Digital tools are accelerating scientific research through larger-scale data collection, automated analysis, simulation, and global collaboration. They increase the speed and scope of innovation, but outcomes depend on data quality, access, transparency, and human oversight.
This topic belongs to the human knowledge context and is shared by SL and HL. A digital system collects, stores, processes, and distributes data, helping researchers identify patterns at scale.
| Change in research | Impact and implication |
|---|---|
| Automated data analysis | Machine-learning systems process large datasets rapidly, but biased or incomplete training data may produce unreliable findings. |
| Computer simulation | Researchers can test models before costly or dangerous physical experiments, although results depend on built-in assumptions. |
| Digital collaboration | Shared databases support international teams, while unequal access can widen the digital divide between institutions. |
| Open digital publishing | Findings circulate rapidly, but weak review may spread inaccurate claims. |
System: AlphaFold2 is an artificial intelligence system developed by DeepMind that predicts a protein's three-dimensional structure from its amino-acid sequence.
Example specifics: In 2020, AlphaFold2 achieved highly accurate results in the CASP14 protein-structure prediction assessment. DeepMind later released predictions through the AlphaFold Protein Structure Database, giving researchers worldwide access for biological research.
Impacts and implications: The system reduced the time needed for useful protein-structure predictions and supported disease and drug research. Opportunities include faster discovery, while risks include overreliance on predictions, unequal computing access, and insufficient scrutiny of model limitations.
Concepts: This demonstrates change because research becomes more data-intensive and automated. It also demonstrates systems, since outcomes emerge through interactions among datasets, algorithms, researchers, institutions, and funding structures.
In an IB response, explain a specific system, distinguish observed impacts from future implications, identify affected people and communities, and evaluate opportunities and limitations. Avoid the misconception that digital outputs are automatically objective or correct.
