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Dataset results
5,155 results for “resolution”
Exceptional multi-year prediction skill of the Kuroshio Extension in the high-resolution CESM decadal prediction system
The Kuroshio Extension (KE) has far-reaching influences on climate as well as on local marine ecosystems. Thus, skillful multi-year to decadal prediction of the KE state and understanding sources of skill are valuable. Retrospective forecasts using the high-resolution CESM show exceptional skill in predicting KE variability up to lead year 4, substantially higher than the skill found in a similarly configured low-resolution CESM. The higher skill is attained because the high-resolution system can more realistically simulate the westward Rossby wave propagation of initialized ocean anomalies in the central North Pacific and their expression within the sharp KE front, and does not suffer from spurious variability near Japan present in the low-resolution CESM that interferes with the incoming wave propagation. These results argue for the use of high-resolution models for future studies that aim to predict changes in western boundary current systems and associated biological fields.
Global Sectoral GDP map at 30'' resolution (SectGDP30) v2.0
<p>- This dataset provides global sector-specific GDP distribution maps (in GeoTIFF format) with a 30-second spatial resolution. It allocates GDP at the 30-arcsecond grid level for three sectors (services, industry, and agriculture) by the distribution of country-level GDP data using high-resolution land cover map.<br>- The source GDP data for allocation is based on nominal GDP for the years 2010, 2015, and 2020, obtained from the World Bank. As the high-resolution land cover map, it uses the built-up area and non-residential area data by the Global Human Settlement Layer (Pesaresi and Politis, 2022) for the service and industrial sectors and the Global cropland map by Potapov et al. (2022) for the agriculture sector. Detailed descriptions of the data creation methodology can be found in Shoji et al. (In Review).<br>- Each pixel represents the monetary value of added value generated by economic activity hypothetically occurring within that pixel. The unit of each pixel value is in millions of USD (current prices for 2010, 2015, and 2020).</p> <p>(Updated to v2.0 on July 1, 2025)</p> <p>This update includes a major change to the spatial allocation method for service and agricultural GDP. For details on the new GDP mapping methodology, please refer to Shoji et al. (In Review). There are no changes to the industrial GDP map.</p> <p> </p> <p>Reference:<br>- Pesaresi M, Politis P.: GHS-BUILT-S R2022A: GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975–2030). European Commission, Joint Research Centre (JRC), 2022.<br>- Potapov P, Svetlana T, Matthew CH, Alexandra T, Viviana Z, Ahmad K, Xiao-Peng S, Amy P, Quan S, Jocelyn C.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3: 19–28, 2022.<br>- Shoji T, Kajiyama K, Yamazaki D, Kita Y, Watanabe M.: Global spatially-distributed sectoral GDP map for disaster risk analysis. In Review.</p>
Quantifying the Impact of Vertical Resolution on the Representation of Marine Boundary Layer Physics for Global-Scale Models
<p>Data supporting the findings of DOI: 10.1175/MWR-D-23-0078.1, a 2023 Monthly Weather Review publication with the same title as this dataset.</p>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</p>
Data from "FICTURE: Scalable segmentation-free analysis of sub-micron resolution spatial transcriptomics"
Open the record for dataset details and reuse information.
India Landslide Susceptibility Map ILSM (100m resolution)
<p>Landslide susceptibility represents the potential of slope failure for given geoenvironmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome these limitations by developing a probabilistic framework that combines imbalance handling and ensemble machine learning for landslide susceptibility mapping. We employ a combination of one-side sampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE) to eliminate class imbalance and develop smaller representative data from big data for model training. A blending ensemble approach using hyperparameter tuned Artificial Neural Networks, Random Forests, and Support Vector Machine, is employed to reduce the uncertainty associated with a single model. The methodology provides the landslide susceptibility probability and a landslide susceptibility class. A thorough evaluation of the framework is performed using receiver operating characteristic curves, confusion matrices, and the derivatives of confusion matrices. This framework is used to develop India's first national-scale machine learning based landslide susceptibility map. The landslide database is carefully curated from global and local inventories, and the landslide conditioning factors are selected from a multitude of geophysical and climatological variables. The Indian Landslide Susceptibility Map (ILSM) is developed at a resolution of 0.001o (~100 m) and is classified into five classes: very low, low, medium, high, and very high. We report an accuracy of 95.73%, sensitivity of 97.08%, and matthews correlation coefficient (MCC) of 0.915 on test data, demonstrating the accuracy, robustness, and generalizability of the framework for landslide identification. The model classified 4.75% area in India as very highly susceptible to landslides and detected new landslide susceptible zones in the Eastern Ghats, hitherto unreported in the government landslide records. The ILSM is expected to aid policymaking in disaster risk reduction and developing landslide prediction models. </p>
Phenotypic differences between interfertile Chlamydomonas species- high-resolution confocal z-stacks for visualizing organelle morphology
<p>This repository contains high-resolution confocal z-stacks of two interfertile <i>Chlamydomonas</i> algal species. The protocol to generate this data is described in the associated publication, "Phenotypic differences between interfertile <i>Chlamydomonas</i> species", and briefly summarized here. Cells were collected from agar plates with TAP medium and suspended in 500 µl of liquid TAP medium in a 1.5 ml eppendorf tube overnight. Cells were pelleted using a microcentrifuge at 2000 x g for 2 min and the supernatant removed. For staining mitochondria, PKMito orange was used at a 1:500 concentration and cells were moved to opaque black microcentrifuge tubes and placed on a tube rotator for 45 min. Cells were pelleted again and washed twice with fresh TAP medium. After the final wash and supernatant removal, cells were resuspended in 25 µl of 1.25% low gelling agar in TAP medium (kept at 45 C). Then 1 µl of the cell/agar mixture was mounted on a #1.5 coverslip with a small wax circle drawn to retain the droplet. Coverslips were flipped and placed on a slide and sealed with VALAP. </p><p>Images were collected on a Nikon CSU W-1 SoRA spinning disk confocal microscope equipped with an ORCA-Fusion BT digital scMOS camera. In order to apply deconvolution in the downstream processing, we needed to oversample (sample beyond Nyquist) in z resolution. To do this, we used a 100×/1.45 NA objective in 2.8× SoRa magnification mode, using ROIs of either 670 × 670 × 81 or 850 × 850 × 91. We imaged with a z-step size of 100 nm for sub-Nyquist sampling. We imaged bright-field first, then 640 nm excitation autofluorescence of chloroplasts, and then 561 nm excitation for PKmito orange dye, because the chloroplasts would bleach after 561 nm excitation. We set exposures to 300 ms with 30% and 50% laser power for 640 and 561, respectively.</p><p>We have included a set of demo data (10 images per species) that accompany the pub hosted on the Arcadia Science webpage (3Dmorpho_demo_data). In addition, we included all of the raw data we collected in this experiment (3Dmorpho_raw_data). Please use the point spread functions (PSF) from the zipped folders for each respective dataset (demo or raw). </p>
Data from "Compact Disks in a High-resolution ALMA Survey of Dust Structures in the Taurus Molecular Cloud"
<p>Continuum fits images for all disks in our ALMA Cycle 4 Taurus disk survey - see details in Long et al., 2018, ApJ, 869, 17 and Long et al., 2019, ApJ, 882, 49</p>
SlumMapVisionBR: A Dataset for Mapping Slums with Medium-Resolution Satellite Imagery
<p>This dataset contains Landsat images (30m/pixel) and masks with slum locations in Brazil. </p><p>This imagery can be used to train models that help keep track of Goal 11.1 of the United Nations Sustainable Development Goals. </p><p>Details on how the data was collected and labelled are available at: <a href="https://github.com/ml-labs-crt/SlumMapVisionBR">https://github.com/ml-labs-crt/SlumMapVisionBR</a>, along with the code to generate the dataset and benchmark results.</p><p>Description of files:</p><ul><li>images.zip contains the images.</li><li>masks.zip contains the masks with slum locations (1 indicates the area has been labelled as a slum).</li><li>metadata.csv contains information about the 116 locations in this dataset.</li><li>metadata_dictionary.txt includes a description of the fields in metadata.csv.</li></ul><p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data more straightforward for the community. Please get in touch with us by <a href="https://github.com/ml-labs-crt/SlumMapVisionBR/issues">posting an issue on GitHub</a>.</p>
Data underlying the publication: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago."
<p><strong>CAR36 dataset</strong></p><p>These data correspond to the <strong>1-year (2019)</strong> simulation from the regional ocean system CAR36. These <strong>daily hindcasts</strong> have been used in the study presented in the paper submitted in GMD editor and entitled: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago", where the CAR36 system is fully described.</p><p><br>The uploaded files are in <strong>netcdf</strong> format:</p><ul><li><i>CAR36_daily_SSH_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Height </strong></li><li><i>CAR36_daily_SST_20190102-20191224.nc </i>= 1-year daily hindcasts of <strong>Sea Surface Temperature</strong></li><li><i>CAR36_daily_SSU_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (zonal component)</strong></li><li><i>CAR36_daily_SSV_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (meridian component)</strong></li></ul><p>All data are projected on the native model tripolar<strong> ORCA grid</strong> <strong>in 1/36° </strong>horizontal resolution.</p><p>NB: In order to filter (in a 1st order) the semi-diurnal tidal signal (with a period of 12h30), the daily mean corresponds to a 25h-average. </p><p><strong>CAR36 software</strong></p><p>The NEMO_CAR36.tar file gathers the <strong>NEMO code configuration</strong> of the CAR36 model. This code follows the same license than NEMO one : <strong>CeCILL</strong>. A file named "License_CeCILL.txt" reminds the details of this license in the NEMO_CAR36.tar file.<br><br>NB.: This model have been renamed CAR36 (English acronym) for the paper instead of ARCAN36 (French initial acronym). In the provided NEMO code, the name ARCAN36 is still used. </p>
Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution
<h2>Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution</h2><p><br>This dataset uses regional atmospheric climate model output to simulate wind and solar power generation across the entire contiguous US. This data is particularly useful for obtaining power production profiles at new locations or locations where only short records exist, or to study climate change impacts. We assume that a generic power plant exists at each grid cell and model the power output for solar at the surface and wind at 80, 100 and 125 meter hub heights. The data consists of a historical period 1980-2022 and several future scenarios that extend from 2020-2099.</p><p><br>- Code: <a href="https://github.com/GODEEEP/tgw-gen/">https://github.com/GODEEEP/tgw-gen/</a><br>- Underlying climate data and description of the future scenarios: <a href="https://tgw-data.msdlive.org/">https://tgw-data.msdlive.org/</a><br>- The NREL reV model was used to produce these profiles: <a href="https://github.com/NREL/reV">https://github.com/NREL/reV</a></p><h3>Data</h3><p>The dataset consists of a series of netcdf files, one per year, grouped together based on resource type, climate scenario, and year range. The data is available as capacity factors which can be scaled to any desired plant size.</p><p> </p><p>Please see the data directory below to find the appropriate record for your purposes.</p><ul><li>solar<ul><li>historical<ul><li><a href="https://doi.org/10.5281/zenodo.10138040">solar historical 1980-2022</a></li></ul></li><li>rcp45cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10138850">solar rcp45cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139076">solar rcp45cooler 2060-2099</a></li></ul></li><li>rcp45hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10139819">solar rcp45hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10139994">solar rcp45hotter 2060-2099</a></li></ul></li><li>rcp85cooler<ul><li><a href="https://doi.org/10.5281/zenodo.10140410">solar rcp85cooler 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140517">solar rcp85cooler 2060-2099</a></li></ul></li><li>rcp85hotter<ul><li><a href="https://doi.org/10.5281/zenodo.10140685">solar rcp85hotter 2020-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10140758">solar rcp85hotter 2060-2099</a></li></ul></li></ul></li><li>wind<ul><li>historical<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182689">wind 80m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10182848">wind 80m historical 2001-2022</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10143911">wind 100m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10144158">wind 100m historical 2001-2022</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161120">wind 125m historical 1980-2000</a></li><li><a href="https://doi.org/10.5281/zenodo.10161276">wind 125m historical 2001-2022</a></li></ul></li></ul></li><li>rcp45cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10183152">wind 80m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10183308">wind 80m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10183360">wind 80m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10192154">wind 80m rcp45cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10145097">wind 100m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10145630">wind 100m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10145720">wind 100m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10145987">wind 100m rcp45cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10161377">wind 125m rcp45cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10161494">wind 125m rcp45cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10161683">wind 125m rcp45cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162136">wind 125m rcp45cooler 2080-2099</a></li></ul></li></ul></li><li>rcp45hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10193740">wind 80m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10198849">wind 80m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10198912">wind 80m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10198962">wind 80m rcp45hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146056">wind 100m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10146108">wind 100m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10146118">wind 100m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10146159">wind 100m rcp45hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162368">wind 125m rcp45hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10162463">wind 125m rcp45hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10162561">wind 125m rcp45hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10162689">wind 125m rcp45hotter 2080-2099</a></li></ul></li></ul></li><li>rcp85cooler<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199074">wind 80m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199103">wind 80m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199362">wind 80m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199411">wind 80m rcp85cooler 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10146346">wind 100m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10150299">wind 100m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10152763">wind 100m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10152830">wind 100m rcp85cooler 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10162728">wind 125m rcp85cooler 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10163251">wind 125m rcp85cooler 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10171504">wind 125m rcp85cooler 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10178654">wind 125m rcp85cooler 2080-2099</a></li></ul></li></ul></li><li>rcp85hotter<ul><li>80 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10199444">wind 80m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10199543">wind 80m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10199587">wind 80m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10199638">wind 80m rcp85hotter 2080-2099</a></li></ul></li><li>100 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10152864">wind 100m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10152945">wind 100m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10160579">wind 100m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10160776">wind 100m rcp85hotter 2080-2099</a></li></ul></li><li>125 meter<ul><li><a href="https://doi.org/10.5281/zenodo.10182205">wind 125m rcp85hotter 2020-2039</a></li><li><a href="https://doi.org/10.5281/zenodo.10182349">wind 125m rcp85hotter 2040-2059</a></li><li><a href="https://doi.org/10.5281/zenodo.10182514">wind 125m rcp85hotter 2060-2079</a></li><li><a href="https://doi.org/10.5281/zenodo.10182574">wind 125m rcp85hotter 2080-2099</a></li></ul></li></ul></li></ul></li></ul><p> </p><p>This research was supported by the <a href="https://godeeep.pnnl.gov">Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP)</a> Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
1/8˚ resolution MOM6-COBALT physical and biogeochemical diagnostics for the Gulf of Mexico, monthly means between 2008-2018
<p>The files in this dataset contain monthly mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2008-2018 for the region between 18-31˚N, 98-80˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, https://github.com/NOAA-GFDL/MOM6 ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at https://rda.ucar.edu/datasets/ds551.0/. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021).</p> <p><br> References:</p> <p><br> Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p><br> Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p><br> Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p><br> Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p><br> Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p><br> Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO2. Journal of Advances in Modeling Earth Systems, 12, e2019MS002043. https://doi.org/10.1029/2019MS002043</p> <p> </p>
ChromoPhyloGen: characterizing copy number alteration patterns in heterogeneous tumor cell populations at Single-Cell Resolution
<p>The human liver cancer cell line Huh7 was obtained from the American Type Culture Collection (ATCC). Huh7 cells were cultivated in Dulbecco's Modified Eagle Medium (DMEM, Gibco, C11995), supplemented with 1% penicillin/streptomycin (Gibco, 15140122), and 10% fetal bovine serum (FBS, Excell, FSP500). Huh7 cell line was maintained under a 95% O2 and 5% CO2 humidified atmosphere in an incubator at 37˚C. </p> <p>The scDNA-seq library was performed using the Chromium Single cell DNA Library & Gel Bead kit (10x Genomics, PN1000040) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip C and D (10x Genomics, 1000022 and 1000042, respectively) according to the manufacturer's user guide and subsequently run on a thermocycler. The barcoded libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p>The scRNA-seq library was generated using the 10x Genomics Chromium Single Cell 3' & Gel Bead Kit v3 (10x Genomics, PN100075) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip B (10x Genomics,1000154) according to the manufacturer's protocol and subsequently run on a thermocycler. The 3' gene expression libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p> </p>
Spectroradiometry with sub-microsecond time resolution using multianode photomultiplier tube assemblies
<p>Raw data (e.g. MA-PMT timeseries), and processed data (e.g. temperature and emissivity fits) for the submitted manuscript. The python script shows a simple example of how to load and plot data from the "..._processed.json" files. The spreadsheet called "Descriptions of json file parameters" explains the meaning of parameters in all the json files for standard experiments. Parameters in "non-standard" json files (e.g. system response .json files; Fig18_accuracy-summary.json) are intended to be self-explanatory.</p>
Data from: Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution
<p>Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE<sub>2</sub> and PGD<sub>2</sub>) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (<em>Nos2</em>) and pro-resolution (<em>Arg1</em>) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.</p>
High resolution wind speed measurements with multicopters of the SWUF-3D UAS fleet - calibration and verification in a wind tunnel with active grid
<p>This dataset contains aggregated measurements from multicopter UAS. The data were measured during the period from October 5, 2022 to October 12, 2022 in the ForWind wind tunnel at the University of Oldenburg with UAS of the SWUF-3D fleet against Constant Temperature Anemometer (CTA). </p><p>Recorded data are provided for measurement flights in different generated wind profiles, i.e. staircase profiles, gusts, velocity steps and statistical turbulence. The measurement data consist of the accelerations measured by the UAS in its longitudinal and lateral axes, as well as the wind speeds measured by the CTA. The latter data were sampled down to the sampling rate of the UAS wind measurement. For the measurements in statistical turbulence, additional files are provided which contain the wind speeds measured by the CTA in its original sampling rate. Each file contains the data for a single measurement flight, as well as information in the header about the ambient conditions in the wind tunnel. The file names contain the following metadata:</p><p>for "gust" files:</p><ul><li>V0 : inertial velocity [m/s]</li><li>V_g : gust velocity amplitude [m/s]</li></ul><p>for "staircase" files:</p><ul><li>uas : the ID of the UAS used [-]</li><li>heading : yaw angle of UAS in relation to longitudinal axes of wind tunnel</li></ul><p>for "turbulence" files:</p><ul><li>V0 : fan wind speed [m/s]</li><li>I : turbulence intensity [%]</li><li>f_cta : sampling frequency of reference sensor [Hz] (for files with original sampling rate)</li></ul><p>for "velocity step" files:</p><ul><li>V0 : lower wind speed</li><li>V_du : wind speed aimed for of upward and downward velocity step</li></ul><p>All filenames end with the test date in YYYY-MM-DD format.</p>
St Barbara statue high resolution
An icon statue of St. Barbara, found in pieces during the excavation of Skriðuklaustur. This statue was made in Utrecht, the Netherlands, in the first half of the 15th century. St. Barbara is usually believed to provide protection against rockslides, earthquakes, fires, and high body temperatures. During the severe plagues of 15th-century Europe, the focus shifted to emphasise her role against feverish illnesses. Thus, she became one of the fourteen saints whose roles were to protect people against the impacts of the plagues that were spreading at this time. The statue is made from terracotta and has a light brown/beige colour. The decoration on the statue seems to have been painted red at one point. National Museum of Iceland. item nr: 2005-36-1566. Found 03/08/2005 by AHP https://sarpur.is/Adfang.aspx?AdfangID=1427151 Source: Objaverse 1.0 / Sketchfab
High-resolution Doryphoros!
Four-million face model of the Doryphoros at the Minneapolis Institute of Art. A lower-resolution version of the model is here: https://skfb.ly/IzR6 More information about the sculpture here: https://collections.artsmia.org/index.php?page=detail&id=3520 Made with about 300 40-megapixel photos, built in PhotoScan. Source: Objaverse 1.0 / Sketchfab
Sharadamba Medium Resolution
Sringeri Sharadamba Medium resolution, Saraswathi devi, godess of knowledge, jagadamba Source: Objaverse 1.0 / Sketchfab
Sringeri Sharadamba Low Resolution
Sringeri Sharadamba Low resolution 3D Model for printing, Saraswathi devi, godess of knowledge, jagadamba Source: Objaverse 1.0 / Sketchfab
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.