Current Issues in Research Informatics

Which of the following is a key trend in research informatics for improving data reproducibility?

• Data anonymization

• FAIR data principles

• Blockchain mining

• Quantum encryption

In research informatics, the term FAIR stands for:

• Free, Accessible, Interoperable, Reliable

• Findable, Accessible, Interoperable, Reusable

• Fast, Accurate, Innovative, Robust

• Functional, Adaptable, Integrated, Repeatable

Which of the following is a major ethical concern in the use of AI for research data analysis?

• Reduced processing time

• High computational costs

• Algorithmic bias

• Lack of internet connectivity

The use of blockchain in research informatics primarily aims to:

• Replace cloud storage entirely

• Facilitate secure and immutable data sharing

• Increase bandwidth speed

• Reduce software licensing costs

Which emerging technology is enabling real-time analysis of large-scale biomedical datasets?

• 3D printing

• Cloud-based high-performance computing (HPC)

• RFID

• Virtual reality headsets

A common challenge in integrating multi-omics data in bioinformatics research is:

• Excessive internet bandwidth

• High dimensionality and heterogeneity of data

• Lack of laboratory equipment

• Unavailability of cloud storage

The European Open Science Cloud (EOSC) initiative is an example of:

• A private research database

• A commercial data analytics software

• A federated infrastructure for open research data

• A blockchain-based cryptocurrency for scientists

In research informatics, which trend focuses on reducing the carbon footprint of large-scale data processing?

• Data minimization

• Green computing

• Microservices architecture

• Edge computing

Which of the following is a key benefit of integrating Artificial Intelligence into systematic literature reviews?

• Removal of all human bias automatically

• Ability to process and analyze vast volumes of publications quickly

• Guarantee of 100% accurate conclusions

• Eliminating the need for peer review

One of the main issues in cross-border research data sharing is:

• Limited local research funding

• Differences in legal and regulatory frameworks

• Slow internet connections

• Too many data visualization tools available