Best Notion AI Alternative for Researchers: GetScholar Academic Workspace
Notion has become the go-to workspace for productivity enthusiasts, project management, and personal knowledge bases. Notion AI adds conversational assistance to this powerful platform. But if you're a researcher, graduate student, or academic professional, you've likely discovered a frustrating truth: Notion wasn't built for research, and it shows.
You can't search academic databases directly. You can't manage citations properly. You can't run code for data analysis. You can't export to LaTeX. The AI is generic, not trained on academic workflows. What works brilliantly for project managers fails researchers at every critical step.
The problem: Notion AI is a general-purpose tool trying to serve specialized needs. Researchers need domain-specific features, academic database integration, and workflows designed around the research lifecycle—not generic note-taking.
The solution: GetScholar is the Notion AI alternative purpose-built for academic research. It combines the collaborative workspace feel of Notion with specialized academic features: multi-database literature search, citation management, multi-model AI assistants, in-browser code execution, and export to academic formats.
Why Notion AI Falls Short for Academic Research
What Notion AI Offers (General Productivity) ✅
Notion deserves credit for creating an excellent general-purpose workspace:
- Flexible Note-Taking: Blocks, databases, pages
- Organization: Folders, tags, relations
- Collaboration: Shared workspaces, comments
- AI Assistance: Summarization, writing help, Q&A
- Templates: Reusable structures
What Researchers Actually Need (But Notion Lacks) ❌
However, for serious academic work, Notion has critical gaps:
1. No Academic Database Integration
- Can't search ArXiv, PubMed, Crossref, DBLP from within Notion
- Must manually copy-paste paper metadata
- Web clipper captures generic pages, not scholarly articles
- No automatic citation extraction
The Impact:
- Researchers spend 2-3 hours per week manually copying paper information into Notion
- Risk of transcription errors in citations
- No way to track where a reference came from
2. Poor Citation Management
- No built-in citation styles (APA, MLA, Chicago, Vancouver)
- Can't generate bibliographies automatically
- No BibTeX or RIS export
- Manual formatting for every citation
The Impact:
- Must use separate tool (Zotero, Mendeley)
- Manually sync between citation manager and Notion
- Copy-paste citations into manuscripts
- Reformatting citations for each journal
3. Generic AI, Not Academic-Specialized
- Notion AI isn't trained on scholarly content
- Can't distinguish peer-reviewed from blog posts
- Doesn't understand research methodologies
- No specialized models for different tasks
Example Failure:
Ask Notion AI: "Compare the experimental designs of these 5 neuroscience papers"
→ Generic summary, misses key methodological differences
→ Treats all sources equally (doesn't prioritize peer-reviewed)
→ Can't critique statistical approaches
Ask GetScholar AI (with Claude Sonnet):
→ Identifies methodology differences (behavioral vs. imaging)
→ Compares statistical power and sample sizes
→ Notes control conditions and potential confounds
→ Suggests methodological improvements
4. No Code Execution
- Can insert code blocks for syntax highlighting
- But can't run code
- No data analysis capabilities
- No visualization tools
The Impact:
- Must switch to Jupyter Notebook or RStudio
- Can't keep code, results, and writing together
- Hard to reproduce analyses months later
- Collaboration on code difficult
5. Limited Export for Academia
- Export to Markdown, PDF, HTML
- But no LaTeX export (required for many academic journals)
- No direct export to citation managers
- PDF export doesn't preserve code formatting
6. No Version Control for Academic Writing
- Basic page history (shows changes)
- But no branching, merging, or structured versioning
- Can't label versions ("submitted-to-journal", "revised-after-review")
- Difficult to compare two specific versions side-by-side
7. Privacy and Data Ownership Concerns
- Cloud-only storage (no local-first option)
- AI training data policies unclear for research content
- Institutional data policies may prohibit cloud note storage
- No self-hosting option for sensitive research
The Real Cost for Researchers
Academic teams using Notion report:
- 45% more time on administrative tasks (copying citations, exporting)
- 6-8 additional tools needed for complete workflow
- Version control chaos when multiple authors collaborate
- Risk of citation errors from manual entry
- Difficulty meeting journal requirements (LaTeX, specific formats)
GetScholar: Purpose-Built for Researchers
GetScholar reimagines the collaborative workspace concept specifically for academic research.
🔬 Academic Database Search (Notion Can't Do This)
GetScholar integrates directly with major scholarly databases:
| Database | Coverage | What You Can Find | |----------|----------|-------------------| | ArXiv | 2.3M+ preprints | Physics, math, CS, quantitative fields | | PubMed | 35M+ citations | Biomedical, life sciences, clinical research | | Crossref | 130M+ records | Multidisciplinary scholarly publications | | DBLP | 6M+ papers | Computer science, engineering | | CORE | 200M+ articles | Open access research across all fields |
Integrated Workflow:
1. Type search query in GetScholar → Multi-database search
2. Results appear with metadata pre-filled → One-click save to collection
3. AI summarizes each paper → Add to your notes
4. Insert citations into writing → Auto-formatted
5. Export bibliography → BibTeX, RIS, or formatted
vs Notion:
1. Google Scholar → Find paper
2. Copy title, authors, year → Paste into Notion
3. Manually format → Database entry
4. Repeat 100x for literature review
5. Export → Manually reformat for citation manager
🤖 Multi-Model Academic AI (vs Notion's Single Generic AI)
GetScholar lets you choose the right AI for each task:
| Task | Best Model | Why | |------|-----------|-----| | Literature synthesis | GPT-4o | Broad knowledge, excellent at integration | | Methodology critique | Claude Sonnet 4 | Deep reasoning, catches flaws | | Real-time information | Perplexity Sonar | Web access for latest developments | | Code generation | GPT-4o | Strong at Python, R, statistical code |
Example: Literature Review
Ask GetScholar (using Claude Sonnet):
"I'm reviewing 15 papers on CRISPR gene editing in embryos. Please:
1. Summarize the main ethical arguments for and against
2. Identify methodological concerns in the experimental studies
3. Note consensus vs. controversial claims
4. Suggest gaps for future research"
Claude Response:
## Ethical Arguments
For:
- Eradication of genetic diseases (cite: Smith 2023, Jones 2024)
- Reduction of healthcare costs (cite: Lee 2023)
Against:
- Designer baby concerns (cite: Kim 2023)
- Unknown long-term effects (cite: multiple papers)
## Methodological Concerns
- Small sample sizes in animal models (cite: Garcia 2024)
- Off-target effects underreported (cite: Wang 2023)
- Publication bias toward positive results
[Continues with detailed, citation-linked analysis]
Compare to Notion AI: Generic response, no citation linking, misses nuanced academic context.
📊 Document Types for Every Research Need
GetScholar offers specialized document types that Notion lacks:
1. Markdown Documents with Citation Integration
# Introduction
Recent advances in transformer architectures [@Vaswani2017; @Devlin2019]
have revolutionized natural language processing.
## Bibliography
[Auto-generated from cited works]
vs Notion: Manual citation insertion, no automatic bibliography, manual reformatting.
2. Code Documents with Execution
import pandas as pd
import matplotlib.pyplot as plt
# Load systematic review data
data = pd.read_csv('extraction.csv')
# Plot effect sizes
data.plot.scatter(x='year', y='effect_size')
plt.title('Effect Sizes Over Time')
plt.show()
# Output appears directly below code block
vs Notion: Can show code syntax, but can't run it. Must screenshot Jupyter output and paste image.
3. Data Extraction Tables
| Study | Year | N | Method | Effect Size | P-value |
|------------|------|-----|------------|-------------|---------|
| Smith | 2023 | 120 | RCT | 0.45 | 0.003 |
| Jones | 2024 | 95 | Cohort | 0.38 | 0.012 |
[Auto-export to CSV for meta-analysis software]
vs Notion: Similar tables, but no direct export to statistical software formats.
4. AI-Generated Diagrams
Ask AI: "Create a flowchart for our systematic review PRISMA diagram:
- 500 records identified
- 200 after title/abstract screening
- 50 full-text assessed
- 25 included"
→ AI generates diagram image
→ Editable in document
→ Export to SVG, PNG
🤝 Research-Specific Collaboration
GetScholar enhances Notion-style collaboration with academic features:
Shared Workspaces for Research Teams
Workspace: "Diabetes Intervention Systematic Review"
Members:
- PI: Dr. Smith (admin, all permissions)
- Postdoc: Dr. Jones (editor, can create/edit documents)
- Grad Students: Alice, Bob (contributors, assigned screening tasks)
- Undergrad RA: Charlie (viewer, can comment)
Folders:
- 📁 Protocol
- 📁 Search Results (200 papers auto-synced)
- 📁 Data Extraction (tables assigned by person)
- 📁 Statistical Analysis (Python notebooks)
- 📁 Manuscript Draft
- 📁 Supplementary Materials
Version Control for Manuscripts
Unlike Notion's linear page history, GetScholar offers structured versioning:
Document: "Manuscript.md"
Versions:
✅ v1.0 - Initial draft (2024-01-15)
✅ v2.0 - Revised after team review (2024-02-10)
✅ v2.1 - Submitted to Journal (2024-03-01) [Tagged]
v2.2 - Addressing reviewer comments (current) [Branch]
Features:
- Compare v2.1 vs v2.2 side-by-side
- Revert specific sections to previous version
- Export v2.1 tagged version for archive
- See exactly who wrote what in v2.2
Collaborative Screening Workflow
Systematic Review Screening Table:
| Title | Abstract | Alice | Bob | Consensus | Notes |
|-------|----------|-------|-----|-----------|-------|
| Paper 1 | [...] | ✅ Include | ✅ Include | ✅ | High quality RCT |
| Paper 2 | [...] | ❌ Exclude | ✅ Include | 🤔 | Discuss Wed meeting |
| Paper 3 | [...] | ✅ Include | ❌ Exclude | 🤔 | Borderline methods |
AI Assistance:
- Auto-summarize abstracts for quick screening
- Flag discrepancies between reviewers
- Suggest eligibility based on inclusion criteria
- Track inter-rater reliability (Cohen's kappa)
💻 In-Browser Code Execution (Game-Changer)
This is where GetScholar fundamentally differs from Notion.
Why Code Execution Matters for Researchers
Without Code Execution (Notion Workflow):
1. Extract data into Notion table
2. Export to CSV
3. Open RStudio or Jupyter
4. Import CSV
5. Write analysis code
6. Generate plots
7. Save plots as images
8. Upload images to Notion
9. Write results in Notion
10. When data changes → Repeat steps 2-9
Result: Fragmented, time-consuming, error-prone
With Code Execution (GetScholar Workflow):
1. Extract data into table (same as Notion)
2. Write analysis code in same document
3. Run code → Results appear automatically
4. Edit data → Re-run code → Updated results
5. Share document → Colleagues see code + results
6. Export → Code and results stay together
Result: Integrated, reproducible, efficient
Real Research Examples
Example 1: Meta-Analysis
import pandas as pd
import numpy as np
from scipy import stats
# Read data from GetScholar table above
studies = pd.DataFrame({
'study': ['Smith2023', 'Jones2024', 'Lee2024'],
'n': [120, 95, 150],
'effect': [0.45, 0.38, 0.52],
'se': [0.12, 0.15, 0.10]
})
# Calculate pooled effect size (inverse-variance weighting)
weights = 1 / studies['se']**2
pooled_effect = np.average(studies['effect'], weights=weights)
pooled_se = np.sqrt(1 / weights.sum())
# Test for heterogeneity
Q = ((studies['effect'] - pooled_effect)**2 * weights).sum()
p_heterogeneity = 1 - stats.chi2.cdf(Q, len(studies)-1)
print(f"Pooled effect size: {pooled_effect:.3f} (SE: {pooled_se:.3f})")
print(f"Heterogeneity Q: {Q:.2f}, p = {p_heterogeneity:.4f}")
# Forest plot
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 6))
y_pos = range(len(studies))
ax.errorbar(studies['effect'], y_pos, xerr=studies['se']*1.96, fmt='o', label='Individual studies')
ax.axvline(pooled_effect, color='red', linestyle='--', label=f'Pooled: {pooled_effect:.3f}')
ax.set_yticks(y_pos)
ax.set_yticklabels(studies['study'])
ax.set_xlabel('Effect Size')
ax.set_title('Meta-Analysis Forest Plot')
ax.legend()
plt.tight_layout()
plt.show()
Output appears immediately in document. Team members see code + results. Reviewers can verify analysis. Future you can reproduce it exactly.
Example 2: Survey Data Analysis
# Quick descriptive statistics for survey data
import pandas as pd
survey = pd.DataFrame({
'participant': range(1, 101),
'age': [22, 25, 28, ...], # 100 participants
'score': [85, 78, 92, ...]
})
print("Descriptive Statistics:")
print(survey['score'].describe())
# Check normality for t-test appropriateness
from scipy.stats import shapiro
stat, p = shapiro(survey['score'])
print(f"Shapiro-Wilk test: W={stat:.3f}, p={p:.4f}")
if p > 0.05:
print("✅ Data is normally distributed, t-test appropriate")
else:
print("⚠️ Consider non-parametric test")
Example 3: Qualitative Coding Visualization
# Visualize themes from qualitative interviews
import matplotlib.pyplot as plt
themes = {
'Barriers to treatment': 34,
'Social support': 28,
'Self-efficacy': 22,
'Healthcare access': 19,
'Stigma': 15
}
plt.figure(figsize=(10, 6))
plt.barh(list(themes.keys()), list(themes.values()))
plt.xlabel('Number of Mentions')
plt.title('Qualitative Themes from 20 Interviews')
plt.tight_layout()
plt.show()
📚 Citation Management (Researcher-First)
GetScholar Citation Features
1. One-Click Citation Insertion
Writing in document:
"Transformer architectures have revolutionized NLP [cite: Vaswani 2017]"
→ GetScholar automatically:
- Finds "Attention is All You Need" in your collection
- Inserts formatted citation
- Adds to bibliography
- Formats according to your style (APA, MLA, etc.)
2. Multiple Citation Styles
- APA 7th Edition
- MLA 9th Edition
- Chicago Author-Date and Notes-Bibliography
- Vancouver (biomedical journals)
- IEEE
- Nature
- Science
- And 100+ other journal styles
3. Export to Citation Managers
- BibTeX (for LaTeX users)
- RIS (for EndNote, Zotero, Mendeley)
- CSV (for custom processing)
4. Automatic Bibliography Generation
# References
[Auto-generated from in-text citations, formatted in selected style]
Vaswani, A., Shazeer, N., Parmar, N., ... (2017). Attention is all you need.
Advances in neural information processing systems, 30.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training
of deep bidirectional transformers for language understanding. NAACL, 1, 2.
vs Notion's Citation Workflow
Notion requires:
- Manual citation entry
- Separate Zotero/Mendeley database
- Manual style formatting
- Copy-paste into Notion
- Manually update if citation changes
- Export → Reformat for submission
GetScholar: Automatic from paper search through to export.
🔄 Complete Research Workflow Example
Let's see GetScholar in action for a complete research project:
Scenario: Graduate Student Writing Thesis Chapter
Step 1: Literature Search
Query: "machine learning climate change prediction"
GetScholar searches:
- ArXiv → 45 papers (mostly CS/ML)
- Crossref → 120 papers (multidisciplinary)
- PubMed → 12 papers (health impacts)
Filter: Last 5 years, highly cited
→ Save 30 relevant papers to collection "Thesis-Chapter2"
Step 2: AI-Assisted Reading
Select 10 most relevant papers
Ask AI (GPT-4o):
"Summarize the main ML approaches used in climate prediction across these papers.
Organize by: data sources, model types, prediction targets, accuracy."
AI generates structured summary with citations → Save to document "Lit-Review-Summary.md"
Step 3: Create Chapter Outline
# Chapter 2: Machine Learning in Climate Prediction
## 2.1 Introduction
[AI-generated draft based on literature]
## 2.2 Data Sources
### 2.2.1 Satellite Imagery [@Smith2023]
### 2.2.2 Weather Station Data [@Jones2024]
...
## 2.3 Model Architectures
### 2.3.1 Convolutional Neural Networks
Recent studies have applied CNNs to spatial climate data [@Lee2023; @Kim2024].
[Continue writing with auto-citations]
Step 4: Add Comparative Analysis
# Create table comparing model performance from papers
import pandas as pd
import matplotlib.pyplot as plt
models = pd.DataFrame({
'Model': ['CNN', 'LSTM', 'Transformer', 'Ensemble'],
'MAE': [0.45, 0.38, 0.32, 0.28],
'R²': [0.78, 0.82, 0.87, 0.91],
'Source': ['Smith2023', 'Lee2023', 'Garcia2024', 'Wang2024']
})
# Comparison plot
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
models.plot.bar(x='Model', y='MAE', ax=ax1, legend=False)
ax1.set_ylabel('Mean Absolute Error')
ax1.set_title('Prediction Error Comparison')
models.plot.bar(x='Model', y='R²', ax=ax2, legend=False)
ax2.set_ylabel('R² Score')
ax2.set_title('Explained Variance Comparison')
plt.tight_layout()
plt.show()
print("Table 2.1: Model Performance Summary")
print(models.to_markdown(index=False))
Step 5: Advisor Review
Share document with advisor
Advisor adds inline comments:
- "Expand on why Transformers outperform LSTMs here"
- "Add discussion of computational costs"
- "Citation needed for this claim"
Student addresses comments in real-time
Advisor sees changes immediately
Step 6: Version Control
Save version: "v1.0-advisor-approved"
Committee requests revisions
→ Make edits
Save version: "v1.1-committee-revisions"
Compare versions to draft response letter
Step 7: Export for Thesis
Export formats available:
- LaTeX (for university template)
- DOCX (for committee member who uses Word)
- PDF (for archive)
Bibliography auto-formatted in:
- Chicago style (humanities committee member)
- APA (psychology committee member)
Comparison: Notion AI vs GetScholar for Researchers
| Feature | Notion AI | GetScholar | |---------|-----------|------------| | Note-Taking | ✅ Excellent | ✅ Excellent | | Organization | ✅ Flexible databases | ✅ Research-optimized structure | | Collaboration | ✅ Real-time editing | ✅ Real-time + version control | | AI Assistant | ✅ Generic writing help | ✅ Multi-model academic AI | | Academic Database Search | ❌ No | ✅ ArXiv, PubMed, Crossref, DBLP, CORE | | Citation Management | ❌ Manual only | ✅ Auto-formatting, multiple styles | | Bibliography Generation | ❌ No | ✅ Automatic, style-specific | | Code Execution | ❌ Syntax highlighting only | ✅ In-browser Python (Pyodide) | | LaTeX Export | ❌ No | ✅ Yes | | BibTeX/RIS Export | ❌ No | ✅ Yes | | Version Control | ⚠️ Basic page history | ✅ Structured versioning | | Specialized for Research | ❌ General productivity | ✅ Purpose-built | | Price (Individual) | $8-10/mo | $9.99/mo (Starter) |
Migration Guide: Notion to GetScholar
For Individual Researchers
Phase 1: Setup (Week 1)
- Create GetScholar account
- Set up workspace structure (mirror your Notion organization)
- Create document templates for common tasks
Phase 2: Content Transfer (Week 2-3)
- Export Notion pages to Markdown
- Import into GetScholar documents
- Recreate important databases as tables
- Add citations from paper searches
Phase 3: Advanced Features (Week 4)
- Connect AI to your workflow (literature summaries, critiques)
- Add code blocks for data analysis
- Set up citation styles for your field
- Test LaTeX export for journal requirements
Phase 4: Full Transition (Ongoing)
- Use GetScholar for new projects
- Gradually migrate active Notion projects
- Keep Notion for non-research use (if desired)
For Research Teams
Team Lead Responsibilities:
- Create team workspace in GetScholar
- Define folder structure and templates
- Invite team members
- Set permissions (who can edit, view, admin)
- Import key shared Notion content
Team Member Onboarding:
- Accept invitation → Access shared workspace
- Review team conventions (citation style, folder structure)
- Complete training project (e.g., literature summary with code)
- Start contributing to active projects
Hybrid Approach (Optional):
- Keep Notion for general project management
- Use GetScholar for research-specific work
- Link between tools as needed
Real-World Use Cases
1. Systematic Review Team (5 Researchers)
Notion Pain Points:
- Manual paper metadata entry for 200+ studies
- Separate Zotero for citations
- Jupyter for meta-analysis
- Google Sheets for data extraction
- Overleaf for manuscript
- Email for discussion
GetScholar Solution:
- Multi-database search → Auto-populate paper collection
- Shared data extraction table with AI-generated eligibility summaries
- Inline Python for meta-analysis in same document as manuscript
- Real-time collaborative writing with version control
- One-click export to journal LaTeX template
Time Saved: ~15 hours per researcher (75 hours total)
2. PhD Candidate (Dissertation)
Notion Pain Points:
- 5 chapters in Notion
- 200+ citations manually entered
- No LaTeX export (university requires LaTeX)
- Code in separate GitHub repo
- Difficult for committee to provide feedback
GetScholar Solution:
- Each chapter as document with auto-citations
- Code and results embedded in methods/results chapters
- LaTeX export for university template
- Committee members invited as collaborators (inline comments)
- Version control for draft history
3. Research Lab (PI + 8 Members)
Notion Pain Points:
- Lab protocols in Notion → Works well ✅
- Literature reviews in Notion → Painful citation management ❌
- Data analysis notebooks → Separate from Notion ❌
GetScholar Solution:
- Keep Notion for general lab management
- Use GetScholar for research outputs
- Each project has GetScholar workspace:
- Literature collection
- Analysis notebooks (code + results)
- Manuscript drafts
- Supplementary materials
4. Interdisciplinary Project (CS + Biology)
Notion Pain Points:
- CS team wants LaTeX, Biology team wants DOCX
- Need to search both ArXiv (CS) and PubMed (Biology)
- Mixed methods: ML code + wet lab data
GetScholar Solution:
- Multi-database search covers both fields
- Code execution for CS analysis
- Tables for biology data
- Export to both LaTeX and DOCX from same source
Frequently Asked Questions
Can I import my Notion content?
Yes. Export your Notion pages as Markdown, then import into GetScholar. Tables and basic formatting transfer automatically. You'll gain additional features (citations, code execution) that Notion doesn't have.
Can GetScholar completely replace Notion?
For research work: Yes, GetScholar is superior (academic features). For general productivity: Notion might still be better for project management, CRM, etc. Best approach: Use GetScholar for research, Notion for other tasks, or fully migrate to GetScholar.
What about privacy and data security?
GetScholar offers:
- Enterprise-grade encryption (in transit and at rest)
- SOC 2 compliance
- GDPR and CCPA compliance
- No AI training on your private data
- Regular security audits
We understand research data is sensitive (pre-publication findings, patient data, proprietary results).
Does GetScholar have templates like Notion?
Yes. Pre-built templates for:
- Literature review
- Systematic review / meta-analysis
- Thesis/dissertation chapters
- Grant proposals
- Lab notebooks
- Meeting notes
- Data extraction forms
Plus, create your own custom templates.
Can I use GetScholar offline?
Documents are cached locally for offline reading and editing. Sync happens when you reconnect. Academic database search and AI chat require internet (same as Notion).
What programming languages are supported?
Currently Python via Pyodide (in-browser). This includes most scientific libraries:
- NumPy, Pandas, Matplotlib
- SciPy, Scikit-learn, Statsmodels
- Seaborn, Plotly
R and Julia support are on our roadmap.
How does GetScholar handle citations?
- Search academic databases → Papers have complete metadata
- Insert citation →
[@Smith2023]or[cite: Smith 2023] - Choose citation style → APA, MLA, Chicago, Vancouver, etc.
- Auto-generate bibliography → Formatted according to style
- Export → BibTeX, RIS, or formatted document
vs Notion: Manual entry, manual formatting, no auto-bibliography.
Can I collaborate with people who don't have GetScholar?
Yes, but with limitations:
- They can view shared documents (read-only link)
- They can download exported DOCX/PDF
- For full collaboration (editing, commenting), they need an account (free tier available)
What's included in the free plan?
Free plan includes:
- 20,000 credits for new users
- Multi-database academic search
- AI chat (all models)
- Document creation and collaboration
- Code execution
- Citation management
- All export formats
Paid plans add higher usage limits and priority support.
How much does GetScholar cost compared to Notion?
| Plan | Notion AI | GetScholar | |------|-----------|------------| | Free | Limited features | 20K credits (full features) | | Individual | $10/mo | $9.99/mo (Starter) | | Team | ~$15/user/mo | $29.99/mo (Standard, ~5 users) |
GetScholar advantage: Academic features included that Notion doesn't offer at any price.
Conclusion: The Academic Workspace You Deserve
Notion AI is an excellent general-purpose productivity tool. But researchers aren't "general-purpose" workers—they have specialized needs that generic tools can't meet.
GetScholar is what Notion would be if it were built for researchers from the ground up:
✅ Academic database integration → Find papers without leaving your workspace ✅ Multi-model AI → Choose the right AI for each task (reasoning, analysis, real-time info) ✅ Code execution → Analyze data where you write about it ✅ Citation management → Auto-formatting, bibliographies, BibTeX export ✅ LaTeX export → Meet journal requirements ✅ Version control → Track manuscript evolution through submission and revision ✅ Research-first design → Every feature built for academic workflows
Keep Notion for: Project management, personal productivity, general note-taking Switch to GetScholar for: Literature reviews, data analysis, manuscript writing, research collaboration
Or just use GetScholar for everything. It's a workspace designed for knowledge work—it just happens to excel at the most demanding knowledge work of all: research.
Ready to research without compromise?
Start free on GetScholar and experience the academic workspace you've been waiting for.
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