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42 results for “Composite indicators”
AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites
<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites</p> <p>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), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. 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)]. </p> <p>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. </p> <p>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. </p> <p>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). </p> <p>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: </p> <p>Vegetation Indices </p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972) </p> <p>(Enhanced Vegetation Index, Huete 2002): </p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) </p> <p>(Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) </p> <p>(Normalised Difference Moisture Index, Gao 1996) </p> <p>(Normalized Difference Vegetation Index, Rouse 1973) </p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) </p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969) </p> <p>(Soil Adjusted Vegetation Index, Huete 1988) </p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002) </p> <p> </p> <p>Built-up Indexes </p> <p>(Band Ration For Built-up Area, Waqar 2012) </p> <p>(Built-up Area Extraction Index, Bouzekri 2015) </p> <p>(Built-up Index, He Et Al. 2010) </p> <p>(Index-based Built-up Index, Xu 2008) </p> <p>(New Built-up Index, Jieli Et Al. 2010) </p> <p>(Normalized Difference Built-up Index, Zha 2003) </p> <p>(Normalized Built-up Area Index, Waqar 2012) </p> <p>(Urban Index, Kawamura 1996) </p> <p> </p> <p>Water Indices </p> <p>(Modified Normalized Difference Water Index, Xu 1996) </p> <p>(Normalized Difference Water Index, Mcfeeters 1996) </p> <p>(Water Index, Fisher 2016) </p> <p> </p> <p>Other Indices </p> <p>(Bare Soil Index, Rikimaru Et Al. 2002) </p> <p>(Bare Soil Index, Wanhui 2004) </p> <p>(Burn Area Index, Martin 1998) </p> <p>(Clay Minerals Ratio, Drury 1987) </p> <p>(Ferrous Minerals Ratio, Segal 1982) </p> <p>(Iron Oxide Ratio, Segal 1982) </p> <p>(Normalized Burn Ratio, Lopez Garcia 1991) </p> <p>(Normalised Difference Snow Index, Hall 1995). </p>
AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites
<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites</p> <p>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), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. 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)]. </p> <p>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. </p> <p>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. </p> <p>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). </p> <p>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: </p> <p>Vegetation Indices </p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972) </p> <p>(Enhanced Vegetation Index, Huete 2002): </p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) </p> <p>(Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) </p> <p>(Normalised Difference Moisture Index, Gao 1996) </p> <p>(Normalized Difference Vegetation Index, Rouse 1973) </p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) </p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969) </p> <p>(Soil Adjusted Vegetation Index, Huete 1988) </p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002) </p> <p> </p> <p>Built-up Indexes </p> <p>(Band Ration For Built-up Area, Waqar 2012) </p> <p>(Built-up Area Extraction Index, Bouzekri 2015) </p> <p>(Built-up Index, He Et Al. 2010) </p> <p>(Index-based Built-up Index, Xu 2008) </p> <p>(New Built-up Index, Jieli Et Al. 2010) </p> <p>(Normalized Difference Built-up Index, Zha 2003) </p> <p>(Normalized Built-up Area Index, Waqar 2012) </p> <p>(Urban Index, Kawamura 1996) </p> <p> </p> <p>Water Indices </p> <p>(Modified Normalized Difference Water Index, Xu 1996) </p> <p>(Normalized Difference Water Index, Mcfeeters 1996) </p> <p>(Water Index, Fisher 2016) </p> <p> </p> <p>Other Indices </p> <p>(Bare Soil Index, Rikimaru Et Al. 2002) </p> <p>(Bare Soil Index, Wanhui 2004) </p> <p>(Burn Area Index, Martin 1998) </p> <p>(Clay Minerals Ratio, Drury 1987) </p> <p>(Ferrous Minerals Ratio, Segal 1982) </p> <p>(Iron Oxide Ratio, Segal 1982) </p> <p>(Normalized Burn Ratio, Lopez Garcia 1991) </p> <p>(Normalised Difference Snow Index, Hall 1995). </p> <p> </p>
AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites
<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites</p> <p>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), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. 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)]. </p> <p>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. </p> <p>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. </p> <p>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). </p> <p>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: </p> <p>Vegetation Indices </p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972) </p> <p>(Enhanced Vegetation Index, Huete 2002): </p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) </p> <p>(Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) </p> <p>(Normalised Difference Moisture Index, Gao 1996) </p> <p>(Normalized Difference Vegetation Index, Rouse 1973) </p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) </p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969) </p> <p>(Soil Adjusted Vegetation Index, Huete 1988) </p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002) </p> <p> </p> <p>Built-up Indexes </p> <p>(Band Ration For Built-up Area, Waqar 2012) </p> <p>(Built-up Area Extraction Index, Bouzekri 2015) </p> <p>(Built-up Index, He Et Al. 2010) </p> <p>(Index-based Built-up Index, Xu 2008) </p> <p>(New Built-up Index, Jieli Et Al. 2010) </p> <p>(Normalized Difference Built-up Index, Zha 2003) </p> <p>(Normalized Built-up Area Index, Waqar 2012) </p> <p>(Urban Index, Kawamura 1996) </p> <p> </p> <p>Water Indices </p> <p>(Modified Normalized Difference Water Index, Xu 1996) </p> <p>(Normalized Difference Water Index, Mcfeeters 1996) </p> <p>(Water Index, Fisher 2016) </p> <p> </p> <p>Other Indices </p> <p>(Bare Soil Index, Rikimaru Et Al. 2002) </p> <p>(Bare Soil Index, Wanhui 2004) </p> <p>(Burn Area Index, Martin 1998) </p> <p>(Clay Minerals Ratio, Drury 1987) </p> <p>(Ferrous Minerals Ratio, Segal 1982) </p> <p>(Iron Oxide Ratio, Segal 1982) </p> <p>(Normalized Burn Ratio, Lopez Garcia 1991) </p> <p>(Normalised Difference Snow Index, Hall 1995). </p>
Figure 3. Cluster Dendrogram indicating 11 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 3. Cluster Dendrogram indicating 11 plant association types in the Lalkoo Valley.
Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites
<p>Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX<sub>86</sub>, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing <sup>14</sup>C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p> <p>Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p> <p>Antarctic δ<sup>18</sup>O<sub>ice</sub> and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ<sup>18</sup>O<sub>ice</sub> and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ<sup>18</sup>O<sub>ice</sub> records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records divided by the square root of the number of stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records. The final Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, expressed in ‰, has the same standard deviation as the δ<sup>18</sup>O<sub>ice</sub> record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, except that the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were corrected for seawater δ<sup>18</sup>O<sub>ice</sub> variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p>
The data table of eleven invasive species in Hungary and Romania: Invasive species' cover, invasive species' traits, basic characteristics, trait composition, functional diversity indices and soil parameters of recipient plant communities
<p>We studied 11 widespread herbaceous invasive alien species of East-Central Europe and their 16 impact metrics (resident plant communities' ecological characteristics, trait composition, functional diversity, and soil parameters) by sampling invaded and similar, uninvaded sites (space-for-time substitution method). Our aim was to (1) investigate the detailed ecological impacts of invasive plants on native plant communities; (2) explore the type of cover-impact relationships across impact metrics and their consistency across species; (3) study whether the cover-impact relationship depends on functional traits of invasive species. We present the data table with the 11 invasive species: the status of the sites (invaded, uninvaded), the cover of invasive species at plot level, the invasive species traits (lifespan, height, SLA, seed mass, clonal spread, flowering duration), community characteristics (species richness and diversity, native vegetation cover and bare ground cover at plot level), trait composition of native plant communities (native vegetation height, CWM height, CWM SLA, CWM seed mass, CWM clonal spread), functional diversity (functional richness, functional evenness, functional divergence, functional distance, RaoQ) and soil properties (N, P, organic C, pH).</p>
Data from: Bryophyte community composition and diversity are indicators of hydrochemical and ecological gradients in temperate kettle hole mires in Ohio, USA
Open the record for dataset details and reuse information.
The data table of eleven invasive species in Hungary and Romania: Invasive species' cover, invasive species' traits, basic characteristics, trait composition, functional diversity indices and soil parameters of recipient plant communities
Open the record for dataset details and reuse information.
Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions: supplementary material
<p>This dataset contains supplementary material to the paper "Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions", doi: 10.1016/j.jvolgeores.2021.107174</p> <p>It contains a set of volcanic ash refractive indices and optical properties derived for certain microphysical properties.</p> <p>For more information please consider the manuscript or contact the authors.</p>
Is the isotopic composition of precipitation a robust indicator for reconstructions of past tropical cyclones frequency? A case study on Réunion Island from rain and water vapor isotopic observations
<p>Isotopic composition of precipitation and water vapor and LMDZ-iso and ECHAM6-wiso simulations associated with the manuscript:</p> <p>Françoise Vimeux, Camille Risi, Christelle Barthe, Sören François, Alexandre Cauquoin, Olivier Jossoud, Jean-Marc Metzger, Olivier Cattani, Bénédicte Minster, and Martin Werner (2024). Is the isotopic composition of precipitation a robust indicator for reconstructions of past tropical cyclones frequency? A case study on Réunion Island from rain and water vapor isotopic observations. Journal of Geophysical Research: Atmospheres, 129, e2023JD039794. https://doi.org/10.1029/2023JD039794</p>
Supplementary material 2 from: Grabowska J, Płóciennik M, Grabowski M (2024) Detailed analysis of prey taxonomic composition indicates feeding habitat partitioning amongst co-occurring invasive gobies and native European perch. NeoBiota 92: 1-23. https://doi.org/10.3897/neobiota.92.116033
Relative abundance of prey categories (%N) (number of given prey category in relation to total number of prey) identified in fish guts at sites Z, R, B in the Western Bug River in August 2007
Supplementary material 1 from: Grabowska J, Płóciennik M, Grabowski M (2024) Detailed analysis of prey taxonomic composition indicates feeding habitat partitioning amongst co-occurring invasive gobies and native European perch. NeoBiota 92: 1-23. https://doi.org/10.3897/neobiota.92.116033
Relative abundance of species (%N) in fish assemblages found at sites Z, R, B in the Western Bug River in August 2007 (Penczak et al. 2010)
Assessment of Body Composition in Children Treated With Growth Hormone for the Indication of Isolated Non-acquired Growth Hormone Deficiency.
ClinicalTrials.gov study NCT07333521. IPD Sharing: NO. Countries: 1. Publications: 2.
Models to calculate composite indicators (Berga and Sutri). HISMACITY Protocol.
<p>Models to calculate composite indicators (Berga and Sutri). HISMACITY Protocol. The folder and .gdb files with the names: Attractiveness and Convertibility contain the data for Tourist Efficiency and Transformability indexes.</p>
Current Adoption of Composite Indices in Evaluating Rheumatoid Arthritis Patients: An Observational Study
ClinicalTrials.gov study NCT00793403. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Low Fat Plant-based Diet Effects on Body Composition Indices
ClinicalTrials.gov study NCT02906072. IPD Sharing: YES. Countries: 0. Publications: 5.
Influence of varying dietary ratios of ω6 to ω3 fatty acids on the hepatic global gene expression, and association with phenotypic traits (growth, somatic indices and tissue lipid composition) in Atla
GEO Series GSE139418. Salmo salar. 16 samples. Type: Expression profiling by array.
Evaluation of Indices of Body Composition During Ultramarathon : Measure by Body Bioelectrical Impedance
ClinicalTrials.gov study NCT01906203. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Kinect Equations for Body Indices and Body Composition
ClinicalTrials.gov study NCT04969588. IPD Sharing: NO. Countries: 1. Publications: 0.
Influence of Cardiorespiratory Fitness and Body Composition on Resting and Post-exercise Indices of Vascular Health in Young Adults
ClinicalTrials.gov study NCT06163456. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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