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Gzip Archive - 2.0 GB - MD5: 7b79b7bf905e7e72c5b234a3968b2971
Structural annotation of various datasets generated in the study using GeMoMa v1.9. Each subfolder contains the structural annotations in GFF3 (.gff3) format, predicted coding sequences as FASTA file (.cds.fa), and the predicted polypeptide sequences as FASTA file (.pep.fa) for the corresponding dataset. File names are based on the plant species na...
Comma Separated Values - 7.5 KB - MD5: 8b6e314c149aa413049e66e977c863cb
Statistics of gene annotation of 82 species and re-annotation of 7 species
Comma Separated Values - 34.6 KB - MD5: d03c4330afa93ae046f183653f66d1c6
Detailed results of Kraken and Bracken classification of all datasets
Plain Text - 17.0 KB - MD5: 09efa63aa55dec812e8d28aa1037efac
README file describing the datasets and files overview
Nov 13, 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, V3
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...
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