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13 results for “Self-Organizing Maps”
Processing of MODIS-Aqua data with Self-Organizing Maps NeuroVaria method for the southern canary upwelling system
<p>Abstract</p> <p>This ocean color dataset is derived from MODIS_Aqua sensor measurements covering the Southern Canary upwelling system. The raw L1A measurements were downloaded from NASA's Ocean Color web site and then processed using the Ocean Biology Processing Group's (OBPG) Multi-Sensor Level-1 to Level-2 (MSL12) code. The l2gen program, based on its standard process, generates Level-2 parameters consisting of the top of atmosphere radiance, the radiance of each ocean and atmosphere component, the measurement angles, Level-2 flags, ... The top of atmosphere radiance is pre-corrected to keep only a dependence on the diffuse transmittance, the aerosol contribution and the water leaving radiance.</p> <p><br> The pre-corrected product and measurement angles are assimilated using the Self-Organizing Map<br> NeuroVaria (SOM-NV) code (Diouf et al., 2013). SOM-NV is an algorithm based on two statistical models<br> that classify a dataset into a map, and then use the information from that map to deliver atmospheric and oceanic parameters from the satellite observation.</p> <p>The parameters of interest are the remote sensing reflectance spectra (Rrs(λ)) and the aerosol optical thickness (AOT) at 869 nm (aot_869). The Rrs at blue (443 and 488 nm) and green (547 nm) are used to calculate chlorophyll-a concentration from the OBPG OCx algorithm (chl_ocx, O'Reilly et al., 1998; Mobley et al., 2016).</p> <p>These geophysical parameters are projected onto a fixed grid at 1/96° resolution and archived in a daily netcdf format files. Each file contains five visible reflectances Rrs(λ) (with λ = 412, 443, 488, 531, and 547 nm), chl_ocx, aot_869, and latitude and longitude coordinates. These parameters are described in the files, along with the global attributes.</p> <p><br> The netcdf files are formatted as follows: SOM-NV-Ayyyydddhhmmss.nc; where yyyy = year; ddd = Julian<br> day; hh = hour; mm = minute; ss = second. The extension "Ayyyydddhhmmss.nc", corresponds to the name<br> of the MODIS_aqua file of the day. When two input files exist for the same day, within 5 minutes, the two<br> scans are concatenated and the orbit keeps the name of the second file.<br> All files are compressed internally to a size of 4, to facilitate transfers.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Résumé</p> <p>Ce jeu de données de couleur de l’eau est issu des mesures du capteur MODIS_Aqua sur la partie sud du système d’upwelling des Canaries. Les mesures brutes L1A ont été téléchargées du site Ocean Color de la NASA, puis traitées à l’aide du code de traitement « Multi-Sensor Level-1 to Level-2 (MSL12) » du groupe Ocean Biology Processing Group (OBPG). La version standard du programme l2gen génère les paramètres de niveau 2 constitués de la luminance totale mesurée, de la luminance de chaque composante du système océan-atmosphère, des angles de mesures, des masques de niveau 2, …. La luminance totale est pré-corrigée pour ne garder qu’une dépendance à la transmittance diffuse, à la contribution des aérosols et à la luminance marine.<br> <br> Le produit pré-corrigé et les angles de mesure sont assimilés à l’aide du code Self-Organizing Map NeuroVaria (SOM-NV) de Diouf et al. (2013). SOM-NV est un algorithme basé sur deux modèles statistiques qui permettent de classer un ensemble de données sur une carte, puis d’utiliser les informations de cette carte pour restituer les paramètres atmosphériques et océaniques de l’observation satellite.<br> <br> Les paramètres restitués sont les spectres de réflectance marine (Rrs(λ)) et l’épaisseur optique des aérosols (AOT) à 869 nm (aot_869). Les Rrs au bleu (443 et 488 nm) et au vert (547 nm) servent à calculer la concentration en chlorophylle-a à partir de l’algorithme OCx de OBPG (chl_ocx).<br> <br> Ces paramètres géophysiques sont projetés sur une grille fixe à 1/96° de résolution et archivés au format de fichiers netcdf journaliers. Chaque fichier netcdf contient cinq réflectances du visible Rrs(λ) (avec λ = 412, 443, 488, 531 et 547 nm), la chl_ocx, l’aot_869, et les coordonnées latitude et longitude. Ces paramètres sont décrits dans les fichiers, ainsi que les attributs globaux.</p> <p><br> Les fichiers netcdf sont formatés comme suite : SOM-NV-Ayyyydddhhmmss.nc ; avec yyyy = année ; ddd =<br> jour julien ; hh = heure ; mm = minute ; ss = seconde. L'extension "Ayyyydddhhmmss.nc", correspond au<br> nom du fichier MODIS_aqua du jour. Dans le cas où deux fichiers existent pour un même jour, à 5 minutes<br> près, les deux scans sont concaténés et l'orbite garde le nom du deuxième fichier.<br> Tous les fichiers sont compressés en interne à un niveau 4, pour faciliter le transfert.</p>
Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic
<p>Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:<br><code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code><br>There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).</p> <p>The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).</p> <p>The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.</p> <h2><strong>Storage</strong></h2> <p>All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.</p> <p>The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3.<br>As for the PFT variables, these values are in percent points.</p> <h2><strong>References</strong></h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.<br>“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,<br><em>J. Geophys. Res. Oceans</em> 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, <strong>2019</strong></li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.<br>“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”<em>,<br>Ocean Science</em> 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, <strong>2024</strong></div> </li> </ul> <h2><strong>Changelog</strong></h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>
Cortical topographic motifs emerge in a self-organized map of object space
<p>The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflects a smooth mapping of a unified representational space.</p>
Cortical topographic motifs emerge in a self-organized map of object space
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High frequency soil sensor data for SOM input - Complex drivers of riparian soil oxygen variability revealed using self-organizing maps
The provided datasets contain the original (non-normalized) high-frequency soil and meteorological observations that were fed to the Self-Organizing Map (SOM) in order to identify ranges of values associated with low and high soil O2 conditions. For the Champlain Valley (CV) site we used the natural breaks algorithm to subset the data into high and low O2 datasets. O2 values were consistently low at the Green Mountains (GM) site, so we ran a single SOM for all O2 values at this site. The original values were then range-normalized before they were fed to the SOM.
Tropical North Atlantic phases: A Self-Organizing Maps (SOM) approach.
<p>Data from SOM analysis</p>
Self-organizing map of electron-impact mass-spectra
<p>This text file is a self-organising map (256 x 256 grid units) of mass spectra.</p> <p>The mass spectra are stored as a continuous list. In the file, consecutive sets of 256 rows in the file are one row of the map (each row has 256 grid units). After every 256th row in the file, a new map row begins (256 rows in total).</p> <p>The mass spectra are electron-impact mass-spectra, as they are typically recorded in gas chromatography. The resolution is unit mass, the spectral range is limited to 29 - 200 m/z, and the spectral intensities are normalized.</p> <p>The mass spectra were collected from the Mass Bank of North America (<em>MassBank of North America</em>. <a href="https://mona.fiehnlab.ucdavis.edu/">https://mona.fiehnlab.ucdavis.edu/</a> (accessed 2018-07-05)) and are used under a CC-BY 4.0 license.</p>
Redefining floristic zones in the Korean Peninsula using high-resolution georeferenced specimen data and self-organizing maps
<p>The use of biota to analyze the distribution pattern of biogeographic regions is essential to gain a better understanding of the ecological processes that cause biotic differentiation and biodiversity at multiple spatiotemporal scales. Recently, the collection of high-resolution biological distribution data (e.g., specimens) and advances in analytical theory have led to the quantitative analysis and more refined spatial delineation of biogeographic regions. This study was conducted to redefine floristic zones in the southern part of the Korean Peninsula and to better understand the eco-evolutionary significance of the spatial distribution patterns. Based on 309,333 distribution data of 2,954 vascular plant species in the Korean Peninsula, we derived floristic zones using self-organizing maps. We compared the characteristics of the derived regions with those of historical floristic zones and ecologically important environmental factors (climate, geology, and geography). In the clustering analysis of the floristic assemblages, four distinct regions were identified, namely, the cold floristic zone (Zone I) in high-altitude regions at the center of the Korean Peninsula, cool floristic zone (Zone II) in high-altitude regions in the south of the Korean Peninsula, warm floristic zone (Zone III) in low-altitude regions in the central and southern parts of the Korean Peninsula, and maritime warm floristic zone (Zone IV) including the volcanic islands Jejudo and Ulleungdo. Totally, 1,099 taxa were common to the four floristic zones. Zone IV showed the highest abundance of specific plants (those found in only one zone), with 404 taxa. Our study improves floristic zone definitions using high-resolution regional biological distribution data. It will help better understand and re-establish regional species diversity. In addition, our study provides key data for hotspot analysis required for the conservation of plant diversity.</p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
Redefining floristic zones in the Korean Peninsula using high-resolution georeferenced specimen data and self-organizing maps
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Integrative analysis of single-cell ATAC-seq and RNA-seq using Self-Organizing Maps [ATAC-seq]
GEO Series GSE89362. Mus musculus. 265 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Integrative analysis of single-cell ATAC-seq and RNA-seq using Self-Organizing Maps [scRNA-Seq]
GEO Series GSE89280. Mus musculus. 568 samples. Type: Expression profiling by high throughput sequencing.
Integrative analysis of single-cell ATAC-seq and RNA-seq using Self-Organizing Maps
GEO Series GSE89285. Mus musculus. 833 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
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