CRC 1253 - Catchments as Reactors
Published September 25, 2020 | Version v1
Dataset Open

Direct Push Color Logging in the Ammer Floodplain - Data and MATLAB codes

Creators

  • 1. ROR icon University of Tübingen
  • 2. ROR icon Martin Luther University Halle-Wittenberg
  • 3. ROR icon Helmholtz Centre for Environmental Research

Description

This data set contains the direct-push field data (spring 2019), lab data (2018 and 2019) and matlab codes used to visualize the results presented in the publication Klingler et al. 2020, Direct-push color logging images spatial heterogeneity of organic carbon in floodplain sediments, Journal of Geophysical Research: Biogeochemistry.

Other (English)

Keywords:

  • sediment color,
  • direct-push,
  • total organic carbon,
  • natural organic matter

Files

ColorLogging_KlinglerEtAl2020_ElectronicAppendix.zip

Files (15.9 MB)

Additional details

Identifiers

Handle
10900.1/8687010a-86e8-43cb-9999-7d7171e463f5

Related works

Is described by
Data paper: 10.1029/2020JG005887 (DOI)

Funding

Deutsche Forschungsgemeinschaft
CAMPOS - CRC 1253: Catchments as Reactors 281741268

Data quality

Accuracy

Not yet specified.

Completeness

Not yet specified.

Conformity

Not yet specified.

Consistency

Not yet specified.

Credibility

Not yet specified.

Processability

Not yet specified.

Relevance

Not yet specified.

Timeliness

Not yet specified.

Understandability

Not yet specified.

Study design and Methodology

Data source type
geographic area
Data type
decimal
General data format
numeric, software, still image
Mode of collection
physical measurements and tests, field/intervention experiment, experiment
Type of instrument
technical instrument(s)

Software documentation

Application category
data analytics and processing software
Download URL
https://www.mathworks.com/
README

The data set includes MATLAB scripts to

  • fit parameters of the Gaussian Mixture Model cluster analysis
  • create plots of the data
  • perform the 2-D example of Gaussian Mixture  Modeling (GMM) and the scoring of the GMM cluster analysis scoring (AIC and BIC)