m2datakit 0.1.103

Data management and scoring tools for the M2C2 project

M2DataKit

Universal loading, assurance, scoring, and export tools for M2C2 cognitive assessment data.

🚀 A Python toolkit for working with M2C2kit data from multiple sources including MongoDB, MetricWire, Qualtrics, UAS, CSV bundles, and the M2C2 API.

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Installation

pip install m2datakit

or

pip3 install m2datakit

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Documentation

📘 https://m2c2-project.github.io/datakit/site/index.html

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Supported Data Sources

M2DataKit supports importing and harmonizing data from multiple platforms and export formats.

Source Type	Loader	Description
MongoDB	MongoDBImporter	Flat or nested MongoDB JSON exports
MetricWire	MetricWireImporter	MetricWire JSON exports
Qualtrics	QualtricsImporter	Qualtrics CSV exports
UAS	UASImporter	Understanding America Study exports
MultiCSV	MultiCSVImporter	Multiple CSV files grouped by activity
M2C2 API	M2C2KitAPIImporter	Direct API access with authentication
M2C2 Static	M2C2StaticImporter	Static flat CSV exports

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L.A.S.S.I.E. Pipeline

Step	Method	Purpose
L	load()	Load raw data
A	assure()	Validate required columns
S	score()	Apply scoring rules
S	summarize()	Aggregate participant/session metrics
I	inspect()	Quality checks and visualization
E	export()	Save tidy outputs and metadata

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Example Usage

MetricWire

import m2datakit as m2

mw = (
    m2.core.pipeline.LASSIE()
    .load(
        source_name="metricwire",
        source_path="data/metricwire/unzipped/*/*/*.json"
    )
)

mw.assure(
    required_columns=m2.core.config.settings.STANDARD_GROUPING_FOR_AGGREGATION_METRICWIRE
)

mw.score()

mw.inspect()

mw.export(
    file_basename="metricwire",
    directory="tidy/metricwire_scored"
)

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MongoDB

mdb = (
    m2.core.pipeline.LASSIE()
    .load(
        source_name="mongodb",
        source_path="data/export.json"
    )
)

mdb.assure(
    required_columns=m2.core.config.settings.STANDARD_GROUPING_FOR_AGGREGATION
)

mdb.score()

mdb.export(
    file_basename="mongodb_export",
    directory="tidy/mongodb_scored"
)

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M2C2 API

api = (
    m2.core.pipeline.LASSIE()
    .load(
        source_name="m2c2kit-api",
        source_path="https://api.m2c2kit.com",
        study_id="YOUR_STUDY_ID",
        username="USERNAME",
        password="PASSWORD"
    )
)

api.score()

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Developer Setup

make clean
make dev-install

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Contributing

Contributions, issues, and feature requests are welcome.

📌 Open an issue: https://github.com/m2c2-project/datakit

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Resources

• 🌎 https://m2c2.io
• 📘 https://m2c2-project.github.io/m2c2kit-docs/
• 📦 https://pypi.org/project/m2datakit/
• 🚀 https://docs.astral.sh/uv/

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Citation

If you use M2DataKit in your research, please cite the project and associated publications.

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Acknowledgements

Development of m2datakit was supported by:

• NIA Grant: 1U2CAG060408-01

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Developers

• Dr. Nelson Roque
  https://orcid.org/0000-0003-1184-202X

• Dr. Scott Yabiku

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🚀 Let's go study some brains!