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4,276 results for “Estimation”
Biomass production at 2085 horizon for the Maurienne valley (French Alps) estimated using a Bayesian Belief Network
<p>In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: while an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of regional grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified.</p> <p>We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2100. We built a Bayesian Belief Network (BBN) from long-term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution, and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios.</p> <p>Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the Conservation LULC scenario (-28% on average between 2020 and 2085), followed by the Tourism development scenario (-7%) and the Business-as-Usual scenario (+3%). Climate change under RCP 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Intensification somewhat compensated effects of climate and LULC changes on biomass production, whereas silvopastoralism offered only marginal gains. The Bayesian network model explicitly captured a future increase in interannual variability in biomass production.</p> <p>Synthesis and application: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannnual variability threaten the current grass-based Protected Designation of Origin production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. </p>
Interim model output of estimated bare peat/erosion density in Aberdeenshire and Angus at 1m resolution.
<p>This dataset contains the estimated unit density of peat erosion accross Aberdeenshire and Angus at 1m resolution. These outputs are derived from high resolution aerial imagery using the PeatNet pipeline for predicting the location and extent of peatland degradation features. This output was created to estimate the particulate organic carbon (POC) emissions from eroding peat based on multiple scenarios.</p>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Estimated Annual Agricultural Pesticide Use for USA48 2000 to 2019 (gridded maps at 250-m resolution)
<p>Estimated low and high pescticide uses based on census data for USA48, provided for the cropland mask at 250-m spatial resolution. The original <a href="https://water.usgs.gov/nawqa/pnsp/usage/maps/">USGS maps</a> are only available as PNGs, so we have prepared here a code to rasterize the values and produce GeoTIFFs. For cropland mask we use the <a href="https://www.usgs.gov/centers/eros/science/national-land-cover-database">National Land Cover Database (NLCD)</a>. All processing is fully documented in: <a href="https://github.com/Envirometrix/pesticide-use-USA48">https://github.com/Envirometrix/pesticide-use-USA48</a>. All maps are projected in the <a href="https://epsg.io/5070-1252">EPSG:5070</a>. Description of the layers:</p> <ul> <li><code>croplands_20**_250m.tif</code> = cropland mask (0–100%) estimated based on NLCD.</li> <li><code>GLYPHOSATE_EPEST.HIGH.KG.KM2_20**_250m.tif</code> = estimated pesticide use per county (high estimate) expressed in kg/km-square.</li> <li><code>diff.GLYPHOSATE_EPEST.HIGH.KG.KM2_250m.tif</code> = annual difference in pesticide use from 2000 to 2019;</li> </ul> <p><strong>Disclaimer</strong>: this code is under construction and USGS makes is clearly available that there are some limitations to this data:</p> <ul> <li>These estimates are made by using projected county crop acres from the previous Census of Agriculture and are expected to be revised upon availability of updated crop acreages in the following Census of Agriculture.</li> <li>The files do not include pesticide use estimates for California. Data for California are obtained from the online Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) database and are typically not available at the time the preliminary pesticide use estimates are generated for the rest of the U.S.</li> </ul> <p>Pease have in mind these limitations when using these maps for further modeling.</p>
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior
<p>Capturing qualitative features of animal behavior requires recording occurrences of behavior over time. Continuous sampling is best for capturing brief behaviors, but can be very time consuming. Instantaneous sampling can reduce the amount of labor required, but can miss short-duration behaviors. We therefore synthesized these techniques by continuously sampling during randomly scattered time intervals; a technique we call piecewise continuous sampling. To optimize and test the efficacy of this technique, we collected a continuous behavioral dataset of harvester ant workers, and then we developed a protocol to estimate the amount of sampling time necessary to reconstruct the proportion of time animals spend in different behavioral states. This protocol finds the sample size needed for the variance of the sample to converge on the variation of the population. We then divided this estimated time into equal-duration intervals that were randomly distributed across the entire continuous dataset. Finally, we calculated both time-dependent and time-independent error from this sample. We found that 4 to 16 sampling intervals minimize both types of error simultaneously. This finding was robust to differences in underlying behavior and was validated with simulations, implying that this method could be used for many types of organisms.</p>
F I G U R E 1 in Staining protocols affect use of otolith to estimate the demography of the damselfish sergeant major (Abudefduf vaigiensis)
F I G U R E 1 Mark quality in different concentrations of stain in the sagittae, lapilli, and asterisci. Error bars represent 95% confidence intervals and letters represent significant differences. Capitalized letters compare across treatments and lower-case letters compare within a treatment.
Figs 31–36 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 31–36. Models of length-weight relationship and functions for a large tunnelers (n=598) dung-beetles: 31–32 – untransformed data; 33–36 – log-logarithmic transformation data; 31 – exponential function model (R2=0.8205, AIC=5.08E05); 32 – logistic function model (R2=0.8276, AIC=4.88E05); 33 – linear function model (R2=0.5047, AIC=30.24); 34 – quadratic function model (R2=0.7814, AIC=17.61); 35 – power function model (R2=0.7089, AIC=21.44); 36 – exponential function model (R2=0.6979, AIC=22.03).
Figs 25–30 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 25–30. Models of length-weight relationship and functions for a large dwellers (figs 25–27, n=599) and large tunnelers (figs 28–30, n=598) dung-beetles: 23–27 – loglogarithmic transformation data; 28–30 – untransformed data; 25 – power function model (R2=0.5119, AIC=10.58); 26 – exponential function model (R2=0.5138, AIC=10.60); 27 – logistic function model (R2=0.5154, AIC=10.59); 28 – linear function model (R2=0.7259, AIC=7.75E09); 29 – quadratic function model (R2=0.8459, AIC=4.36E05); 30 – power function model (R2=0.813, AIC=4.77E05). 25
Figs 1–6 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 1–6. Models of length-weight relationship and functions for a small dwellers (n=1441) dung-beetles: 1–5 – untransformed data; 6 – log-logarithmic transformation data; 1 – linear function model (R2=0.5970, AIC=569.91); 2 – quadratic function model (R2=0.6094, AIC=554.40); 3 – power function model (R2=0.6103, AIC=553.25); 4 – exponential function model (R2=0.6113, AIC=551.78); 5 – logistic function model (R2=0.6104, AIC=553.01); 6 – linear function model (R2=0.6817, AIC=32.78).
Figs 7–12 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 7–12. Models of length-weight relationship and functions for a small dwellers (fig. 7, n=1441) and medium dwellers dung-beetles (figs 8–12, n=1808): 7 – log-logarithmic transformation data; 8–12 – untransformed data; 7 – quadratic function model (R2=0.6819, AIC=34.77); 8 – linear function model (R2=0.6535, AIC=3151.70), 9 – quadratic function model (R2=0.6937, AIC=2788.40); 10 – power function model (R2=0.6932, AIC=2793.50); 11 – exponential function model (R2=0.6922, AIC=2802.20); 12 – logistic function model (R2=0.6926, AIC=2897.20).
Figs 13–18 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 13–18. Models of length-weight relationship and functions for a medium dwellers (Figs 13–17, n=1808) and large dwellers (Fig. 18, n=599) dung-beetles: 13–17 – log-logarithmic transformation data; 18 – untransformed data; 13 – linear function model (R2=0.6242, AIC=24.92); 14 – quadratic function model (R2=0.6307, AIC=26.57); 15 – power function model (R2=0.6308, AIC=26.56); 16 – exponential function model (R2=0.6308, AIC=26.56); 17 – logistic function model (R2=0.6308, AIC=26.56); 18 – linear function model (R2=0.5320, AIC=11563). 23
Figs 43–47 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 43–47. Models of length-weight relationship and functions for a medium tunnelers (n=988) dung-beetles (log-logarithmic transformation data): 43 – linear function model (R2=0.8622, AIC=11.06); 44 – quadratic function model (R2=0.8638, AIC=12.99); 45 – power function model (R2=0.8639, AIC=12.99); 46 – exponential function model (R2=0.8609, AIC=13.14); 47 – logistic function model (R2=0.8632, AIC=13.02).
Figs 37–42 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 37–42. Models of length-weight relationship and functions for a large tunnelers (Fig. 37, n=598) and a medium tunnelers (Figs 38–42, n=988) dung-beetles: 37 – loglogarithmic transformation data; 38–42 – untransformed data; 37 – logistic function model (R2=0.5743, AIC=28.57); 38 – linear function model (R2=0.8348, AIC=5807.50); 39 – quadratic function model (R2=0.8419, AIC=5560.30); 40 – power function model (R2=0.8419, AIC=5560.10); 41 – exponential function model (R2=0.8418, AIC=5562.10); 42 – logistic function model (R2=0.8406, AIC=5607).
Figs 19–24 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East
Figs 19–24. Models of length-weight relationship and functions for a large dwellers (n=599) dung-beetles: 19–22 – untransformed data; 23, 24 – log-logarithmic transformation data; 19 – quadratic function model (R2=0.5353, AIC=11484); 20 – power function model (R2=0.5353, AIC=11483); 21 – exponential function model (R2=0.5355, AIC=11479); 22 – logistic function model (R2=0.5352, AIC=11486); 23 – linear function model (R2=0.5150, AIC=8.57); 24 – quadratic function model (R2=0.5160, AIC=10.58).
Data for "Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols"
<p>This repository contains calculated/simulated data on the imaginary part of the complex refractive index, single scattering albedo, and/or optical depth for dust aerosols in the visible band or at the wavelength of 550 nm.</p> <p>For detailed information on (1) the acquisition and utilization of this data, (2) comprehensive configurations for model simulations, (3) the principal findings, and (4) the methodology employed to achieve these findings, please refer to the article authored by Li, Mahowald et al. (2024; Commun. Earth Environ).</p> <p>Other datasets, including the code and laboratory observations presented in the paper, can be found elsewhere (refer to the Data and Code Availability sections of the paper).</p> <p>For any clarification regarding the data and code, inquiries related to the publication, or potential collaboration, please contact Longlei Li (<a href="mailto:ll859@cornell.edu">ll859@cornell.edu</a>) or Natalie M. Mahowald (<a href="mailto:mahowald@cornell.edu">mahowald@cornell.edu</a>).</p>
F I G U R E 3 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 3 (a) Posterior distribution of the difference between the true value and posterior distribution estimates (in percentage of the true value) of annual total smolt abundance obtained from DM (Dirichlet-multinomial) model M5 (simulation exercise). Each line is a year, and the 10 (one for each replicate) distributions are overlaid. The overall average difference is indicated in the left corner of the panel and represented by a red dashed vertical line. (b) C.V. of the posterior distributions of total abundance obtained from DM model M5. Each blue dot corresponds to the C.V. of a unique year/replicate, and each dashed blue horizontal line corresponds to the average C.V. of a replicate across years. The average C.V. across years and replicates (red dashed line) is also indicated in the top-right corner of the panel.
F I G U R E 4 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 4 (a–c) Posterior distribution of the relative contribution of the smolt abundance associated with each upstream rotary screw trap (RST) and (d) posterior distribution of the relative contribution of the "rest" of the smolt abundance in relation to the total abundance for the DM (Dirichletmultinomial) model M5 (simulation exercise). The red dots correspond to the true values in the simulated dataset.
F I G U R E 1 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 1 Restigouche River catchment map with main streams and lakes. Red dots indicate the location of the rotary screw traps (RST). Black polygons indicate subcatchments draining into upstream RSTs. Top-right panels: zoom on the location of the two downstream RSTs and example of a smolt with a streamer tag. Photo credit: Marie-Camille St-Amour.
F I G U R E 5 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 5 (left panels, a–e) Posterior distributions of the estimated annual catchability θt,k at each rotary screw trap (RST), (middle panels, f– h) posterior distributions of the annual smolt abundance estimates associated with each upstream RST (Nmt,i), (i) the total smolt abundance for the Restigouche catchment (Nmtot), and (right panels, j–m) posterior distributions of the annual relative proportions of the total smolt abundance t of the Restigouche catchment estimated for each upstream RST and for the unsampled areas (rest, bottom-right panel) using the Restigouche dataset. In the catchability panels (a–e), the dashed gray line is the median, and the light and dark gray areas indicate the 2.5th–97.5th and 25th– 75th interquantile ranges, respectively, of the hyperparameter μθ, that is, the average catchability of each RST of the time series. In the relative k proportions panels (f–i) the thick dashed colored line in the top three panels indicates the average proportion of the total catchment abundance estimated at the upstream RST. The thick dashed gray lines indicate the proportions of the Restigouche catchment wetted areas of the subbasins sampled at the upstream RST(j–l) and the wetted area for the remainder of the Restigouche catchment not sampled by any upstream RST (m); the proportions for the rest of the catchment area change over years based on the specific upstream RSTs that operated in a given year. For all panels, the colored dot is the median, and the thin and thick vertical segments indicate the 2.5th–97.5th and 25th–75th interquantile ranges, respectively.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.