Introduction
- This module is part of an R programming certificate series.
- The course is beginner-friendly, no prior experience in bibliometric analysis or programming is required.
- Taking notes is mandatory and must be submitted to project managers, as it will impact individual research project placements.
About the Instructor
- Taylor Pape is the chief data officer at ThinkNeuro.
- Bachelor's in Biology with minors in Computer Science and Data Analytics from New Jersey Institute of Technology (NJIT).
- Played soccer for four years.
- Pursuing a Master's in Advanced Computer Science at the University of Essex.
- Planning to pursue a PhD in computational biology, focusing on neuroscience.
Research Experience
- Began research at NJIT's Biosmart laboratory, building biosensors for pain quantification.
- Transitioned to computational research at NJIT's STG laboratory of neuron and circuit dynamics.
- Currently running a project computationally modeling neural circuits in crustaceans.
What is Bibliometrics?
- Bibliometrics is the quantitative analysis of scientific publications.
- It involves using numbers to study published science.
- Includes analyzing:
- Number of papers published on a topic.
- Citation frequency of papers.
- Common keywords.
- Collaboration networks (researchers, institutions, countries).
- Bibliometrics is "the science of measuring science."
Real-World Example: AI in Neuroscience
- If curious about AI in neuroscience, bibliometrics can:
- Analyze papers from the last 10 years to see research growth.
- Identify popular keywords like deep learning and brain imaging.
- Determine which journals publish the most on the topic.
- Identify top authors and institutions.
- This helps researchers, students, and policymakers understand the field's current state.
- Guides literature reviews to identify research gaps and inform data-driven decisions.
Why Bibliometrics Matters
- Tracks scientific trends:
- Shows the growth or decline of research areas like AI in medicine over time.
- Identifies influential research:
- Highlights most cited articles and leading authors.
- Reveals collaboration patterns:
- Shows which researchers, institutions, or countries collaborate.
- Guides literature reviews:
- Helps find the most relevant and impactful papers.
Example: Impact of Data Sharing in Neuroscience
- Research studied the impact of sharing digital reconstructions of neuromorphology via neuromorpho.org on article citations.
- Two groups were compared:
- Sharing articles (data publicly available): ~1,600 papers.
- Unsharing articles (data not shared): ~3,000 papers.
Graph A: Yearly Citation Distribution
- Papers grouped by citations per year (logarithmic scale).
- Blue line: sharing articles.
- Orange line: unsharing articles.
- Sharing articles had a higher average citation rate.
- Sharing articles averaged 8.91 citations per year vs. 6.19 for unsharing articles (44% increase).
Graph B: Citation Increase Due to Data Reuse
- Distribution of citation increase due to data reuse.
- Average increase: ~13.8%.
- Data reuse explains less than one-third of the citation boost.
Takeaway
- Sharing data boosts visibility, with increased exposure and credibility being the primary drivers of citation advantage.
- Bibliometrics provides quantitative evidence of the impact of data sharing on research influence.
R vs. RStudio
- R: The programming language that runs the code and performs calculations (the engine).
- RStudio: An interface for writing, editing, and running R code (the dashboard).
- Both are needed to get started.
- RStudio automatically detects R.
Installation
- Install R first.
- Install RStudio second.
- Default installation paths are acceptable.
- In R installer, select the following:
- Main files
- 64-bit files
- Message translations
- Accept the defaults during R installation and name it as "r" for simplicity.
- Create a desktop shortcut and save version number in registry during installation.
Important Notes
- A Google form will be sent out every Wednesday in the Slack channel for questions to be brought to office hours; submit by the following Monday at 11:59 PM Eastern Standard Time.
- Office hours: Wednesdays, 5:30 PM - 6:30 PM Eastern Standard Time on Zoom (link in Slack).
- General questions: use the s u 25 questions channel on Slack.
- Notes must be taken and submitted.
- Citation for the research article used in the example is provided.
RStudio Environment
- RStudio is organized into four main panes:
- Console: where R runs code (like a calculator).
- Script editor: where you write and save R scripts.
- Environment: stores variables, data frames, and functions.
- Files: manages files, plots, packages, etc.
Console
- Type commands directly here and see the output right away.
Script Editor
- Write and save R scripts.
- Click run to send code to the console.
Environment
- Stores variables, data frames, and functions.
- History tab shows all previous actions.
Files
- Manages files and shows plots.
- Packages tab lists installed libraries.
Programming Basics: Assigning Variables
- Assign the number 10 to a variable called citation:
citation <- 10
print(citation) # Output: 10
- Assign a list or vector to a variable:
citation <- c(10, 20, 30)
print(citation) # Output: 10 20 30
Common Built-In Functions
length(): get the length of a vector.
length(citation) # Output: 3
sum(): sum the elements of a vector.
sum(citation) # Output: 60
mean(): find the mean of a vector.
mean(citation) # Output: 20
max(): find the maximum value in a vector.
max(citation) # Output: 30
sort(citation, decreasing = FALSE) # Output: 10 20 30 (ascending)
Creating a Data Frame
- Create a small data frame with titles, years, and citation counts:
titles <- c("paper a", "paper b", "paper c")
years <- c(2021, 2022, 2022)
citations <- c(5, 12, 8)
papers <- data.frame(title = titles, year = years, citation = citations)
print(papers)