MECODA

MECODA (ModulE for Citizen Observatory Data Analysis) is an online tools repository to facilitate the analysis and viewing of all sorts of citizen science data.

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Service description:

MECODA is a platform to provide you with tools to analyse and view all sorts of data. It also allows you to create and share new ones for everyone to use. Anyone can use it. Moreover, if you use MECODA, you can choose the data you want to analyse. Therefore, it provides tools to analyse all sorts of data -biodiversity, environmental, social, etc.- coming from citizen observatories such as Natusfera, other data sources or your own sets.

Development & functioning :

This service has been adapted from an existing package, MOODA (Module for Ocean Observatory Data Analysis), following principles of reusability. The primary function of MOODA is to help scientists analyse data from the EMSO-ERIC facilities. MOODA, developed by CSIC under the framework of the H2020 project EMSODEV, is an open-source package in python that is adaptable and scalable. It was designed mainly for oceanographers and marine science students. It is an expandable  power scripting system that currently has the following functionalities: 

  • User-friendly access to various marine data repositories  
  • Customizable data-quality control
  • Customizable data-analysis methods
  • Data-viewing tools that are commonly used in the oceanographic community
  • Exports data in different formats (such as netCDF or CSV)

In this line, MECODA has been adapted from MOODA to be used by scientists and students doing research and training activities associated with citizen science and citizen observatories.

Innovation for citizen observatories:

  • MECODA will offer user interfaces, such as Jupyter, to make the execution of analytics experiments on citizen science datasets straightforward and user-friendly
  • MECODA and MOODA may collaborate and share products in developments applied to citizen science in marine environments. This type of collaboration could be extended to other environmental and citizen science research infrastructures in the future.
We are working on this service, it will be available soon!
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Keywords:

Python, Jupyter, data analysis, data quality, data visualization.

Coordinator:

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Help us to co-create and test the new generation of services for citizen observatories!