Conceptualising field research

Research ethics and IRB/ethics committee application preparation

Project management for research timelines and deliverables

Defining problem statements and identifying relevant research questions

Selecting appropriate research methodologies for different project types

Data collection and analysis

Qualitative research (interviews, focus groups, etc.)

Using platforms like Qualtrics or SurveyMonkey to create and manage surveys

Building respondent pools and tracking engagement metrics

Evaluating data validity and reliability across sources

AI tools, workflows and automation for transcription and analysis

Preparing comprehensive literature reviews

Conducting structured literature reviews and quantitative meta-analyses (PRISMA)

Performing pre-publication peer review

Synthesizing academic arguments and identifying key gaps

Creating annotated bibliographies using citation managers

Meeting citation standards and stylesheet guidelines for journals

Manuscript formatting and submission for target journals

Preparing manuscripts in LaTeX

Refining and copy-editing manuscripts for clarity and scholarly tone

Aligning content to specific journal scopes and editorial requirements

Writing grant proposals to meet technical requirements

Grant budget development and financial reporting

Reviewing funder guidelines and formatting proposals accordingly

Preparing executive summaries and logic frameworks for donors

Statistical analysis and data visualization (using AI)

Research software proficiency (SPSS, R, NVivo, ATLAS.ti)

Coding and analyzing qualitative/textual data

Integrating AI tools with traditional software to enhance outputs

Performing cross-tabulation, correlation, and regression analysis

Writing code in Python or MATLAB to automate data analysis

Employing tools like Jupyter Notebooks or R Markdown

Machine learning algorithms and data mining techniques

Building statistical models and simulations (Monte Carlo, etc.)

Scraping online databases and academic archives using code

How to use AI for automation, data scraping and mining

Creating publication-quality graphs, tables, and infographics (Excel, Tableau, Python/R)

Designing diagrams and models using Adobe Illustrator or CorelDRAW

Creating effective research posters and academic conference presentations

Embedding visual storytelling in research deliverables

Using AI to design slide decks and visual assets quickly

Proficiency in Zotero, Mendeley, EndNote

Managing bibliometric indicators and impact factors

Collaborative writing and version control (Git, Google Docs)

Setting up shared research folders with synced citation managers

Automating reference updates across long manuscripts

Organizing, cleaning, and transforming raw datasets

Managing large datasets with SQL, Hadoop, Spark

Anonymizing sensitive data (GDPR/IRB compliance)

Using AI for transcription and analysis of interviews

Understanding legal considerations around data ownership and sharing