and Big Data in materials science become increasingly prevalent, a critical prerequisite has emerged: Data Standardization. There is a common misconception that standardization is primarily a complex technical hurdle. However, before addressing the technicalities, we must recognize that its essence lies in a “social contract”—a fundamental agreement among people.
Put simply, data standardization is akin to choosing a common language to communicate with people from different nations. Imagine if every researcher continued to record data in their own idiosyncratic way. Even the most powerful AI would exhaust its resources merely trying to interpret disparate meanings, or worse, it would produce critical errors. Therefore, data standardization begins when a community agrees to call the same measurement by the same name and record values in unified units. Only when this “promise” is kept can data produced in different laboratories be merged into a single, massive dataset capable of revealing meaningful patterns.
Consider, for example, the data representing the “ratio of raw materials mixed” when developing a new composite material or chemical product. One researcher might record this as the Blending Ratio, another as the Mixing Ratio, and yet another as the Composition Ratio. All these terms refer to the same physical concept: the proportion of each raw material added to create the final substance. However, if we process this data without accounting for these synonymous relationships, they will be treated as entirely different data points.
What if we agreed that all raw material input ratios should be standardized under the term “Composition Ratio” and recorded in “wt%”? Such an agreement would allow us to collect, integrate, and compare materials data from across the globe with ease. This alignment of terms and units—seemingly trivial at first glance—is the very tool that transforms fragmented information into actionable Big Data in the field of materials science.
A Detailed Discussion on the Essence of Data Standardization: Consensus and Commitment within the Community
In this context, data standardization should be viewed not merely as an engineering process of developing specific technologies or software, but rather as a social consensus reached by experts within the field. Just as the word “apple” evokes the same fruit in everyone’s mind in daily life, the materials science community must share a fundamental commitment: “From now on, we shall call this property by this specific term and record it strictly in this unit.”
No matter how advanced an AI model may be, if the underlying data formats are inconsistent, the collected Big Data becomes useless. This fragmentation has been a critical weakness that undermines the very foundation of AI development, especially in complex academic disciplines. In materials science, if researchers record data using only the terms or abbreviations familiar to themselves, an outsider—or a computer—will inevitably perceive these as unrelated, disparate pieces of information. This severs the logical connection between data points, which is the very core of building a materials ontology. Data that is not connected is nothing more than fragmented numbers; AI cannot learn correlations or predict new properties from such disorder.
Ultimately, data standardization is the process by which a community’s collective intelligence imposes logical order onto disordered data. This process of consensus—translating the experience and knowledge accumulated by countless researchers in their respective labs into a common language—is the solid foundation that supports the vast knowledge system of materials Big Data. The more actively experts commit to and participate in these “promises,” the more sophisticated and powerful our ontological network will become.
The Practical Limitations of Materials Big Data: The Sluggish Pace of Data Standardization and the Time Gap
Despite the ideal goals of data standardization, existing global methods suffer from a significant weakness: a severe imbalance between the speed of data generation and the speed of its formalization. Traditional data standardization processes typically involve several rigid and complex stages:
- Formation of Expert Committees: It begins with assembling leading authorities in specific material fields to establish a forum for discussion.
- Proposal of Terms and Specifications: Terms used disparately across laboratories and industries are collected to derive an optimal representative specification.
- Repeated Deliberation and Resolution: A grueling process follows to verify academic and industrial validity and to reach a consensus among diverse stakeholders.
- Promulgation and Distribution: The finalized standards are formalized and disseminated to the research field.
The problem is that this process consumes a vast amount of time—ranging from several months to many years. While field data is pouring out in real-time through advanced experimental equipment and automated systems, the speed of creating the “vessel” (the standard) to hold that data is moving at a snail’s pace.
This Time Gap causes serious side effects in research environments. Researchers who need immediate Big Data analysis using AI cannot afford to wait indefinitely for standardized guidelines. Ultimately, exhausted by the wait, they revert to recording data using their own familiar methods and terminology. This leads to a vicious cycle that further deepens data fragmentation.
These limitations inevitably result in the sluggish construction of Materials Ontologies, which define the meanings and relationships of data. An ontology is a sophisticated task of designing logical connection structures beyond simple terminology lists; as long as data standardization is delayed, building the framework of the ontology remains stagnant. Trying to establish “relationships” between data points when even their “names” are not unified is akin to building a castle on sand.
Ultimately, at this juncture, the greatest challenge facing global data standardization methods is the severe discord between the momentum of the Big Data era and rigid, outdated standardization processes. While the engine of research innovation driven by Big Data relies on overwhelming “volume” and near real-time “velocity,” current methods are exposing the following critical limitations:
- Decreased Utility: Data held back while waiting for finalized standards often becomes “legacy data” by the time it is released. In a rapidly evolving research environment, its analytical value drops precipitously.
- Lack of Flexibility: The slow pace of deliberation simply cannot keep up with the daily influx of new material trends and cutting-edge experimental techniques.
- High Barriers to Adoption: Even when data standardization is completed, the protocols are often so complex and vast that the cost of learning and applying them is too high, leading researchers in the field to ignore them altogether.
Consequently, even if standards are eventually established and an ontological framework is set, a “Data Debt” phenomenon occurs—where the cost and time required to re-organize and map the vast amounts of non-standardized data already accumulated exceed the original investment.
To truly activate materials Big Data, there is an urgent need for a more flexible and innovative data governance system that can drastically shorten the slow pace of data standardization and ontology construction. This requires the development of a new dimension of technology where Artificial Intelligence itself can propose standards and autonomously structure data.
A New Solution: Agile Ontology to Drastically Shorten the Data Standardization Process
To fundamentally shift the paradigm of data standardization, we must develop new technologies leveraging AI. While this might seem to contrast with the idea that standardization is a “social contract,” it is, in fact, a new paradigm that elevates the essence of “agreement” and “consensus” into the realm of technological execution. Instead of waiting for protracted standardization processes, we should pursue an Agile system where data standardization occurs in real-time alongside ontology construction. This approach is essential to narrowing the gap between data generation and standard definition to near zero. We propose three core strategies to realize this:
- AI-Assisted Mapping Even before a community consensus on standardized terms is finalized, we must introduce technology where AI learns from vast amounts of existing literature and data to determine similarities between different terms and automatically map them to standard terms. Specifically, by utilizing Namespaces to manage unique terminology from individual provenance separately, we can prevent term conflicts while ensuring logical connectivity. This allows researchers to maintain their familiar terminology without compromising the data’s origin or context, while simultaneously securing interoperability that links to global standards in real-time.
- Bottom-up Dynamic Standardization We need a system that collects the terms and data structures most frequently used in actual research fields in real-time and reflects them immediately as “dynamic standards.” This method directly synchronizes the collective intelligence of the field with the database, recognizing the terms and relationship settings chosen by the majority of researchers as the “De facto standard.” This upward approach bridges the gap between the authority of standardization bodies and practical field work, completing a vibrant standardization framework that reflects rapidly changing materials research trends in the ontology as quickly as possible.
- Modular Ontological Structure Rather than delaying system construction until a massive, comprehensive standard is perfectly designed, a flexible modular design should be introduced that can be immediately applied and expanded starting from verified sub-sectors. Like assembling Lego blocks, independent ontology modules for specific areas—such as processes, properties, and structures—are developed and deployed first. These are later interconnected to form a vast knowledge network. such a structure ensures that modifications to a specific part do not paralyze the entire system and provides extreme Agility, allowing researchers to select and use only the modules they need for immediate database construction.
Specifically, this Agile Ontology is a concept that goes beyond a mere listing of terms; it encompasses the creation and practical application of a Meta-Ontology—the very rules that define how an ontology should be structured and expanded. This serves as the starting point for an intelligent system where data defines its own structure and evolves autonomously.
This evolution of the Agile approach overcomes the limitations and traditional syntax of conventional ontology construction. It ultimately leads to a technology we call “Transcendental Ontology,” which maintains perfect logical consistency while minimizing human expert intervention. (This is a proprietary concept and technology proposed by this blog, and we will explore its details in future posts.)
In the end, Agile Ontology will be the key to overcoming the physical limitation of the “Time Gap.” it will build a truly intelligent research ecosystem where materials Big Data stays alive in real-time, expanding its own knowledge base.
Conclusion: From “Waiting for Standards” to “Creating Standards Together”
We have reached a point where we must rethink data standardization to ensure it no longer acts as a bottleneck for materials Big Data research. We must drastically shorten the standardization process, transforming the language of the research field into data assets immediately, and thereby establishing a powerful foothold for the success of materials ontologies. As discussed, rather than a thick manual of standards born from months of deliberation, let us work together toward an agile standardization framework that allows data entered today to be analyzed by AI tomorrow.