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5,506 results for “variability”
A three-dimensional map of the Milky Way using 66,000 Mira variable stars
<p>We provide here full Table 1 from Iwanek, P., et al., 2023, "A three-dimensional map of the Milky Way using 66,000 Mira variable stars", ApJS (accepted for publication, DOI: 10.3847/1538-4365/acad7a), which contains mean magnitudes, distances, extinction values, and photometric chemical types for 65,981 Galactic Miras (Iwanek2023_Table1_GalMirasDist.txt file). The corner plot, i.e., the two-dimensional projections of the multi-dimensional posterior parameter spaces fitted to the Galactic Miras distribution is presented in Figure Iwanek2023_corner_plot.png.</p> <p> </p> <p>Patryk Iwanek is partially supported by Kartezjusz program No. POWR.03.02.00-00-I001/16-00, founded by the National Centre for Research and Development, Poland. Szymon Kozłowski acknowledges the support from the National Science Centre, Poland, via grant OPUS 2018/31/B/ST9/00334. </p> <p>This publication makes use of data products from WISE, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration (NASA). This work is based in part on archival data obtained with the Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA.</p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Monthly climate variables of isotope-enabled climate model simulations over the last millennium (850-1849CE) version 2
<p>Here we provide climate fields in monthly resolution for five isotope-enabled model: ECHAM5-wiso (Sjolte et al. 2018, Werner et al. 2016), GISS-E2-R (Lewis and Legrande 2015, Colose et al 2016), iCESM (Brady et al. 2019, Stevenson et al 2019), iHadCM3 (Bühler et al. 2021, Tindall et al. 2009), and isoGSM (Yoshimura et al. 2008) in supplement to Buehler et al. (2021, submitted to Clim. Past. Discuss.). The model simulations were performed with different sets of boundary conditions as described in Bühler et al. (2021, submitted to Clim. Past. Discuss.) in line with the PMIP3 protocoll (Schmidt et al. 2012). We provide output for surface temperature (in K), total precipitation amount (in mm month^-1), evaporation (in mm month^-1), latent heat (in W m^-2), and oxygen isotope ratios of precipitation (in permil).<br> Additionally, we provide simulation output extracted at cave locations within the SISAL v.2. database (https://researchdata.reading.ac.uk/256/, Comas-Bru et al. (2020)). We include output for sites that pass the resolution and dating screening, meaning that have at least 2 radiometric dates (or are lamina-counted) and provide 36 oxygen isotope ratio measurements within the last millennium.</p> <p>For version 2, we updated the damaged ECHAM5 precipitation file and the time axis to the iCESM precipitation and tsurf files.</p>
Prediction of Conformational Variability for RRM proteins in inter3m data base
<p>Predictions for protein Conformational Variability for the entries in InteR3M (<a href="https://inter3mdb.loria.fr/">https://inter3mdb.loria.fr/</a>), performed with the software ConforMine (in preparation).</p>
CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia
<p><strong>General Info</strong></p> <p>This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively. </p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Dataset Contents </strong></p> <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> <p>The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET).</p> <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> <p>The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>)</p> <pre><code>model_scripts.tar</code></pre> <p>Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset. </p> <pre><code>glacier_final.tar</code></pre> <p>Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).</p>
Simulations of Miocene Antarctic ice-sheet variability under increased precipitation and sub-shelf melt, using the ice-sheet model IMAU-ICE
<p>To demonstrate the viability of a precipitation regime change leading to a fundamentally different volume-to-area ratio of the Antarctic ice sheet, we deploy the 3D thermodynamical ice sheet/shelf model IMAU-ICE v1.1.1. In the standard set-up (<a href="https://doi.org/10.5194/cp-2023-12">Stap et al., 2021a</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">2021b</a>), climate forcing follows from pre-run warm and cold snapshot climate simulations. The applied climate forcing is transiently calculated based on the prescribed CO<sub>2</sub> concentration and the modelled ice sheet size, through a matrix interpolation method. Equilibrium experiments are performed at various CO<sub>2</sub> levels between preindustrial and 3x preindustrial CO<sub>2</sub> values, with insolation at present-day levels and initiated from an ice-free Miocene Antarctic topography (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109">Hochmuth et al., 2020</a>). Here, we perform additional sensitivity experiments, in which we apply a fixed precipitation increase and extreme sub-shelf melt rates. The precipitation anomaly is calculated as 25% of the warm snapshot precipitation fields, sub-shelf melt rates are set to 400 m/yr.</p> <p> </p>
TESS Confirmed and First Identified SuperWASP Variable Stars
<p>This catalog consists of the TESS-confirmed and First Identified SuperWASP Variable Stars of types $\delta$ Scuti, $\gamma$ Doradus, RR Lyrae, eclipsing binary systems with pulsating components, rotating variables, and others. This dataset is part of a short summary submitted to RNAAS entitled "Identifying SuperWASP Detected Candidate Variables with TESS" (Zhou, A.-Y., 2023 Research Notes of the AAS, vol.7). </p><p> </p>
Integrated freshwater abundance and connectivity clusters at the Hydrologic Unit 8 scale for the Midwest and Northeast U.S.A. – freshwater metric variables and k-means cluster assignment
This dataset includes integrated freshwater abundance and connectivity cluster output, principal component scores, and lake, wetland, and stream abundance and connectivity metrics measured at the Hydrologic Unit 8 (HU8) scale for 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of the integrated freshwater landscape that includes lakes, wetlands, and streams and their surface connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). The integrated freshwater clusters were created through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics for lakes, streams, and wetlands separately, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of freshwater abundance and connectivity in the landscape.
Freshwater connectivity clusters for lakes, wetlands, and streams at the Hydrologic Unit 12 scale in the Midwest and Northeast U.S.A. – freshwater metric variables and K-means cluster assignment
This dataset includes freshwater connectivity cluster output and principal component scores for lakes, wetlands, and streams measured at the Hydrologic Unit 12 (HU12) scale in 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of freshwater connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). Freshwater connectivity clusters were created separately for lakes, wetlands, and streams through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of lake, wetland, and stream connectivity in the landscape.
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/26/11. The abstract below was extracted from the Level 0 data package and is included for context: Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
Multiple biogeochemical variables were measured for organic and mineral soils on Arctic LTER experimental plots in moist acidic and non-acidic tundra, Arctic LTER Toolik Field Station, Alaska 2013.
Measures of soil nutrient content (available N and P, Extractable N and P, Total C, N and P), and microbial biomass and activity (exoenzyme activity) were measured for organic and mineral soils on Arctic LTER experimental plots at Toolik field station in moist acidic and non-acidic tundra (organic soils only).
Soil biogeochemical variables collected on the Arctic LTER experimental plots in moist acidic, moist non-acidic, wet shrub and shrub tundra, Arctic LTER Toolik Field Station, Alaska 2015
We investigated the effect of long-term warming on multiple soil and microbial carbon, nitrogen, and phosphorus pools, and microbial extracellular enzyme activities, with a particular focus on phosphorus, in Alaskan tundra plots underlain by permafrost
Soil and plant biogeochemical and soil temperature variables collected at brown lemming (Lemmus trimucronatus) and tundra vole (Microtus oeconomus) structure sties near Nome, Toolik Lake, and Utqaigvik, Alaska, summer 2018-2020
Soil and plant sampling analysis under small mammal-built structures and controls sites from near the Team Vole fences: Nome, Toolik, Utqiagvik, AK 2018-2020.
Artificial Communities Experiment: Predictor Variables
This study considers the relative role of competition and facilitation in primary succession of the boreal forest floodplain of Bonanza Creek Experimental Forest. Alder (Alnus tenuifolia Nutt.) is an early colonist of young floodplain sites, where it contributes greatly to soil development through the addition of nitrogen to the soil. White spruce (Picea glauca (Moench.) Voss) typically colonizes such sites later; it becomes established beneath a canopy of alder and willow (Salix spp.) which is sometimes overtopped by young balsam poplar (Populus balsamifera L.). The alder may potentially influence white spruce both positively and negatively. Possible facilitative effects include protection of small seedlings from excess heat or heavy siltation accompanying flooding and addition of soil nitrogen, an effect that may extend for many years. Competition for light (and other resources) before the white spruce has overtopped the alder canopy may prove to be a major negative effects. This study seeks to determine the net balance of these effects on the floodplain successional sequence.
Field survey of mangrove regeneration, porewater variables, and light in mangrove forests in Everglades National Park, Florida, USA, July 2020 - August 2022
This dataset package encompasses measurements from field surveys of mangrove regeneration, porewater variables, and light conditions across six mangrove sites in the coastal Everglades. The goal of this project was to quantify mangrove regeneration of seedlings and saplings in mid- and downstream locations within three estuaries in Everglades National Park, Florida, USA. We assessed the effects of porewater variables and light conditions on the observed regeneration patterns. The package includes seven datasets: FCE1268_Porewater: Contains measurements of porewater salinity, sulfide, ammonia, nitrite, orthophosphate, and nitrate at a 30 cm depth. Porewater surveys were conducted biannually from 09-10-2020 to 05-17-2022. See also similar porewater data for Florida Coastal Everglades (FCE) long-term sites in data packages knb-lter-fce.1169 and knb-lter-fce.1171, which contain data for SRS-5 and SRS-6, available in the FCE LTER website's data catalog or the EDI repository. FCE1268_Foliar_Nutrient_Content dataset, collected in August 2022, includes measurements of foliar nutrient content (total carbon, total nitrogen, and total phosphorus) for three mangrove species (A. germinans, L. racemosa, R. mangle) of two life stages—seedlings (height < 1 m) and saplings (height ≥ 1 m and Diameter at Breast Height (DBH) < 2.5 cm). FCE1268_Light contains light intensity (foot-candle) measurements taken at 1-hour intervals from 09-18-2020 to 08-29-2022 at mangrove sites and converted photosynthetic active radiation values from an outdoor mesocosm experiment. FCE1268_Sapling_Density provides biannual count measurements of individuals at the sapling plot level (4 m^-2) within each site from 07-09-2020 to 08-29-2022. FCE1268_Seedling_Density contains biannual count measurements of individuals at the seedling plot level (m^-2) within each site from 07-07-2020 to 08-29-2022. FCE1268_Sapling_Regeneration contains height, crown area, and stem elongation measurements of tagged sapling indiv
Dryland soil mycobiome response to long-term precipitation variability at the Jornada Basin LTER site, 2013-2019
This dataset contains data and code for the paper "Dryland soil mycobiome response to long-term precipitation variability depends on host type" published in Journal of Ecology in 2022. Data were collected at the Jornada Basin LTER site in southern New Mexico, USA. Soil samples were sent to the University of Georgia, for DNA extraction and sequencing, performed at the Georgia Genomics and Bioinformatics Core in Athens, GA. Sequencing data are archived at NCBI under project identifier PRJNA884111.
Perennial grass tiller and stolon counts in plots with experimentally altered precipitation variability at the Jornada Basin LTER site, 2012-2014
This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation variability manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009, has five annual precipitation treatments, and contains 50 plots (10 per treatment). This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs to 2.5 x 2.5 meter plots in a desert grassland. There are high, low, and ambient (control) precipitation variability treatments. Ambient plots receive natural precipitation each year, while variability treatments alternate between 20% and 180% (high variability), or 50% and 150% (low variability) of ambient precipitation each year. Perennial grass tiller and stolon counts were made annually in each plot from 2012-2014. This is an ongoing study and the dataset will be updated as needed.
Ecophysiological variables of common shrub and grass species during the growing season following simulated sandblasting trials at the Jornada Experimental Range, New Mexico, USA, 2018 and 2019
In this dataset, we report ecophysiological variables of contrasting perennial grass (Bouteloua eriopoda, Sporobolus airoides, and Aristida purpurea) and shrub (Prosopis glandulosa, Atriplex canescens, and Larrea tridentata) functional groups before and after a series of simulated sandblasting events with various intensities and frequencies. We hypothesized that grass species are more susceptible to the resulting "sandblasting" (i.e., abrasive damage by wind-blown particulates) than shrubs, thus contributing to the shift from grass to shrub dominance. To test this, we conducted a wind tunnel experiment at the USDA Jornada Experimental Range in 2018 and 2019 growing seasons. Potted plants were subjected to different levels of sandblasting in a novel portable wind tunnel, and plants’ ecophysiological responses including leaf gas exchange and nighttime leaf stomatal conductance were quantified. All tested plants were then grown in benign greenhouse conditions to investigate plant recovery post sandblasting. This dataset contains data about plant biomass and height, leaf chlorophyll content, leaf gas exchange, stomatal conductance, and water use efficiency (WUE) under the experimental treatments above. This study is complete.
Shrimp populations variability in numbers and sizes in response to disturbance and seasons on 20 pools along the reach of Quebrada Prieta, Luquillo Experimental Forest
Shrimp populations were monitored at approximately 3 week intervals to determine the variability in numbers and sizes of each species in response to disturbance and seasons. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
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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.