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134 results for “global estimates”
Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p>In-situ observations collected from publications, grid-level maps showing the predicted NCE (%) of global cropland and data-driven model codes</p>
Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p>Field observation data collected from publications, the references from the main text and data sources , grid-level maps showing prediction of global cultivated land NCE(%) and data-driven model codes </p>
Dataset for "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet"
<p>The dataset supports the article "Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet", which is accepted to be published in the Environmental Data Science journal. <br><br>The description of the files is as follows:</p> <ol> <li>ERA5.nc: <br> <ul> <li>Dimensions: (location: 11, time: 166560)<br>Coordinates:<br> longitude (location) float32 ...<br> latitude (location) float32 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2018-12-31T23:00:00<br> year (time) int64 ...</li> <li>Variables: 10ws, 100ws, 100alpha, 975ws, 950ws, 975wsgrad, 950wsgrad, zust, i10fg, t2m, skt, stl1, d2m, msl, blh, cbh, ishf, ie, tcc, lcc, cape, cin, bld, t_975, t_950, 2mtempgrad, sktempgrad, dewtempsprd, 975tempgrad, 950tempgrad, sinHR, cosHR, sinJDAY, cosJDAY, 10ws_delta1, 10ws_delta2, 10ws_delta3, 10ws_delta4, 10ws_delta5, 10ws_delta6, 100ws_delta1, 100ws_delta2, 100ws_delta3, 100ws_delta4, 100ws_delta5, 100ws_delta6, 975ws_delta1, 975ws_delta2, 975ws_delta3, 975ws_delta4, 975ws_delta5, 975ws_delta6, 950ws_delta1, 950ws_delta2, 950ws_delta3, 950ws_delta4, 950ws_delta5, 950ws_delta6</li> </ul> </li> <li>2000.nc: <ul> <li>Dimensions: (obs: 11, time: 8784, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2000-01-01 ... 2000-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> </ul> </li> <li> 2001.nc: <ul> <li>Dimensions: (obs: 11, time: 8760, heightAboveGround: 12)<br>Coordinates:<br> lat (obs) float64 ...<br> lon (obs) float64 ...<br> * time (time) datetime64[ns] 2001-01-01 ... 2001-12-31T23:00:00<br> * heightAboveGround (heightAboveGround) float64 10.0 15.0 ... 400.0 500.0<br>Dimensions without coordinates: obs<br>Data variables:<br> data (obs, time, heightAboveGround) float64 ...</li> <li>Data is the wind speed at multiple height levels</li> </ul> </li> </ol>
A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022 (V1.0)
<p>Our released data mainly contains four types of data:</p> <p>(1) Half-hourly or hourly gap-filled LE data: The data are well gap-filled LE data using the novel bias-corrected RF algorithm. In the filenames, “HH” or “HR” indicate half-hourly or hourly scale data, respectively. The time information in the data files includes a pair of timestamps consistent with those in FLUXNET2015. The data are recorded at local time. The start time is “2000-02-18, 00:00:00”, and the end time is the same as the observation time at each site. For the quality control flags (QC), a value of 0 indicates observed data, while 1 indicates gap-filled data.</p> <p>(2) Prolonged daily LE data: This dataset provides the prolonged daily LE data using the novel bias-corrected RF algorithm. The seamless data covers the period from February 18, 2000, to December 31, 2022. For the prolonged part, the quality flag is set to 2. The rest data is consistent with the aggregated daily LE data.</p> <p>(3) Aggregated daily, monthly and yearly LE data: The hourly dataset is aggregated from the gap-filled half-hourly data to a daily scale. The start time is “2000-02-18”, and the end time is the same as the observation time at each site. Data quality control flags are also provided, with the values representing the percentage of hourly observations for each day. The monthly and yearly LE data are aggregated from the prolonged daily LE data. Quality control flags represent the proportion of days with more than 90% of hourly observations in a given month or a given year. No distinction is made between prolonged data and data with complete missing observations within a day. The start time for the monthly data is March 2000, and that for the yearly data is 2001.</p> <p>All files are formatted as csv files. NDVI and debiased reference variables from ERA5-Land are also provided.</p> <p>For more details of our data, please refer to a companion research article submitted to ESSD. <span>Li, W., Yao, Z., Qu, Y., Yang, H., Song, Y., Song, L., Wu, L., and Cui, Y.: A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000-2022, Earth Syst. Sci. Data, under review, 2024.</span> </p>
Data from: Global, satellite-driven estimates of heterotrophic respiration
While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least observed carbon fluxes, particularly outside the mid-latitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4x5o spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System- Flux (CMS-Flux) with an optimally-scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially-varying carbon use efficiency retrieved in a model-data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6 ± 19.3 Pg C/yr. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input GPP and NEP datasets than to the assumption of a static CUE value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, with new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes, and are 30% greater overall (43.6 Pg C/yr vs. 33.4 Pg C/yr). The uncertainty ranges of both methods are comparable, except poleward of 45 degrees North, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.
FIGURE 5 in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 5. Present distributions of Syncordulia species in South Africa. Uppermost box shows the distributions of all Syncordulia species, lower boxes show individual species distributions: S. gracilis (top and top left); S. legator (top right); S. serendipator (bottom left); S. venator (bottom right).
FIGURE 3. R8S in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 3. R8S analysis on a 26-taxon tree; Geological maps adapted from figures on rst.gsfc.nasa.gov.
FIGURE 4 in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 4. Ancestral distributions; DIVA analysis optimized with 2 regions; larger letters indicate the scenarios discussed in the text
FIGURE 1. Strict consensus tree from a in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 1. Strict consensus tree from a PAUP parsimony heuristic search; 10,000 addition sequence replicates; bootstrap support shown above branches
FIGURE 2. Consensus tree from a in Biogeography and divergence time estimation of the relict Cape dragonfly genus Syncordulia: global significance and implications for conservation
FIGURE 2. Consensus tree from a PHASE analysis; 10 million generations. Posterior probabilities shown above branches
Figure 2 in A global biodiversity estimate of a poorly known taxon: phylum Tardigrada
Figure 2. Estimated global tardigrade diversity;, known numbers of taxa;, estimated species diversity; error bars are 95% confidence intervals; lower bars are not included as these are lower than the number of known species; y-axis is a logarithmic scale. A, marine species; B, limnoterrestrial species; and C, combined species.
Figure 1 in A global biodiversity estimate of a poorly known taxon: phylum Tardigrada
Figure 1. Images of a limnoterrestrial and marine tardigrade: A, scanning electron micrograph (SEM) of the limnoterrestrial eutardigrade Calohypsibius ornatus (Richters, 1900) (photo courtesy of Diane R. Nelson); B, drawing of the marine heterotardigrade Tanarctus bubulubus Jørgensen & Kristensen, 2001. Used with permission of the authors and publisher.
Figure 3 in A global biodiversity estimate of a poorly known taxon: phylum Tardigrada
Figure 3. Number of distinct first-author surnames for taxonomic papers listed in the checklist for tardigrades as a function of year;, terrestrial;, marine.
Data from: Estimating global biodiversity: the role of cryptic insect species
<p>How many species are there on Earth and to what groups do these species belong? These fundamental questions span systematics, ecology, and evolutionary biology. Yet, recent estimates of overall global biodiversity have ranged wildly, from the low millions to the trillions. Insects are a pivotal group for these estimates. Insects make up roughly half of currently described extant species (across all groups), with ~1 million described species. Insect diversity is also crucial because many other taxa have species that may be unique to each insect host species, including bacteria, apicomplexan protists, microsporidian fungi, nematodes, and mites. Several projections of total insect diversity (described and undescribed) have converged on ~6 million species. However, these projections have not incorporated the morphologically cryptic species revealed by molecular data. Here, we estimate the extent of cryptic insect diversity. We perform a systematic review of studies that used explicit species-delimitation methods with multi-locus data. We estimate that each morphology-based insect species contains (on average) 3.1 cryptic species. We then use these estimates to project the overall number of species on Earth and their distribution among major groups. Our estimates suggest that overall global biodiversity may range from 563 million to 2.2 billion species.</p>
Supplemental data for "Uncertainty in Land Use Obscures Global Soil Organic Carbon Stock Estimates"
<p>These are supporting data for the manuscript: "Uncertainty in Land Use Obscures Global Soil Organic Carbon Stock Estimates." They include data for the figures showing the spatial dynamics of cropland and LULCC-induced SOC loss etc, including Fig.3, Fig.6, Fig.9, and Fig.10.</p>
Data used in "A novel response priority framework for an urban coastal catchment using global weather forecasts-based improved flood risk estimates"
<p>The datasets used in "A novel response priority framework for an urban coastal catchment using global weather forecasts-based improved flood risk estimates" have been provided as rar files. Further details and instructions are provided in readme.txt in each folder of the rar file.</p>
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
<p>Code to reproduce the work in the manuscript 'A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes', Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, Lilong Liu, submitted to GMD. </p>
Estimating the Global Need for Palliative Care for Children
ClinicalTrials.gov study NCT02553148. IPD Sharing: YES. Countries: 1. Publications: 7.
Data from: Global, satellite-driven estimates of heterotrophic respiration
Open the record for dataset details and reuse information.
Data from: A global synthesis of survival estimates for microbats
Open the record for dataset details and reuse information.
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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
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.