European Centre for Research and Teaching in Environmental Geoscience

CEREGE (Centre Européen de Recherche et d’Enseignement de Géosciences de l’Environnement) is a joint research center located in Provence, at the Technopôle Environnement Arbois Méditerranée, Petit Plateau de l’Arbois (Aix-en-Provence, Les Milles) and on the St Charles campus of AMU in Marseille. Thanks to its theoretical, methodological, and technological approaches to research, CEREGE is strongly interdisciplinary.

Research themes
  • Climate
  • Sustainable Environment
  • Earth and Planets
  • Ressources, Réservoirs and Hydrosystems
CEREGE regroups around 130 permanent staff (45 university lecturers and professors, 40 researchers and 45 engineers, technicians, and administrative staff), and 110 temporary staff including around 60 graduate students. CEREGE belongs to OSU-Institut PYTHEAS (Observatoire des Sciences de l’Univers), participates in the research federation ECCOREV (CNRS- INEE), contributes to two operations EQUIPEX : ASTER-CEREGE and NANO-ID, LABEX OT-Med and SERENADE (A*MIDEX).
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

31 to 40 of 45 Results
Jupyter Notebook - 331.4 KB - MD5: de477606342adce6dd7fecc70d03da0b
Code
This notebook will calculate the following variables: precipitation amount (yearly) and seasonality index (dimensionless) and the coefficient of variation of precipitation (CV)
Python Source Code - 9.2 KB - MD5: 9122c11174bdf1944f009d4fa8b4c973
Code
This code will calculate the following variables: precipitation amount (yearly) and seasonality index (dimensionless) and the coefficient of variation of precipitation (CV)
Jupyter Notebook - 215.2 KB - MD5: aea19aa99261170a0c1ab8a775eb5622
Code
This notebook will process and plot the climate space (precipitation amount, seasonality index or precipitation CV) of tropical peatland areas in HR or LR
Python Source Code - 19.4 KB - MD5: 6761d5c0833253811a79547ff622a3ee
Code
This code will process and plot the climate space (precipitation amount, seasonality index or precipitation CV) of tropical peatland areas in HR or LR
Jupyter Notebook - 557.1 KB - MD5: 8324b04aef79baaa64e5c07ecc02fc6a
Code
This notebook will allow estimating past changes in precipitation amount and/or seasonality using peat δDn-C29 values compared with a wide range of δDprecip values at the CEN-17.4 site computed using an empirical approach based on modern climate data
Python Source Code - 21.7 KB - MD5: 51f4f64aba511a2b0c61bcf4e4a34c5a
Code
This code will allow estimating past changes in precipitation amount and/or seasonality using peat δDn-C29 values compared with a wide range of δDprecip values at the CEN-17.4 site computed using an empirical approach based on modern climate data
Plain Text - 1.0 KB - MD5: dfd566a1d8d9674b85545246dd78122b
Code
rbacon code for age/depth models
Tabular Data - 269 B - 5 Variables, 9 Observations - UNF:6:sRazKBE7YsXHLsA1uOO5Gw==
Data
14C dates of core BDM1-7 with topcore assigned to year 2019
Tabular Data - 747 B - 5 Variables, 24 Observations - UNF:6:HUgIdSVW3yTYTdxEsVPikQ==
Data
14C dates of core CEN-17.4 with topcore assigned to year 2014
Tabular Data - 284 B - 5 Variables, 10 Observations - UNF:6:M6e3BLVRS8Uz3IwpqSIgig==
Data
14C dates of core LOK5-5 with topcore assigned to year 2020
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.