cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6908501
title: AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 GeoMAD Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.6084/m9.figshare.19422296
title: Attempts to use breeding approaches in Aedes aegypti to create lines with distinct and stable relative Wolbachia densities - Final

description: Wolbachia densities
DOI: 10.6084/m9.figshare.19422296.v1
title: Attempts to use breeding approaches in Aedes aegypti to create lines with distinct and stable relative Wolbachia densities - Final

description: Wolbachia densities
DOI: 10.5281/zenodo.6916567
title: A computational study of accelerating, steady and fading negative streamers in ambient air

description: This dataset contains the data used to generate the results presented in the article "A computational study of accelerating, steady and fading negative streamers in ambient air". This dataset includes (1) the source code; (2) two transport data files, which contain a list of included reactions, with their reaction rate coefficients, and transport coefficients; (3) configuration files for running the simulations and (4) output log files, which contain information about the physics and numerical properties of the simulations.
DOI: 10.5281/zenodo.6400856
title: A computational study of accelerating, steady and fading negative streamers in ambient air

description: This dataset contains the data used to generate the results presented in the article "A computational study of accelerating, steady and fading negative streamers in ambient air". This dataset includes (1) the source code; (2) two transport data files, which contain a list of included reactions, with their reaction rate coefficients, and transport coefficients; (3) configuration files for running the simulations and (4) output log files, which contain information about the physics and numerical properties of the simulations.
DOI: 10.5281/zenodo.6908580
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6908579
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6908627
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6908626
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6909469
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6909468
title: AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites

description: AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), https://www.opendatacube.org/]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: Vegetation Indices (Atmospherically Resistant Vegetation Index, Kaufman 1972) (Enhanced Vegetation Index, Huete 2002): (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) (Normalised Difference Moisture Index, Gao 1996) (Normalized Difference Vegetation Index, Rouse 1973) (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) (Simple Ratio Vegetation Index Jordan, C.F.1 969) (Soil Adjusted Vegetation Index, Huete 1988) (Visible Atmospherically Resistant Index, Gittleson 2002) Built-up Indexes (Band Ration For Built-up Area, Waqar 2012) (Built-up Area Extraction Index, Bouzekri 2015) (Built-up Index, He Et Al. 2010) (Index-based Built-up Index, Xu 2008) (New Built-up Index, Jieli Et Al. 2010) (Normalized Difference Built-up Index, Zha 2003) (Normalized Built-up Area Index, Waqar 2012) (Urban Index, Kawamura 1996) Water Indices (Modified Normalized Difference Water Index, Xu 1996) (Normalized Difference Water Index, Mcfeeters 1996) (Water Index, Fisher 2016) Other Indices (Bare Soil Index, Rikimaru Et Al. 2002) (Bare Soil Index, Wanhui 2004) (Burn Area Index, Martin 1998) (Clay Minerals Ratio, Drury 1987) (Ferrous Minerals Ratio, Segal 1982) (Iron Oxide Ratio, Segal 1982) (Normalized Burn Ratio, Lopez Garcia 1991) (Normalised Difference Snow Index, Hall 1995).
DOI: 10.5281/zenodo.6916163
title: Erythroxylum anguifugum zanfona 1

description: .
DOI: 10.5281/zenodo.6916162
title: Erythroxylum anguifugum zanfona 1

description: .