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53 results for “Derived variable”
Long-term seasonally and annually aggregated climatic variables for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert, derived from single-day NASA Daymet images, 2000 to 2022
This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Collective Variable for Metadynamics Derived from AlphaFold Output
<p>AlphaFold is the state of the art method for prediction of 3D structures of proteins from the amino acid sequence by neural networks. One of the outputs of AlphaFold is a probability profile of inter-residue distances for all residue pairs. We used this profile to evaluate any conformation of the studied protein to express its compliance with the AlphaFold prediction. This value can be used as a collective variable in metadynamics or parallel tempering metadynamics to accelerate protein folding in a molecular simulation. We applied this approach on folding of mini-proteins Trp-cage and beta hairpin. See V. Spiwok, M. Krečka & A. Křenek: <a href="http://doi.org/10.3389/fmolb.2022.878133">Collective Variable for Metadynamics Derived from AlphaFold Output</a> <em>Frontiers in Molecular Biosciences</em> <strong>9</strong> 878133 (2022) DOI: 10.3389/fmolb.2022.878133.</p>
WaterGAP2.2d model derived Potential evapotranspiration and Renewable water resources variables with standard and modified PET calculation methods
<p>This data set is produced as a part of the ''Improving the quantification of climate change hazards by hydrological models: A simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration" journal publication (in preparation). WaterGAP2.2d global hydrological model with two different settings; 1) with standard PET method Priestley-Taylor (PT) and 2) with modified approach (PT-MA) (please refer to the publication for more details on the method) used to derive the data set. The bias-adjusted GCM-derived (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) climate data under RCP2.6 and RCP8.5 emission scenarios were used as the input. The model-derived potential evapotranspiration and the renewable water resources variables are available from 1981 to 2099 on the monthly scale for each land grid cell (spatial resolution: 0.5 degrees x 0.5 degrees). The data files are in the netCDF format (.nc4). </p>
Dataset of five years of in-situ and satellite derived chlorophyll a concentrations and its spatiotemporal variability in the Rotorua Lakes, New Zealand
<p><strong>rotorua_chl_fields_2015-2020.nc</strong> is a time series of 283 <em>Chl</em> fields of 13 of the lakes derived from Sentinel-2 MSI images with a regionalised parametrization of the C2RCC algorithm at 60 m pixel resolution. It also includes C2RCC and Idepix masks as well as a shoreline-and-shallow-water-buffer for flexible quality flagging.</p> <p><strong>rotorua_chl_spatial_variability.tif</strong> is a GeoTIFF that illustrates the representativeness of each grid cell for the <em>Chl</em> distribution in each lake and thus indicates recurring spatial patterns. The file contains three bands. Each band shows the relative frequency (in %) which <em>Chl</em> concentration was found near the median, or upper or lower quartile, respectively. The intervals around the median and quartiles are 5% to either side.</p> <p><strong>rotorua_insitu_chl_2015-2019.csv</strong> contains 831 in situ <em>Chl</em> measurements from 12 of the lakes collected between 2015 and 2019. The majority of these measurements (802) have been taken as part of the monthly Bay of Plenty lake water quality monitoring programme, in which 11 lakes are monitored. The data set also contains samples from field work under the <em>Eye on Lakes</em> project (University of Waikato) obtained by one of the authors (MKL). These 29 samples also include two measurements at Lake Rotokakahi, which is not part of the monthly monitoring program.</p> <p><strong>shoreline_shallow_water_buffer.zip</strong> contains a shapefile with polygons of the valid water pixels of all lakes to remove areas contaminated by bottom reflectance in remote sensing products. Each lake has a 120 m shoreline buffer to avoid mixed land-water pixels to reduce adjacency effects. It further excludes lake areas shallower than the 95%-quantile of all Secchi depth measurements of the Bay of Plenty lake water quality monitoring programme.</p>
Vallée de la Sionne Snow Avalanche n. 20213009: High-speed camera recording and derived variables
<p>This repository hosts data obtained from high-speed camera measurements conducted within a large powder snow avalanche (No. 20213009) that occurred naturally at the Vallée de la Sionne test site in Switzerland. Positioned 14 meters above the ground on a vertical pylon, the high-speed camera captures visualizations of snow particles within the aerial layers. These images reveal diverse particle clusters, identifiable as bright spots due to their higher light reflectance compared to the surrounding air-snow crystal mixture.</p> <p>Contained within this repository is an overview video recording along with corresponding data on the average brightness of each image captured by the high-speed camera. This dataset facilitates the reconstruction of the temporal evolution and frequency of particle clustering, with brightness intensity acting as a proxy for mass transport. The average brightness for each image is computed from the averaging of values from 2048 x 2048 pixels (greyscale 0 to 255). These datasets complement the findings presented in the following publication:</p> <p>B. Sovilla, E. Marchetti, M. Kyburz, A. Koehler, P. Huguenin, I. Calic, M.J. Kohler, E. Surinach, and C. Pérez-Guillén, under review. "The dominant source mechanism of infrasound generation in powder snow avalanches," submitted to Geophysical Research Letters.</p>
Summary Table of AHG Parameters, Hydraulics, Morphological and geophysical variables, and Suspending Sediment Concentration Derived at 1246 USGS River Monitoring Stations
<p>This table contains information about:</p> <p>1) At-A-Station Hydraulic Geometry Parameters,</p> <p>2) Hydraulics Variables,</p> <p>3) Morphological and Geophysical Variables, and</p> <p>4) Suspending Sediment Concentration and Fraction of Sand, Silt, and Clay,</p> <p>derived at 1246 USGS river monitoring stations across the conterminous United States. </p>
Radiometer network dataset of 10 Hz spectral irradiance and derived variables (LIAISE campaign)
<p><strong>Description</strong></p> <p>Measurements from the spatial network of 15 radiometers deployed at the LIAISE field campaign in 2021.</p> <p>14 July 2021 until 28 July 2021.</p> <p><strong>Dataset Contents</strong></p> <ul> <li><em>rsds: </em>Total shortwave downwelling solar irradiance @ 10 Hz, 1 sec, and 1 minute resolution (Level 2, derived dataset)</li> <li><em>prw: </em>Total column integrated water vapour @ 1 sec resolution (Level 2, derived dataset)</li> <li><em>spectrum: </em>Pre-calibrated solar spectral irradiance measurements @ 10 Hz resolution (Level 1, source dataset)</li> <li><em>raw data:</em> straight from the sensors (Level 0)</li> </ul> <p><strong>Dataset Quality</strong></p> <p>All data is quality controlled and completed with metadata and quality flags. Level 2 data is calibrated against high quality references, and derived from Level 1 data.</p> <p>Methodology, performance, and usage all described in detail in an upcoming pre-print.</p> <p><strong>References and more info</strong></p> <ul> <li><a href="https://egusphere.copernicus.org/preprints/2022/egusphere-2022-726/">Radiometer reference paper (FROST)</a></li> <li>LIAISE <a href="https://liaise.aeris-data.fr/">campaign website</a>, <a href="https://liaise.aeris-data.fr/page-catalogue/?uuid=594dbc5d-986e-4679-b0ec-7d1e29b5cab9">LIAISE database</a></li> <li>Dataset description paper: <a href="https://arxiv.org/abs/2307.06980">pre-print on Arxiv</a></li> <li>Code to produce these data <a href="https://doi.org/10.5281/zenodo.10159129">on Zenodo</a></li> </ul> <p><strong>Version History</strong></p> <p>v1.2: fixed bug of incorrect wavelength labeling, all bands but 900 and 940 nm were affected. This only concerns the 10 Hz spectrum data, rest unchanged.</p> <p>v1.1: added raw (level 0) data, fixed typo in file name for the 10 Hz spectrum zip (L2 -> L1).</p>
Downscaled climate grids of California at 90m for a variety of bioclimatic variables from 1971-2000, derived from historical climate grids
This dataset is comprised of 90 Geotiff images of selected bioclimatic variables for the state of California (extended past state lines to river basin boundaries). Originally created to model plant species distributions in California (Franklin et al. 2013. Modeling plant species distributions under future climates: how fine-scale do climate projections need to be? Global Change Biology 19: 473-483).
Making andesite through shallow hybridization of magmas derived from variably enriched lithospheric mantle
<p>We integrate textural and in situ compositional information from plagioclase and clinopyroxene (Cpx) phenocrysts together with groundmass compositions in early Cretaceous andesite dykes within the Sulu belt of China to propose a new petrogenetic model for andesite. Plagioclase phenocrysts are mostly andesine; they are depleted in high field strength elements (HFSE). However, clinopyroxene (Cpx) phenocrysts are either reversely-zoned (type I) or homogeneous (type II), with the zoned Cpx divided into subtypes IA and IB. All Cpx has high Mg#, low Na<sub>2</sub>O and generally low Al<sub>2</sub>O<sub>3</sub>, with depletions in HFSE and variably high <sup>87</sup>Sr/<sup>86</sup>Sr ratios, suggesting crystallization above the Moho from magmas derived from enriched lithospheric mantle. The cores of type IA/IB and type II Cpx have normal major- and trace-element compositional variations and similar <sup>87</sup>Sr/<sup>86</sup>Sr ratios to each other and to plagioclase, consistent with fractional crystallization from a common magma (magma 1). The rims of type IA and IB Cpx also have normal major- and trace-element compositional variations, but these are not as evolved as the cores, and the rims have lower <sup>87</sup>Sr/<sup>86</sup>Sr ratios, demonstrating crystallization from an isotopically-distinct magma (magma 2). Based on modelled major and rare earth element compositions of magmas inferred to have been in equilibrium with different Cpx (± plagioclase) domains, the measured groundmass compositions can be reproduced by variable mixing between the two magmas. Our study demonstrates for the first time that andesite magma can be made through fractionation and shallow hybridization of magmas derived from variably enriched lithospheric mantle.</p>
Slow Yogic-Derived Breathing and Respiration and Cardiovascular Variability in Spinal Cord Injury Patients
ClinicalTrials.gov study NCT05480618. IPD Sharing: NO. Countries: 1. Publications: 1.
Derived variables and coordinates to assess the ecological relevance of multiscale bathymetry for coral species distribution modelling across the Great Barrier Reef
Open the record for dataset details and reuse information.
Data derived state probabilities for Z. noltei monitoring study. Observed variables were shoot density at four sites in this study
<p>1. In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well-known situation. Model transferability and adaptability may be extremely beneficial - approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context.</p> <p>2. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterisation and quantification of the model, and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN.</p> <p>3. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterised the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses.</p> <p>4. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximise model reuse and minimise re-development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.</p>
Dataset of "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" (3/3)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" by T. Kuroda, E. Yiğit and A.S. Medvedev.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees.</p> <p>data210rdc.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc.tar.xz: for Ls=240-270 (46 Sols)</p> <p>data270rdc.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc.tar.xz: for Ls=330-360 (56 Sols)</p>
Dataset of "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" (2/3)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" by T. Kuroda, E. Yiğit and A.S. Medvedev.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees.</p> <p>data090rdc.tar.xz: for Ls=090-120 (64 Sols)</p> <p>data120rdc.tar.xz: for Ls=120-150 (60 Sols)</p> <p>data150rdc.tar.xz: for Ls=150-180 (54 Sols)</p> <p>data180rdc.tar.xz: for Ls=180-210 (49 Sols)</p>
Dataset of "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" (1/3)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" by T. Kuroda, E. Yiğit and A.S. Medvedev.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees.</p> <p>data000rdc.tar.xz: for Ls=000-030 (61 Sols)</p> <p>data030rdc.tar.xz: for Ls=030-060 (66 Sols)</p> <p>data060rdc.tar.xz: for Ls=060-090 (67 Sols)</p>
Figure 5. Minimum spanning haplotype network derived from a 658 base-pair cytochrome c oxidase subunit I in Six degrees of separation in barnacles? Assessing genetic variability in the sea-turtle epibiont Stomatolepas elegans (Costa) among turtles, beaches and oceans
Figure 5. Minimum spanning haplotype network derived from a 658 base-pair cytochrome c oxidase subunit I (COI) fragment from 57 Stomatolepas elegans collected from nine different Lepidochelys olivacea nesting on Playa Teopa, Jalisco, Mexico, six S. elegans from Caretta caretta from the western Atlantic, and six S. praegustator from C. caretta from the western Atlantic. Circle sizes are proportional to the frequency of each haplotype, with haplotype 1 being most common. Coloured pie slices are also proportional, and represent the number of S. elegans from each turtle characterized by the respective haplotype. Colours represent the nine Mexican turtles randomly sampled for S. elegans populations. Open circles with numbers indicate Atlantic haplotypes. Solid black circles designate hypothetical missing haplotypes. The network includes S. elegans haplotypes 1–21, and S. praegustator haplotypes 19, 26–30. Haplotypes 1–17, shown in colour, represent Jalisco, Mexico specimens collected from nine different turtles in the Pacific, and haplotypes 18–21 and 26–30, shown as unshaded circles, represent southeastern United States Atlantic specimens collected from six different C. caretta (see Table 1).
Derived data for: "Variability of the interplanetary magnetic field as a driver of electromagnetic induction in Mercury's interior"
<p>Derived data for "Variability of the interplanetary magnetic field as a driver of electromagnetic induction in Mercury’s interior", accepted for publication in the Journal of Geophysical Research: Space Physics.</p>
FIGURE 10 in Taxonomy and nomenclature of Kalanchoe beharensis (Crassulaceae subfam. Kalanchooideae), a variable, arborescent species from Madagascar, with reference to its horticultural derivatives
FIGURE 10. Material included in Kalanchoe beharensis Hairy Group (continued). A. Kalanchoe beharensis 'Napoleon's Hat'. B. Kalanchoe beharensis 'Oakleaf. C. Kalanchoe beharensis 'Pixel'. All photographs: Gideon F. Smith.
FIGURE 9 in Taxonomy and nomenclature of Kalanchoe beharensis (Crassulaceae subfam. Kalanchooideae), a variable, arborescent species from Madagascar, with reference to its horticultural derivatives
FIGURE 9. Material included in Kalanchoe beharensis Hairy Group. A–C. Large growing plants with leaves variously covered in a golden-bronze tomentum are marketed and sold under a range of monikers, some of which are likely trade designations only. The plants illustrated in A to C are generally available under K. beharensis 'Aureo-aeneus', although this is not an established cultivar name. The variant illustrated in B has entire leaf margins. The plant of K. beharensis illustrated in C is cultivated in the Shoenberg Temperate House in the Missouri Botanical Garden, St Louis, U.S.A. D–F. Cultivar names, or at least monikers presented as cultivar names, such as K. beharensis 'Brown Dwarf', K. beharensis 'Monstrose', K. beharensis 'Nana', and K. beharensis 'Curly', are generally applied to a range of small-growing, hairy variants of K. beharensis, some of which are illustrated here. All photographs: Gideon F. Smith.
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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.