Topic 1

The Open Science Movement

Open science is transforming how research is conducted, shared, and evaluated. This movement toward transparency and accessibility is reshaping the scientific enterprise.

What is Open Science?

Open science is an umbrella term for practices that make scientific research, data, and dissemination accessible to all levels of society—amateur or professional.

The Pillars of Open Science

Open Access

Research publications freely available to all readers, without subscription barriers

  • Gold OA: Journal makes free
  • Green OA: Self-archive preprint
  • Diamond OA: Free to read and publish

Open Data

Research data shared publicly for verification and reuse

  • FAIR principles
  • Data repositories
  • Data documentation

Open Source

Research software and code freely available for inspection and use

  • GitHub/GitLab repositories
  • Open source licenses
  • Reproducible workflows

Open Methods

Detailed protocols and materials shared for replication

  • Protocols.io
  • Detailed methods sections
  • Supplementary materials

Open Peer Review

Transparent review processes with identities and/or reviews public

  • Signed reviews
  • Published reviews
  • Post-publication review

Citizen Science

Public participation in scientific research and data collection

  • Crowdsourced data
  • Community-based research
  • Public engagement

Preregistration and Registered Reports

Preregistration

Publicly documenting your research plan before collecting data

  • Hypotheses
  • Methods
  • Analysis plan
  • Timestamped and public

Platforms: OSF, AsPredicted, ClinicalTrials.gov

Registered Reports

Peer review of study design before data collection

  • Stage 1: Review methods
  • In-principle acceptance
  • Stage 2: Review results
  • Publication guaranteed if followed

350+ journals offer this format

Why Preregister?

  • Prevents HARKing (hypothesizing after results are known)
  • Distinguishes confirmatory from exploratory analyses
  • Reduces p-hacking and selective reporting
  • Creates transparency about research decisions
  • Publication not contingent on "significant" results

FAIR Data Principles

F

Findable

  • Unique persistent identifiers (DOIs)
  • Rich metadata
  • Indexed in searchable resources
A

Accessible

  • Retrievable by identifier
  • Open, standardized protocol
  • Metadata always accessible
I

Interoperable

  • Standard formats
  • Controlled vocabularies
  • Links to other data
R

Reusable

  • Clear usage license
  • Detailed provenance
  • Community standards

Getting Started with Open Science

You don't have to do everything at once. Start with:

  • Share your papers via preprint servers (arXiv, bioRxiv, PsyArXiv, SocArXiv)
  • Preregister your next study on OSF
  • Share data and code when you publish
  • Use open-source tools when possible
  • Add open science practices incrementally
Topic 2

AI and Machine Learning in Research

Artificial intelligence is revolutionizing research across disciplines—from accelerating discovery to automating analysis. Understanding both the opportunities and challenges is essential for today's researchers.

AI Applications in Research

Literature Discovery

AI tools that find, summarize, and synthesize research literature

  • Semantic Scholar
  • Elicit
  • Consensus
  • Research Rabbit

Data Analysis

Machine learning for pattern recognition and prediction

  • Image classification
  • Natural language processing
  • Predictive modeling
  • Anomaly detection

Scientific Discovery

AI accelerating hypothesis generation and testing

  • Drug discovery
  • Protein structure prediction
  • Materials science
  • Climate modeling

Writing Assistance

AI tools for drafting, editing, and improving text

  • Grammar and style checking
  • Drafting assistance
  • Translation
  • Summarization

Code Generation

AI assistants for programming and analysis

  • GitHub Copilot
  • Code debugging
  • Analysis script generation
  • Documentation

Lab Automation

AI-driven experimental systems

  • Robotic labs
  • Automated experimentation
  • Real-time optimization
  • Self-driving labs

Using Large Language Models (LLMs) Responsibly

Appropriate Uses

  • Brainstorming and ideation
  • Editing and improving your own writing
  • Explaining complex concepts
  • Drafting outlines
  • Coding assistance
  • Translation assistance
  • Literature search starting points

Problematic Uses

  • Generating text to pass off as your own
  • Fabricating citations or references
  • Replacing critical thinking
  • Submitting AI-generated reviews
  • Undisclosed use in publications
  • Using for confidential data

Principles for Responsible AI Use

Transparency

Disclose AI use as required by journals/institutions

Verification

Always verify AI-generated content for accuracy

Accountability

You remain responsible for all content

Privacy

Never input confidential or sensitive data

Challenges and Limitations

Hallucinations

AI can generate plausible-sounding but false information, including fake citations. Always verify.

Bias

AI models reflect biases in training data. Be aware of potential systematic distortions.

Black Box

Many AI models lack interpretability. Understanding why they produce outputs can be difficult.

Intellectual Property

Questions about ownership of AI-generated content and training data rights remain unresolved.

Reproducibility

AI models can change over time, making exact reproduction of results challenging.

Skills Atrophy

Over-reliance on AI may diminish development of fundamental research skills.

AI Policies Are Evolving

Journals, funders, and institutions are rapidly developing AI policies:

  • Many require disclosure of AI use in manuscripts
  • AI cannot be listed as an author (lacks accountability)
  • Some prohibit AI in peer review
  • Policies vary—always check specific requirements

Stay current with evolving norms and guidelines.

Topic 3

Team Science and Collaboration

Research is increasingly conducted by teams rather than individuals. Understanding how to work effectively in large, diverse, and distributed teams is becoming essential.

The Rise of Team Science

90%+
of papers now have multiple authors
4x
increase in average team size since 1960
35%+
of papers involve international collaboration

Why Teams?

Complexity: Modern problems require diverse expertise

Scale: Big data and global challenges need big teams

Funding: Major grants favor team-based projects

Technology: Digital tools enable distributed work

Types of Research Teams

Lab/Research Group

PI-led group at single institution

  • 5-20 members
  • Shared space/resources
  • Mentor-trainee relationships

Multi-PI Collaboration

Multiple PIs on joint project

  • Complementary expertise
  • May span institutions
  • Shared leadership

Research Center

Institutionalized collaboration hub

  • Dedicated infrastructure
  • Long-term funding
  • Multiple projects

Consortium

Multi-institution coordination

  • Standardized protocols
  • Pooled data/samples
  • Distributed work

Many Labs/Multisite

Large-scale replication efforts

  • 50-200+ labs
  • Identical protocols
  • Crowdsourced research

Virtual/Distributed

Online-only collaboration

  • No physical proximity
  • Global membership
  • Asynchronous work

Making Teams Work

Shared Vision

Clear, compelling goals that unite the team. Everyone understands and commits to the mission.

Clear Structure

Defined roles, responsibilities, and decision-making processes. Everyone knows who does what.

Effective Communication

Regular, transparent communication. Right tools for the task. Psychological safety to speak up.

Trust and Respect

Value diverse contributions. Follow through on commitments. Handle conflicts constructively.

Project Management

Track progress systematically. Manage timelines and deliverables. Adapt when needed.

Fair Credit

Transparent authorship discussions. Recognize all contributions. Use CRediT taxonomy.

The CRediT Taxonomy

The Contributor Roles Taxonomy (CRediT) standardizes how contributions are described:

Conceptualization
Data curation
Formal analysis
Funding acquisition
Investigation
Methodology
Project administration
Resources
Software
Supervision
Validation
Visualization
Writing – original draft
Writing – review & editing

Many journals now require or encourage CRediT statements to clarify who did what.

Team Science Competencies

Develop these skills for effective team work:

  • Cross-disciplinary communication
  • Conflict resolution
  • Cultural awareness and sensitivity
  • Collaborative leadership
  • Giving and receiving feedback
  • Virtual collaboration tools
Topic 4

Reproducibility and Research Quality

The "reproducibility crisis" has prompted fundamental changes in how research is conducted and evaluated. Understanding and implementing practices that enhance research quality is now essential.

The Reproducibility Challenge

Many published findings fail to replicate when researchers attempt to reproduce them:

70%+

of researchers have failed to reproduce others' results

50%+

have failed to reproduce their own results

$28B

estimated annual cost of irreproducible preclinical research (US)

Contributing Factors

Publication Bias

Only "positive" results published

Low Statistical Power

Studies too small to detect effects reliably

P-Hacking

Analyzing data until p < .05

HARKing

Post-hoc hypotheses presented as predictions

Poor Documentation

Methods not detailed enough to replicate

Incentive Structures

Pressure to publish novel, significant findings

Definitions Matter

Reproducibility

Same data + Same analysis = Same results

Can others get your results using your data and code?

Replicability

New data + Same methods = Same conclusions

Can others get similar findings with new data?

Robustness

Same data + Different analysis = Same conclusions

Do findings hold with alternative analytical approaches?

Generalizability

Different context = Similar conclusions

Do findings apply to other populations/settings?

Improving Research Quality

Preregistration

Commit to hypotheses and analysis plans before seeing data

Power Analysis

Ensure studies are large enough to detect effects

Code Sharing

Share analysis scripts so others can verify and reuse

Data Sharing

Make data available for verification and secondary analysis

Detailed Methods

Describe procedures in enough detail to replicate

Direct Replication

Conduct and publish replications of important findings

Effect Sizes

Report effect sizes and confidence intervals, not just p-values

Multi-Site Studies

Test effects across multiple labs/sites simultaneously

Reporting Guidelines

Use established reporting guidelines to ensure completeness:

CONSORT

Randomized trials

STROBE

Observational studies

PRISMA

Systematic reviews

ARRIVE

Animal research

JARS

Psychological research

SRQR

Qualitative research

Find more at: EQUATOR Network

A Positive Framing

The "reproducibility crisis" is also a credibility revolution:

  • Science is self-correcting—we identified the problem
  • Better tools and practices are emerging
  • New generation of researchers trained in open science
  • Funders and journals requiring better practices
  • The goal is more trustworthy, cumulative knowledge
Topic 5

Emerging Paradigms and Practices

Research practices continue to evolve. This topic explores emerging trends that are shaping the future of how we create and share knowledge.

New Models of Publication

Preprints

Papers shared before peer review

  • Immediate dissemination
  • Faster feedback
  • Priority establishment
  • arXiv, bioRxiv, PsyArXiv, etc.

Overlay Journals

Peer review of preprints

  • No traditional journal infrastructure
  • Lower cost
  • Curation of preprint servers

Post-Publication Review

Review after publication

  • PubPeer, journal comment sections
  • Ongoing quality assessment
  • Community engagement

Micropublications

Small, focused contributions

  • Single figures/datasets
  • Null results
  • Methods papers
  • Rapid publication

Alternative Metrics

Beyond citations and impact factors, altmetrics capture diverse forms of research impact:

Downloads

Page views

Social media mentions

News coverage

Blog posts

Policy citations

Saves/bookmarks

Syllabus mentions

DORA: Rethinking Research Assessment

The San Francisco Declaration on Research Assessment (DORA) calls for moving beyond journal impact factors to assess research on its own merits:

  • Assess research based on its content, not publication venue
  • Consider all research outputs (data, software, etc.)
  • Use multiple indicators for hiring/promotion
  • Value quality over quantity

Emerging Research Practices

Remote & Hybrid Research

Post-pandemic shift to distributed work. Virtual data collection, online experiments, and hybrid collaborations are now standard.

Participatory Research

Engaging communities as partners, not just subjects. Co-design of research with stakeholders. Community-based participatory research.

Global South Partnerships

Moving beyond colonial research models. Equitable partnerships with researchers and institutions in lower-income countries.

Sustainable Research

Reducing environmental footprint of research. Green labs, sustainable conferences, carbon-conscious practices.

Convergence Science

Deep integration of disciplines to address complex problems. Beyond multidisciplinary to true synthesis.

Responsible Innovation

Anticipating and addressing societal implications of research. Embedding ethics throughout the research process.

Skills for the Future

Computational Literacy

Programming, data science, AI/ML basics

Data Management

FAIR data, repositories, documentation

Open Science Tools

OSF, GitHub, preregistration, preprints

Team Science

Collaboration, communication, leadership

Science Communication

Public engagement, social media, storytelling

Adaptability

Continuous learning, embracing change

The Future is Collaborative, Open, and Responsible

Research is moving toward:

  • More open: Data, code, and publications freely available
  • More collaborative: Teams across disciplines and borders
  • More reproducible: Rigorous methods, transparent practices
  • More engaged: Connected to society and stakeholders
  • More responsible: Ethical, sustainable, equitable

The researchers who thrive will embrace these changes while maintaining the core values of curiosity, rigor, and integrity.

Summary

Module 18 Key Takeaways

What You've Learned

  • Open science—open access, open data, preregistration—is becoming the norm; start incorporating these practices now
  • AI and machine learning are powerful tools but require responsible use, verification, and transparency
  • Team science requires intentional attention to communication, structure, credit, and collaboration skills
  • The reproducibility movement is improving research quality through better methods, transparency, and incentives
  • Emerging practices emphasize participation, sustainability, and responsibility alongside traditional scientific values
🎉

Congratulations!

You've completed Research for Everybody—all 18 modules covering the complete research journey from foundations to the future of science.

You now have the knowledge and tools to:

  • Design rigorous research studies
  • Collect and analyze data effectively
  • Write and publish your findings
  • Navigate research ethics
  • Build your research career
  • Contribute to the future of open, collaborative science

Now go forth and do research that matters. The world needs your curiosity, your rigor, and your discoveries.

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Practice

Future-Ready Exercises

Capstone Activities

  1. Open Science Audit: Review your current research practices against open science principles. Create a plan to incorporate 2-3 new practices in your next project.
  2. AI Tool Exploration: Try three AI research tools (e.g., Semantic Scholar, Elicit, GitHub Copilot). Evaluate their usefulness and limitations for your work.
  3. Reproducibility Check: Take a recent analysis you've done. Could someone else reproduce it from your files? Create documentation to make it reproducible.
  4. Research Vision: Write a 1-page vision statement for your research career:
    • What problems do you want to solve?
    • What impact do you want to have?
    • How will you embrace future practices?
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