edeniss2020 (EDEN ISS 2020 Telemetry Dataset)

Overview

The edeniss2020 dataset is a time series dataset. It consists of equidistant sensor readings stemming from 97 sensors in the EDEN ISS research greenhouse.

EDEN ISS was a (almost) closed loop research greenhouse build under the lead of the German Aerospace Center to study Controlled Environment Agriculture (CEA) techniques and plant growth for future long-term space missions. EDEN ISS was deployed in Antarctica in 2018 next to the german Neumayer III polar station and has been in operation for four years.

The data contained in the edeniss2020 dataset was recorded during the third mission year in 2020 between 2020/01/01 and 2020/12/30. Every sensor within the dataset is related to one of the following Subsystems |Acronym | Description | |--|--| | AMS‑FEG | Atmosphere Management System (AMS) of the Future Exploration Greenhouse (FEG) | | AMS‑SES | Atmosphere Management System AMS of the Service Section (SES)| | ICS | Illumination Control System | | NDS | Nutrient Delivery System | | TCS | Thermal Control System|

Specification

Contents

The dataset includes the following files: - ams-feg/*.csv: Sensor readings related to the AMS-FEG - ams-ses/*.csv: Sensor readings related to the AMS-SES - ics/*.csv: Temperature readings related to the ICS - nds/*.csv: Sensor readings related to the NDS - tcs/*.csv: Sensor readings related to the - edeniss2020.csv: Description and Units of the measurements. - README.md: This file.

Usage

The dataset can be used for uni- and multivariate analysis.

To read a uni-variate time series with pandas do:

import pandas as pd

# Example of loading ams-feg/co2-1.csv

data = pd.read_csv('./ams-feg/co2-1.csv')
print(data.head())

To read the data for a whole subsystem (e.g. AMS-FEG) as a multivariate time series:

import pandas as pd
import glob

SUBSYSTEM = 'ams-feg'

combined_df = pd.DataFrame()
for i, file in enumerate(glob.glob(f'./{SUBSYSTEM}/*.csv')):
    df = pd.read_csv(file, header=0, usecols=[0, 1])
    if i == 0:
        combined_df = df
    else:
        combined_df = pd.merge(combined_df, df, on='time')

combined_df = combined_df.sort_values(by='time').reset_index(drop=True)

print(combined_df)

License

Creative Commons Attribution 4.0 International (CC BY 4.0 Deed)

Citation

If you use this dataset, please cite it as follows:

Rewicki, F., Norman, T., Vrakking, V., (2024). edeniss2020. Zenodo. http://doi.org/10.5281/zenodo.11485183

Contact

Ferdinand Rewicki: ferdinand.rewicki@dlr.de

Version History

• v1.0 Initial Version