2,851 to 2,860 of 8,524 Results
Oct 26, 2022 -
Supporting data and code for the African Rice Panreference produced by the frangiPANe software
Tabular Data - 4.6 MB - 4 Variables, 152411 Observations - UNF:6:J8wm0efC8Lf7KQQ/txrx4Q==
CSV file with the position of the new sequences on the genome reference. It contains one line per sequence, each containing 4 columns : (1) the sequence id, (2) its length (bp), (3) the chromosome name, (4) its position on the chromosome.
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Oct 21, 2022 - UMR CEREGE
Garcin, Yannick; Schefuß, Enno; Dargie, Greta C.; Hawthorne, Donna; Lawson, Ian T.; Sebag, David; Biddulph, George E.; Crezee, Bart; Bocko, Yannick E.; Ifo, Suspense A.; Mampouya Wenina, Y. Emmanuel; Mbemba, Mackline; Ewango, Corneille E.N.; Emba, Ovide; Bola, Pierre; Kanyama Tabu, Joseph; Tyrrell, Genevieve; Young, Dylan M.; Gassier, Ghislain; Girkin, Nicholas T.; Vane, Christopher H.; Adatte, Thierry; Baird, Andy J.; Boom, Arnoud; Gulliver, Pauline; Morris, Paul J.; Page, Susan E.; Sjögersten, Sofie; Lewis, Simon L., 2022, "Hydroclimatic vulnerability of peat carbon in the central Congo Basin: codes for age-depth models, geospatial data processing and analysis", https://doi.org/10.23708/FO2HGM, DataSuds, V1, UNF:6:ewI8zj0Z/m4OMWGoSn0k3Q== [fileUNF]
This dataset includes two packages: 'Age_depth_models' and 'Tropical_Peats' for the creation of the age-depth models and the processing and analysis of the geospatial data, which are presented in the article "Hydroclimatic vulnerability of peat carbon in the central Congo Basin".... |
Jupyter Notebook - 8.6 KB -
MD5: 48a731c69580534c9dc3435e2b0331f4
This notebook will download raster data (monthly precipitation) in high-resolution (~1x1 km), clip the tropical region and downsample raster at low-resolution (~10x10 km). |
Python Source Code - 5.3 KB -
MD5: 7b4cb0723bfc35ab6251601c993a94e4
This code will download raster data (monthly precipitation) in high-resolution (~1x1 km), clip the tropical region and downsample raster at low-resolution (~10x10 km). |
Jupyter Notebook - 11.5 KB -
MD5: 40073efc9b96795aa894a027776558f3
This notebook will download and process (reproject, rasterize, synchronize in HR or LR and encoding) Africa peat shapefiles |
Python Source Code - 6.4 KB -
MD5: 750b408358fe0d10469db8eb7a728a43
This code will download and process (reproject, rasterize, synchronize in HR or LR and encoding) Africa peat shapefiles |
Jupyter Notebook - 13.1 KB -
MD5: 4acb1d26c60c15e8ad472188b965c80c
This notebook will download and process (reproject, union South and North America, rasterize, synchronize in HR or LR and encoding) America peat shapefiles |
Python Source Code - 7.6 KB -
MD5: ee5dc376ed5f2be49e4125105bde2bea
This code will download and process (reproject, union South and North America, rasterize, synchronize in HR or LR and encoding) America peat shapefiles |
Jupyter Notebook - 12.6 KB -
MD5: 45dd12aa67af30d36636be39e3436737
This notebook will download and process (reproject, union Southeast Asia and Oceania, rasterize, synchronize in HR or LR and encoding) Asia and Oceania peat shapefiles |
Python Source Code - 7.3 KB -
MD5: 7aadf793ebc05409196952a7e7477680
This code will download and process (reproject, union Southeast Asia and Oceania, rasterize, synchronize in HR or LR and encoding) Asia and Oceania peat shapefiles |
