Published October 8, 2022 | Version v1
Dataset Open

C-RIDGE: Indoor CO2 Data Collection System for Large Venues Based on Prior Knowledge

  • 1. School of Large Aircraft Advanced Training Center, Beihang University
  • 2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University
  • 3. School of Instrumentation and Optoelectronic Engineering, Beihang University
  • 4. Research Institute for Frontier Science, Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Key Laboratory of Precision Opto-mechatronics Technology and School of Instrumentation and Optoelectronic Engineering, Beihang University
  • 5. Shenzhen International Graduate School, Pengcheng Laboratory, Tsinghua University

Description

This CO2 of C-RIDGE system dataset contains the high spatial and temporal resolution of the CO2
measures with the corresponding timestamp  of static wireless sensors. 
45 sensors are densely deployed on the stand in the venue. The id and relative positions of sensors 
are shown in device message. The sampling rate is adaptively adjusted according to the competition schedule. 
The sampling interval is 5 minutes during the competition and 15 minutes otherwise. Please refer to 
the description of the experimental setup in the data descriptor paper.

In the process of data collection, the data cleaning process is performed  to remove 
and calibrate outliers and abnormal trend data. The script of data cleaning algorithm is 
provided in this repository. For details about the data cleaning process, please refer to the script in 
this repository and data descriptor paper.

The dataset in this repository is processed version. The raw dataset is not included in this repository.

Data is stored as CSV file. Each device is numbered in order of placement. There are 45 sensors in total,
1 to 45 in the csv file are sensor numbers, timestamp as the China Standard Time (GMT+8), Each timestamp 
corresponds to 45 CO2 concentration data from different sensors.

To access the dataset, any programming language that can access the CSV file is appropriate. Users can 
also directly open the CSV file. To successfully execute script files, Pycharm with Python 3.0 is required.

Files

Cleaning Data.csv

Files (980.7 kB)

Name Size Download all
md5:b9baf0408e5637d93303ebaa5047a881
974.1 kB Preview Download
md5:a7a37e92c8ed3704b5e5431d2e9dc8fd
1.2 kB Preview Download
md5:29fa44a8b32d5dd5b01fada4e31184e1
2.5 kB Preview Download
md5:8c5c91e0af85c50834b95b05109c7e06
3.0 kB Preview Download