Research That Sticks: The Strategic Framework for Turning Technical and Academic Papers into Lasting Knowledge

While I typically focus on software, Data Science (with ML), and product engineering, today I'm exploring a challenge that every knowledge worker faces: effectively reading and retaining insights from papers, articles, and books. As a Master's student, someone interested in reading technical papers, or even a seasoned researcher, you've likely faced the difficult task of reading a fully loaded paper like a treasure hunt filled with hidden gems. Having tried different methods - reading the entire paper in one sitting, or reading each section with a 5-minute break interval - I encountered a useful approach: The Three-Pass Approach, with a little twist: adding Tiago Forte's Building a Second Brain method (using PARA). This article explores how this new approach turned my challenge into a structured, enjoyable journey.
Using the real-world example of the research paper "Study on digital twin computing for predicting general road traffic volume" by Mikiko Tanaka, I’ll show how to uncover practical insights while keeping notes organized and actionable.

The Problem of Navigating Technical Papers

Research papers contain complex ideas, data, and, sometimes, “jargon” or difficult terms, making them challenging to read without a plan. For someone like me, studying topics like digital twins, the challenge is twofold: understanding the science and applying it to real-life issues such as declining public transport and traffic congestion. Without a clear method, it’s easy to get lost in details or miss the big picture, especially when budgets limit data collection, as seen in regional cities. This is where a structured approach becomes a lifesaver.

The Three-Pass Approach with Tiago Forte’s Twist

The three-pass approach, paired with Tiago Forte’s note-taking strategies, helps me break down papers into manageable, understandable steps, turning raw information into a personal knowledge vault. Here’s how I apply it, step by step, with examples from the research paper. Click here to find the final version of the note.

Pass 1: Look at the Big Picture - What is the Story about?

The first pass involves conducting a high-level survey to understand the paper’s structure and main concepts. It’s like flipping through a picture book. Firstly, begin by examining the title: “Study on digital twin computing for predicting general road traffic volume”. Next, read the abstract, which hints at using digital twins to ease congestion and optimize routes. Review cool figures such as the LPWA diagram network (Fig. 1) to get a feel for the author’s setup.

Following Tiago Forte’s approach, create a note in your preferred knowledge management system (e.g., Notion, Apple Notes). Give the note a title, for example, “Tanaka’s Digital Twin Paper”, in the “Resources” folder. In a quick sentence, I write the main idea and keywords obtained from the abstract. This sentence is highlighted. This step is like figuring out if the story is about a T-Rex or a spaceship!

Summary:

• Check the Title and read the Abstract. Glance at the sub-headings, pictures (charts, headings), to get a feel of what is inside the paper.

• Ask yourself: What are the scientists/authors (will use interchangeably) trying to find out (discover, achieve)? This helps to figure out the main idea behind the story.

• Note-taking guide:

  • Write a quick sentence (and highlight it) about the paper’s main idea from the abstract.

  • Take screenshots of cool pictures, figures, charts, and note what they show (headings).

Figure 1 - A diagram illustrating my notes for Pass 1

Pass 2: Find the Key Parts - What is the main adventure?

The second pass focuses on retrieving essential information from the introduction, results, and discussion/conclusion sections. In this pass, dive into the paper like you are following the main path of a story. Focus or look for the big ideas, for example, “what did they achieve or find out?”. Add a "Key Findings" section to your notes with concise summaries, e.g., “By inputting bus position and speed into the traffic learning model, it outputs a congestion map image that helps figure out travel routes and times“. Emphasize the most critical finding through formatting to maintain focus during future reviews.

Note: In this pass, I try to identify and write the top three ideas for each section, picking the top one (1) from each section, resulting in my top-three key findings. From these three, I emphasize the most critical finding.

Summary:

• Focus on:

  • Introduction: shows why the authors care about the topic and what they want to learn (i.e., why the study matters).

  • Results: they share what they found/discovered.

  • Discussion & Conclusion: They explain what their findings mean and what they did not do.

    To get the big ideas, e.g., what did they achieve or discover?

• Do not worry about - Methods, Mathematics, etc, for now.

• Note-taking guide:

  • Add a sub-heading/section like “Key Findings”

  • Write 2-3 sentences summarizing the introduction (why the study matters), results (what they found), and discussions (what it means).

  • highlight/bold the most critical sentence or phrase (just one) - indicates the main finding. This is the progressive summarization.

Figure 2 - A diagram illustrating how I take notes for Pass 2

Pass 3: Dig Deeper - Refine and connect the Treasure

Finally, if you want to know more, you can further explore the skipped or tricky sections to gain a comprehensive understanding. Read the methods section to see how they did their experiment. Look at the charts or data closely to understand their proof. If some words are confusing, think of them like puzzle pieces—you can guess their meaning or ask someone (or me!) to explain. This step is like finding hidden gems in the story that make it even more awesome! Here, a “Deep Dive” or “Technical Analysis“ section is added, containing the detailed summaries identified.

Summary:

• Focus:

  • If you want to know more, explore the parts you skipped, e.g., methods, data, etc.

  • Read the methods to see how they carried out their experiments (if it can be reproduced).

  • Look closer at charts or data to deeply understand their proof.

  • Introduction: shows why the authors care about the topic and what they want to learn (i.e., why the study matters).

• Note-taking guide:

  • Add a sub-heading/section like “Deep Dive”, and summarize key details from skipped and tricky sections, e.g., methods, data, literature reviews, etc, in simple, clear, concise sentences.

  • Review earlier highlights and only bold the must-know points again.

  • Add important tags or links to related topics, e.g., further readings like references

  • If the paper sparks an idea, note the idea in the Projects folder.

  • If this note is no longer useful, move to the Archive section; otherwise, leave it in Resources.

Figure 3 - A diagram illustrating how I take notes for Pass 3

Extra Tips:

1. Write short, clear sentences - if a word is hard, explain in simpler terms in brackets, e.g., digital twin (a copy of the real world)

2. Use/Have a digital copy of your notes for easy backup

3. Check out the PARA method for easy organization.

4. Review and Reuse - try looking at your highlighted notes weekly.

5. Do not overdo it - only take useful notes, skip sections that are not useful (LLMs can help refine your notes🚀)

Case Study: Analyzing and Applying the Passes to Tanaka’s Research Paper

The Tanaka research addresses critical issues facing rural Japan: declining public transportation and increasing vehicle congestion due to population decline. The final version of the note - Click here. Applying the three-pass methodology:

Title: Study on Digital Twin Computing for Predicting General Road Traffic Volume

Main Idea (from Abstract) - This paper is about a study on digital twins, focusing on transportation and traffic control, proposing a method to build a digital twin by collecting and inputting small amounts of data, and supplementing the data with machine learning.

Keywords - Digital Twin, Transportation, Traffic Flow Control

Figures/Pictures:

Figure 1 - Diagram of the proposed transportation digital twin configuration built on an LPWA network

Key Findings:

1. Regional cities face challenges like limited budgets and traffic congestion, requiring a new digital twin system that uses minimal data from bus sensors and existing sources like Google Maps.

2. Main Finding: By inputting bus position and speed into the traffic learning model, it outputs a congestion map image that helps identify travel routes and times.

3. The system is designed to be accurate and real-time at a low cost, better than older systems like Google Maps, supporting new transportation solutions such as shared buses in rural areas.

Deep Dive:

• Figures:

  • Figure 1 is the architecture they are building.

  • Figure 2: The function diagram - how different components of the system interact with each other.

  • Figure 3: Only the bus is sending data to LoRa, instead of getting data from all moving vehicles.

  • Figure 4: A conceptual diagram of the digital twin system.

• Main roles of what they are trying to build:

  • Real-time road transport monitoring and management to easily identify congestion, accidents, and allow for rapid response.

  • Simulation and forecasting to predict future events.

  • Optimize traffic flows.

  • Urban planning and infrastructure development - simulating different scenarios in the design of new infrastructure and urban planning.

  • Improve safety in dangerous locations and prevent accidents early enough.

  • The government can plan transport policies and simulate their impacts in advance, leading to the creation of more effective policies.

• Methods/Detailed Structure (from Figure 4):

  • Physical layer: contains sensors (public transport GPS), weather data, past traffic flow data from Google Maps, and current event data

  • Database layer: Past physical layer data, analysis model, analysis result data

  • Cyberspace layer: An analysis model is constructed on a virtual map using data stored in the database as input

  • Analysis and optimization layer: A machine learning-based transportation model generates congestion maps from public transit GPS data to calculate required travel times.

  • Visualization layer: public visualization - web app, published as an iOS or Android app.

• Data sources:

  • GPS Information from Public Transportation (e.g., Buses): collected from sensors installed on buses to track their location and speed in real time.

  • Congestion Information from Google Maps: Existing traffic congestion data from Google Maps is used to supplement the digital twin system.

  • Weather Data: to simulate how weather affects traffic.

  • Event Information: For example, festivals or gatherings are used to account for traffic changes caused by special occasions.

In closing, this approach has fundamentally transformed my approach to research and knowledge management. Rather than simply reading papers and hoping to remember key insights, I now have a systematic process that turns every research paper into a valuable asset in my knowledge repository. The combination of structured analysis with intentional note-taking has made complex research accessible and immediately applicable to real-world challenges. I highly recommend this integrated approach to anyone struggling to extract meaningful value from academic papers, whether you're a researcher, student, or professional seeking evidence-based solutions. I'm grateful to both the original creators of the three-pass method and Tiago Forte for developing frameworks that make working with knowledge more effective. Moving forward, I plan to continue refining this system and explore how it can be adapted for different types of technical literature and research domains.

🧠📚🔍 #ResearchMethods #ResearchSkills #ThreePassApproach #TiagoForte #KnowledgeManagement #BuildingASecondBrain #AcademicResearch #DigitalTwin #ProductivitySystems