Insight Readiness Level (IRL) Assessment Tool

Quantifying research maturity to drive greater impact.

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Welcome to the TerraCipher IRL Assessment Tool

The Insight Readiness Level (IRL) score is a metric designed to quantify the maturity and robustness of a code-based research project. It provides a single, easy-to-understand number (from 1 to 9) that helps researchers gauge how close their insight is to achieving real-world impact by assessing Analytical Accuracy, Code Stability, and Data Value.

This tool guides you through a self-assessment to generate a detailed IRL report with an actionable plan. If you're just starting, you can upload tabular data to receive initial R or Python code to kickstart your analysis. For existing projects, you can include your code to get a review with specific feedback and commented code examples designed to help you refactor your work and lift its IRL impact.

Explore the Insight Readiness Levels

Click on a level to understand the focus, roles, and impact opportunities at each stage.

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The IRL Assessment Tool is designed to help you quantify your research maturity, secure funding, and increase the impact of your work. By analyzing your code, data, and processes, you get a clear, actionable roadmap for improvement.

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Step 1: Define Your Project Goal

1. Select your mode of interaction:

2. What is your primary role?

3. Briefly describe your code-based insight (what it is, what it does, and the problem it solves):

4. What is the primary coding language used?

5. Upload your code files for structural analysis (optional, but highly recommended for specific feedback):

Accepts multiple files (e.g., .py, .R, .jl). The content will be analyzed for modularity and stability.

3. Upload your data file (CSV, TXT, or similar tabular format):

4. Select the target programming language:

5. Select the desired analysis type:

What is your primary impact goal for your code-based research insights?

Step 2 of 4: Analytical Accuracy (Insight Quality)

This measures how well the insight or model reduces uncertainty and provides reliable, predictable results.

Q6. How has the performance of the analysis/model been validated?

It has not been formally validated. Its performance was checked on the same data it was developed on. It was tested on a separate 'hold-out' dataset not used for development. It has been systematically tested against multiple, different subsets of the data.

Q7. Has the model's error or uncertainty been formally measured?

No, the error rate has not been measured. Yes, there is a general sense of when it's right or wrong. Yes, standard performance metrics have been calculated (e.g., Accuracy, R-squared). Yes, the range of potential error for its predictions has been formally estimated.

Q8. How does the analysis handle new or unexpected data?

The model often fails or gives strange results. The model works for typical data, but edge cases have not been tested. The model includes basic error handling for common issues (e.g., missing values). The model has been tested for robustness and handles unexpected inputs gracefully.

Step 3 of 4: Code Stability (Know-How Quality)

This measures the structural organization of the code and its resistance to failure, reflecting the certainty of the implemented "know-how."

Q9. How is the code managed and versioned?

No version control; code exists as files on a local machine. Code is in version control (e.g., Git) but with an inconsistent history. Code is managed in version control with a clear and consistent history of changes. Code is managed in a shared repository (e.g., GitHub) with a formal review process.

Q10. How is the code tested?

Testing is done manually by running the script and observing the output. Separate, informal scripts are used to validate parts of the code. Automated tests exist for the most important parts of the code. A full suite of tests is run automatically whenever the code is updated.

Q11. How accessible is the code for others to use?

The code can only be run by the author on their specific machine. The code can be shared, but requires manual setup of the environment and dependencies. The code is packaged for easier use (e.g., with a requirements.txt, Docker container). The code is deployed as an accessible service or installable package.

Step 4 of 4: Data Generalizability (Scope & Reach)

This measures the degree to which the insight is built on standardized, reproducible data, determining its potential reach and interoperability.

Q12. How relevant is the data to the real-world problem?

Data is synthetic or generated for a specific experiment. Data is from a real-world source but is a small, specific sample. Data is a representative sample of the target real-world environment. Data is comprehensive, covering a wide range of real-world scenarios.

Q13. How standardized is the data's format and schema?

Data has no formal schema; it's a custom, ad-hoc format. Data has a well-documented internal structure but is not a public standard. Data structure follows a widely accepted public standard for the field. Data is structured as part of a formal, governed data model (e.g., an ontology).

Q14. How are data updates and model reproducibility managed?

There is no process; new data overwrites old data. New data is manually added, but old datasets are not easily accessible. Both the data and the code are version-controlled, allowing for reproducible results. An automated system manages data updates and can re-run analyses as needed.

Insight Readiness Level (IRL) Report

Overall Readiness Score

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Current Readiness Level

N/A

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Analytical Accuracy

N/A

Code Stability

N/A

Data Generalizability

N/A

Overall Assessment

Highest Priority Action

Specific Factor Improvements

Goal Alignment

Suggested Code Improvements (Files to Download)

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Data, Code, and Privacy

No data or code you upload is stored on our servers; it is used only for the duration of your session to provide assessment and feedback. However, if you are using sensitive data or code containing intellectual property, please ensure you have all necessary approvals in place before uploading. The IRL assessment tool can still provide useful feedback without requiring any code or data to be uploaded.