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

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 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

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

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

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

Sindh Madressatul Islam University

Assistant Professor

Journal of Journalism, Media Science & Creative Arts, https://jjmsca.com/index.php/jjmsca

Journal of Journalism, Media Science & Creative Arts

Pakistan journal of Media Science https://media-sciences.com/index.php/pjms

Editor in Chief

pakistan journal of media Science,https://media-sciences.com/index.php/pjms

pakistan journal of media Science

University of Karachi

Senior Lecturer

Federal Urdu University of Arts, Science & Technology, Karachi.

Abdullah government Girls College 

College Lecturer

Dr. Naureen Aleem is a Journalism Professor and Expert at Sindh Madressatul Islam University (SMIU), where she educates and mentors aspiring journalists and media professionals. With a Ph.D. in Investigative Journalism from the Federal Urdu University of Arts, Science & Technology, Karachi, she has a strong academic background and a passion for advancing knowledge in the field.

She is also the Editor-in-Chief of the Pakistan Journal of Media Science, a leading publication that sets the standards and trends of media research in the country. She also serves as the Editor for the Journal of Journalism, Media Science & Creative Arts, and as the Associate Editor for the Journal of Social Sciences and Media Studies, showcasing her versatility and expertise across various domains. She has authored a book based on her Ph.D. thesis and has multiple publications in reputable journals. Her professional experience includes working as a reporter and a feature editor for prominent newspapers and news agencies, demonstrating her adeptness in investigative journalism. Dr. Aleem is a distinguished figure in the field of journalism, with a robust academic and professional profile.

Dr.Naureen Aleem

How to Write a Thesis (Short Section-by-Section Guide)
📌 Preliminary Pages
Cover Page: Thesis title, your name, supervisor, department, university, date.
Acknowledgment: Thank people who supported your research.
Abstract: 150–300 word summary of problem, methods, results, conclusion.
Table of Contents: Chapter titles with page numbers.
List of Figures/Tables: All figures/tables with page numbers.
📘 Chapter 1: Introduction
Background: Basic context of the study.
Research Problem: Issue or gap being addressed.
Gap: What past studies missed.
Objectives: What your study aims to achieve.
Rationale: Importance and significance of the study.
Scope/Delimitations: What is included and excluded.
Thesis Structure: Summary of chapters.
📗 Chapter 2: Literature Review
Agreements/Disagreements: What scholars say.
Gap: Missing areas in existing research.
Lead to RQs/Hs: How literature shapes your research questions/hypotheses.
Theoretical Framework: Theories guiding your study.
📙 Chapter 3: Methods
RQs/Hypotheses: Main questions or hypotheses.
Design: Type of research (qualitative, quantitative, etc.).
Sample: Participants and selection method.
Instruments: Tools used (questionnaires, interviews, observations).
Data Collection: How data was gathered.
Analysis: Statistical or thematic analysis methods.
Ethics: Consent, confidentiality, permissions.
📕 Chapter 4: Results
Findings to RQs/Hs: Answers to each research question/hypothesis.
Stats/Themes: Quantitative = numbers; Qualitative = themes.
Tables/Figures: Visual presentation of results.
📓 Chapter 5: Discussion
Interpret: Meaning of the results.
Compare: Link findings with previous research.
Explain: Reasons behind results.
Implications: Why the findings matter.
Limits: Study limitations.
Future: Directions for future research.
📒 Chapter 6: Conclusion & Recommendations
Recap: Summary of the study and findings.
Main Takeaway: Core conclusion.
Contribution: What new knowledge your study adds.
Recommendations: Policy or practical suggestions.
Limitations: Brief reminder of limitations.
Future Research: What others can explore next.
📄 Ending
References: All cited works in required style (APA, etc.).
Appendices: Extra materials (questionnaires, interview guides, raw data, ethics forms).

Professor specializing in research skills and research design, Editor-in-Chief of the two journals PJMS and JJMSCA. Experienced researcher, freelance journalist, and PhD thesis focused on investigative journalism.

Step-by-Step Thesis Writing: Useful Starter Phrases for Each Section
01) Introduction 
Context:
Problem/Gap:
Purpose/Aim
Research Question(s)/Objectives:
Contribution/Significance:

Use the starters to write:
The purpose of this study is to examine ___ in order to address gaps in ___.
Although previous studies have investigated ___, limited attention has been given to ___.
This paper aims to build on earlier findings by focusing on ___.
This study is guided by the question: ___?
The importance of this research lies in providing additional evidence on ___.

02) Literature Review 

Use the starters to write:
Previous research has shown that ___, but results remain inconsistent.
Several studies have suggested ___, while others have reported ___.
The majority of existing literature has focused on ___, leaving ___ underexplored.
Recent studies have highlighted the need for further investigation into ___.
This review summarizes key findings and identifies the main research gap addressed in this study.”

03) Methods 
Design/Approach: 
Participants/Sampling: 
Measures/Instruments:
Procedure/Data Collection:
Analysis:
Ethics:

Use the starters to write:
This study used a [qualitative/quantitative/mixed] approach to examine __
Data were collected through __ and analyzed using __
The sample consisted of __ participants, selected using __
All variables were defined according to standard measures in the field.
Statistical analysis was carried out using ___ to test the hypotheses.
Tip: Methods should align tightly with your research questions and claims.

04) Results
Primary outcomes 
Key statistics .
Tables/Figures to summarize complex results 

Use the starters to write:
The results show that ___ is significantly related to ___.
The analysis indicates that ___ increased/decreased across the study period.
Contrary to expectations, ___ was not found to have a significant effect.
The findings are summarized in Table X and Figure Y.
The main outcome of the study is that ___.


05) Discussion 
Answer the research question(s) in plain language.
Compare with prior studies: .
Mechanisms/Explanations: .
Implications:
Limitations:
Future research:

Use the starters to write:
These findings are consistent with previous studies that reported _.
The results differ from earlier work, which suggested _.
One possible explanation for the findings is ___.
The study contributes to the literature by providing new evidence on ___.
Implications of the results include ___, and they suggest directions for future research.

06) Conclusion 
One-paragraph recap 
Contribution: 
Limitations + future work 
Closing sentence

Use the starters to write:
This study examined _ and found that 
The results support the conclusion that _
The study makes a contribution by addressing the gap identified in the literature.
Limitations include ___, which should be considered when interpreting the results.
Further research is needed to confirm these findings and explore ___ in more detail.

16 tools for checking plagiarism in your manuscripts.
🔹 Types of Plagiarism 
It identifies 10 types of plagiarism, each with a unique nature:
1-Direct Plagiarism – Copying someone else’s work word-for-word without citation.

2-Self-Plagiarism – Reusing your own previously published work without acknowledgment.

3-Mosaic Plagiarism – Patching together phrases or ideas from different sources without credit.

4-Accidental Plagiarism – Failing to cite sources or paraphrasing improperly due to ignorance.

5-Paraphrasing Plagiarism – Rewriting another's work in your own words without citation.

6-Source-Based Plagiarism – Citing incorrect or non-existent sources.

7-Complete Plagiarism – Submitting someone else’s work as your own.

8-Artistic Plagiarism – Copying creative work like art, music, or graphics without credit.

9-Code Plagiarism – Using someone else’s programming code without permission.

10-Collusion – Collaborating with someone inappropriately to produce plagiarized work.

🔹 AI Tools for Detecting Plagiarism 
1-Turnitin:Widely used in academia to detect textual matches from large databases.
2-Grammarly:Mainly a grammar checker, but it includes a basic plagiarism detection feature.

3-Copyscape:Detects duplicate content online, commonly used for websites and blogs.

4-Quetext :Offers deep search and citation assistance for plagiarism detection.

5-PlagScan: (by Turnitin)Academic-level tool integrated with Turnitin’s system

6-Unicheck: Cloud-based tool for academic writing with detailed similarity reports.

7-PlagiarismCheck.org: Checks for originality and offers detailed reports for students and teachers.

8-SmallSeo: ToolsFree SEO and plagiarism checker, commonly used by bloggers and web writers.

9-AnswerThis: AI content checker with paraphrasing and answer generation analysis.

10-ViperOffers : downloadable reports and checks against 10+ billion sources.

11-Paperpal : AI writing assistant that includes plagiarism detection.

12-ScribbrPopular among students, offers similarity check and citation help.

13-Plagium :Offers deep search plagiarism analysis and Google Docs integration.

14-BibMe.org: Focused on citation generation, includes basic plagiarism check.

15-Copyleaks :Advanced AI that checks code, text, and academic content across languages.

16-Duplichecker: AIMulti-function platform for grammar, SEO, and plagiarism checks.
Activate to view larger image,

top 25+ Journal Selection Tools Every Researcher Should Know (With Direct Links!)
🎯 Finding the right journal can make or break your publication journey.
 Here’s a complete, updated list of tools and databases — from publisher-specific platforms to multi-publisher and ranking resources — to help you choose the best journal for your research.

💡 1. Publisher-Specific Journal Finders
1️⃣ Elsevier: https://lnkd.in/g3gENHpC
2️⃣ Springer: https://lnkd.in/g2Q2-Y96
3️⃣ Taylor & Francis: https://lnkd.in/gcX8Fn8M
4️⃣ IEEE: https://lnkd.in/gMH69k69
5️⃣ Emerald: https://lnkd.in/gttt2pgx
6️⃣ RSC: https://lnkd.in/ghRSYUDD
7️⃣ Wiley: https://lnkd.in/gBVVk4Cu
8️⃣ SAGE: https://lnkd.in/ggQetX7D
9️⃣ ACS: https://acs.chronoshub.io/
🔟 MDPI: https://lnkd.in/gKXX5KGs

💡 2. Tools That Work Across Publishers
1️⃣ Researcher.life: https://lnkd.in/gVTe95Pu
2️⃣ Edanz: https://lnkd.in/g889wrZq
3️⃣ Trinka: https://lnkd.in/g496AGY8
4️⃣ Journal Advisor: https://lnkd.in/gUsdyk2A
5️⃣ JANE: https://lnkd.in/gm2uiHT4
6️⃣ Sciencegate: https://lnkd.in/gZGK4eJY
7️⃣ Master Journal List: https://lnkd.in/g4dJTpXJ
8️⃣ Journal Searches: https://lnkd.in/ghApXZbw
9️⃣ Review-it: https://review-it.ai/

💡 3. Journal Databases / Ranking Lists
0️⃣ Scopus: https://lnkd.in/gpUvs7wT
1️⃣ Web of Science: https://lnkd.in/gxyVCQMX
2️⃣ ABDC Journal List: https://lnkd.in/gVJhDZbH
3️⃣ Directory of Open Access Journals (DOAJ): https://doaj.org/
4️⃣ PubMed Central: https://lnkd.in/g3tGAqYZ
5️⃣ SCImago Journal Rank (SJR): https://www.scimagojr.com/
6️⃣ European Reference Index for Humanities: https://lnkd.in/ggfq4km3
7️⃣ Google Scholar Metrics: https://lnkd.in/gDzc_uC7
8️⃣ CORE: https://core.ac.uk/,

A Complete Guide To Qualitative and Quantitative Research Method 
Quantitative Research : Introduces the concept of quantitative research.
Goal (Quantitative): Aims to measure user behavior and experiences through numerical data analysis.
Questions (Quantitative): Answers "what" and "how much" through numerical findings.
Quantitative research methods: Lists specific methods for collecting numerical data.
Surveys: Collects numerical data from many respondents using structured, closed-ended questions.
A/B testing: Compares two versions to see which performs better based on numerical metrics.
Behavioral Data Analysis: Examines numerical data of user actions, often via analytics.
"What is Qualitative Research : Introduces the concept of qualitative research.
Goal (Qualitative): Aims to understand user motivations, emotions, and perceptions.
Questions (Qualitative): Answers "why" and "how" through in-depth understanding.
Qualitative research methods: Lists specific methods for gathering non-numerical insights.
Interviews: Gathers detailed personal insights through one-on-one conversations.
Usability Testing: Observes user interactions to identify issues and understand experiences.
Focus Groups: Explores opinions and experiences through small group discussions.
Open-ended surveys: Collects detailed, free-form written responses from participants.

how to wrte Grant Proposal
PI / Co-PI Information
Collaborator (if any)
Project Details
Budget Requested
Executive Summary
Project Objectives
Implementation Method
Timeline
Key Milestones
Final Deliverables
SDGs Covered
Equipment Summary
Local Impact
Partnerships
Signatures & Undertaking

How to write a Grant Proposal 

COMPARISON BETWEEN QUALITATIVE & QUANTITATIVE
1. Methods Used
Qualitative : Uses methods such as focus groups, in-depth interviews, and document reviews to analyze themes. These methods help understand people's experiences, emotions, and opinions in detail
Quantitative : Uses structured surveys, observations, and records/documents to collect numeric data. The focus is on measurable and statistical analysis
2. Process Type
Qualitative : Follows an inductive process, meaning it starts with observations and then develops theories or hypotheses. The goal is to explore concepts in-depth
Quantitative : Follows a deductive process, meaning it starts with a hypothesis or theory and tests it through data collection and analysis
3. Subjectivity vs. Objectivity
Qualitative : More subjective, as it describes experiences from the perspective of participants. Researchers interpret meanings rather than just measuring data.
Quantitative : More objective, as it relies on observed facts and statistical interpretation rather than personal opinions.
4. Data Type
Qualitative: Text-based, meaning data is collected in the form of words, narratives, or open-ended responses.
Quantitative : Number-based, meaning data is collected in the form of numbers, percentages, or statistical figures.
5. Depth of Information
Qualitative : Provides more in-depth information on fewer cases. 
Quantitative : Provides less depth but covers a large number of cases.
6. Response Options
Qualitative : Uses fixed response options, such as predefined themes or coding for analysis
Quantitative : Uses unstructured or semi-structured response options, allowing more flexibility in data collection
7. Statistical Tests
Qualitative : Uses statistical tests for analysis, but mostly relies on thematic analysis
Quantitative : Does not use statistical tests in the same way; instead, it focuses on structured numerical analysis
8. Validity and Reliability
Qualitative : Validity and reliability depend on the measurement device or instrument used, meaning the way data is collected affects its trustworthiness.
Quantitative : Validity and reliability depend on the researcher's skill and detail, as precise measurements and analysis are crucial
9. Time Expenditure
Qualitative : More time-consuming in the planning phase because data collection requires in-depth methods like interviews and focus groups. 
Quantitative: Less time-consuming in planning but heavier in analysis since statistical processing of large datasets takes time
10. Generalizability
Qualitative : More generalizable in terms of deep understanding of a specific issue but may not apply broadly
Quantitative : Less generalizable, as it focuses on numerical data rather than the complexity of human experiences
11. Research Model
Qualitative : Follows a human behavior model,it studies emotions, perceptions, and interactions
Quantitative : Follows a natural science model, it applies mathematical and statistical approaches similar to scientific experiments

Turning Your Thesis into a Journal Article

🔹 Step 1: Identify the Core Focus
 Extract the main objective or question from your thesis.
 Why: Journals prefer focused studies, not broad theses.
 Example: From a thesis on media and youth, focus only on social media's impact on youth political engagement.

🔹 Step 2: Select the Right Journal
Choose a peer-reviewed journal that matches your research topic.
 Why: Each journal has unique aims, scope, and formatting requirements.
 Example: For education-related media research, consider Journal of Media Literacy Education.

🔹 Step 3: Shorten and Streamline Content
 Trim your thesis (15,000–80,000 words) down to a paper (~4,000–7,000 words).
 Why: Journals require concise, direct writing.
 Focus on:
One problem
One method
One main set of findings

🔹 Step 4: Restructure into Research Paper Format
 Organize the paper using the standard journal structure:
i--Title (10–15 words)
ii-Abstract (150–250 words)
iii-Keywords (4–6)
iv-Introduction (500–700 words)
v-Literature Review (800–1000 words)
vi-Methodology (500–700 words)
vii-Results (500–800 words)
viii-Discussion (700–1000 words)
ix-Conclusion (200–300 words)
x-References

🔹 Step 5: Rewrite in a Concise, Academic Style
 Avoid repetition, reduce literature discussion, and cut lengthy background info.
 Why: Academic papers require clarity and brevity.
 Tip: Use active voice and present tense where appropriate.

🔹 Step 6: Update the Literature
 Add the most recent studies to your literature review.
 Why: Journals want current and relevant references (last 5 years is ideal).

🔹 Step 7: Ensure Originality and Avoid Self-Plagiarism
 Reword thesis content, especially if your thesis is online or previously submitted.
 Why: Journals check for originality and duplication.

🔹 Step 8: Prepare for Journal Submission
Format according to journal guidelines
Include cover letter
Proofread thoroughly

🔹 Step 9: Submit and Respond to Reviewer Feedback
Submit your paper
If rejected, revise and resubmit to another journal
If accepted with changes, revise carefully and respond to all comments

🔹 Step 10: Celebrate Your Publication!
 Once published, share your work on LinkedIn, ResearchGate, or academic platforms

Research, Simplified with Dr. Naureen Aleem
 🌟 Scholar to Mentor
 Research, Simplified with Dr. Naureen Aleem is a space dedicated to empowering scholars, early-career researchers, and academics navigating the complexities of research design, thesis writing, and publication. With years of experience as a professor, editor-in-chief, and mentor, Dr. Naureen Aleem shares practical strategies, insightful tips, and inspiring guidance to help you move from being a learner to a confident mentor in your own academic journey.
 Whether you are drafting your first thesis, polishing a research article, or aiming for journal publication, this newsletter offers clarity, encouragement, and expert advice—turning overwhelming challenges into achievable milestones.

Plagiarism undermines academic integrity and can occur in many forms. Understanding these types—and knowing which tools can help detect them—is essent

Open article: 16 Tools for Checking Plagiarism in Your Manuscripts by Dr.Naureen Aleem • 2 min read

16 Tools for Checking Plagiarism in Your Manuscripts

Stop Overthinking! Choose a Winning Research Topic with This 3-Step Hack
Step 1: Techniques for Finding and Choosing a Research Topic
This step involves using creative techniques and rational techniques to identify potential topics.
Creative Techniques
 1-Keeping a Notebook of Ideas
Example: A researcher interested in mental health keeps notes of articles, documentaries, and daily observations, leading to the idea of studying mental health portrayals in movies.
2-Brainstorming
Example: During a brainstorming session, the researcher lists related ideas like mental health in Netflix dramas or mental health awareness campaigns.
3-Exploring Personal Preferences Using Past Projects
Example: If the researcher previously worked on public health campaigns, they might focus on media’s influence on mental health awareness.
4-Concept Mapping
Example: Create a concept map that branches topics like social media and mental health or stigma reduction strategies.
Rational Techniques
1-Scanning the Media
Example: The researcher reads media reports on increasing anxiety levels post-pandemic, sparking an idea for a study.
2-Searching the Literature
Example: Exploring academic journals reveals limited studies on how movies shape public opinion on mental health.
3-Discussion
Example: Talking to experts highlights that youth mental health is under-researched.
4-Examining Your Strengths and Interests
Example: The researcher’s background in psychology and media studies leads them to focus on “mental health representation.
5-Past Project Titles
Example: Titles like Mental Health in Television” from others inspire narrowing the focus.
Step 2: Refining the Research Topic/Idea
This involves a cyclical process:
1-Start with a Research Topic
Example: Mental health portrayal in movies
2-Determine if the Topic is Too Broad or Too Narrow
Example: The topic is too broad. Narrow it to Mental health portrayal in Netflix dramas during 2020–2022.
2-Concept Mapping the Idea
Example: Map the idea by exploring subtopics like depressionbyouth audiences, and emotional triggers in storytelling.
3-Refine the Topic Using Concept Map Information
Example: Finalize the topic as Impact of Netflix dramas on youth perceptions of depression
4-Literature Search
Example: Find relevant studies or identify gaps, such as the lack of research on the impact of visual storytelling on young adults.
5-Finalize the Research Idea
Example: Analyzing the role of Netflix dramas in shaping youth perceptions of depression 
topic using concept mapping.
Central Theme: Obesity
1-What?
Example: What are the risk factors of obesity?
2-Who?
Example: Focus on specific groups like adolescents or elderly individuals.
3-Why and How?
Example: Why is obesity rising in urban areas? or How do lifestyle choices affect obesity?
4-Where?
Example: Limit to a region, e.g., Obesity trends in Australia
5-When?
Example: Narrow to a time frame, e.g., Obesity rates during 2023–2024
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Quantitative (Numerical) vs Qualitative (Textual) Data Analysis Techniques

🔢 Quantitative Analysis (Numerical Data)
1️⃣ Descriptive Analysis: Summarize data using metrics such as mean, median, mode, and standard deviation to uncover patterns and trends in the dataset.
Software: Excel, SPSS, R, Python
2️⃣ Inferential Analysis: Test hypotheses and make predictions about a population based on sample data using methods like t-tests, ANOVA, chi-square tests, confidence intervals, and more.
Software: SPSS, R, Python (SciPy, Statsmodels), Minitab, SAS
3️⃣ Predictive Analytics: Utilize historical data and machine learning algorithms to forecast future outcomes, identifying potential trends or behaviors.
Software: IBM SPSS Modeler, R (Caret, randomForest), Python (Scikit-learn, TensorFlow, Keras)
4️⃣ Regression Analysis: Explore and quantify relationships between variables to make data-driven predictions and informed decisions.
Software: R, Python (Statsmodels, Scikit-learn), SAS, SPSS
5️⃣ Correlation Analysis: Measure the strength and direction of the relationship between two or more variables to identify potential associations.
Software: R, Python (Pandas, Statsmodels), Excel, SPSS
6️⃣ Time Series Analysis: Analyze data points collected or recorded at specific time intervals to identify trends, seasonal patterns, and cyclical behavior.
Software: R (forecast, ts), Python (Statsmodels, Pandas), SAS, EViews
7️⃣ Factor Analysis: Reduce the number of variables and identify underlying factors or dimensions that explain the correlations among observed variables.
Software: SPSS, R (psych, FactoMineR), SAS

Qualitative Analysis (Textual Data)
1️⃣ Thematic Analysis: Identify recurring themes or patterns within textual data to interpret underlying meanings and insights.
Software: NVivo, ATLAS.ti, MAXQDA, Dedoose
2️⃣ Content Analysis: Systematically categorize and analyze textual data by counting the frequency of specific words, phrases, or concepts to uncover trends.
Software: NVivo, ATLAS.ti, QDA Miner, R (tm package)
3️⃣ Narrative Analysis: Examine personal stories, narratives, or experiences to understand the context and perspectives of individuals or groups.
Software: NVivo, MAXQDA, ATLAS.ti, Transana
4️⃣ Grounded Theory: Build a theory based on data through a systematic, iterative process, where data collection and analysis occur simultaneously.
Software: NVivo, MAXQDA, ATLAS.ti, Dedoose
5️⃣ Discourse Analysis: Study language use within texts and conversations to understand social meanings, power dynamics, and communication patterns.
Software: NVivo, ATLAS.ti, QSR International, R (quanteda package)
6️⃣ Phenomenological Analysis: Focus on individuals' lived experiences, exploring how they perceive and make sense of their world.
Software: NVivo, MAXQDA, ATLAS.ti, Dedoose
7️⃣ Case Study Analysis: In-depth examination of a specific instance or case, focusing on detailed contextual understanding to generate insights.
Software: NVivo, MAXQDA, ATLAS.ti, Caseware

⭐ The Key Pillars of Research Paradigms: From Ontology to Methodology
1. Research Paradigm
A philosophical framework that guides research based on beliefs about reality, knowledge, and methods.
⭐ Key Pillars of Research Paradigms
2. Ontology
The nature of reality—whether it is single or multiple.
3. Epistemology
How knowledge is created and the researcher’s relationship with it.
4. Methodology
The tools and strategies used to collect and analyze data.
⭐ Research Philosophy vs. Research Paradigm
5. Research Philosophy
Beliefs and assumptions about how knowledge develops; includes ontology and epistemology.
6. Research Paradigm
A broader worldview combining philosophy, methodology, and tools (e.g., positivism, interpretivism).
⭐ Research Paradigm vs. Research Design
7. Research Design
The detailed plan for conducting a study, including sampling, data collection, and analysis.
8. Research Paradigm
The guiding worldview that shapes the entire research approach.
⭐ 5 Steps to a Paradigm Shift
9. Identify the element to change
Choose the belief or behavior you want to improve.
10. Write your goals
Clearly define what you want to achieve.
11. Adjust your mindset
Shift thinking to support your new goals.
12. Do uncomfortable things
Take actions outside your comfort zone to grow.
13. Practice who you want to become
Repeat new habits until they feel natural.
⭐ 10 Types of Research Paradigms
14. Positivism
Belief in one measurable, objective reality.
15. Post-Positivism
Reality exists but cannot be perfectly measured.
16. Constructivism
Reality is shaped by human interaction and experiences.
17. Interpretivism
Focuses on understanding meanings behind human behavior.
18. Pragmatism
Uses methods that best solve the research problem.
19. Transformative
Emphasizes social justice and empowering marginalized groups.
20. Post-Modernism
Rejects universal truths; values multiple perspectives.
21. Feminism
Challenges gender inequalities through research.
22. Critical Theory
Examines power, oppression, and seeks social change.
23. Participatory/Action Research
Researchers and participants work together to solve real-world problems.

How to Conduct a Rigorous Quasi-Experimental Research Study
1. What is a Quasi-Experimental Study?
A research design that tests cause-and-effect relationships without random assignment to groups.
6 Steps to Conduct a Quasi-Experimental Study
1. Define Research Questions and Objectives
Identify what you want to investigate, including:
Independent Variable (IV): What you change.
Dependent Variable (DV): What you measure.
Hypothesis: Predicted relationship.
2. Select a Quasi-Experimental Design
Choose a design that fits your goals and available conditions (e.g., pretest–posttest, nonequivalent groups).
3. Identify and Recruit Participants
Select participants from naturally existing groups (e.g., school classes). Take informed consent.
4. Collect Data
Gather information before and after the intervention using consistent tools (e.g., same test).
5. Analyze Data
Use statistical techniques (t-test, ANOVA, regression) to compare groups and measure change.
6. Interpret Results
Explain what the findings mean, whether there were differences, and how strong the effects were.
7. Draw Conclusions
Summarize findings, note limitations, and suggest future research directions.
Types of Quasi-Experimental Designs (Short Definitions)
Nonequivalent Groups Design
 Compares existing groups without random assignment.
Pretest–Posttest Design
 Measures outcomes before and after an intervention.
Combination Design
 Uses multiple quasi-experimental methods together.
Interrupted Time Series Design
 Analyzes trends before and after an intervention over time.
Regression Discontinuity Design
 Assigns groups based on a cutoff score and compares outcom

How to Write a Research Paper | Essential Tips and Guidelines for Success
📌 Research Paper Structure
Abstract Summary Section
Introduction and Background
Current Literature Review
Research Methodology Details
Research Findings Presentation
Discussion and Implications
Conclusion of Findings
Citations and References
📌 Key Elements of Good Paper
Clear Thesis Statement
Strong Research Support
Logical Paper Flow
Proper Source Citation
Academic Writing Style
Relevant Paper Content
Smooth Idea Transitions

📌 Types of Research Papers
Analytical Research Paper
Argumentative Research Paper
Experimental Study Paper
Survey-Based Research Paper

📌 6-Step Writing Process
Planning and Preparation
Research and Documentation
Organizing and Drafting
Writing the Introduction
Body Paragraph Development
Revision and Finalization

📌What are the 7 steps of writing a research paper?

Choose and narrow your topic
Research thoroughly using credible sources
Create a detailed outline
Write your first draft, starting with the introduction
Include proper citations and references
Revise for content, organization, and clarity
Edit and proofread for grammar, spelling, and formatting
Each step builds upon the previous one, ensuring a well-structured and properly supported academic paper.

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