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Publication Year: 2025

411 to 420 of 1,109 Results

8.Dataset1_all_records.xlsx Jul 22, 2025 - Systematic Literature Review (SLR) Data MS Excel Spreadsheet - 79.9 KB - MD5: 6d7eeab716f7b4526f67dbc91f3239f6
9.Dataset2_all_records_without_two_papers.xlsx Jul 22, 2025 - Systematic Literature Review (SLR) Data MS Excel Spreadsheet - 79.9 KB - MD5: 6d2ef58bdb37b657e210e8d8b74caf9e
ReadMe.txt Jul 22, 2025 - Systematic Literature Review (SLR) Data Plain Text - 8.0 KB - MD5: eac2be6ca9eeb82a51191766f7b0e778
Genome sequence and annotation of Victoria cruziana Jul 22, 2025 Nowak, Melina Sophie; Harder, Benjamin; Meckoni, Samuel Nestor; Friedhoff, Ronja; Wolff, Katharina; Pucker, Boas, 2025, "Genome sequence and annotation of Victoria cruziana", https://doi.org/10.60507/FK2/5DS0JZ, bonndata, V1 The genome of a Victoria cruziana plant was sequenced with nanopore long reads. The genome sequence was assembled with Verkko2, scaffolding was conducted with CPhasing and the gene models were predicted by BRAKER3 and GeMoMa. The functional annotation was predicted based on sequence similarity to well characterized Arabidopsis thaliana sequences.
README_Victoria_cruziana.txt Jul 22, 2025 - Genome sequence and annotation of Victoria cruziana Plain Text - 5.5 KB - MD5: a0e0115aace0787d4824afa79d4f4a0d
Victoria_cruziana_VB03.tar.gz Jul 22, 2025 - Genome sequence and annotation of Victoria cruziana Gzip Archive - 1.1 GB - MD5: 47796d87218347dfcf56f5f4ec32dece
MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring Jul 16, 2025 Chong, Yue Linn, 2025, "MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring", https://doi.org/10.60507/FK2/OX9XTM, bonndata, V1 Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a s...
LAI_biomass_and_metadata.zip Jul 16, 2025 - MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring Shapefile as ZIP Archive - 2.0 MB - MD5: 9c70fd138fe386669ed38fccc4884dd1
MuST-C_Readme.txt Jul 16, 2025 - MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring Plain Text - 7.0 KB - MD5: 795e65a631cea2e9746f6f3ac338e3f4
folder_structure.pdf Jul 16, 2025 - MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring Adobe PDF - 70.1 KB - MD5: 84590d0232820eba14a5c0c99674d847

8.Dataset1_all_records.xlsx

Jul 22, 2025 - Systematic Literature Review (SLR) Data

MS Excel Spreadsheet - 79.9 KB -

9.Dataset2_all_records_without_two_papers.xlsx

Jul 22, 2025 - Systematic Literature Review (SLR) Data

MS Excel Spreadsheet - 79.9 KB -

ReadMe.txt

Jul 22, 2025 - Systematic Literature Review (SLR) Data

Plain Text - 8.0 KB -

Genome sequence and annotation of Victoria cruziana

Jul 22, 2025

Nowak, Melina Sophie; Harder, Benjamin; Meckoni, Samuel Nestor; Friedhoff, Ronja; Wolff, Katharina; Pucker, Boas, 2025, "Genome sequence and annotation of Victoria cruziana", https://doi.org/10.60507/FK2/5DS0JZ, bonndata, V1

The genome of a Victoria cruziana plant was sequenced with nanopore long reads. The genome sequence was assembled with Verkko2, scaffolding was conducted with CPhasing and the gene models were predicted by BRAKER3 and GeMoMa. The functional annotation was predicted based on sequence similarity to well characterized Arabidopsis thaliana sequences.

README_Victoria_cruziana.txt

Jul 22, 2025 - Genome sequence and annotation of Victoria cruziana

Plain Text - 5.5 KB -

Victoria_cruziana_VB03.tar.gz

Jul 22, 2025 - Genome sequence and annotation of Victoria cruziana

Gzip Archive - 1.1 GB -

MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring

Jul 16, 2025

Chong, Yue Linn, 2025, "MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring", https://doi.org/10.60507/FK2/OX9XTM, bonndata, V1

Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a s...

LAI_biomass_and_metadata.zip

Jul 16, 2025 - MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring

Shapefile as ZIP Archive - 2.0 MB -

MuST-C_Readme.txt

Jul 16, 2025 - MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring

Plain Text - 7.0 KB -

folder_structure.pdf

Jul 16, 2025 - MuST-C Dataset: The Multi-Sensor and Multi-Temporal Data Set of Multiple Crops for In-Field Phenotyping and Monitoring

Adobe PDF - 70.1 KB -

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