Work Description

Title: Magneto-inductive sensor characterization data Open Access Deposited

h
Attribute Value
Methodology
  • Data collected using a PNI RM3100 magnetometer in different lab settings.
Description
  • The data contained in the file comprises those collected during the characterization of the sensor as described in the article "Investigation of a low-cost magneto-inductive magnetometer for space science applications" (cited below). This includes:

  • Resolution

  • Stability

  • Linearity

  • Frequency response
Creator
Depositor
  • lregoli@umich.edu
Contact information
Discipline
Funding agency
  • National Aeronautics and Space Administration (NASA)
Keyword
Resource type
Curation notes
  • Addendum to README added June 1, 2018 regarding several files not used in preparing the manuscript with which the dataset is associated.
Last modified
  • 09/18/2020
Published
  • 03/23/2018
DOI
  • https://doi.org/10.7302/Z2SX6BD1
License
To Cite this Work:
Regoli, L. H. (2018). Magneto-inductive sensor characterization data [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/Z2SX6BD1

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This work is not a member of any user collections.

Files (Count: 2; Size: 258 MB)

Title: Magneto-inductive sensor characterization data
Author: Leonardo H. Regoli
Contact: Dr. Leonardo H. Regoli lregoli@umich.edu
Grant: NASA NNX16AH47G and NASA NNX16AT35A

The data contained in the file comprises those collected during the characterization of the sensor as described in:

Regoli, L. H., Moldwin, M. B., Pellioni, M., Bronner, B., Hite, K., Sheinker, A., & Ponder, B. M.: Investigation of a low-cost magneto-inductive magnetometer for space science applications, Geosci. Instrum. Method. Data Syst., 7, 129-142, https//doi.org/10.5194/gi-2017-53, 2018.


Research overview:

The paper presents the characterization of a magneto-inductive sensor
with space applications in mind. The measurement principle of the sensor is ideal for
small spacecraft applications, allowing for low cost and power consumption functioning
with measurement capabilities that exceed those of other commercial magnetometers and
are similar to other sensors specifically designed for small spacecraft.

The research presented in the paper was conducted in 2017 at the Department of Climate
and Space Sciences and Engineering and the Space Physics Research Laboratory at the
University of Michigan.

Methods:

The data used in the study were collected using a PNI RM3100 magnetometer at a sampling
frequency of 40 Hz. The data were collected either at the Michigan Moldwin Magnetics Lab
or at a copper room, both located in the Space Research Building in Ann Arbor, MI, USA.
The data were collected using a program in C language and analyzed using Matlab.

File inventory:

The data are contained in the MagnetometerData.zip file. The compressed file contains
three separate folders and one file.

The FrequencyResponse folder contains all the files used for the frequency response
analysis in the paper (section 3.4).

The Linearity_data folder contains the data used for the linearity analysis in the paper
(section 3.3).

The Sensitivity folder contains the data used for the resolution analysis in the paper
(section 3.1).*

The stability_data_apr2017.txt file contains the data used for the stability analysis and
for the noise floor analysis in the paper (sections 3.2 and 3.1 respectively).


Variables:

For all the files, the first column corresponds to time (in seconds) since the beginning
of the sampling and the rest of the columns correspond to the X-, Y- and Z-components of
the magnetic field (in nT). For the Linearity tests, a fifth column is present, which
represents the field measured by an external fluxgate magnetometer during the data
acquisition. For the Sensitivity tests, the last column represents whether the data were
recorded inside (1) or outside (0) the shield can.

Use and Access:
This data set is made available under a Creative Commons Public Domain license (CC0 1.0).

To Cite Data:
Regoli, Leonardo H. (2018). Magneto-inductive sensor characterization data [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/Z2SX6BD1

*ADDENDUM June 1, 2018:
The following files were not used in preparation of the manuscript and can be disregarded; the meaning of the fifth column in these files does not correspond to that of the other sensitivity files:
-sensitivity_data2_100.000000
-sensitivity_data30_1.000000
-sensitivity_data30_2.000000
-sensitivity_data30_3.000000
-sensitivity_data30_4.000000
-sensitivity_data30_5.000000

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