Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,023
datasets available to search
ShareScore release 0.9.0
Dataset results
5,023 results for “landing”
Selected land variables from CESM1(BGC) simulations for 1850 to 2300
<p>Here, we provide the selected fields used in our study, <em>Quantifying Carbon Cycle Extremes and Attributing Their Causes Under Climate and Land Use & Land Cover Change from 1850 to 2300 (in review), </em>from CESM1(BGC) fully-coupled runs from 1850 to 2300, comprising of historical (1850-2005), Representative Concentration Pathway 8.5 (2005-2100), and Extension Concentration Pathway 8.5 (2101-2300). In these simulations, land and ocean biogeochemical processes experienced the same trajectory of increasing atmospheric CO<sub>2</sub>. The ensemble member that has constant plant functional type (PFT) distribution has "pftcon" in the file name; the other simulations have dynamic LULCC forcing. Fore more information of the comparison between simulations with LULCC and without LULCC of CESM1(BGC), please see <em>Mahowald et. al. (2017).</em></p> <p>We acknowledge and thank the Community Earth System Model (CESM) Biogeochemistry Working Group (BGCWG) for performing the CESM1(BGC) simulations.</p> <p><strong>DOI for related CESM1(BGC) simulations:</strong></p> <p><a href="https://doi.org/10.5065/D6BC3X8Q">https://doi.org/10.5065/D6BC3X8Q</a></p> <p><strong>Related article : </strong></p> <p>J. T. Randerson, et al., 2015: Multi-century changes in ocean and land contributions to climate-carbon feedbacks, Global Biogeochem. Cycles, 29, 744-759, doi:10.1002/2014GB005079</p> <p>Mahowald, N. M., Randerson, J. T., Lindsay, K., Munoz, E., Doney, S. C., Lawrence, P., Schlunegger, S., Ward, D. S., Lawrence, D., and Hoffman, F. M. (2017), Interactions between land use change and carbon cycle feedbacks, Global Biogeochem. Cycles, 31, 96– 113, doi:10.1002/2016GB005374</p> <p><span>Sharma, B.</span>, <span>Kumar, J.</span>, <span>Collier, N.</span>, <span>Ganguly, A. R.</span>, & <span>Hoffman, F. M.</span> (<span>2022</span>). <span>Quantifying carbon cycle extremes and attributing their causes under climate and land use and land cover change from 1850 to 2300</span>. <em>Journal of Geophysical Research: Biogeosciences</em>, <span>127</span>, e2021JG006738. <a href="https://doi.org/10.1029/2021JG006738">https://doi.org/10.1029/2021JG006738</a></p> <p> </p> <p> </p>
daleihao/Topographic_Effects: Codes and data for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"
<p>Codes and data to reproduce all results and plot all figures for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"</p>
Data for "Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1"
<p>The domain file and surface data file that used to run ELMv1, and processed ISIMP2a runoff data that used in <a href="https://gmd.copernicus.org/preprints/gmd-2021-401/">https://gmd.copernicus.org/preprints/gmd-2021-401/</a></p> <p>ELM_runoff_parameter_post.nc contains the ELM runoff generation relevant parameter posteriors at a global half degree spatial resolution.</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Royer & Pastres, Kalman Filter applied to Oxygen estimation in Land-based Aquaculture
<p>These two csv files contains the data relative to the paper "<strong>Data assimilation as a key step towards the implementation of an efficient management of dissolved oxygen in land-based aquaculture</strong>" submitted to Aquaculture International.</p>
Data for: Visual guidance of honeybees approaching a vertical landing surface
<p>Landing is a critical phase for flying animals, whereby many rely on visual cues to perform controlled touchdown. Foraging honeybees rely on regular landings on flowers to collect food, crucial for colony survival and reproduction. Here, we explore how honeybees utilize optical-expansion cues to regulate approach flight speed when landing on vertical surfaces. Three sensory-motor control models have been proposed for landings of natural flyers. Landing honeybees maintain a constant optical-expansion-rate set-point, resulting in a gradual decrease in approach velocity and gentile touchdown. Bumblebees exhibit a similar strategy, but they regularly switch to a new constant optic-expansion-rate set-point. Meanwhile, landing birds fly at a constant time-to-contact to achieve faster landings. Here, we re-examined the landing strategy of honeybee by fitting the three models to individual approach flights of honeybees landing on platforms with varying optic-expansion cues. Surprisingly, the landing model identified in bumblebees proves to be the most suitable for these honeybees. This reveals that honeybees adjust their optic-expansion-rate in a stepwise manner. Bees flying at low optic-expansion-rates tended to stepwise increase their set-point, while those flying at high optic-expansion-rates tend to stepwise decrease it. This modular landing control system enables honeybees to land rapidly and reliably under a wide range of initial flight conditions and visual landing platform patterns. The remarkable similarity between the landing strategies of honeybees and bumblebees suggests that this may also be prevalent among other flying insects. Furthermore, these findings hold promising potential for bioinspired guidance systems in flying robots.</p>
Sahel Land Cover OSO 2018
<p>A land cover map of the Sahel region for the reference year 2018 produced by supervised classification of Sentinel-2 image time series and using the <a href="https://land.copernicus.eu/global/products/lc">CGLS</a> maps as reference data for supervision.</p> <p>The nomenclature is the following:</p> <p>tree cover:10<br> forest:100<br> evergreen needleleaf closed forest:111<br> evergreen broadleaf closed forest:112<br> deciduous needleleaf closed forest:113<br> deciduous broadleaf closed forest:114<br> closed forest mixed:115<br> closed forest unknown type:116<br> evergreen needleleaf open forest:121<br> evergreen broadleaf open forest:122<br> deciduous needleleaf open forest:123<br> deciduous broadleaf open forest:124<br> open forest mixed:125<br> open forest unknown type:126<br> shrubs:20<br> herbaceous vegetation:30<br> cropland:40<br> urban:50<br> bare / sparse vegetation:60<br> snow & ice:70<br> permanent water bodies:80<br> temporary water bodies:81<br> herbaceous wetland:90<br> sea:200<br> continental land mass not classified:255</p> <p>The map was produced using <a href="http://iota2.net">iota2</a>, a free and open source platform for automatic map production using satellite image time series.</p>
Early Stage Crop/ Land Cover Classifcation Results Datasets
<p>This dataset provides results related to the work and report of work done by NPA in a close collaboration with project the Sentinels for Common Agricultural Policy - Sen4CAP consortium. The report (cross referenced below) gives an overview of a collaboration between NPA and Sen4CAP focussed on the 2020 claim year. The main idea is to check if any results generated before and during applications submission period could be used as preliminary data. By using results of land cover classification, crop classification and activity monitoring it would lead to availability to check if it provides benefit in the beginning and during declaration period as preliminary data of crop type (summer, winter, land cover, land use). </p>
Data set and analytic codes supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia"
<p>This deposit contains data set and analytic codes** (accompanied with a meta data) supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia". We investigated the impacts of land-use change on semi-aquatic bug (Gerromorpha, Hemiptera) communities in Sabah, Malaysia.</p> <p>Semi-aquatic bugs were collected from streams in old-growth forest, logged forest, and oil palm with and without riparian buffer strips. A range of environmental parameters were also collected to represent environmental conditions (associated with land-use change). Environmental data were collected at catchment, riparian, and stream scales, and were used separately for the assessments of their effects on the bugs. We looked at the effects on the abundance, biomass, species richness, and community composition of semi-aquatic bugs. We also assessed the effects on the proportion of juveniles, winged individuals, and female <em>Ptilomera</em> sp. (a morphospecies with clear sexual dimorphism in this study).</p> <p>This research was funded by the Jardine Foundation, the Cambridge Trust, the Natural Environment Research Council (NERC) (studentship 1122589), Proforest, the Varley Gradwell Travelling Fellowship, the Tim Whitmore Fund, the Panton Trust, the Cambridge University Commonwealth Fund, the Hanne and Torkel Weis-Fogh Fund, and the S.T. Lee Fund.</p> <p> </p> <p>** For reproducibility of outputs of the Canonical Correspondence Analysis (CCA), do insert the following function in the R Markdown before the line of "anova.cca(CCAEnvInsect, by = 'terms', first = TRUE)":</p> <p>set.seed(42) # About set.seed: <a href="https://stackoverflow.com/questions/13605271/reasons-for-using-the-set-seed-function">r - Reasons for using the set.seed function - Stack Overflow</a></p>
The Influence of Land Surface Temperature on Mental Health in Lausanne - Data
<p>Dataset contains LST data for Lausanne used for the report.</p> <p>GeoPackage is in ESPG:21781 and provides coordinated in ESPG:4326 as well.</p> <p>Data contains mean and median LST for Summer (June - August) 1998, 2008 and 2018 and the delta. The delta is calculated by subtractig the data from 2018 (for example deltalstmedian0818 = lst_2018 - lst_2008).</p> <p>Find the code here: https://github.com/aamir-s18/InfluenceLSTGAF</p>
Rare soil microbial taxa regulate the negative effects of land degradation drivers on soil organic matter decomposition
<p>1. Land degradation drivers, including loss in vegetation and eutrophication, are expected to impact soil biodiversity and functions in drylands world-wide. Soils contain both common and rare microbial taxa that drive multiple soil functions. Yet, little is known about how these microbial taxa influence the impacts of land degradation drivers on ecosystem functions. Obtaining this information is essential to determine whether rare taxa need to be protected, or if protecting only common taxa would be enough to sustain and protect ecosystem functions and services.</p> <p>2. Here, we conducted an experiment to investigate the effects of N-enrichment and vegetation loss (plant removal), which are two major land degradation drivers in semi-arid grasslands, on the diversities of common and rare soil bacterial and fungal taxa and soil function [soil organic matter (SOM) decomposition] in a long-term experiment.</p> <p>3. Six years after N-enrichment and vegetation loss, we found that N-enrichment decreased the alpha diversities of common and rare soil bacteria and rare soil fungi, while vegetation loss only decreased the alpha diversity of rare soil fungi. Both N-enrichment and vegetation loss altered the community composition of common and rare bacteria and fungi, except for the lack of response of common soil fungi to the vegetation loss. Moreover, both structural equation modelling and variation partitioning analyses show that land degradation drivers reduce SOM decomposition, and these were also indirectly associated with changes in the diversity of rare microbial taxa, especially that of bacteria.</p> <p>4.<em> Synthesis and applications</em>. Collectively, this work shows that land degradation can have negative impacts on soil biodiversity and functions, and the rare microbial taxa indirectly regulate the impacts of land degradation on ecosystem functioning. These results indicate that the rare microbial taxa can be used as one of the ecological indicators for identifying land degradation in the semi-arid grasslands. These findings are essential to understand the future impacts of desertification and land degradation on rare microbial taxa–function relationships in global drylands.</p>
Data for: Recreational land use contributes to the loss of marine biodiversity
<p>This repository includes data for the article: Virtanen, E. A., N. Kallio, M. Nurmi, S. Jernberg, L. Saikkonen, L. Forsblom. Recreational land use contributes to the loss of marine biodiversity, People and Nature.</p> <p> </p>
Data for: Improving estimates of land protection costs in a tropical biodiversity hotspot
<p>Accurate estimates of the cost of land protection are useful for understanding where biodiversity conservation goals can be achieved at the lowest cost to society. However, because good cost maps are rare in tropical countries, conservation planning studies often ignore cost or rely on untested proxies, such as agricultural rent or land use intensity. Here, we show how analysts can estimate land protection costs using original data of public land acquisitions, global predictor datasets, and simple machine-learning models. For the Colombian Andes, a global biodiversity hotspot, we find that the principal driver of land protection cost is urban proximity, not agricultural rent. We derive cost estimates that predict public land protection costs more accurately than available cost proxies and identify new protection priorities for 143 threatened bird species. A more systematic collection of cost records of land protection will help inform public decisions on national and global biodiversity protection priorities.</p>
Perturbation exercise in elderly CLBP participants: Water and land exercise outcomes
<p>The blinded data of clinical outcomes of perturbation exercises in elderly CL P participants before and after of 6 weeks.</p>
High Resolution Greenspace Land Cover in Philadelphia, Pennsylvania
<p>This dataset provides a high resolution (1-m) land cover map for Philadelphia, Pennsylvania in the United States of America during the summer of 2017. This dataset was created to differentiate two types of green space in Philadelphia: tree and grass cover. The dataset includes four numerically coded land cover classes.</p> <p><strong>Input data:</strong></p> <p>This classification is derived from National Agriculture Imagery Program (NAIP) 1-m aerial imagery captured in the State of Pennsylvania during June of 2017. To improve classification accuracy, NAIP data was stacked with Sentinel-2 level 1C 10-m and 20-m data using the .addBands() function in Google Earth Engine. For the Sentinel-2 data, a median composite was calculated from cloud-masked images collected between April and October of 2017. Sentinel-2 input bands included blue, green, red, red edge 1, red edge 2, red edge 3, near infrared, and shortwave infrared 1. An additional normalized difference vegetation index (NDVI) was calculated from the NAIP and Sentinel-2 bands using the formula:</p> <p>NDVI = (Near infrared - Red) / (Near infrared + Red)</p> <p><strong>Classification methods:</strong></p> <p>We classified the input data using a Random Forest classifier with 200 trees. Data was classified into four coded land cover classes:</p> <p>1 - Tree</p> <p>2 - Grass</p> <p>3 - Human-built structures</p> <p>4 - Open water</p> <p>8,961 land cover reference points were collected with 70% used to train and 30% to test the classifier. Results were smoothed using a 3x3 square kernel based on the mode of a pixel’s neighbors.</p> <p><strong>Accuracy:</strong></p> <p>Measures of accuracy including overall accuracy and per class user’s (UA) and producer’s accuracy (PA) of the random forest classifier were calculated.</p> <p>Overall accuracy: 93%</p> <p>Tree: UA = 89.73% PA = 93.90%</p> <p>Grass: UA = 93.41% PA = 88.21%</p> <p>Human-built structures: UA = 98.28% PA = 97.47%</p> <p>Open water: UA = 93.56% PA = 98.95%</p> <p><strong>Code link:</strong></p> <p>The Google Earth Engine code used in this analysis is publicly available.</p> <p><a href="https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f">https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f</a></p> <p><strong>Data for download:</strong></p> <p>Two files are available for download.</p> <ol> <li>Philadelphia_classification_points.zip</li> </ol> <p>Contains a shapefile of the 8,961 reference points used to train and test the classifier.</p> <p> 2. Philadelphia_Landcover_2017.zip</p> <p>Contains a GEOTIFF of the classified image over Philadelphia, Pennsylvania for the summer of 2017.</p> <p> </p>
Fijian sea krait behavior relates to fine‐scale environmental heterogeneity in old‐growth coastal forest: The importance of integrated land–sea management for protecting amphibious animals
<p><span>Here the data for "Fijian sea krait behaviour relates to fine-scale environmental heterogeneity in old growth forest: the importance of integrated land-sea management for protecting amphibious animals" by</span><span> Lowe, C., Keppel, G., Waqa, K., Peters, S., Fisher, R.N., Scanlon, A., Osborne-Naikatini, T, and Thomas-Moko, N </span><span> is provided. This article investigates the habitat of </span>Yellow Lipped Sea Kraits, <em>Laticauda</em> <em>colubrina</em>, in the terrestrial realm on Leluvia Island, a small, topographically flat atoll in Fiji with coastal forest. The investigation uses concurrent microclimate measurements and behaviour surveys, as well as vegetation surveys, and the data collected for these analyses are provided here. Microclimates were significantly related to canopy cover, leaf litter depth, and distance from the high-water mark (HWM). Sea kraits were almost exclusively observed in coastal forest within 30 m of the HWM. Sloughing of skins only occurred within crevices of mature or dying trees. Resting <em>L</em>. <em>colubrina</em> were significantly more likely to occur at locations with higher mean diurnal temperatures, lower leaf litter depths, and shorter distances from the HWM. On Leleuvia, behaviour of <em>L</em>. <em>colubrina</em> therefore relates to environmental heterogeneity created by old-growth coastal forests, particularly canopy cover and crevices in mature and dead tree trunks. The importance of healthy coastal habitats, both terrestrial and marine, for <em>L</em>. <em>colubrina</em> suggests it could be a good flagship species for advocating integrated land-sea management. Furthermore, our study highlights the importance of coastal forests and topographically flat atolls for biodiversity conservation. Effective conservation management of amphibious species that utilise land- and seascapes is therefore likely to require a holistic approach that incorporates connectivity among ecosystems and environmental heterogeneity at all relevant scales.</p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Harmonising the land-use flux estimates of global models and national inventories for 2000-2020: background data
<p>This online repository includes all the relevant data used in the paper "Harmonizing the land-use flux estimates of global models and national inventories for 2000-2020" (Grassi et al. 2023), plus some additional methodological information, organised in the following files:</p> <p>1) "<strong>Global model</strong><strong>s</strong> <strong>land CO2 </strong><strong>data 2000-2020</strong>" (MS Excel Format), including for each country data for:</p> <p>a. Land-use CO2 fluxes from each of three Bookkeeping Models (BMs) used, and for different categories (net LULUCF, deforestation, forest, other transitions, organic soils). </p> <p>b. The ensemble mean of the ‘natural terrestrial sink’ estimated by 16 Dynamic Global Vegetation Models (DGVMs), filtered with maps of intact/non-intact forest.</p> <p>The global model data included here are consistent with those included in the Global Carbon Budget 2022 (Friedlingstein et al., 2022).</p> <p>2) “<strong>National inventories LULUCF data 2000-2020</strong>” (version Dec 2022, MS Excel Format), including a comprehensive collection of LULUCF CO2 data based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). The data here represent a slight update of the dataset included in Grassi et al. (2022).</p> <p>3) “<strong>Processing steps for DGVM results”,</strong> describing the protocol used to filter the results of DGVMs with maps of intact/non-intact forest and further details on the maps (PDF Format). </p> <p>4) “<strong>Intact and non-intact forest maps</strong>”, available in two files with different resolutions (0.5 and 0.05 degrees) in NetCDF format. Grassi et al. (2023) used the 0.5 degree resolution.</p> <p>5) "<strong>IntactAndNonIntactForest_0.5deg_script.js</strong>", the Google Earth Engine Java script to produce the forest maps (.js/text format)</p> <p>For further details, please refer to:</p> <p>Grassi et al. (2023) Harmonising the land-use flux estimates of global models and national inventories for 2000-2020. Earth Syst. Sci. Data.</p> <p>Other references:</p> <p>Friedlingstein et al. (2022) Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900.</p> <p>Grassi et al (2022) Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting. Earth Syst. Sci. Data, 14, 4643-4666.</p> <p> </p> <p> </p>
Genova - Land and property value in proximity to NBS
<p>Results of a questionnaire evaluating the land and property value change in proximity to NBS in Genova.</p>
Data from: The impact of land surface properties on haboobs and dust lofting
<p>Haboobs are dust storms formed by strong surface winds in convective storm outflow boundaries, or cold pools, which can loft large quantities of mineral dust as they propagate. Both cold pools and the dust they loft are impacted by land surface properties resulting in complex surface interactions on haboobs. As a result of these additional complexities brought about by surface interactions, it is unclear which surface parameters and physical processes are important for predicting haboob intensity and dust concentrations. Here we applied the Morris one-at-a-time (MOAT) global sensitivity statistical method to an ensemble of 120 idealized simulations of daytime and nighttime haboobs to investigate the land surface properties that affect both dust mobilization and cold pool dynamics. MOAT identifies and ranks the importance of different input factors, which for the prediction of haboob strength and dust concentrations are 1) initial cold pool temperature, 2) surface type (vegetation), 3) soil type (clay content), and 4) soil moisture. The underlying physical mechanisms driving these feedbacks were then analyzed using a traditional one-at-a-time factor analysis. Time of day is significant for determining boundary layer height and dissipation via surface fluxes, leading to shallower, more intense cold pools/haboobs at night. Most of the land parameters modify the cold pool through impacts on surface fluxes, while surface type is dominated by roughness length effects. By ranking the importance of these surface factors, we have identified which variables are most sensitive and must be constrained via observations and data assimilation in numerical dust prediction models.</p>
ScienceDex guides
Understand access before you commit
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.