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2,191 to 2,200 of 11,133 Results
metadata_environment.tsv
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Tab-Separated Values - 10.1 KB - MD5: e6c1253d43a1a8902a6212dfc7d7ca05
ms_neg.RDS
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Unknown - 1.9 MB - MD5: ded2a5b55b93101fe907cc5e854515c8
ms_pos.RDS
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Unknown - 2.3 MB - MD5: 90265a85d2c8b05ec07c2cf3124c1592
nutrient.csv
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Comma Separated Values - 1.7 KB - MD5: f27fee6c2fd062e31dc8d019b0e43638
rooted_tree.qza
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Unknown - 686.6 KB - MD5: 14bc1a0dd529da2bdbbf69693d77f4ef
rooted_tree.tsv
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Tab-Separated Values - 1.3 MB - MD5: 6300da2c6a1e69aeefe7823a5f89c193
Unfiltered_ASV_taxonomy.csv
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Comma Separated Values - 3.0 MB - MD5: c6fd14924073b6363cdb187eba9d8025
Unfiltered_ASV_taxonomy.qza
Jan 20, 2025 - Replication data for: Cold Seeps and Coral Reefs in Northern Norway: carbon cycling in marine ecosystems with coexisting features
Unknown - 979.0 KB - MD5: 81de5976f8afda4989e261fe2e0ca6b7
Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells
Jun 25, 2021
Opstad, Ida S.; Godtliebsen, Gustav; Sekh, Arif Ahmed, 2021, "Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells", https://doi.org/10.18710/IDCUCI, DataverseNO, V1
Abstract: Segmenting sub-cellular structures in living cells from fluorescence microscopy images is a ground truth (GT) hard problem. The microscope’s 3-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution, and complex morphol...
0_ReadMe-1.txt
Jun 25, 2021 - Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells
Plain Text - 15.6 KB - MD5: e132db0a0f357ed56600fb8b42da3583
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