Lancaster University

- Implementing and driving the institute's research strategy across several research areas. 
- Leading new international strategic partnerships, increasing institute impact and research income.
- Developing new research opportunities and driving growth across the UK and South East Asia.
- Building an academic network,identifying key topics and experts across disciplines and institutions.
- Overseeing the scientific research program, managing a portfolio of projects and budgets.

Director of the Future Cities Research Institute

- Driving development and growth across key national and international partners/clients, increasing, identifying new business opportunities of follow-on and as well as new projects. 
- Developing and leading strategic projects and teams in the health, healthcare and circular economy areas, ensure profitability and high client satisfaction. 
- Engaging in thought leadership (e.g. publications, presentations, press, blogs) and development of intellectual property.

Professor in Global Health Design Innovation

- Setting and leading the direction of the institute on research, innovation and business engagement on sustainable cities, digital cities, resilient cities and liveable cities in the UK and Malaysia.
- Leading new international strategic partnerships, increasing institute impact and income sources.
- Identifying new business opportunities and driving development and growth across key clients


The Future Cities Research Institute (FCRI) is a new joint research institute between Sunway and Lancaster Universities, helping to tackle significant challenges associated with the rapid urbanisation experienced across the global south.

Co-Director of Future Cities Research Institute

- Generating insights and recommendations for global health interventiosna dn products in Africa,  Souteast Asia and Europe
- Leading the development of cross-department international training and stakeholder engagement, generating over 20 new stratregic connections 
- Consultancy to national and international research bodiest and the UK Research Institute on Digital Health innovations
- Engaging in thought leadership (e.g. publications) and development of intellectual property for use on future engagements
- Coaching of staff and external stakeholders on global innovation methods

Director for Health and International Research at ImaginationLancaster

- Developed training programmes on innovation, increasing numbers of clients 
- Developed and managed over 5 externally funded international innovation and research projects, increasing institutional income and outputs
- Trained and mentored small and large groups of adults in design thinking, innovation and
design, user research, achieving high client satisfaction
- Led division marketing leading to increased income from new students

Senior Lecturer/Associate Professor in Design Interactions at ImaginationLancaster

- Set and led strategic department direction in health innovation and research increasing research income
- Developed international special interest group in Global Health, leading to new strategic
partnerships, increasing organisation impact and influence

Strategic Lead for Health & Wellbeing Research

EPFL

Visiting Researcher/Professor at EPFL+ECAL Lab, EPFL.

• Co-developed new Digital Health Innovation Research Framework
• Delivered presentations and talks on speculative design and design innovation.
• Collaborated with interdisciplinary teams to drive cutting-edge research initiatives.

Visiting Researcher/Professor

Sunway University

Visiting Researcher/Professor at Sunway University as part of my role as Director of the Future Cities Research Institute.

• Led CRI Half-Day Ideation Series to brainstorm ideas and facilitate collaboration between SU and LU colleagues.
• Developed strategies for NetZero Cities, Healthy Communities/Healthy Planet, and Urban Poverty initiatives.
• Collaborated with Sunway University colleagues to drive research and innovation in Future Cities.
Conducted capability development workshops:
- Early Career Research Network (ECRN) Seminar – Beyond Journal: Redefining Research Impact
- Post Graduate Academic Career Mentoring and PhD Training

Brunel University

- Developed new and restructured existing training programmes, increasing organisation
income and making savings on resources

Course Director for Multimedia Technology and Design

- Developed and managed innovation project with the NHS, leading to successful completion
- Supervised students placed in the industry making new industrial links and gathering industrial insights for the organisation

Lecturer/Assistant Professor

- Managed complex project of 9 partners from 7 different countries with 25+ staff to successful completion
- Led technical work packages and generated insights from a range of sources leading to innovation development
- Contributed to the research application leading to funding success

Co-Investigator, Project Manager and Engagement Coordinator

• Conducted technical and user research leading to successful completion of project on multi-national EU funded project (IP-Based Networks, Services and TermINals for Converged SysTems -INSTINCT), that led to new insights and additional funding for the organisation.

Research Assistant

I help PhDs, Postdocs & Lecturers secure tenure-track jobs and navigate academia | Insider knowledge tips on how to succeed | Academic Career Mentor | Professor & Research Director

I was a lost PhD student. Confused. Overwhelmed. Ready to quit.

Sitting in my tiny office at 2 AM, drowning in research papers, I made a promise...

"If I somehow make it through this, I'll help others navigate what I'm struggling with."

Today, I'm a Professor at a top UK university. But here's what matters more...

I've kept that promise by helping over 10,000 researchers complete their PhDs and secure academic positions.

The brutal truth about academic careers:
→ 80% of PhD graduates never get academic jobs
→ Universities teach research, not career strategy
→ Brilliant minds give up because they lack guidance
→ Hiring committees have secret rules nobody explains

The brutal truth about doing a PhD whilst working:
→ You're figuring it out alone with minimal supervisor support
→ You lose sleep worrying about failure and its professional consequences
→ You're paralyzed by pressure - procrastinating makes deadlines feel impossible
→ Impostor syndrome hits hard: "Everyone else knows what they're doing, but I'm lost"
→ You're overwhelmed juggling your demanding job with complex academic requirements

My mentors saved my career. Without their guidance, I would have quit. Now I'm that mentor for you.

My transformation story:
→ From PhD uncertainty to Professor by 40
→ £9 million in research funding secured
→ 130+ publications across top journals
→ Research reached 30+ million people globally
→ 220K researchers follow my guidance daily

But here's my real achievement:
Thousands of researchers now have the roadmap I desperately needed.

FREE RESOURCES I PROVIDE:
✅ Weekly newsletter (10K+ subscribers) - PhD completion & job search strategies -  www.phdtoprof.com/newsletter
✅ Daily LinkedIn content - Insider academic career tips
✅ Free academic resources - www.phdtoprof.com/store
✅ AI tools and strategies for ethical research productivity

PREMIUM & PERSONALISED SERVICES:
🎯 1:1 Career Coaching - Personalized academic job search strategy
🎯 Academic Job Application Reviews - CV, cover letters, research statements
🎯 The 6P Success System - 6-week academic job accelerator program -  www.phdtoprof.com/6p-succes

🎯 Premium PhD Coaching - For busy professionals completing their PhD
🎯 Expert Dissertation Review - Academic editing and proofreading of PhD dissertations
🎯 PhD Proposal Development - Support in developing and successfully passing proposals

My mission is simple:
Help you complete your PhD on time AND secure your dream academic position.

Academic careers don't have to be a mystery. You just need the right guide.

Emmanuel Tsekleves

My PhD took 4.5 years. It should have taken 3. I wasted 1.5 years because no one taught me what actually matters. Now I teach my students what I wish someone had taught me.

Year 4 of my PhD.

I was drowning in research.

47 journal articles read. 23 more on my list.

"I need to read everything before I can write," I told myself.

That belief cost me 18 months.

Here's what I didn't understand:
Reading more papers doesn't make your thesis better.
It just delays your writing.

I finally started writing in Year 4.
By then, I'd forgotten half of what I'd read.

I had to re-read dozens of papers.
That cost me another 6 months.

When I finally submitted in Year 4.5, my supervisor said something that haunted me:
"Your thesis could have been submitted 1.5 year ago. You were overthinking it."

1.5 years wasted.

Because I'd confused activity with progress.

I read a lot. Attended every seminar. Took extensive notes.

But I wasn't writing.

And PhDs are judged on what you write, not what you read.

That experience changed how I supervise students.

I now teach them what I learned the hard way:

The 7 non-negotiables for 3-year completion:

1. Read purposefully (30 min daily):
Not 6-hour reading marathons
Focused 30 minutes with specific questions works better

2. Kill distractions completely:
Every notification costs 23 minutes of refocus
I lost weeks to email interruptions
Deep work requires protection

3. Write from Day 1:
I waited 3 years to start writing
That was my biggest mistake
Write rough drafts immediately

4. Just 150 words daily:
I waited for "full writing days" that never came
150 words feels insignificant
But it compounds to 54,750 words yearly

5. Rest strategically:
I worked 7 days/week with guilt
Burned out in Year 3
Strategic rest prevented 8 months of paralysis

6. Outline early:
I researched aimlessly for 2 years
No structure = no progress
Early table of contents would have saved a year

7. Stop perfecting:
I rewrote my intro 23 times
Draft 3 was good enough
Perfection killed 9 months of my life

My student Mark learned from my mistakes.
He implemented all 7 from Day 1.
Result: 3-year completion.

Quality as good as my 4.5-year thesis.
Just without the wasted time.

At his viva celebration, he thanked me:
"You taught me what no one taught you."

That meant everything.

I can't get my 1.5 years back.

But I can prevent other students from losing theirs.

If you're in your PhD now:

Don't wait to start writing.
Don't confuse activity with progress.
Don't sacrifice 2-3 years to habits that don't serve you.

Learn from my failure so you don't repeat it.

What habit is stealing your PhD time right now? What would you tell your Year 1 self to do differently?

Note: 
UK/Europe/Canada/Australia/Gulf countries full-time PhD, typical completion is 3-4 years. Part-time is 5-7 years
US full-time PhD, typical completion is 4-6 years. Part-time is 7-9 years

#PhDJourney #AcademicLife #ThesisWriting #PhDLessons #GradSchool

I help PhDs complete their PhDs on time & without burnout & Postdocs secure academic jobs | The insider roadmap universities don’t teach | 10,000+ researchers guided | AI + proven strategies

I couldn't afford the courses I needed as a PhD student.
How to build world-class research skills when you have zero budget—9 free courses from top universities:

Every skill I lacked had a £1,500-£3,000 price tag.

Systematic reviews. Scientific writing. Data analysis.

The courses existed. The expertise was available.

I just couldn't access it.

That financial barrier nearly ended my academic career.

But I refused to let money determine my potential.

So I searched. And searched. And searched.

Turns out, elite knowledge is often freely available.

You just need to know where to look.

Now, as a Professor, I've made it my mission:

Find every high-quality free course.

Make them accessible to researchers facing the same barriers I did.

Level the playing field.

I've spent years perfecting this collection.

Here are 9 elite courses that'll transform your research capabilities:

When you're building methodology skills:
→ Yale: Qualitative Research Fundamentals
→ NUS: Quantitative Research for Beginners 
→ University of London: Understanding Research Methods

When you're learning to write for publication:
→ École Polytechnique: How to Write and Publish Scientific Papers
→ Stanford: Writing in the Sciences

When you're advancing your research:
→ Johns Hopkins: Systematic Review and Meta-Analysis
→ Harvard: Persuasive Writing & Public Speaking
→ University of Cape Town: Research for Impact
→ The Open University: Data Analysis in Excel

Same universities. Same professors. Same quality.

Free.

I've completed 4 of these myself.

My students have completed them all.

Every single one reported the same thing:

"This is as good as any paid course I've taken."

Your financial situation shouldn't determine your research potential.

The image above has every direct link.

Save this post. Share it with someone who needs it.

Which research skill do you most need to develop?

Tell me in the comments. I'll recommend which course to start with and why.

#PhDLife #ResearchSkills #AcademicJourney #FreeEducation #PhDSuccess

JSESSIONID

li_at

Talks about #phd, #postdoc, #phdcareers, #academicjobs, and #highereducationjobs

The PhD student of a colleague of mine almost got expelled for using AI.
I tested 12 literature review AI tools. Only 3 maintain integrity.

She didn't know which tools were safe.

Neither did he.

So I spent 3 weeks testing every literature review AI tool I could find.

She came to my office in tears.

"A reviewer flagged my literature review for AI detection."

She'd used an AI tool to help organize papers.

Seemed harmless.

But the tool had synthesized text without proper citations.

Journal editor threatened to reject and report her.

That same week, I got asked variations of the same question 47 times:

"Can I use this AI tool for my literature review?"
"Is Elicit allowed?"
"Will ChatGPT get me in trouble?"

I had no good answer.

This was my wake-up call.

PhD students are using AI tools without knowing which ones maintain integrity.

So I tested 12 popular tools.

Real literature reviews. Actual papers. Full testing.

The results split into two clear groups:

THE 3 SAFE TOOLS (Maintain Academic Integrity):

Semantic Scholar
→ AI-powered paper discovery
→ Finds sources you'd miss manually
→ No text generation

Elicit
→ Extracts findings from papers
→ Organizes themes automatically
→ Every finding linked to source

Research Rabbit
→ Maps citation networks visually
→ Shows research connections
→ Pure visualization tool

THE 9 RISKY TOOLS (Integrity Issues):

Immediate Red Flags:
→ ChatGPT for literature synthesis
→ Claude for writing review sections
→ Gemini for academic content
→ Perplexity for creating summaries

Sophisticated but Detectable:
→ Trinka (AI writing assistant with text generation)
→ SparkDocAI (writing assistant for papers)
→ PopAI (Academic Paper Generator)
→ TypeAI (AI writing for academic text)
→ Any undisclosed AI tool

The pattern became clear:

✅ SAFE: Tools that help you FIND and ORGANIZE papers
❌ RISKY: Tools that WRITE or SYNTHESIZE for you

I now give every PhD student this list on day 1:

Use AI to find papers. Never to write them.

My literature reviews are faster and more comprehensive.

Because I'm using AI for discovery, not writing.

Are you using AI tools for literature reviews right now?

Which ones? I'll tell you if they're safe or risky.

#PhDLife #AcademicIntegrity #ResearchTips #LiteratureReview #PhDSuccess

No alternative text description for this image

My paper was rejected 7 times.
Here's how to fix the one thing that's killing your acceptance rate - your introduction:

Seven rejections. Same research. Same data. Same groundbreaking conclusions.

But every reviewer said the same thing:

"The introduction fails to engage."

That feedback destroyed me.

I spent 3 years on that research.

And reviewers weren't even reading past page 1.

Here's what I discovered:

Academic introductions aren't about being "scholarly."

They're about making reviewers CARE.

I rebuilt my introduction using this 7-step framework:

1. Importance Hook
→ Start with global impact, not your study
→ "Climate change threatens 1 billion coastal residents"
→ NOT "This study investigates coastal vulnerability"

2. Background Context 
→ Show existing work WITHOUT listing every paper
→ Create a narrative, not a bibliography

**3. Problem Statement**
→ Make the gap feel urgent
→ "But nobody has studied X in context Y"

4. Research Gap Revelation
→ Your "aha" moment for readers
→ "This missing piece changes everything"

5. Evidence Foundation
→ Back your claims with data
→ Prove this isn't just your opinion

6. Local Context Connection
→ Make abstract research tangible
→ "This affects real communities, real people"

7. Study Objectives Bridge
→ Connect everything to your specific aims
→ "Here's exactly what we'll achieve"

I rewrote my introduction following this framework.

Submission #8: Accepted by a top-tier journal.

Reviewer comments: "Compelling introduction that clearly establishes significance."

Same research. Different introduction. Different outcome.

Your brilliant work deserves to be read.

Not rejected because of a weak first page.

Which of these 7 steps is hardest for you right now?

Tell me in the comments. I'll share specific examples from my accepted papers.

#AcademicWriting #ResearchTips #PhDLife #AcademicSuccess #PublishOrPerish

5 journal rejections taught me one lesson about research papers. 
This is how to fix yours:

The feedback was brutal: "Lacks structure and clarity."

Back in my PhD days I thought good research was enough.

I was wrong.

I spent 18 months collecting data.

Ran every analysis perfectly.

Had findings that could change my field.

But my paper kept getting rejected.

After rejection 5, I almost gave up.

Then a senior colleague read my draft.

She finished and said: "Your research is solid. Your structure is chaos."

She drew something on the whiteboard that changed everything.

A simple body diagram.

Each section of a research paper mapped to a body part.

Each part answers one specific question.

Here's what she taught me:

Abstract (The Head)
This is your 30-second elevator pitch.
What's the problem? What did you find? Why does it matter?

Introduction (The Neck)
What is known? Set up our world understanding.
Hook readers with relevance. Make them care.

Literature Review (The Shoulders)
What is unknown? What gap are you filling?
Show you understand the conversation.

Methodology (The Arms)
How should we fill the gap? What did you do?
Make it so clear others can replicate.

Results (The Torso)
What findings did you get?
Present data without interpretation. Clean and focused.

Discussion (The Hips)
How do the findings bridge the gap?
Connect your results to the bigger picture.

Conclusion (The Legs)
What does this mean going forward?
Future directions. Leave readers wanting more.

References (The Feet)
Honor the giants you stand on.
Show the depth of your research journey.

She said: "Each section answers ONE question. Answer it clearly. Move on."

I rewrote my paper using her framework.

Same data. Same findings. Different structure.

Submitted to the same journal that rejected me twice.

Accepted with minor revisions.

Reviewer comment: "Well-structured and clear presentation."

The difference was not my research.

The difference was my structure.

Since then:

My next 3 papers accepted on first submission

I now teach this framework to every PhD student I supervise

The mistake most researchers make:

They think great data makes great papers.

Actually, great structure makes great papers.

Your research deserves to be read.

But first it needs to be structured so readers can follow.

The visual shows the complete anatomy I now use.

One body. Eight sections. Eight questions answered.

I wish someone had drawn this for me on day one.

Would have saved me 5 rejections and 2 years.

What section of research paper writing challenges you most right now?

Abstract? Literature review? Discussion?

Drop it below. I'll share specific tips for that section.

#AcademicWriting #ResearchPaper #PhDLife #AcademicPublishing #PhDSuccess

Academic writing isn't about sounding smart. It's about being understood. Here's 17 Golden Rules of Academic Writing:

Back in my PhD days, I thought I was a good writer. Academic writing proved me wrong.

That chapter took me 3 weeks to write.

I'd read everything. Cited everyone. Used all the fancy academic words.

My supervisor's summary: "I can't follow your argument."

I was devastated.

She sat me down and said something I'll never forget:

"Academic writing isn't about sounding smart. It's about being understood."

Then she gave me a list of rules she'd learned over 30 years.

17 rules that transformed my writing.

Here's what she taught me:

1. Start with Your Argument
"My thesis is X" gives your writing focus. Know your point before you write.

2. Write Before You're Ready
Skip "more reading first." Writing reveals what you don't know.

3. One Idea Per Paragraph
Need "and also"? Start a new paragraph.

4. Use Signpost Sentences
"This section examines..." tells readers where you're going.

5. Cut Academic Bloat
"Utilize" becomes "use." Simple beats pretentious.

6. Show, Don't Just Tell
"This affects 2 million people" beats "This is significant."

7. Write Transitions
"Having established X, I turn to Y." Connect paragraphs.

8. Vary Sentence Length
Short sentences punch. Longer ones develop complexity. Mix them.

9. Kill Your Darlings
Brilliant sentence doesn't fit? Delete it. Clarity beats clever.

10. Read It Aloud
Awkward on tongue means awkward on page.

11. Front-Load Key Information
"Smith argues that..." not "...which Smith argues..."

12. Use Active Voice
"We conducted" not "experiments were conducted."

13. Define Before Using
Explain terms before assuming readers know them.

14. Cite Generously
Show your scholarly conversation.

15. Write Conclusions That Conclude
"X matters because Y" not "more research is needed."

16. Revise With Fresh Eyes
Tomorrow's edit beats today's tired revision.

17. Done Beats Perfect
"Good enough to submit" stops perfectionist paralysis.

I rewrote my chapter using these 17 rules.

Same research. Same citations. Different writing.

My supervisor's response: "Now I can follow your thinking."

That chapter became the foundation of my thesis.

Since learning these rules:

My papers get accepted faster

Reviewers comment on clarity

I spend less time revising

I now give this list to every PhD student I supervise.

Week one. Before they write anything.

The transformation is always the same.

They go from "trying to sound academic" to "being understood."

The biggest shift?

Rule 5: Cut Academic Bloat.

Most PhD students think complex words show intelligence.

Actually, simple words show confidence.

The visual shows all 17 rules I use.

Print it. Pin it above your desk.

Your writing will thank you.

Which rule do you struggle with most?

#AcademicWriting #PhDLife #WritingTips #PhDSuccess #AcademicSkills

My PhD student submitted her thesis chapter last week. 3 citations were completely fabricated. Now we're retracing every citation in her entire thesis

Authors that don't exist. Journals that were never published. Studies that never happened.

She had no idea.

ChatGPT hallucinated them. She trusted the AI. She didn't verify.

This is the AI hallucination crisis nobody's talking about openly.

AI generates fake citations so convincingly that even careful researchers miss them.

The fabrications look legitimate:

"Smith et al. (2022) found 85% effectiveness in Journal of Advanced Research, 45(3), 234-251."

Author name sounds real. Year is plausible. Journal title sounds academic. Volume and page numbers perfectly formatted.

Everything looks right except one thing: None of it exists.

My student isn't careless. She's brilliant and thorough.

But she made one critical mistake: She treated AI as an authority instead of an assistant.

When AI suggested the citation, she assumed it must be correct.

But AI hallucinates constantly. 
It generates plausible-sounding citations by pattern-matching. 
Sometimes those citations are real. Sometimes they're complete fiction.

The stakes are devastating:

Your academic reputation built over years can be questioned by a single fabricated citation.

Manuscripts face retraction and potential academic misconduct allegations.

Time spent correcting errors diverts energy from actual research.

After finding her fabricated citations, I taught her the TRUST framework for using AI in research:

T) Treat AI as an assistant, not an authority. Every AI suggestion needs verification.

R) Rely on verification, not assumption. Check the original source exists and says what AI claims.

U) Uphold intellectual authorship. AI can organize, but synthesis must be yours.

S) Study research critically. Don't let AI summaries replace reading key papers.

T) Transparency about AI use. Document how you used AI with tools like Paperpal AI Footprint and AI Disclosure features.

Now she checks everything:

Author names against database searches
Journal titles against real publication lists
Statistics by reading original methodology sections
Dates by verifying publication records

It's more work than trusting AI blindly.

But her thesis won't be retracted for fabricated citations.

Her academic reputation stays intact.

I recorded a free webinar with Paperpal on exactly how to avoid these hallucinations. It covers verification techniques, transparency tools, and practical examples.

Link in comments.

Universities need to teach this from Day 1. 
Before students use AI for the first time.

Are you verifying every AI-suggested citation? Or assuming AI wouldn't make up references?

Try Paperpal for free using my link: https://lnkd.in/enZukV7D

#AIinResearch #AcademicIntegrity #PhDLife #ResearchEthics #ChatGPT

Universities teach PhD students to waste 4 months on literature reviews. Using methods from 2005. In 2025, this is academic malpractice.

I just finished supervising my 14th PhD student through their literature review.

Every single one started the same way:
→ Print 100+ papers
→ Read every word manually
→ Take handwritten notes
→ Synthesize with Word documents

The same process I used in 2005.

The same process my supervisor used in 1985.

Nothing has changed.

Meanwhile, AI tools that could help them work 65% faster sit unused.

Why?

Because universities refuse to update their methods.

Last year, I ran an experiment.

I had 150 papers to review for a new project.

Using traditional methods: 4 months estimated.

I decided to use AI assistance instead.

The result: 3 weeks.

Same quality. Better organization. Complete integrity.

Here's what AI helped with:

Initial filtering 
- Scanned 150 abstracts for relevance in 2 hours (vs 2 days)
- I verified every recommendation by reading abstracts
Sample tools: SciSpace, Bohrium, ResearchRabbit

Paper summaries:
- Generated structured summaries focusing on methodology and findings
- I spot-checked 25% against original papers for accuracy
- Saved 60+ hours of manual note-taking
Sample tools: Paperpal, SciSummary, Scholarcy

Pattern identification:
- Found contradictory findings across studies
- Identified research gaps faster
I verified patterns by reading relevant papers myself
Sample tools: Consensus, Liner, Anara

What didn't change:
→ I read the critical papers in full
→ I formed all the insights and interpretations
→ I wrote the entire synthesis myself
→ I made all the intellectual connections

AI removed mechanical work.

It didn't replace my expertise.

But here's what makes me angry:
I've taught this process to 40+ PhD students.
They all cut their review time by 50-70%.
Yet their universities still teach the 1995 method.

PhD supervisors say things like:
"That's not how we do literature reviews."
"You need to read every word of every paper."
"Using AI shows you're not serious about research."

This is gatekeeping disguised as rigour.

Reading every word of 150 papers doesn't make you a better researcher.

It just makes you exhausted.

What makes you a better researcher:
→ Spending more time on analysis
→ Having energy for critical thinking
→ Actually completing your PhD on time

The tools exist to help PhD students work smarter.

Universities need to stop treating efficiency as cheating.

If you're struggling with literature reviews:
--> The problem isn't you.
--> It's the outdated methods you've been taught.

Did your PhD program teach you efficient research methods? Or did they just tell you to "read more papers"?

#PhDChat #AcademicResearch #HigherEducation #PhDLife #ResearchMethods

SciSpace

Bohrium

ResearchRabbit

Paperpal

SciSummary

Scholarcy

Consensus

Liner

Anara

Academic publishers made $10 billion last year. From research they didn't fund & academics they don't pay. Most research papers are available for free. You just need to know where to look...

Let me explain how academic publishing works:
Step 1: University pays researcher's salary
Step 2: Researcher conducts study using public grants
Step 3: Researcher writes paper (unpaid by publisher)
Step 4: Researcher submits to journal (pays submission fee)
Step 5: Journal sends to peer reviewers (volunteers, unpaid)
Step 6: Researcher revises based on feedback (still unpaid)
Step 7: Journal accepts paper (researcher pays publication fee)
Step 8: Journal sells access back to universities for ~$40/paper

Publishers contribute: formatting and hosting.

Publishers profit: billions.

This is the most profitable scam in academia.

And most researchers accept it as "how things work."

But here's what they don't tell you:
Most research papers are already free.
You just need to know where to look.

I've compiled 24 legitimate platforms that give you access to millions of papers across every discipline.

Not pirate sites. Not illegal downloads.

Official repositories, preprint servers, and open-access journals.

The complete list:

1) General platforms: ResearchGate, Academia.edu, CORE, Semantic Scholar, BASE

2) Preprint servers: arXiv, Open Science Framework, DOAJ, ScienceOpen, Open DOAR

3) Medical/health: PubMed Central, BioRxiv, MedRxiv, PLOS

4) By discipline: PsycArxiv, ERIC, PhilPapers, SSRN

5) Open publishers: Springer Open, Frontiers, MDPI

6) Access tools: Unpaywall, Open Science Framework, World Digital Library

See Comment for weblinks to these.

These platforms exist because researchers and institutions fought back.

But most PhD programs never mention these platforms.

They assume students have full journal access.

They don't teach researchers how to navigate the paywall system.

That needs to change.

Every PhD student should learn about open access in Week 1.
Every researcher should know these platforms exist.

Access to knowledge shouldn't cost £847 per year.

It shouldn't depend on your university's subscription budget.

It shouldn't favor wealthy institutions over developing countries.

The infrastructure for free access exists.

We just need to use it.

Save it. Share it. Use it to bypass the paywall system.

How much have academic paywalls cost you? Should publicly-funded research be free to the public?

#OpenAccess #AcademicPublishing #ResearchFunding #PhDChat #ScienceForAll

Most PhDs write their thesis first, then extract papers. That's backwards. Here's why and how to do this:

I finished my PhD with a 250-page thesis and zero publications.

My supervisor had looked at my CV and said: "You've wasted three years of publishable research."

Those words destroyed me.

I'd worked so hard on that thesis.

Every chapter polished. Every reference checked.

But job committees didn't care about my thesis.

They wanted publications.

I had nothing to show them.

Application after application. Rejection after rejection.

Then my mentor shared something that changed everything.

"You're thinking like a student. Start thinking like a publisher."

She drew my thesis structure on a whiteboard.

Then drew lines connecting chapters to potential papers.

She called it the 2+2 Publication Strategy.

Here's what she taught me:

2 Core Papers from Results Chapters

Your Results Chapter 1 becomes Research Paper 1.
Take that chapter. Add relevant methods. Add focused literature.
One clear research question. One complete story.

Your Results Chapter 2 becomes Research Paper 2.
Same approach. Different findings. Another publication.

2 Supporting Papers from Other Chapters

Your Literature Review becomes a Review Paper.
Expand it. Add synthesis. Comprehensive reviews get cited heavily.

Your Methodology becomes a Methodology Paper.
Novel methods deserve their own publication.
Frameworks you developed can stand alone.

The key insight:

Every thesis chapter should tell a complete story.

Not fragments of a bigger story.

Complete stories that can be extracted and published.

I applied this strategy during my postdoc.

Before I started writing, I mapped which papers each chapter would become.


Most PhDs discover this too late.

They write a 300-page thesis.

Then spend years trying to extract papers afterward.

By then, the research feels old. Motivation fades. Careers stall.

Start thinking about publications from day one.

Map your thesis chapters to future papers.

Write each chapter as a standalone story.

Your thesis becomes a collection of related papers.

Not one giant document nobody reads.

What's your current thesis chapter that could become a paper?

Drop it below. I'll tell you which paper type it fits best.

#PhDLife #AcademicPublishing #PhDSuccess #ResearchStrategy #AcademicCareer

47 scholarship emails sent. 3 profs replied. 
My Masters student, Priya, was ready to abandon her PhD dream. Here's the exact pattern that changed everything:

"Maybe I'm not cut out for this," she told me after rejection 40.

I'd seen this before.

Brilliant students giving up.

Not because they lacked talent.

Because no one taught them how to write emails that professors actually read.

Here's what most students don't know:

Professors get on average 15+ scholarship support requests every day.

Your email gets 8 seconds of attention.

Maybe less.

Most students write these emails like cover letters.

Long paragraphs about their passion. 
Generic praise about "impressive research." 
Detailed life stories.

Professors delete them in seconds.

I taught Priya to flip the script entirely.

Stop selling yourself.

Start proving you've done your homework.

Here's the exact pattern that changed everything:

→ Subject line: Name the specific scholarship AND deadline 
→ First sentence: Reference their recent paper by exact title 
→ Second sentence: Connect your research to theirs specifically 
→ Bullet points: Show qualifications (no paragraphs) 
→ Last line: Suggest 15-minute call with specific time options 

No dreams. No generic praise. No life story.

Just evidence you understand their work.

The result?

Within 48 hours, 3 professors responded.

Two offered to support her application.

One became her PhD supervisor.

She's now 6 months into her fully-funded PhD.

Same student. Same qualifications.

Completely different approach.

Most students think scholarship emails are about selling themselves.

They're actually about showing you understand the professor's work.

The infographic in this post shows the template that worked.

Save it. Use it. Adapt it to your field.

Have you sent scholarship emails that disappeared into the void?
What did you wish someone had told you before you started?

#PhD #PhDScholarship #AcademicCareer #ResearchFunding #GradSchool

Most PhD students pay hundreds for research methods training. These 9 courses cost nothing. Here are 9 I give every PhD student.

Yu was in her second year.

Her supervisor wanted her to use mixed methods.

Problem: She'd never learned qualitative research.

The university methods course cost £800. She didn't have it.

She came to my office almost in tears.

"I can't afford the training I need to do my own research."

I remembered struggling with the same problem during my PhD.

Expensive courses. Limited funding. Skills gaps everywhere.

So I'd spent days collecting free alternatives from top universities.

Courses that teach the same content. From better institutions. At zero cost.

I sent Yu the list.

She completed three courses in four months.

Qualitative methods from University of Amsterdam.
Statistical inference from Johns Hopkins.
Writing in the Sciences from Stanford.

Her supervisor's feedback after: "Your methods section is the strongest I've seen from a second-year student."

Free courses. World-class training.

Here are the 9 courses:

1. Understanding Research Methods (University of London)
Build your research foundation. Start here if you're new to research.

2. Qualitative Research Methods (University of Amsterdam)
Learn to find stories that numbers can't tell.

3. Research Design (University of North Texas)
Plan your research with clarity before you start collecting data.

4. Writing in the Sciences (Stanford)
Transform findings into compelling narratives. My personal favorite.

5. Academic English (UC Irvine)
Refine your academic writing. Essential for non-native speakers.

6. Introduction to Systematic Review (Johns Hopkins)
Master comprehensive literature reviews. Critical for any PhD.

7. Statistical Inference (Johns Hopkins)
Understand what your data actually means. Not just how to run tests.

8. Quantitative Methods (University of Amsterdam)
Strengthen your quantitative analysis skills.

9. Research for Impact (University of Cape Town)
Ensure your research reaches beyond academia.


The universities offering these include Stanford, Johns Hopkins, and Amsterdam.

The same institutions charging thousands for in-person programs.

Their online versions are free.

Most PhD students don't know these exist.

They pay for expensive workshops.

Or struggle without proper training.

You don't have to.

Save it. Share it with PhD students who need it.

Which research skill do you most need to develop right now?

#PhDLife #FreeCourses

My journal reviewer destroyed my conclusion in 3 words: "Weak and unconvincing." Here's how to write a strong conclusion that gets your paper accepted

That feedback hurt.

I spent 8 months on that research.

The methodology was solid. The data was clean. The findings were important.

But my conclusion?

"Just summarize your findings and wrap it up."

That's what I thought conclusions were for.

I was so wrong.

After 3 rejections with the same feedback, I finally figured it out:

The best conclusions don't end your paper.

They start someone else's research journey.

Here's what I changed (and why my next paper got accepted):

Before: I summarized my findings
After: I opened with my biggest discovery

Don't repeat what you already said.

Lead with what matters most.

Before: I stayed in my lab
After: I connected to real-world problems

Show how your petri dish findings affect actual humans.

Make abstract research tangible.

Before: I listed contributions vaguely
After: I stated exactly what I added to the field

Not: "This research contributes to the literature..."

But: "This is the first study to show X in context Y."

Before: I wrote "Further research is needed"
After: I posed the next burning question

Not: "More work should explore..."

But: "Why does X happen under Y conditions? This question could reshape how we understand Z."

I rewrote my conclusion following this approach.

Same research. Different conclusion.

The result?

→ Accepted by a top-tier journal on first submission
→ Two researchers reached out about collaborations
→ One citation became ten within six months

The reviewers specifically mentioned my conclusion:

"Compelling synthesis that clearly articulates significance and future directions."

Your conclusion isn't just summary.

It's your last chance to make readers care.

The best conclusions leave readers with a question they didn't know they had.

That's how you get cited.

That's how you build collaborations.

That's how research actually moves forward.

Look at your current conclusion right now.

Does it just summarize? Or does it make readers think about tomorrow?

What's the one question your research uncovered that keeps YOU up at night?

Drop it in the comments. I'll tell you if it's the kind of question that hooks readers.

#AcademicWriting #ResearchTips #PhDLife #AcademicSuccess #PublishingStrategy

Wasting weeks reading random papers for literature reviews? This is how to fix it.
A PhD student in my department wasted 6 weeks reading the wrong papers.

His supervisor asked: "Do you even know who the key scholars are in this field?"

He didn't.

James came to me for advice three months into his PhD.

Drowning in papers.

Downloaded 127 articles. Read 83 of them.

His literature review was a mess.

Random papers. No coherent narrative. Missing major scholars.

His supervisor said:

"James, you're reading randomly. You need to map the landscape first."

"What does that mean?" James asked.

I explained: "Find the key scholars. Understand who cites whom."

He tried for two days to manually map citations.

Drew diagrams. Created spreadsheets. Got more confused.

I showed him something a colleague recommended:

"Try Bohrium Scholar. It maps everything visually."

James was skeptical but desperate.

We spent 30 minutes exploring his research area.

What he discovered:

Step 1: He searched his research area
Found the 10-20 most cited scholars instantly.
Complete research profiles showing their focus areas.

Step 2: The Global Impact Map
Showed which papers cite whom.
He could see where work is cited globally.
Citation flows made sense visually.

Step 3: ScholarTalk Feature
He asked questions about scholars' methodologies.
Got insights into their expertise and philosophies.

Step 4: Academic Tree
Showed collaboration patterns he'd never noticed.
Clicked on collaborators to see co-authored papers.

Step 5: Reading List Builder
Identified must-read papers from influential scholars.
His literature review foundation in 30 minutes.

The difference:

Before: 6 weeks, 83 random papers, confused mess

After: 30 minutes mapping, targeted reading list, clear narrative


James now starts every literature review this way.

The 5-step framework:

1. Identify key scholars (10-20 most cited)
2. Track citation flows (who cites whom)
3. Understand approaches (ScholarTalk)
4. Map collaboration networks (Academic Tree)
5. Build targeted reading list (from profiles)

30 minutes of mapping saves weeks of random reading.

Are you writing a literature review right now?

If you want to check out your global impact map and academic tree, you can claim your scholar profile.

Try Bhohrium Scholar for free: https://lnkd.in/earmR__B

Use the special code: EMMANUEL02. This code offers a one-week membership, which will remain valid for redemption for one year, so you can fully experience the Bohrium platform and its features.

What's your biggest challenge - finding key scholars, understanding connections, or building a reading list?

#PhDLife #LiteratureReview #ResearchTips #PhDSuccess #AcademicResearch

My PhD student's conference paper got initially rejected for "inadequate English." Here's how she fixed it in 5 days and got accepted.

Ying sat in my office trying not to cry.

"I knew my English wasn't good enough. I should never have tried to submit to international conferences."

The rejection email was brutal: "The language does not meet our standards."

Her research was excellent. Strong methodology. Novel findings.

But English was her third language.

She wrote "very big impact" instead of "substantial impact."

"Every day" instead of "daily."

"More" instead of "greater."

The grammar was correct. The sentences made sense. 

But they didn't sound academic.

There was another conference in 3 weeks.

Different venue, but equally prestigious.

Ying needed her paper fixed fast.

One of her peers suggested she hire a professional editor.

She looked at the quotes: £800 for a full edit.

She didn't have it.

The university offered editing services.

Wait time: 6 weeks. She had 3 weeks.

Then I remembered a tool a colleague had mentioned.

Inkwell . AI-powered but designed to teach, not just fix.

I suggested Ying try it.

She was skeptical about AI editing academic work.

"Won't conference reviewers reject AI-edited text?"

I explained: Inkwell doesn't write for you. It teaches you to write better.

Suggests corrections but you apply them yourself.

Ying tried it that evening.

Uploaded her paper

Got instant feedback on every language issue.

Grammar. Syntax. Academic register. Tone.

But here's what convinced her:

Inkwell explained WHY each change mattered

She wasn't just fixing errors.

She was learning academic English.

Ying revised her paper using Inkwell's explanations.

Submitted to the second conference.

The decision came back: "Accepted. Well-written and clearly presented"

That was 6 months ago.

Ying has now presented at 2 conferences.

Published 1 journal paper.

Each time, her writing gets stronger.

The 5-step process she uses:

Step 1: Upload draft (Word, PDF, or LaTeX)
Step 2: AI review identifies all language issues instantly
Step 3: Read explanations for each suggested correction
Step 4: Apply corrections independently while learning
Step 5: Export publication-ready document

I now recommend Inkwell to every international PhD I supervise.

And to all PhDs who struggle with academic language.

Conference deadlines are tight.

Professional editors are expensive and slow.

You need something fast that actually teaches you.

What's the biggest academic language barrier you face when submitting to conferences or journals?

#AcademicWriting #PhDLife #AcademicEnglish #ConferencePaper #PhDSuccess

For a limited time, activate any subscription with your first month completely risk-free, and explore every feature with no limitations: https://inkwell.net/

And students and faculty anywhere in the world receive 50% off, permanently.
Check your eligibility in seconds: https://lnkd.in/eTGU_Hti

graphical user interface, application, Word

I spent 6 hours building a conference presentation last week. Then I discovered I could do it in 10 minutes. Here's how.

The conference deadline was in 5 days.

I had a 40-slide PowerPoint to create from my research paper.

My usual process: Copy text from the paper. Paste into slides. Format everything manually. Find images. Resize charts. Fix alignment issues.

6-8 hours minimum. Every single time.

Then a colleague showed me something that changed everything.

"Just paste your paper into Gamma. It builds the presentation for you."

I was skeptical.

AI-generated presentations usually look terrible.

Generic templates. Wrong emphasis. Missing the actual point of the research.

But I was desperate. 5 days. 40 slides. No time for my usual approach.

I tried it.

Pasted my entire paper into Gamma.

Selected "presentation" and hit continue.

Chose my slide settings: text length, visual types, number of slides.

Picked a theme.

Hit generate.

10 minutes later, I had a complete presentation.

Not a rough draft. A complete, professional presentation.

The structure followed my paper's logic.

Key findings were highlighted correctly.

The visual hierarchy made sense.

I added my own charts and diagrams. Gamma integrated them beautifully.

Made a few tweaks. Changed some images. Adjusted two slide titles.

Total time: 15 minutes of active work.

The presentation at the conference?

Three people asked for my slides afterward.

Two collaboration inquiries.

One journal editor approached me about submitting the full paper.

All from a presentation I built in 15 minutes instead of 6 hours.

Here's the 5-step process I now use:

Step 1: Set your presentation
Select your paper. Create new Gamma. Choose "Paste in Text" option.

Step 2: Prepare your text
Copy and paste your paper. Select presentation format. Hit continue.

Step 3: Design and customize
Define slide settings: text length, visuals, number of slides.
Select theme. Hit generate.
Drag and drop your own charts and diagrams.

Step 4: Review and refine
Add or remove slides as needed.
Change images, add videos, adjust text.
Make it yours.

Step 5: Export and present
Export to PDF, PowerPoint, or Google Slides.
Share with your team.
Present with confidence.

The time I used to spend on formatting?

Now I spend on rehearsing.

Which actually matters for the presentation.

What takes you longest when building research presentations?

The initial structure? Finding visuals? Formatting slides?

#AcademicPresentations #PhDLife #ConferenceTips #ResearchPresentation #AcademicProductivity

👉 Try with Gamma for free, using my link: https://lnkd.in/eXvi6QxR

My mentee's journal paper got rejected twice. Reviewers found papers she'd completely missed. Here's how she fixed it.

Sophie had reached out through my mentorship program

Frustrated after the rejection

Reviewer 2's comment: "The literature review is incomplete. The authors missed 15 papers directly relevant to this research question"

Environmental psychology papers for her urban planning study

She'd spent 6 months on her literature review

She'd searched every urban planning database

But she only used keywords: "urban AND design AND health"

The problem?
Psychologists don't call it "urban design"
They call it "built environment"
Climate scientists call it "urban infrastructure"
Sociologists call it "spatial justice"

She missed 40% of relevant papers because they were hidden in other disciplines

When she came to me, I showed her the BRIDGE method using Consensus

Here's what we did:

B: Begin with BROAD semantic searching
Asked Consensus: "How does urban green space affect mental health?"
Not keywords. Natural language
Found papers from public health, psychology, urban planning, environmental science.

R: RELEVANCE verification
Every statistic checked against original source
"47% improvement" meant 47% in elderly only, not all populations
Never cited without reading the relevant sections.

I: INTERDISCIPLINARY mapping
Asked Consensus Threads: "What fields have studied this?"
Created a map showing which disciplines contribute what insights
Tracked terminology differences across fields.

D: DEPTH checking
Started with Consensus AI summary
Read abstracts of top 15 papers
Deep dive into methods of key papers
Flagged papers needing full reading vs summary.

G: GAP identification
Mapped what each discipline covered vs hadn't covered
Found real gaps, not just unusual terminology
Connected dots AI hadn't seen.

E: ETHICAL synthesis
Used Consensus for discovery, not writing
Created original frameworks from connections found
Wrote analysis in her own voice
Cited AI assistance transparently: "Initial papers identified using Consensus AI"

She spent 4 weeks revising her literature review

This time, she found papers from 7 different disciplines

Her revised manuscript was 40% stronger

She resubmitted. Accepted with minor revisions

The lesson?
Traditional keyword searching misses 40% of relevant papers.

They're not hidden. They're just called different things in different disciplines

Semantic AI like Consensus finds them

But you still need critical thinking to evaluate them

If AI does everything, who gets the credit?

Consensus accelerates discovery.

But your critical thinking, synthesis, and original insights make the paper publishable.

Repost and Comment 'guide' to get the full PDF guide for FREE

#AcademicPublishing #LiteratureReview #AIforResearch 

Try Consensus for free using my link: https://lnkd.in/ecpZQj3v - and get 1 week free of Consensus Pro
Use my promo code for 25% off for the year: phdtoprof25

🩺I take researchers from goals to published results 🇮🇳 🇬🇧 Founder II Academic Writing Mentor || AI researcher || Homeopath ✅️Need Research Solutions❓️ Go to my ABOUT section.🔻

I specialize in testing AI tools for literature reviews, academic writing, editing and statistical analysis, sharing insights that enhance research productivity and workflow.

As a PhD in Life Sciences, I am passionate about guiding academics and PhDs from research concept to completion, ensuring they have the right tools, methodologies and mentorship to succeed.

🎯 How I Help Researchers & PhDs??

Through 

🟪 RESEARCH MADE CLEAR 🟪

I offer consultancy services in:

✅ Scientific Writing & Editing: Ensuring clarity, coherence and impact in research papers.
✅ PhD Mentorship & Research Methodology: Supporting scholars in thesis writing, research design, and proposal development.
✅ Data & Statistical Analysis: Providing expertise in biostatistics, data analysis and result interpretation.
✅ Faculty & Student Development: Training in AI-powered research tools & ethical considerations.
✅️ Webinars & Workshops: How to use AI tools for Literature Review, research writing and Research Management, ethically.

🔍 My Experience & Research Contributions:

With 10+ years of experience across multidisciplinary research projects, I have:
📌 Led & coordinated multiple research trials and longitudinal surveys as a Research Manager.
📌 Published 8+ peer-reviewed papers in evidence-based medical writing.
📌 Developed the MAPNI questionnaire—a validated tool assessing perceived stigma towards mental illness in India.
📌 Taught Research Methodology & Biostatistics as an 'Assistant Professor', mentoring 500+ academic professionals in thesis & dissertation writing.
📌 Supervised successful scholarship applications for the Short-Term Studentship in Homoeopathy (STSH, CCRH, India).

📢 Let’s Connect
✅️Need expert guidance in research writing, AI tools or statistical analysis?
✅️Looking for collaborations, training sessions or mentorship?

💌 Email: dps@researchmadeclear.com
📩 DM me here on LinkedIn!

Dr Priya Singh PhD💜MD (Hom.)

❓Are you still doing every part of your research manually, when AI can do half the heavy lifting?

Between managing references, digging through papers, writing drafts and presenting findings 

It often feels like we’re juggling ten different jobs.
 
However, AI can make your research journey much smoother if you know where to look.

That’s exactly why I curated this list of Top 30 AI Tools for researchers

From finding literature and identifying research gaps to writing, editing and visualizing data, these tools save time, boost productivity and help you stay focused:

1. Literature Review:
ResearchRabbit: https://lnkd.in/eZ4PMRup
ResearchPal: https://lnkd.in/e_nPmJTK
Litmaps: https://www.litmaps.com/
Liner: https://lnkd.in/epvViufW (get 40% off)

2. Data Extraction:
SciSpace: https://lnkd.in/ehbWNQdr (PRIYA40, PRIYA20)
Covidence.org: https://www.covidence.org/
Elicit: https://elicit.com/

3. Note taking:
Notebook LM: https://notebooklm.google/
DocChat: https://docchat.iferp.in/
Microsoft One Note

4. Research Gap:
Scite: https://scite.ai/
AnswerThis (YC F25): https://answerthis.io/
Consensus: https://consensus.app/

5. Writing & Editing:
Paperpal: https://lnkd.in/eWjg4UKk (PAP20)
ProWritingAid: https://prowritingaid.com/
Jenni AI: https://jenni.ai/
Trinka.ai: https://www.trinka.ai/

6. Data Visualisation:
R studio
Julius AI: https://julius.ai/
Tableau: https://www.tableau.com/
Flourish: https://flourish.studio/

7. Reference Management:
Zotero: https://www.zotero.org/ 
Mendeley: https://lnkd.in/efjwZPhC
EndNote: https://lnkd.in/ebG2a5zG

8. Presentation:
Gamma: https://gamma.com.ai/
Microsoft copilot for PowerPoint
Simplified 

9. Research Management:
Otio: https://otio.ai/
Notion:  
ClickUp 

📌 Remember: You don’t need all 30 tools.
 
Pick 1 or 2 from each category to match your current research stage and build from there.

Let AI support your research, not stress it.

You’ll be amazed how much time and mental energy you save.
 
Repost to help fellow PhDs, MPhils, and early-career researchers build their research toolbox.

PS: What tools would you add to this list?
Share in the comments.

Most PhDs overcomplicate qualitative research.

I suggest my mentees to follow these guidelines for doing strong qualitative research:

1. Start with a focused question, not a vague topic

 Bad: “I want to explore leadership.”
 Better: “How do first-time managers in X context make decisions under pressure?”

The clearer your question, the easier every next step becomes (interview guide, coding, themes).

2. Talk to the right people, not just the available ones
 
Your sample should be:
Directly connected to your research question
Able and willing to share experiences
 
Don’t just recruit whoever says yes. Be intentional.

3. Design interviews like a conversation, not an interrogation

Use:
Warm-up questions (“Tell me a bit about your role…”)
Open questions (“Can you walk me through…”)
Gentle probes (“Can you tell me more about that?”)
 
Your goal is to help participants tell stories, not give one-word answers.

4. Record your decisions as you go
 
Keep a simple research diary:
Why you changed a question
Why you added/dropped a code
What surprised you in the data

 This becomes gold later when you write methods, reflexivity, and limitations.

5. Transcription: good enough > perfect

Capture:
What was said
Key emotions (laughs, long pause, sighs) when they change meaning
 
Perfectionism in transcription can drain your energy before the real analysis starts.

6. Coding isn’t about “finding the right answer”
 
Think of coding as labeling pieces of meaning:
First round: messy, descriptive labels (what is being said?)
Later rounds: more focused (what does this mean in relation to my question?)

 You’re moving from labels → patterns → themes.

7. Your themes must answer your research question
 
A theme is not just a topic like “communication” or “stress”.
 A strong theme sounds like an insight:
“Feeling unsupported by seniors increases self-doubt in new PhDs.”
 
Ask yourself for each theme: “So what? What does this explain or reveal?”

8. Use quotes wisely – not as decoration

A good quote should:
Be short and sharp
Clearly illustrate the point you’re making
Be followed by your interpretation
 
Don’t let quotes do all the work. Your job is to connect them to the story.

9. Aim for clarity, not complexity, in your writing
 
If your methods and findings can’t be understood by a smart Master’s student, they’re probably too complicated.
 
Avoid filler phrases like “It is important to note that…” – be direct and specific.

PS:  What part of qualitative research do you struggle with most –
 interviews, coding, or writing up themes?

Share in the comments.

REPOST to help others.

Guide to using Qualitatuve Research Methodology

The biggest research mistake?
Choosing a topic before choosing a problem.

A well-crafted research question is not just a sentence… it is the backbone of your entire research project.

Here are simple, practical tips to help you write research questions that are clear, focused, and worth answering:

1. Start with the Problem, Not the Literature Dump 
Don’t begin by thinking, “What topic should I choose?”
 
Start by asking: “What real-world problem or gap actually needs to be solved or understood?”

Your research question should grow from a problem that genuinely puzzles you.

2. Make Your Question Answerable 

A great research question is:
 ✔ Specific
 ✔ Narrow
 ✔ Observable
 ✔ Measurable (even in qualitative research, “measurable” means “can be explored through data”)

Avoid questions like:
 ✖ “Why is education important?”
 That’s an essay, not research.
 
Instead aim for:
 ✔ “How do first-year teachers experience the shift to AI-assisted classrooms?”

3. Choose the Right Question Type
Ask yourself: What exactly do I need to know?

For quantitative studies → your question usually aims to:
• Compare groups
 • Test relationships
 • Measure an outcome
Examples:
 ✔ “Does training frequency predict employee retention in startups?”
 ✔ “Is there a difference in burnout levels between remote and on-site nurses?”

For qualitative studies → your question usually aims to:
• Explore experiences
 • Understand meanings
 • Unpack processes
Examples:
 ✔ “How do women describe their decision-making process after receiving an infertility diagnosis?”
 ✔ “What challenges do teachers face when integrating assistive technology for autistic learners?”

4. One Question = One Direction
A common mistake: asking questions that are trying to do too many things at once.
Keep your RQ laser-focused.

If you feel tempted to add “and” again and again — split it into sub-questions.

5. Make Sure Someone Actually Needs the Answer
A strong research question has impact.

Ask yourself:
 👉 If I answer this, who benefits?
 👉 Does this move the field forward?
 👉 Will this help someone make better decisions?
If the answer is “not really,” revise it.

6. Let Your RQ Guide Your Entire Design
This is the part most students underestimate.
 Your research question should naturally lead to:
 • Your methodology
 • Your sampling decisions
 • Your analytical strategy
 • Your hypotheses (if quantitative)
 • Your interview questions (if qualitative)
If your question doesn’t point clearly to the method, there’s a mismatch.

7. Don’t Aim for Perfect — Aim for Clear
Research questions evolve.

Refining your RQ is part of the research process, not a sign that you're doing it wrong.

PS: What’s one mistake you made when framing your first research question — and what did it teach you?
Share in the comments.

REPOST to help others.

Guide to writing RQ and hypothesis

Most PhD students overthink the results section…
But do you know where reviewers decide whether your paper is strong?

The Introduction.
Because if your Introduction is weak, unfocused, or confusing…
your reader is already gone before you show them your brilliant work.

Below are the guidelines, I wish every researcher knew about writing a powerful Introduction:

1. Start with the big picture, not the details
Give your reader a clear map.
Set the broad context: Why does this topic matter?

Think of it as the “invite” to your research.

2. Narrow it down step by step
Move from general → specific.
 
Show the logical flow from the broader problem to the exact issue you are addressing.
If your introduction feels like a jump-scare, you wrote it too fast.

3. Highlight what we already know (briefly)
Summarise key literature, not everything you read.

Pick the 4–6 most relevant findings that lead directly to your research gap.

4. Make the research gap unmistakably clear
This is where 80% of papers fail.
 
Explain what is missing, unclear, or contradictory in the current knowledge.
 
Your gap should be specific, not a generic statement like “few studies have explored…”.

5. Show why your study matters
Tell the reader why this gap is worth addressing:
 
Does it solve a real problem?
Challenge old assumptions?
Improve practice or policy?

6. State your aim and research questions clearly
 
One sentence for the aim.
One or two questions or hypotheses.
No jargon, no long stories.

7. End with a mini-roadmap
Briefly outline what comes next:
 “This paper next discusses the methodology, followed by results and implications…”
It helps the reader stay oriented and improves clarity.

PS: What's your biggest struggle with introductions?
Share in the comments.

Introduction Section for Research Papers

How to cut your literature review time from 6 weeks to 6 hours (without missing the important papers) ?

Most PhD students don’t struggle with ideas 
They struggle with information overload.

You open databases for 10 minutes… and 3 hours + 27 tabs later, you’re still not sure what the field actually says.

This is where this Literature Review Agent + Research Tracer together can be genuinely helpful, without replacing your brain.

How I tell my mentees to use them:
1. Go to https://lnkd.in/eiHTBi36
2. Start wide with the Literature Review Agent. 

Type your topic
✅ It searches through over 200 million academic papers to highlight key works in your field and identify research gaps. 
✅Instead of spending weeks wondering if you've covered the foundations, you get a clear map of the landscape in minutes.

Treat this as a map of the territory, not the final word. Mark which themes actually match your research question.

3. Then zoom in with Research Tracer
Once you know the main themes, use Research Tracer for:

✅Tracing how ideas evolved over time
✅Seeing which papers started a line of work – and which ones challenged it
✅Spotting newer trends and shifts you might miss with manual searching 

Start with the Literature Review to understand the big picture and spot the gaps. 
Then use the Research Tracer to follow the thread of specific concepts that matter to your work. 

You can even feed it your rough notes (up to 2,500 characters) and it'll find papers in the same domain to compare against what you've written.

📌 Even with both agents, you still need to:
Read the important papers yourself (methods, limitations, not just the abstract)
Check that citations actually support the claims
Decide which debates matter for your thesis
Rewrite all AI outputs in your own words and voice

Think of these agents as research assistants who help with the heavy lifting of searching, tracing, and organising so you can spend more time thinking, critiquing, and writing.

If you’re a PhD or early-career researcher:
 👉 Use AI to cut down search time, not thinking time.

Try it here: https://lnkd.in/eiHTBi36
 40% off the first month only valid for the Liner Pro monthly plan.
 Use code: 2025priya1 for the final discounted price.

PS: How many literature reviews have you started over because you weren't sure you'd covered everything?

Share in the comments

REPOST to help others

How to write a review that journals actually want to publish??

Review studies look straightforward… until you start one.

Very quickly, you realise they demand more organisation, more clarity, and more discipline than almost any other research design.

Here are a few things I wish, I knew earlier about literature reviews, scoping reviews, systematic reviews, and meta-analyses:

1. Your review is only as strong as your question
Most researchers jump into searching before sharpening their question.
 
But a vague question = vague search = vague conclusions.

A tight question instantly tells you:
what to search,
what to ignore,
and which type of review makes sense.
Think of the question as your GPS, if it's unclear, expect detours.

2. Build your “evidence pipeline” before you search
A good review is 50% planning, 50% execution.

Before touching a database, set up:
where you’ll store papers,
how you’ll label and code them,
who checks what,
and what happens if you disagree on a study.

This upfront setup saves you from chaos later.

3. Don’t aim for perfect, aim for transparent
Review studies often trigger perfectionism: “what if I missed a paper?”
 
Instead of perfection, focus on clarity.
If your process is:
logical,
well-documented, and
reproducible,
…your review holds up even if you refine it later.

Transparency beats perfection.

4. The magic happens after you extract the data
Most beginners think the hard part is screening.

It’s not.
The real challenge begins when you have 40–80 papers and must turn scattered findings into a clear story. 

That requires:
pattern-spotting,
grouping ideas,
comparing contradictions,
and identifying what the field still doesn’t know.

This is where your analytical voice shines.

5. Use your review to discover, not just to summarise
A high-quality review doesn’t simply restate what others said.
 
It tells the reader:
what’s missing,
where the evidence is thin,
where the field is confused,
and what the next research step should logically be.

The purpose is not to show everything you read, but to create clarity for someone who hasn’t read any of it.

6. Design your review for your future self
Reviews take months.

Document decisions as if someone else will continue your project tomorrow:
Why did you exclude that study?
Why did you choose that database?
Why did you change your keywords?

Your future self will thank you.

7. And finally: pace yourself
Review studies can be surprisingly draining.

Set:
weekly screening goals,
a time limit for reading each abstract,
and clear “stop” criteria.

Slow and steady > burnout and backlog.

PS: Do you prefer reading 100 papers before you write or writing as you read?
Share in the comments.

REPOST to help others.

How to find the papers you didn’t even know you were missing?

Most researchers don’t struggle with finding papers.

They struggle with making sense of the papers they’ve already found.

You collect PDFs, bookmark links, highlight paragraphs…
but when it’s finally time to connect the dots, the literature feels scattered and overwhelming.

That’s exactly where AnswerThis (YC F25) Canvas surprised me.

It’s not just another AI tool — it’s a full research system:
Search → Analyse → Chat → Map → Write all in one clean interface.

How I used it for a citation map:
I started with a simple research question.
 
Canvas instantly generated a literature review with real citations, then surfaced additional studies I hadn’t even thought to include.

This is where it gets genuinely useful:
 you can filter, trim, and refine the papers until you’re left with exactly the set you want to analyse.

Then, with one click, you can run a bibliometric snapshot showing:
Which years had publication spikes
Which keywords dominate the field
Who the influential authors are
Which studies have shaped the conversation

And my favourite part?

You can visualise how 50+ papers connect —
which ones are central, which ones sit on the edges, and which foundational studies you might have overlooked.

It’s like watching your literature review untangle itself.

Try this workflow, here’s the link: https://lnkd.in/e_HfEfN8 

PS: Would a citation map change how you approach your next paper or PhD chapter?

Share in the comments

REPOST to help others.

Google quietly released a new AI-powered research tool

Google Scholar Labs, an experimental AI-powered feature that actually understands your research question and brings back papers with context. 

If you’ve ever spent hours searching, clicking, and still missing key literature… this tool might change your workflow.

In my new video, I break down:
✨ What Scholar Labs is
 ✨ How the AI-powered search works
 ✨ A full demo using real research questions
 ✨ When to use it (and when not to)
 ✨ How to save hours in your literature review

If you’re a PhD student, early-career researcher, or anyone doing academic writing — this is worth knowing.

🎥 Watch the full video here: https://lnkd.in/eNintd5Q

PS: Do you think AI will become a standard part of academic literature searching in the next 2–3 years?
Share in the comments.

REPOST to help others.

The biggest determinant of PhD success? 

Not your GPA. Not your topic. Not your supervisor. 

Your resilience.

When I look back at my own PhD, I remember a moment that completely changed how I understood progress.

It wasn’t during a breakthrough.
It wasn’t after a great supervision meeting.
It wasn’t even when a chapter finally clicked.
It was during a period where I had completely burned out... a quiet afternoon where I sat staring at my laptop, unable to even open the document.

At that moment, I didn’t need a productivity strategy.
I didn’t need a new writing tool.
I didn’t need a better timeline.
I needed recovery.

And no one had ever told me how crucial that was.

Over the years, supervising students, I’ve realised something powerful:

The students who finish strong are the ones who know how to come back after they fall.

Because in a PhD, you will fall.

Not once.
Not twice.

But repeatedly, and in ways that feel personal.

You will go through:
 • intellectual droughts
 • confidence crashes
 • emotional exhaustion
 • rejection after months of effort
 • life moments that interrupt everything
 • days when every sentence feels wrong

📌 The overlooked skill that carries you through a PhD is:
Knowing when to pause.
Knowing how to restore your mind.
Knowing how to speak to yourself with kindness when you feel behind.
Knowing the difference between discipline and self-punishment.
Knowing that rest is not time lost, it is part of the process.

🔥 My message to PhDs today:
Your capacity to keep going will not come from working harder.
It will come from restoring your ability to think, feel, and function.

Because research is not a sprint.
It’s not a marathon either.

It’s a long, uneven landscape full of climbs, dips, plateaus, and unexpected storms.

Success belongs to those who learn how to rest without quitting.

PS: What does recovery look like for you and when was the last time you allowed yourself to do it without guilt?

Someone else reading the comments might need your answer.

Share in the comments

REPOST to help others.

“How do I start this sentence?” 

If you’ve ever stared at a blank screen wondering, you are not alone.

Academic writing involves both good ideas and finding the right language to communicate them with clarity and precision.

This guide of starter phrases for all major sections of a research paper, and it reminded me how powerful syntactic scaffolding can be.

Thank you for sharing Muhammad Muneeb

Sometimes, we don’t need new ideas, we just need the right sentence structure to express them.

This resource offers such practical, ready-to-use phrasing 👇 

Save this for later. 

REPOST to help others

The EduTech Guide: AI Transformations in Learning | Your AI guide to the digital world | Follow me to learn how you can use AI to 10x your productivity and accelerate your career.

100 starter phrases for research writing.

I personally found this guide very useful for writing.

This guides you how to write each of the following sections.

- Introduction
- Literature review
- Methods
- Results
- Discussion 
- Conclusion

#phd #research #writing

Research writing

Biomedical science finally gets an AI built just for its complexity

Meet the SciSpace BioMed Agent 
What impressed me most is how purpose-built it is.

With 150+ biomedical software integrations and 100+ specialized tools, 
it can automate tasks that normally require multiple platforms and a lot of manual effort.
 
Clinical workflows, 
variant interpretation, 
literature review, 
experimental design, 
drug discovery… all in one place.

The original SciSpace Agent still handles broad research across all disciplines.

But BioMed Agent is built only for biomedical work, from genes and pathways to patients, drugs, and trials. 

It understands the language, the complexity, and the workflow.

To test it, I asked a real research question:
 👉 “Show me recent studies, biomarkers, therapeutic targets, and ongoing clinical trials for Duchenne Muscular Dystrophy.”

And it delivered — structured, clear, up-to-date insights within seconds.
 
I also tried the new biomedical illustration feature 

I simply prompted it to create a clinical trial phases diagram… and it generated a clean, publication-ready figure instantly.

For someone who has spent years making figures for papers and lectures, this felt like magic.

If you’re in biomedical research, this isn’t just another tool.

It genuinely feels like having an AI co-scientist—one that saves you hours, reduces cognitive overload, and helps you focus on the work that actually matters.

🔗 Try SciSpace BioMed Agent : https://lnkd.in/eieAfd-5
Use code:
DPSBIO40: offer 40% off on annual plan
DPSBIO20: offer 20% off on monthly plan

DPSBIO30: offer 30% off on annual plan (Indian users)

PS: If AI could automate ONE step of your biomedical pipeline this week, what would you choose?

Share in the comments

REPOST to help others.

#scispace #scispacebiomed #aiinresearch #biomedicalscience #researchtools #phdlife #drugdiscovery #clinicalresearch

❓ Mam...“How do I use AI to support my research without letting it override my own thinking?”

Most researchers I mentor ask this similar question now 👆 

I believe the answer lies in the workflow.

Recently, I experimented with AnswerThis (YC F25) simply to understand how such systems might support early-stage exploration, especially the thinking before writing phase. 

That exploration led me to sketch a simple, ethical framework that any researcher can adapt.

A Suggestive Framework for Using AI in Early Research Thinking:

Draft → Extract → Evaluate → Interpret → Rewrite

1. Draft (AI for scaffolding only)
Tools like AnswerThis (YC F25) can generate a very rough first draft or outline based on a research question.
 
This isn’t the end product.
It’s a starting structure.
 
It helps researchers see how the field might be framed before they begin shaping it themselves.

2. Extract (AI for organising information)
One feature I found quite useful was the ability to extract limitations, gaps, and future directions from the papers AI itself cited.

It’s simply about accelerating the information-gathering phase so you can spend more time thinking and less time manually hunting for recurring themes.

3. Evaluate (human verification is essential)
Every AI-generated insight must be checked against the original papers.

Accuracy, nuance, context 
that responsibility remains yours.
 
AI can summarise, but it cannot read like a researcher.

4. Interpret (the core intellectual work)
Your analysis determines:
✅ which gaps are meaningful
✅how ideas connect
✅what questions matter
✅where a study could contribute

This is where your voice, expertise, and originality shape the argument.

5. Rewrite (claim the draft as your own)
Even if AI helps produce scaffolding, the final text should be your reasoning, your words, your structure.

This framework matters, as:
It keeps the researcher at the centre.
 
It uses AI for the parts that slow you down — summarisation, structuring, organising 

while protecting the parts that define scholarship:
judgment, interpretation, and argumentation.

For me, this reframes AI not as a shortcut, but as a thinking partner during early exploration.

If you want to try the workflow with AnswerThis, go here: https://lnkd.in/e_HfEfN8 
For discounts on subscription, use 'PRIYA10'

PS: Are you experimenting with tools like AnswerThis for early drafts or deep dives?
What boundaries or workflows help you keep the research rigorous and ethical?
Share in the comments.

REPOST to help others.

Follow for more research workflows.

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