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Data from: A COMPARATIVE STUDY OF NUMERICAL APPROXIMATIONS FOR SOLVING THE...
- figshare.com
- scielo.figshare.com
jpegUpdated Jun 2, 2023
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EmailClick to copy linkLink copiedCiteM. Singh; G. Kaur; J. Kumar; T. De Beer; I. Nopens (2023). A COMPARATIVE STUDY OF NUMERICAL APPROXIMATIONS FOR SOLVING THE SMOLUCHOWSKI COAGULATION EQUATION [Dataset]. http://doi.org/10.6084/m9.figshare.7942064.v1jpegAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.7942064.v1Dataset updatedJun 2, 2023Dataset provided bySciELO journalsAuthorsM. Singh; G. Kaur; J. Kumar; T. De Beer; I. NopensLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionABSTRACT In this work, numerical approximations for solving the one dimensional Smoluchowski coagulation equation on non-uniform meshes has been analyzed. Among the various available numerical methods, finite volume and sectional methods have explicit advantage such as mass conservation and an accurate prediction of different order moments. Here, a recently developed efficient finite volume scheme (Singh et al., 2015) and the cell average technique (Kumar et al., 2006) are compared. The numerical comparison is established for both analytically tractable as well as physically relevant kernels. It is concluded that the finite volume scheme predicts both number density as well as different order moments with higher accuracy than the cell average technique. Moreover, the finite volume scheme is computationally less expensive than the cell average technique.
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Aggregated values of the alternatives obtained from different existing...
- plos.figshare.com
xlsUpdated May 10, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDilshad Alghazzawi; Aqsa Noor; Hanan Alolaiyan; Hamiden Abd El-Wahed Khalifa; Alhanouf Alburaikan; Qin Xin; Abdul Razaq (2024). Aggregated values of the alternatives obtained from different existing operators. [Dataset]. http://doi.org/10.1371/journal.pone.0303139.t007xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0303139.t007Dataset updatedMay 10, 2024Dataset provided byPLOS ONEAuthorsDilshad Alghazzawi; Aqsa Noor; Hanan Alolaiyan; Hamiden Abd El-Wahed Khalifa; Alhanouf Alburaikan; Qin Xin; Abdul RazaqLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionAggregated values of the alternatives obtained from different existing operators.
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MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high...
- data.niaid.nih.gov
- zenodo.org
Updated Jul 19, 2024+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteHengl, T. (2024). MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2009 at 1-km [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4515694Dataset updatedJul 19, 2024Dataset provided byHengl, T.
Parente, L.LicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionLayers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2009. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included.
To access and visualize maps use: OpenLandMap.org
To download and compute with maps using Cloud-Optimized-GeoTIFF see this tutorial.
If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels:
Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues
General questions and comments: https://disqus.com/home/forums/landgis/
All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:
clm = theme: climate,
lst = variable: land surface temperature,
mod11a2.daytime = determination method: MOD11A2 product, day time values,
d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile,
1km = spatial resolution / block support: 1 km,
s0..0cm = vertical reference: land surface,
2000..2020 = time reference: from 2000 to 2020,
v1.1 = version number: 1.1,
Median and IQR of the PA measures aggregated over three sessions per...
- plos.figshare.com
xlsUpdated May 30, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMedian and IQR of the PA measures aggregated over three sessions per participant for both measurement methods (GPAQ and SenseWear). [Dataset]. https://plos.figshare.com/articles/dataset/Median_and_IQR_of_the_PA_measures_aggregated_over_three_sessions_per_participant_for_both_measurement_methods_GPAQ_and_SenseWear_/5011133xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0177765.t002Dataset updatedMay 30, 2023AuthorsMichelle Laeremans; Evi Dons; Ione Avila-Palencia; Glòria Carrasco-Turigas; Juan Pablo Orjuela; Esther Anaya; Christian Brand; Tom Cole-Hunter; Audrey de Nazelle; Thomas Götschi; Sonja Kahlmeier; Mark Nieuwenhuijsen; Arnout Standaert; Patrick De Boever; Luc Int PanisLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionNumber of participants included in the analysis is 122.
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Monthly average and annual aggregated cost.
- plos.figshare.com
xlsUpdated Jun 7, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteBorja García-Lorenzo; Carla Fernández-Barceló; Francisco Maduell; Laura Sampietro-Colom (2023). Monthly average and annual aggregated cost. [Dataset]. http://doi.org/10.1371/journal.pone.0247450.t004xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0247450.t004Dataset updatedJun 7, 2023Dataset provided byPLOS ONEAuthorsBorja García-Lorenzo; Carla Fernández-Barceló; Francisco Maduell; Laura Sampietro-ColomLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionMonthly average and annual aggregated cost.
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FacebookTwitterEmailData from: A COMPARATIVE STUDY OF NUMERICAL APPROXIMATIONS FOR SOLVING THE SMOLUCHOWSKI COAGULATION EQUATION
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
ABSTRACT In this work, numerical approximations for solving the one dimensional Smoluchowski coagulation equation on non-uniform meshes has been analyzed. Among the various available numerical methods, finite volume and sectional methods have explicit advantage such as mass conservation and an accurate prediction of different order moments. Here, a recently developed efficient finite volume scheme (Singh et al., 2015) and the cell average technique (Kumar et al., 2006) are compared. The numerical comparison is established for both analytically tractable as well as physically relevant kernels. It is concluded that the finite volume scheme predicts both number density as well as different order moments with higher accuracy than the cell average technique. Moreover, the finite volume scheme is computationally less expensive than the cell average technique.