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438 results for “space time”
Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011
The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.
Plant aboveground biomass dry weight record for Space for Time plots in PIE LTER.
Aboveground biomass measurements were conducted annually near peak biomass to evaluate aboveground plant production and determine differences in relation to other biotic and abiotic factors. In a 0.053 m2 plot, aboveground biomass was clipped to the soil surface at Space For Time plots, dried, and weighed to capture dry weight.
Test-Retest qt-dMRI datasets for "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time"
<p>We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." <em>Medical Image Analysis</em> (2017). More detailed information about the use of these data sets can also be found in the publication.</p> <p>We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500<span class="math-tex">\(\mu\)</span>m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent.</p> <p>- The diffusion MRI data are contained in the files with 'dwis' in the name.<br> <br> - The corpus callosum masks are contained in the files with 'mask' in the name.</p> <p>- The acquisition parameters are contained in the .txt files.</p>
Short term accretion measured using marker horizon of feldspar at Space For Time locations.
This dataset provides a survey of short term accretion across the PIE LTER at both high and low marsh sites. A marker horizon plot was established and sampled annually to determine depth of accretion since deployment.
Soil core metrics taken at Space for Time plots across the PIE LTER.
Soil cores were collected and evaluated at three different depth intervals for bulk density, organic matter by loss on ignition (LOI), pore water ammonium, and pore water salinity. These biotic and abiotic factors differ across marsh types and elevations and help to parameterize other measurements of marsh structure and function, as well as provide insight into marsh heterogeneity within a single site.
Phlorest phylogeny derived from Lee & Hasegawa 2013 'Evolution of the Ainu Language in Space and Time'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S, Hasegawa T (2013) Evolution of the Ainu Language in Space and Time. PLoS ONE 8(4): e62243. doi: 10.1371/journal.pone.0062243</p> </blockquote>
CLDF dataset derived from Lee and Hasegawa's "Evolution of the Ainu Language in Space and Time" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee Sean, Hasegawa Toshikazu (2013). Evolution of the Ainu Language in Space and Time. PLOS ONE 8(4): e62243. https://doi.org/10.1371/journal.pone.0062243</p> </blockquote>
A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation
<p>Thermocatalytic CO<sub>2</sub> hydrogenation to methanol is an attractive decarbonization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In<sub>2</sub>O<sub>3</sub>-, and ZnO-ZrO<sub>2</sub>-based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models (<em>R</em><sup>2</sup> > 0.85) are developed to predict the methanol space-time yield (<em>STY</em>) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 g<sub>MeOH</sub> h<sup>−1</sup> g<sub>cat</sub><sup>−1 </sup>between the actual and predicted methanol<em> STY</em>. The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization.</p>
Climatology of deep O+ dropouts in the night-time F-region in solar minimum measured by a Langmuir Probe onboard the International Space Station
<p>Dataset contains data pertaining to an accepted JGR Space Physics article of the same name as the dataset. The link to the article is the following: <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>. The dataset contains the high level data that were used to generate Figs 2-5 in the aforementioned paper. </p> <p>The observations recorded by ISS FPMU will be uploaded to NASA SPDF as well. A previous dataset already exists in CDAweb under ISS/FPMU. The O+ information will be added with the new upload.</p> <p>For any questions about the data or the tools used to derive the figures from the data, please take a look at the paper <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>, or contact Shantanab Debchoudhury at debchous@erau.edu. </p> <p> </p>
Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020
<p>Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".</p> <p> </p> <p>Please cite the original paper:</p> <p>Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” <em>Applied Geography </em> 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.</p> <p><br>WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).</p> <p>All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.</p> <p>Three WUI maps were generated using structures built on of before the years indicated below:</p> <p>2000 - "CA_WUI_2000.tif"</p> <p>2010 - "CA_WUI_2010.tif"</p> <p>2020 - "CA_WUI_2020.tif" </p> <p> </p> <p>Acknowledgments -</p> <p>We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.</p>
Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation
<p>This data archive provides simulated hourly heating and cooling building energy demand for current and future RCP85 climate for 8 representative cities for a single-family and small office building archetype.</p> <p>The data forms part of the following publication:</p> <p><em>Eggimann S.; Fiorentini M. (2024): Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114348</em></p> <p><strong>Attributes</strong></p> <ul> <li>ID_origin: City ID of source city</li> <li>ID_destination: City ID of target city</li> <li>Signature_Cooling: Cooling demand determined by the signature approach</li> <li>Model_Cooling: Cooling demand determined by EnergyPlus</li> <li>Absolute_Diff: Absolute difference</li> <li>Percentage_Diff: Relative difference</li> <li>Daily_Tout: Average daily dry-bulb ambient temperature</li> </ul> <p><strong>Instruction</strong></p> <p>To obtain the simulation and energy signature-based results, it is required to filter the dataset and set the source ID to the destination ID. The city IDs are provided in the file city_table_ID.</p> <p><strong>Source</strong></p> <p>The archetypes are provided by the Office of Energy Efficiency & Renewable Energy: https://www.energycodes.gov/prototype-building-models</p>
RTI Experiment Simulation assuming Convective and Diffusive Interstitial Transport in the Brain: Concentration over time (and space)
<p>Simulation of interstitial transport in the brain assuming convective and diffusive transport with perivascular efflux routes. The movie shows the transient concentration of TMA ions in a real-time iontophoresis (RTI) experiment, where a small molecular probe is applied to brain tissue at a known rate and its concentration measured over time a a point 100-200um away, here 150 um. RTI experiments are used to characterize the properties of interstitial tissue to determine its void volume and tortuosity, 0.18 and 1.85 for the condition shown here. In this simulation, a model of combined diffusion and convection (superficial velocity=50 um/min) is applied to fit experimental data and range. (Convection assumes Darcy's Law with a hydraulic conductivity of 2x10<sup>-6</sup> cm<sup>2</sup> mmHg s<sup>-1</sup> and pressure difference of 2.15 mmHg). The model domain is a cube 750 um on a side with 8 penetrating arterioles and 8 penetrating venules. The first and third columns from the left are venules and the second and fourth are arterioles, with convective flow from arteriole to venule. As transport of molecules in the perivascular space is known to be faster than in the interstitium, the concentration is assumed to be c=0 at the vascular walls. The solute (TMA) must pass through a perivascular wall with lower diffusivity than the interstitium to leave the domain through a vascular wall (D<sub>wall</sub>=5%D<sub>interstitium</sub>). Although it is difficult to see in the movie, both the presence of convection and the perivascular efflux routes cause range(variability) in the measured concentration curves for different source and detection point combinations that is consistent with experimental data--see additional posted data. Computations performed using FEniCS, movie made using Paraview. </p>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
PIE LTER plant biomass associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains the mass of dried grasses collected from clip plots in the PIE LTER Space for Time Sea Level Rise study. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Grasses are sampled in plots from 4 transects per site with five plots per site, arranged from the tidal creek's edge to no more than 150 meters back from the creek. Other related data files include: HTL-RO-ST-MAR-Sites, HTL-RO-ST-MAR-Birds, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls
PIE LTER herbivory measurement associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains aggregated observations of predation and herbivory on tethered bait in each quadrat of sites around the Rowley River and the south side of Sawyer Island at the Plum Island LTER. Measurements consist of the consumption status of tethered squid or kelp pieces as a measure of energy transfer between trophic levels. Pieces were left in the field for five days and observers recorded the status of bait over time as either entirely missing, partially consumed, having scrape marks, or fully intact. These measurements can be used to calculate consumption rates (i.e. energy transfer) over time. Notes include fields discussing any additional observations - e.g., if a stick was found missing.
PIE LTER fish and crab trap data associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset consists of assessments of crab and fish abundances using traps placed in creeks adjacent to marsh community survey transects at sites around the Rowley River and the south side of Sawyer Island at the Plum Island LTER. At each site, four sets of crab and fish traps were deployed for one week. Traps were sampled daily and all individuals were identified and then returned to the creek away from the area where traps were placed. See HTL-RO-ST-MAR-Sites for site description.
PIE LTER quadrat percent cover associated with marsh sites used in space for time sea level rise study, Rowley, MA.
To assess community structure, first each 1 square meter is first examined by moving grass around to scan the substrate for mussels, crab burrows, amphipod burrows, and Littorina littorea snails. Researchers then estimates the percent live cover of a variety of plant species, checking against a standard list of species. Species not on the list are also recorded, and, if unknown, for identification in the lab after sampling. Each species has its cover recorded individually, which could lead to greater than 100% cover. Percent cover of bare space, detritus, and wrack are also recorded. Last, Melampus bidentata snails are recorded in 10 cm x 10 cm at each corner of the quadrat.
Data on parties' positions in multiple dimensions across time and space
<p>These files contain data on parties’ positions in latent political spaces, reconstructed based on data from the <a href="https://manifestoproject.wzb.eu/">Manifesto Project</a> (also known as the ‘Comparative Manifestos Project’) using the method published in <em>Political Analysis</em> as <a href="https://doi.org/10.1093/pan/mps042">“A Dynamic State-Space Model of Coded Political Texts”</a>.</p>
Space, time and beyond
<p>During the third Project Presentation Session on <strong>Monday</strong> <strong>23.07.2018</strong> 14:15 - 15:45<strong> </strong> the following 3 projects were presented:</p> <ul> <li><strong>Victor Westrich</strong> (Johannes Gutenberg University Mainz, Germany): "A spatial approach to the digital visualization of medieval sources"</li> <li><strong>Giovanni Pietro Vitali</strong> (Université de Poitiers, France): "Rethinking Rome as an Anthology: The Poeti der Trullo's Street Poetry"</li> <li><strong>Stefan Jänicke</strong> (Universität Leipzig, Germany): "The Value of Infographics for Timeline Visualizations"</li> </ul>
Data supplementing the article "Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13."
<p>These data supplement the article Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13.</p> <p>The data can be used freely for academic purposes, provided the aforementioned reference is appropriately cited.</p> <p>The following files are available for experiment 2 of the article:</p> <p>allData.mat</p> <p>Includes the datamatrix allData with the following columns:</p> <p>1) Line used for analysis in the article (0 - no, 1-yes).<br> Possible reasons for exclusion:<br> a. fixation duration smaller than 50ms or larger 1000ms<br> b. fixation adjacent to a blink (preceding or following)<br> c. fixation outside the image</p> <p>2) ID of observer (1-24)</p> <p>3) ID of condition (1:grayscale, 2: reduced luminance, 3: reduced contrast, 4: equalized luminance, 5: equalized contrast, 6: phasenoise)</p> <p>4) ID of image (48 unique numbers between 1 and 135)</p> <p>5) horizontal eye position</p> <p>6) vertical eye position</p> <p>7) fixation duration in ms</p> <p>8) value of empirical map generated from search condition of experiment 1 at fixated location</p> <p>9) value of empirical map generated from preference condition of experiment 1 at fixated location</p> <p>10) value of empirical map generated from memorization condition of experiment 1 at fixated location</p> <p>11) value of empirical map generated from joining memorization and preference condition of experiment 1 at fixated location</p> <p>12) value of empirical map generated from condition 1 at fixated location</p> <p>13) value of empirical map generated from condition 2 at fixated location</p> <p>14) value of empirical map generated from condition 3 at fixated location</p> <p>15) value of empirical map generated from condition 4 at fixated location</p> <p>16) value of empirical map generated from condition 5 at fixated location</p> <p>17) value of empirical map generated from condition 6 at fixated location</p> <p>18) value of empirical map generated from condition 1 at fixated location leaving out the current observer</p> <p>19) value of empirical map generated from condition 2 at fixated location leaving out the current observer</p> <p>20) value of empirical map generated from condition 3 at fixated location leaving out the current observer</p> <p>21) value of empirical map generated from condition 4 at fixated location leaving out the current observer</p> <p>22) value of empirical map generated from condition 5 at fixated location leaving out the current observer</p> <p>23) value of empirical map generated from condition 6 at fixated location leaving out the current observer</p> <p>24) luminance at fixation</p> <p>25) luminance contrast at fixation</p> <p>26) edge density at fixation</p> <p>27) eccentricity of fixation</p> <p> </p> <p>usedData.Rdata</p> <p>- for all lines that are used for analysis (allData(:,1)==1) a field in an R dataframe is created, which contains the following fields (for details, see description of matlab file above):</p> <p>obsNum: the ID of the observer (1-24)</p> <p>condNum: the ID of the condition (1-6)</p> <p>imgNum: the ID of the image (48 unique numbers between 1 and 135)</p> <p>fixDur: fixation duration</p> <p>LUM, LCG, ED, ECC: luminance, contrast, edge density and eccentricity at fixation</p> <p>empMapFromSearch, empMapFromPref, empMapFromMem, empMapFromJoint: values of empirical maps generated from data of experiment 1 (search, preference, memorization task as well as combination of the latter two) at fixation</p> <p>empMapFromC1 through empMapFromC6: value of empirical map generated from condition 1 through 6 at fixated location</p> <p>empMapFromC1loo through empMapFromC6loo - value of empirical map generated from condition 1 through 6 at fixated location leaving out the current observer</p> <p>x,y - coordinates of fixation</p> <p> </p> <p>modelsFigure7.R - computes all models for figure 7 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p>modelsFigure8.R - computes all models for figure 8 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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