Science | Ville Langén

How to Publish Analyses on Zenodo

Fri, 11 Jul 2025 00:00:00 +0000

How to Build a Zero-Cost Academic Homepage Using Open-Source Tools

Thu, 10 Jul 2025 00:00:00 +0000

Writing GitBooks Tutorial

Thu, 10 Jul 2025 00:00:00 +0000

GitHub Without Tears: From Zero to Push in Minutes

Tue, 07 Jan 2025 00:00:00 +0000

How to Run Macros in Word

Thu, 19 Dec 2024 00:00:00 +0000

Introduction to Echocardiography

Tue, 17 Dec 2024 00:00:00 +0000

AffiliWizard: Author-Affiliation Formatting Tool

Sun, 01 Dec 2024 00:00:00 +0000

AffiliWizard v1.0.0 is a lightweight GUI tool created by Ville Langén, designed to solve a common problem in academic writing: formatting author-affiliation lists with proper numbering and structure.

The Problem

When writing academic papers with multiple authors from different institutions, creating a properly formatted author-affiliation list can be tedious and error-prone. You often need to:

Number affiliations sequentially
Match authors to their respective institutions
Format everything consistently
Export to various formats for different journals

The Solution

AffiliWizard streamlines this process with a simple, intuitive interface that transforms your Excel data into professionally formatted author lists.

Key Features

Simple Copy-Paste Interface: Copy data directly from Excel spreadsheets
Automatic Numbering: Intelligently assigns superscript numbers to affiliations
Multiple Export Formats: Save as .docx, .html, or plain text
Cross-Platform: Available for macOS, Windows, and web browsers
No Installation Required: Use the online version instantly

How to Use

Prepare Your Data: Organize your author-affiliation data in Excel with columns for:
- Author Name (including degrees/titles)
- Affiliation 1
- Affiliation 2 (optional)
- Additional affiliations as needed
Copy and Paste: Select and copy your data from Excel, then paste it into AffiliWizard’s text box
Export: Choose your preferred format (.docx, HTML, or Plain Text) and save

Input Format Example

Author Name | Affiliation 1 | Affiliation 2
John Doe, MD | Bogus Institute, CA, USA | Example Institute, TS, USA
Jane Doe, MD | Sample University, TX, USA |

Sample Output (HTML Format)

Authors: John Doe, MD^1,2, Jane Doe, MD³

Affiliations:

Bogus Institute, CA, USA
Example Institute, TS, USA
Sample University, TX, USA

Download Options

Desktop Applications:

macOS Apple Silicon: AffiliWizard-macOS-arm64.dmg
Windows: AffiliWizard.exe

Online Version: For immediate use without downloads: https://affiliwizard.netlify.app/

For Linux Users

Linux users can build AffiliWizard from source:

Install Python 3 (recommended 3.8+)

Set up virtual environment:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Run directly:
```
python AffiliWizard.py
```

Technical Details

Built with: Python and tkinter for the GUI
License: MIT License - free to use and modify
Dependencies: Minimal Python libraries for maximum compatibility
File Size: Lightweight application under 10MB

Use Cases

Perfect for:

Academic researchers preparing manuscripts
Journal editors formatting submission requirements
Conference organizers creating program listings
Grant applications with multiple institutional collaborators

Development & Support

AffiliWizard is actively maintained and open source. Contributions, bug reports, and feature requests are welcome on the GitHub repository.

Questions or issues? Feel free to reach out through the project’s GitHub issues page.

Why AffiliWizard?

In academic writing, the little details matter. AffiliWizard ensures your author-affiliation formatting is:

Consistent across all your publications
Professional and journal-ready
Error-free with automatic numbering
Time-saving with streamlined workflow

Transform your author-affiliation formatting from a tedious task into a one-click solution with AffiliWizard!

atctools: R Package for ATC Code Matching and Validation

Sun, 01 Dec 2024 00:00:00 +0000

atctools is an R package created by Ville Langén that facilitates Anatomical Therapeutic Chemical (ATC) matching and ensures the integrity of ATC data in pharmaceutical datasets. This specialized tool is essential for pharmaceutical research, drug safety studies, and healthcare data analysis.

Overview

The atctools package provides comprehensive tools for working with ATC codes, which are used worldwide to classify drugs according to the organ or system on which they act and their therapeutic, pharmacological, and chemical properties. The package bridges the gap between drug names and their standardized ATC classifications.

Key Features

🔍 ATC Matching Functions

Advanced drug name matching using sophisticated algorithms including Levenshtein distance for fuzzy matching, ensuring accurate identification even with spelling variations or formatting differences.

🏷️ ATC Code Matching

Direct ATC-to-drug name matching for reverse lookups and data verification workflows.

✅ ATC Data Integrity Checks

Comprehensive validation tools to verify the structure and validity of ATC codes using authoritative reference lists.

Core Functions

drug_match()

Matches drug names to ATC codes without stripping trailing characters like “mg”, “g”, etc.

Usage:

library(atctools)

output_data <- drug_match(example_drug_data, "drug_name", example_reference_data)
output_data

Key Features:

Preserves original drug name formatting
Uses fuzzy matching algorithms for robust identification
Includes confidence scoring through drug_name_flag

drug_match_strip()

Matches drug names to ATC codes with trailing characters stripped for cleaner matching.

Usage:

output_data_stripped <- drug_match_strip(example_drug_data_with_mg, "drug_name", example_reference_data)
output_data_stripped

Perfect for:

Drug names with dosage information (e.g., “paracetamol 500mg”)
Standardizing drug nomenclature
Handling varied formatting in clinical datasets

atc2drug()

Matches ATC codes back to drug names directly with no fuzzy matching for precise lookups.

Usage:

output_data_atc2drug <- atc2drug(example_atc_data, "ATC_code", example_reference_data)
output_data_atc2drug

Use Cases:

Reverse lookups from ATC to drug names
Data verification and validation
Creating drug dictionaries from ATC classifications

🔍 ATC Validation Functions

validate_atc()

Checks if ATC codes follow the correct structural format (length, character pattern).

Usage:

validate_output <- validate_atc(example_atc_data_for_validation, "atc_1")
validate_output

Validation Criteria:

Proper ATC code length and structure
Character pattern verification
Format compliance with WHO ATC standards

validate_atc_by_reference()

Checks if ATC codes exist in a given reference list (e.g., WHO ATC index or validated datasets).

Usage:

validate_by_ref_output <- validate_atc_by_reference(example_atc_data_for_validation, "atc_2", example_reference_data)
validate_by_ref_output

Combined Validation Workflow

Comprehensive validation using both format and reference checking:

output_combined <- example_atc_data_for_validation %>%
 validate_atc("atc_1") %>%
 validate_atc_by_reference("atc_2", example_reference_data)

output_combined

Output Flags:

atc_1_invalid: Flag for invalid format (1 = invalid, 0 = valid)
atc_2_invalid: Flag for “not found in reference list” (1 = not found, 0 = found)

Understanding drug_name_flag

The drug_name_flag column provides matching confidence in outputs from drug_match() and drug_match_strip():

0: First 3 characters of drug name match the first 3 of matched ATC code ✅
1: Mismatch detected between drug name and ATC prefix ⚠️

This flag helps identify potential matching errors and enables quality control in large datasets.

Example Datasets

atctools includes comprehensive example datasets for testing and learning:

example_drug_data

Drug names without dosage information for clean matching scenarios.

example_drug_data_with_mg

Drug names with trailing dosage units (e.g., “paracetamol 500mg”) for strip matching.

example_atc_data

ATC codes for testing atc2drug() – includes valid, invalid, and NA values.

example_reference_data

Reference dataset for drug name ↔ ATC code matching workflows.

example_atc_data_for_validation ✨ New!

Contains ATC codes across multiple columns for testing both structural and reference-based validation.

Installation

Install the development version directly from GitHub:

# install.packages("devtools")
devtools::install_github("vljlangen/atctools")

Use Cases

Perfect for:

Pharmaceutical researchers standardizing drug classifications
Clinical data scientists validating medication datasets
Epidemiologists conducting drug utilization studies
Healthcare analysts ensuring data quality in EHR systems
Regulatory affairs professionals working with drug databases
Pharmacovigilance teams processing adverse event data

Key Updates & Features

✅ New Validation Functions

validate_atc() checks format validity against WHO standards
validate_atc_by_reference() verifies presence in reference datasets

🧪 Enhanced Testing

New example_atc_data_for_validation dataset
Comprehensive validation workflow examples

🔁 Improved Documentation

Updated function examples with real-world scenarios
Clear validation workflow documentation

🧹 Performance Improvements

Enhanced atc2drug() function with better column detection
Optimized matching algorithms for large datasets

Why atctools?

Working with ATC codes manually is error-prone and time-consuming. atctools solves this by:

Automating matching between drug names and ATC codes
Ensuring data quality through comprehensive validation
Handling edge cases intelligently (dosage units, spelling variations)
Providing confidence metrics for quality control
Supporting research workflows with robust, tested functions

Technical Details

Built for: R statistical computing environment
Specialty: Pharmaceutical and healthcare data analysis
Dependencies: Minimal dependencies for maximum compatibility
License: Open source (check repository for specific license)
Maintained: Actively developed with regular updates

Development & Support

atctools is open source and welcomes contributions from the pharmaceutical data science community. Bug reports, feature requests, and pull requests are encouraged on the GitHub repository.

Questions or issues? Feel free to reach out through the project’s GitHub issues page.

Transform your pharmaceutical data workflows from manual, error-prone processes into automated, validated pipelines with atctools!

Divergent Git Branches - From Problem to Solution

Sun, 01 Dec 2024 00:00:00 +0000

tablestylizer: R Package for Styling and Formatting Tables

Sun, 01 Dec 2024 00:00:00 +0000

tablestylizer is an R package created by Ville Langén that provides a comprehensive set of functions for styling and formatting tables, making them more suitable for publication in academic papers and reports.

Overview

The tablestylizer package enhances the visual appeal and clarity of tables generated in R, transforming raw output into polished, publication-ready formats. It’s particularly useful for researchers and data analysts who need to present statistical results in a professional manner.

Key Features

stylize_tableone() Function

Processes a tableone object to a lean, publication-ready format:

Converts rownames to column names for better structure
Transfers percentage symbols appropriately
Removes unnecessary symbols that clutter the output
Automatic p-value processing using the stylize_p() function when p-value columns are present

stylize_p() Function

Specialized p-value formatting for improved readability:

Rounds to 2 decimal places whenever feasible
Handles small values: Values less than 0.001 are expressed as "<0.001"
Smart 0.05 handling: Avoids outputting exactly "0.05" since this may not hold practical meaning when alpha = 0.05
Preserves precision: If rounding would result in "0.050", the original value is retained
Exact 0.050 preservation: Original values of exactly 0.050 are kept without rounding

Usage Examples:

stylize_p(0.05795) # Returns: "0.06"
stylize_p(0.0499) # Returns: "0.0499"
stylize_p(0.002) # Returns: "0.002"
stylize_p(0.0005) # Returns: "<0.001"

stylize1() Function

Stylizes numeric values with custom rules, targeting 1 decimal place:

Rounds to 1 decimal place unless they round to a smaller digit
Threshold parameter: Optional specification for displaying values as "<threshold"
Scientific notation: Very large or small values are expressed as powers of 10
Intelligent rounding: Preserves precision when rounding would lose significant information

Usage Examples:

stylize1(-0.05) # Returns: "-0.1"
stylize1(0.002) # Returns: "0.002"
stylize1(0.0005, 0.001) # Returns: "<0.001"

stylize2() Function

Similar to stylize1(), but targeting 2 decimal places:

Rounds to 2 decimal places with intelligent precision handling
Scientific notation: Very large or small values expressed as powers of 10
Threshold parameter: Optional for "<threshold" display
Consistent formatting: Maintains uniform decimal place presentation

Usage Examples:

stylize2(-0.05) # Returns: "-0.10"
stylize2(0.002) # Returns: "0.002"
stylize2(0.0005, 0.001) # Returns: "<0.001"

Installation

Install the development version directly from GitHub:

# Install the development version from GitHub
devtools::install_github("vljlangen/tablestylizer")

Use Cases

Perfect for:

Academic researchers preparing statistical tables for publication
Data analysts creating professional reports
Biostatisticians formatting clinical trial results
Journal submissions requiring specific formatting standards
Presentations where clear, readable tables are essential

Why tablestylizer?

Statistical tables often contain formatting inconsistencies that make them difficult to read and unprofessional in appearance. tablestylizer solves this by:

Standardizing number formatting across all table elements
Improving readability through consistent decimal places and scientific notation
Handling edge cases intelligently (like the 0.05 p-value scenario)
Saving time with automated formatting rules
Ensuring publication standards are met consistently

Technical Details

Built for: R statistical computing environment
Dependencies: Minimal dependencies for maximum compatibility
License: Open source (check repository for specific license)
Maintained: Actively developed and supported

Development & Support

tablestylizer is open source and welcomes contributions. Bug reports, feature requests, and pull requests are encouraged on the GitHub repository.

Questions or issues? Feel free to reach out through the project’s GitHub issues page.

Transform your R table formatting from tedious manual work into a streamlined, professional process with tablestylizer!

Step-by-Step Guide with Example Videos for Creating R Packages

Sun, 16 Jun 2024 00:00:00 +0000

Beginner's Guide to R Programming

Tue, 05 Mar 2024 00:00:00 +0000

Easily Import Custom Citation Styles into Zotero

Tue, 05 Mar 2024 00:00:00 +0000

How to add a (downloaded) citation style to Zotero

Click Zotero —> Preferences

Select the Cite tab and click on the + sign

Navigate to the csl file, select it and click Open

If you succeeded to add the new style, it is displayed in the list of the styles in the Style Manager in Zotero

Many thanks to Anton C. for sharing the style that is showcased in the above image.

Easy Flowchart Creation with Free yEd Software

Tue, 05 Mar 2024 00:00:00 +0000

The free software yEd allows for the creation of flowcharts very quickly. There is no need to manually stretch boxes or similar actions - the program does this automatically upon request. Below are quick instructions for using the program.

(Click to this external link for a Finnish version of this tutorial.)

Creating and Writing in a Box (or Other Element)

Drag the element from the right onto the white canvas
Activate the element by clicking on it with the mouse
Press enter
Write text
If you want to change lines, press Shift+Enter
Write more text
Finally, press enter

Panning and Zooming

Command + mouse scroll zooms in and out
Panning is possible by holding down the right mouse button

Creating Arrows Between Elements

Click on one element, but do not release the mouse button
Drag the mouse to the location of the next element
Release the mouse button

Automatic Width Adjustment Based on Text for Elements

There is no need to manually stretch elements to fit text neatly and ensure all elements are the same size - the program does this automatically upon request.

Select the elements you want to be the same size
From the top menu, select Tools -> Fit Node to Label
If you want all elements to be the same size, select Adapt to Maximum Node
Do not select Ignore Width and Ignore Height
Press Ok
Note: If you want this process to apply only to selected items, make sure to choose “Act on Selection Only”

Changing the Properties of an Element (or Multiple Elements)

Activate one or more elements
Press Command-I or alternatively, right-click and select Properties
Modify the properties as desired

Creating Beautiful Labels for Arrows

Click on the arrow
Press enter
Write text for the arrow label and press enter
Select the arrow label
- If you want all arrow labels, press Command-A at this stage
Right-click on the label and select Properties
Change the background color of the label to white and the Model to Centered
Press Ok

Saving the Flowchart Created with yEd

Remember to save your flowchart properly — not just export it as a .jpeg file or similar! You may need to modify the flowchart later and export it again, perhaps in a different format like a .png file.

From the top menu, select File -> Save As…
Give your yEd flowchart project file a name
Click Save

Exporting the Flowchart Created with yEd

While yEd saves projects in the graphml format, which is great for editing, some scenarios may require exporting the flowchart to bitmap formats like .tiff or .png, especially for submission to scientific journals.

Below are instructions for exporting your flowchart in .png format:

From the top menu, select File -> Export…
Choose the format from the dropdown menu, here we select “PNG Format (*.png)”
Give your exported file a suitable name
Click Save
Click on the Size field and select Custom Width
For Custom Width, input at least 1000 pixels to ensure a high-quality image for the journal
Make sure the Scaling Factor is set to exactly 1.0 to prevent any distortion in the image
Click OK

Verifying Image Quality

After exporting your flowchart, it’s crucial to ensure that the image maintains its high quality. Sometimes, what you see in the software might differ from the final exported result. Here’s a quick step to verify the image quality:

Always double-click on the exported image file on your hard drive to open and inspect it
This ensures that the image appears as intended and in high resolution

By taking this extra step, you can catch any potential issues with image clarity or resolution before sharing or submitting it.

Lightning Fast GitLab Guide for UTU Users (and the Rest of Us)

Tue, 05 Mar 2024 00:00:00 +0000

Seamless Transition: Importing Mendeley References to Zotero Made Easy

Tue, 05 Mar 2024 00:00:00 +0000

Introduction

Given the ongoing developments with the newer ‘Mendeley Reference Manager’, some authors might feel the need to migrate from Mendeley to Zotero. The future will tell, whether “Mendeley Reference Manager” becomes as handy as the former version “Mendeley Desktop”, but for now, you may want to consider the migration to Zotero.

Installation of Zotero (unless you already have it)

Before continuing with tutorial, you’ll have to download and install Zotero on your computer from:

https://www.zotero.org/

There are many installation tutorials on the internet, for instance the following by Georgia State University Library:

https://www.youtube.com/watch?v=pueO_XYiS1w

Signing up in Zotero (unless you already have done it)

The following tutorial video by mr. Haq also explains how to sign up for Zotero, which you will need to do before continuing to the migration tutorial I showcase below.

https://www.youtube.com/watch?v=ERU2-unnCOQ

Adding the Zotero plug-in for Word (unless you already have the plug-in installed, which you may verify as follows)

Click Zotero → Preferences

Click Cite → Word Processors → and if needed, click to the icon that installs the MS Word Add-in (in my case depicted in the image below, it has already been installed and there is no need to do it again)

Migration from Mendeley to Zotero

After having installed Zotero and signed up in Zotero, you can migrate your references from Mendeley to Zotero. This can be very easily done by following these steps:

Sync your Mendeley references to Mendeley servers

Be sure to sync your Mendeley library first. If you are on the older software “Mendeley Desktop”, click the “Sync” button in it. If you are using the newer “Mendeley Reference Manager”, your references have already been synced and you don’t have to care about this step.

Back to Zotero, click File → Import

Select “Mendeley…” and click “Continue"

Type in your Mendeley credentials and click “Continue”.

The tick on “Place imported…” may be left on. Click “Continue”.

Ready. I’ve been using Mendeley for years, and the importation of my 1119 items took around 20 minutes.

Usage of Zotero as compared with Mendeley

The usage of Zotero is very intuitive and resembles Mendeley so much that I won’t cover the usage nor the minor differences between these softwares here. You may find excellent and fast tutorials online on sites like YouTube.

I’ll conclude by reminding that if you have an old Word document that has citations made with the old “Mendeley Desktop” software, the citations can readily be read by the Zotero plug-in in Word, which is a wonderful feature. There is no need to specifically “import” those old MS Word files for use with Zotero.

Unlocking IPCW Analysis in R: A Step-by-Step Tutorial (in Finnish)

Tue, 05 Mar 2024 00:00:00 +0000

VPN Article Search and Reference Management Tutorial for University of Turku Researchers (in Finnish)

Tue, 05 Mar 2024 00:00:00 +0000