LIEC

Le LIEC est une unité mixte de recherche (UMR 7360) créée en janvier 2013 par fusion de l'UMR 7146 LIEBE, de l'UMR 7137 LIMOS et de l'UMR 7569 LEM. L'objectif premier du LIEC est de comprendre le fonctionnement des écosystèmes continentaux fortement perturbés par l'activité humaine, avec pour finalité leur réhabilitation. Dans ce but, nous mettons en oeuvre une recherche interdisciplinaire alliant les concepts et méthodes de la minéralogie environnementale, de la science du sol, de l’écologie microbienne, de la physico-chimie colloïdale, de l’écotoxicologie, de l’écologie fonctionnelle. Pour plus d'informations : https://liec.univ-lorraine.fr/

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.

1 to 10 of 12 Results

data_individual_images.zip Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach ZIP Archive - 47.9 MB - MD5: cd8500aadd6467be2568e3209f697bd7
dataset_generator_multithread.py Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Python Source Code - 8.3 KB - MD5: 713f6d4d9546624acb71a6287f675f7c
data_utils.py Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Python Source Code - 4.8 KB - MD5: 84c114248ffbd1ba9bc7857f34434b98
generator.py Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Python Source Code - 12.1 KB - MD5: 3227a0c498c1a2e4e3ef24ea0f0cb94e
image_utils.py Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Python Source Code - 1.9 KB - MD5: c9bd8395b3dc40cbc0ed71ccab08a38c
model_id_map.tab Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Tabular Data - 1.7 KB - 2 Variables, 166 Observations - UNF:6:WAcYIGvggM/oSHwrm9hPqQ==
README.md Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Markdown Text - 2.2 KB - MD5: e394090eb3a569fee7b37615f3917ce7
ref_img.jpg Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach JPEG Image - 7.1 KB - MD5: 146caec920813db3d5990c77710e3394
synt_environment.yml Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Unknown - 1.5 KB - MD5: 9aa4e688470051f7f7c94555b0c448c0
utils.py Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach Python Source Code - 3.3 KB - MD5: 2569de264dbc5dbff10e170df2a5e1a7

data_individual_images.zip

Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach

ZIP Archive - 47.9 MB -

dataset_generator_multithread.py

Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach

Python Source Code - 8.3 KB -

data_utils.py

Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach

Python Source Code - 4.8 KB -

generator.py

Jul 21, 2023 - Dataset for publication: Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach

Python Source Code - 12.1 KB -