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@@ -14,10 +14,10 @@ machine learning techniques for time series analysis and predictive modelling.
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# Usage
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## Downloading the dataset
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-There are a few ways to download the dataset ([mep_dataset.zip](mep_dataset.zip)). Since it is a fairly small filesize (~300MB), it can just be downloaded through the web interface.
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+There are a few ways to download the dataset ([data/mep_dataset.zip](data/mep_dataset.zip)). Since it is a fairly small filesize (~300MB), it can just be downloaded through the web interface.
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Or from the commandline:
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```bash
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-wget https://gin.g-node.org/JanYperman/motor_evoked_potentials/raw/master/mep_dataset.zip
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+wget https://gin.g-node.org/JanYperman/motor_evoked_potentials/raw/master/data/mep_dataset.zip
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```
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Alternatively, you may clone the repository to your local machine, which will also include the dataset:
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@@ -29,7 +29,7 @@ git clone https://gin.g-node.org/JanYperman/motor_evoked_potentials.git
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For more ways of accessing the data, please refer to GIN's [FAQ](https://gin.g-node.org/G-Node/Info/wiki/FAQ+Troubleshooting#how-can-i-access-the-data).
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## Structure
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-The dataset itself is stored in [mep_dataset.zip](mep_dataset.zip). The general structures is as follows:
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+The dataset itself is stored in [data/mep_dataset.zip](data/mep_dataset.zip). The general structures is as follows:
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* __patient.csv:__ Contains the records for the various patients.
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* __visit.csv:__ Contains the records for the various visits.
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@@ -37,12 +37,12 @@ The dataset itself is stored in [mep_dataset.zip](mep_dataset.zip). The general
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* __measurement.csv:__ Contains the records for the various measurements.
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* __edss.csv:__ Contains the records for the various edss measurements.
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-Besides these files the dataset also contains textfiles for each of the actual time series. The filenames of these files contain a unique identifier which can be used to link back to the column "timeseries" in the measurement.csv file. Some code to automate this linking (in Python) is included in [create_df_from_portable_dataset.py](create_df_from_portable_dataset.py).
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+Besides these files the dataset also contains textfiles for each of the actual time series. The filenames of these files contain a unique identifier which can be used to link back to the column "timeseries" in the measurement.csv file. Some code to automate this linking (in Python) is included in [code/create_df_from_portable_dataset.py](code/create_df_from_portable_dataset.py).
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More details about specifics fields can be found in the dataset descriptor.
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## Getting started
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-It is highly recommended to have a look at the included [jupyter notebook](sample_use_case.ipynb) to familiarize oneself with the dataset.
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+It is highly recommended to have a look at the included [code/jupyter notebook](code/sample_use_case.ipynb) to familiarize oneself with the dataset.
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It includes a sample use case and goes over how to work with the dataset.
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To run the jupyter notebook a few Python packages are required:
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