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Data entering, data management and QC

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HAL Id: hal-02805406

https://hal.inrae.fr/hal-02805406

Submitted on 6 Jun 2020

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Data entering, data management and QC

Matieu Henry, Laurent Saint-André, Nicolas Picard

To cite this version:

Matieu Henry, Laurent Saint-André, Nicolas Picard. Data entering, data management and QC. Na-tional technical staff training on allometric equations (ae) under the UN-REDD Programme, Jun 2012, Hanoi, Vietnam. 7 p. �hal-02805406�

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Data entering, data management and

QC

Dr. Matieu Henry & Dr. Laurent Saint- André

& Dr. Nicolas Picard

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Objective:

To transfer field data into ONE electronic database

Different types of software:

-Microsoft Office

-Access

-MySQL

http://science.nature.nps.gov/im/datamgmt/index.cfm

How data resulting from inventory and monitoring efforts will be acquired, managed,

processed, checked, analyzed, distributed, and archived.

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In order to avoid errors:

To check all the data several times: double entry

Comparison of the two files to detect the errors

To well define the variables to be measured (text of numbers)

DATA ENTERING

Exemple: Geographic coordinates

Lat

Long

Qualitative

Lat

Long

Quantitative

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When the measurements were performed

Details of the code and acronyms

Definition of the variables

Height of the measured diameter or circumference etc.

Units of the measured parameters

Any additional information

METAINFORMATION

Crucial to allow the use of the data at

different time period and different users

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DIFFERENT LEVELS

Tree data

Branche data

Location

Etc…

If you have different level of information, you need to have

different tables

This structure of information minimize the repetition of the

information

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DATA FILTERING

Field Data

Sheet

http://g-redd.cartong.org/aboutInventory.html

Software

http://nrs.fs.fed.us/inventory_monitoring/inventory/data_tools_services/

Objective: Delete the artifacts/ impossible data

The difficulty depends on identifying what is normal or not normal

Very often, the errors come from a change of units

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QUALITY CONTROL

Graphic analysis

Descriptive statistics

Average

Standard error

Number of measurements

Etc.

DATA STRUCTURE

One line for one individual of the sampled population

One column -> one descriptive variable

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