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3,788 results for “indices”
Data for: Parasitoids indicate major climate-induced shifts in Arctic communities
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Predictable evolution toward larger brains and lower hand-wing indices in long-tailed birds
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Data from: Acoustic indices estimate breeding bird species richness with daily and seasonally variable effectiveness in lowland temperate Białowieża forest
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Data from: Enriched East Asian oxygen isotope of precipitation indicates reduced summer seasonality in regional climate and westerlies
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Data and code for: An anchovy ecosystem indicator of marine predator foraging and reproduction
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Global Fire Weather Indices - DSR using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - ISI using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - FFMC using overwintered DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - FWI using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Global Fire Weather Indices - BUI using default DC start-up
<p>This dataset was developed by Natural Resources Canada using the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5-HRS Reanalysis product (C3S, 2017) as inputs to the Canadian Forest Fire Danger Rating System R Package (Wang et al. 2017). The dataset provides gridded values of the Canadian Fire Weather Index (FWI) System indices of fuel moisture and fire behaviour, including the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), Fire Weather Index (FWI), and Daily Severity rating (DSR). Each of these indices are produced using two calculation methods applied at the beginning of fire season start-up. The first method used the default DC value (DC=15) to start-up the FWI System calculation and only accounted for the longest stretch of active fire season each year (as determined by Wotton and Flannigan, 1993). The second method used the overwintered DC value, calculated from the DC value of the last day of the previous fire season and a percentage of overwinter precipitation, and accounted for all periods of fire season throughout the year. We recommend users of this data use indices where DC has been overwintered in regions where the fire season shuts off for winter and where low overwinter precipitation occurs (eg. parts of western Canada, the western US and the Siberian Boreal forest).</p> <p>References:</p> <p>Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Accessed June 20<sup>th</sup> 2019. <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a></p> <p>Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M. A., Anderson, K., Moore, B., & Flannigan, M. D. (2017). cffdrs: an R package for the Canadian forest fire danger rating system. Ecological Processes, 6(1), 5.</p> <p>Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187-192.</p>
Data package from: Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network
<p>This data package contains the very high resolution maps of natural forests as well as map of natural forests dominated by Cecropia hololeuca or Tibouchina pulchra presented in the Figure 8 of the paper "Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network". These maps have be produced with WorldView-2 and WorldView-3 very high resolution images and a deep learning method for image segmentation called U-net (methods and data are fully described in the article). When using this data, please cite the original article <a href="https://doi.org/10.1371/journal.pone.0229448">https://doi.org/10.1371/journal.pone.0229448</a>.</p> <p> </p>
A dash indicates that data were not taken for that sample. a in Isolation of an archaeon at the prokaryote eukaryote interface
A dash indicates that data were not taken for that sample. a The iTAG analysis was performed for samples in which an increase of about 10 times or more in 16S rRNA gene copy numbers of MK-D1 was observed after incubation;data were analysed by qPCR assay.Detailed results are shown in Supplementary Table 1. b Final concentration of casamino acids was 0.05% (w/v). c Final concentration of each amino acid was 0.1 mM. d Powdered milk for baby (Hohoemi, Meiji) was used at a final concentration of 0.1% (w/v). e The concentration of hydrogen gas was in the head space of the culture bottle. f2-BES was added to inhibit methanogens. g Addition of nitrate completely suppressed the growth of MK-D1.This is probably because nitrate inhibits formate dehydrogenase activity of MK-D1 95. h Archaeal cell membrane components were a mixture of phytol,intact polar lipid–glycerol-dialkyl-glycerol tetraethers and core lipid– glycerol-dialkyl-glycerol tetraethers (each at a final concentration 50 ng ml −1). We used the archaeal membrane components as these have a positive effect on the growth of some archaeal species:(i) archaeal cell extract including membrane lipids stimulates the growth of the extremely thermophilic archaeon Thermocaldium modestius 96, and (ii) the hyperthermophilic archaeon Thermofilum pendes requires the polar lipids for growth,which was obtained from the archaeal species Thermoproteus tenax 97.
Indicator Species Analysis Results
<p>Paper: Diversity and composition of fungal soil communities across prescribed burn areas in temperate hardwood forests</p> <p>Authors: S.D. Russell & M.C. Aime</p> <p>A spreadsheet containing the combined DESeq2 log2FoldChange and Random Forest MeanDecreaseAccuracy metrics for each OTU. Supplementary material for the paper above.</p>
Linking individual physiological indicators to the productivity of fish populations: A case study of Atlantic herring
<p>Raw data for i) heart rate measurements of Atlantic herring larvae reared at three different temperatures (7, 11 and 15°C); ii) compilation of Baltic herring larval growth rates measured in the field and in the laboratory; and iii) time series of thermal estimates (derived from satellite sea surface temperatures) in Greifswald Bay (Baltic Sea) between 1992 and 2017, as well as three herring productivity proxies in the area: weekly larval abundance in Greifswald Bay (one of the major spawning grounds), 1 yr-old juvenile abundance in the western Baltic basin, and the recruitment estimate from the International Council for the Exploration of the Sea (ICES) Herring Assessment Working Group for the Area South of 62°N.</p>
Pseudo-Soundings for Publication "Changes in the simulation of instability indices over the Iberian Peninsula due to the use of 3DVAR data assimilation"
<p>These data are made available as part of paper: S. J. González-Rojí, S. Carreno-Madinabeitia, J. Sáenz, and G. Ibarra-Berastegi (2021) "Changes in the simulation of atmospheric instability over the Iberian Peninsula due to the use of 3DVAR data assimilation", published in <em>Hydrology and Earth System Sciences (HESS) (</em><a href="https://doi.org/10.5194/hess-25-3471-2021">https://doi.org/10.5194/hess-25-3471-2021</a><em>)</em>. The dataset holds selected postprocessed files that allow to reproduce all the results in the paper.</p> <p>Two WRF experiments nested in ERA-Interim were prepared. The first one (N) was configured as in standard numerical downscaling experiments. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 hours. The original experiments covered period 2010-2014 after a year of spin-up (2019). </p> <p>The following files are included:</p> <p>- PseudoSoundings_EXP_YYYY: Contains the Pressure, Temperature and Mixing Ratio on each point included in the mask defined for the Iberian Peninsula. The values of each model level are included. The values at 00 and 12 UTC are included. </p> <p>- ListPointsWRF.txt: Provides the order followed in the nPoints dimension of the nc files. The longitud and latitude of each point is also provided in that list. </p> <p>The reference data of the soundings was download from the University of Wyoming, and they are freely available in this website: <a href="http://weather.uwyo.edu/upperair/sounding.html">http://weather.uwyo.edu/upperair/sounding.html</a></p> <p> </p> <p> </p>
Extended indicators for AONBs (appendix 2)
<p>A complete list of the indicators identified throughout the study. They have been categorized according to the UK Government 25 Year Environment Plan headline indicators. Each indicator has also been coded dependent on which of the 5 categories it fits, as identified within the paper - it is submission for appendix 2</p>
Targeted indication of imaging for detection of vesicoureteric reflux after pediatric febrile urinary tract infections based on a multiparametric computational tool
<p>This is the original data used for our study on differentiated indication of a voiding cystourethrography after pediatric febrile urinary tract infections. </p>
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Inventory of principles, criteria and indicators in relation to SAT-ProBio blueprint
<p>The dataset provide indication for principles and criteria in relation to SAT-ProBio blueprint as well as a compilation of available data related to sustainability principles, criteria and indicators provided by EN 16751 and STAR-ProBio project partners USC-UNITELMA et al. on social indicators and Quantis et al. on selected environmental indicators and AUA et al. on techno-economic indicators.</p>
World Health Indicators
<p><strong>Dataset Description:</strong></p> <p>The <strong>World Health Indicators</strong> dataset has been built merging data obtained from the United Nations website (http://data.un.org/) and from The World Bank site (https://data.worldbank.org/indicator). The dataset gives indicators regarding public health, including economic and social data for different countries and regions worldwide for the years 2005, 2010 and 2019.</p> <p> </p> <p><strong>Attribute description:</strong></p> <p><strong>country_code</strong> – ISO3 country code</p> <p><strong>country</strong> – Country name</p> <p><strong>year </strong>– Year</p> <p><strong>population</strong> – Total population</p> <p><strong>population_grow</strong> – Population growth (% annual)</p> <p><strong>population_under_14</strong> – Population between 0-14 years old (% total population)</p> <p><strong>population_above_65</strong> – Population above 65 years old (% total population)</p> <p><strong>gdp </strong>- GDP: Gross domestic product (million current US$)</p> <p><strong>gdp_growth_rate</strong> – GDP growth rate (annual %, constant 2010 prices)</p> <p><strong>unemployment</strong> – Unemployment (% of labour force)</p> <p><strong>education_gov_expenditure</strong> – Government expenditure in education (% of GDP)</p> <p><strong>health_expenditure</strong> – Current expenditure in health (% of GDP)</p> <p><strong>life_expectancy_fem</strong> – Life expectancy, female (years)</p> <p><strong>life_expectancy_male</strong> – Life expectancy, male (years)</p> <p><strong>non_commun_disease_death</strong> – Cause of death, by non-communicable diseases (% of total)</p> <p><strong>commun_disease_death</strong> – Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)</p> <p><strong>tuberculosis</strong> – Incidence of tuberculosis (per 100,000 people)</p> <p><strong>hiv </strong>– Prevalence of HIV, total (% of population ages 15-49)</p> <p><strong>infant_mortality </strong>– Infant mortality rate (per 1,000 live births)</p> <p><strong>undernourishment</strong> – Prevalence of undernourishment (% of population)</p> <p><strong>hospital_beds</strong> – Hospital beds (per 1,000 people)</p> <p><strong>physicians</strong> – Physicians (per 1,000 people)</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.