M.I.C.E labs - Electives
Day 4:
Reference Management & AI in Research
We explored reference management tools that are crucial for efficiently organizing and citing research. We examined Zotero and Mendeley, learning how to seamlessly store, manage, and format references. We also got introduced to AI-powered tools like SciSpace, which help summarize and extract key insights from research papers.
Day 5:
Topic Selection & Base Paper Search
On this day of our elective, we put our learning into practice. Divided into four groups, we selected research topics and identified relevant base papers to support our work. This hands-on activity strengthened our literature search and journal selection skills while fostering teamwork, critical thinking, and strategic research planning.
Day 6:
Deep Dive into Literature Search & Review
Building on the previous day’s efforts, Day 6 was dedicated to deepening our literature review. Each group refined their research questions and conducted a comprehensive search to identify gaps in existing studies.
We practiced advanced search techniques, applied Boolean operators, and critically assessed research papers for relevance and credibility. This process strengthened our research framework, ensuring a solid and well-supported foundation for our projects.
Day 7:
Introduction to Data Analysis & Jamovi Basics
We explored data analysis software, a vital aspect of research. While we were introduced to tools like R, Python, SPSS, and Jamovi, the primary focus was on Jamovi—an open-source statistical software designed for intuitive and efficient analysis.
We gained hands-on experience with data input and descriptive statistics in Jamovi, learning how it simplifies complex statistical computations. This session was a crucial step toward practical research applications, emphasizing the importance of data analysis in drawing meaningful conclusions from research findings.
Day 8:
Data Visualization & Chatbots for Analysis
Effective data visualization plays a crucial role in enhancing research presentations. We explored several AI-powered visualization tools, including:
- Miro & Infogram – Designed for collaborative visual storytelling.
- Piktochart & Datawrapper – Ideal for creating infographics and data visualizations.
- Flourish & Chartblocks – Useful for building interactive charts.
- RawGraphs – Best suited for visualizing raw data.
These tools enable researchers to transform complex datasets into clear, visually engaging, and easily interpretable formats.
