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1,255 results for “High-resolution”
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</p>
High-resolution wind power generation time series for Germany in the period 2000-2015
<p>High-resolution wind power generation time series for Germany in the period 2000-2015. A paper describing the applied methodology can be found in this repository as well.</p> <p>The final temporal resolution is hourly and the spatial resolution NUTS 3. The data is stored in csv and hdf5 files.</p> <p>More information on the data set, e.g. missing time stamps and versioning, can be found in readme.txt.</p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (1/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file with the name starting 'data' contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) (unit: hPa) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T) (unit: K), zonal wind velocity (u) (unit: m/s), meridional wind velocity (v) (unit: m/s) and vertical wind velocity (w) (unit: m/s), in snapshots of every 1/6 Sol for the periods of 30 degrees in Ls per a file as described below. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020), which is based on the observed dust opacity in Mars Year 24 (MY34).</p> <p>data180rdc-my34.tar.xz: for Ls=180-210 (49 Sols)</p> <p>data210rdc-my34.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc-my34.tar.xz: for Ls=240-270 (46 Sols)</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p>The file 'flux61ls5-my34.tar.xz' contains the three-dimensional fluxes and physical parameters calculated from the model output with the MY34 dust scenario. The contents are (T')^2, (u')^2, (v')^2, u'v', u'w', v'w' T(bar), u(bar), v(bar), squared Brunt-Vaisala frequency, and geopotential height. (bar) denotes the sum of the total wavenumber s=0-60 components, and the dash denotes the deviation from (bar), i.e. sum of the total wavenumber s=61-106 components. There are 36 time grids between Ls=182.5 and Ls=357.5 with the step of Ls=5 degrees. Kinetic and potential energies can be derived from these values using the formulae in the paper.</p> <p>The file 'flux61ls5-lowdust.tar.xz' is the same as 'flux61ls5-my34.tar.xz', except the model output with the 'low-dust' scenario (Kuroda et al., 2019; Kuroda, 2019a, 2019b).</p> <p>The file 'scripts.zip' contains the FORTRAN scripts to derive the fluxes and physical parameters equivalent to the file 'flux61ls5-my34.tar.xz' from the model outputs in this dataset and Kuroda (2020), i.e. data180rdc-my34.tar.xz, data210rdc-my34.tar.xz, data240rdc-my34.tar.xz, data270rdc-my34.tar.xz, data300rdc-my34.tar.xz and data330rdc-my34.tar.xz. Also, the fluxes and physical parameters equivalent to the file 'flux61ls5-lowdust.tar.xz' can be derived with those scripts from the model outputs data180rdc.tar.xz, data210rdc.tar.xz, data240rdc.tar.xz, data270rdc.tar.xz, data300rdc.tar.xz and data330rdc.tar.xz which are available in Kuroda (2019a, 2019b).</p>
An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times
<p>Catalog of 440,697 earthquakes of the 2016-2017 Central Italy seismic sequence semi-automatically generated by Spallarossa et al. (2020). The catalogue covers one year of aftershocks following the first mainshock of the sequence (from 08242016 to 08312017).</p> <p>The catalog has been generated using the Complete Automatic Seismic Processor (CASP) procedure (Scafidi et al., 2019) to detect the events and an advanced picker engine (RSNI-Picker<sub>2</sub>; Scafidi et al., 2018; Spallarossa et al., 2014) to determine their phase arrival times. The final set of about 7 million P- and 10 million S-wave arrival times have been used to locate the events using a non-linear location algorithm (NonLinLoc; Lomax et al. 2000), with a 1D velocity model calibrated for the area (De Luca et al., 2009) and station corrections. For each event, also local magnitudes (M<sub>L</sub>) has been calculated as well as a locations quality.</p> <p>Earthquake locations quality has been classified by means of the procedure proposed by Michele et al., (2019) consisting of the combination of diverse uncertainty parameters provided by the NonLinLoc location code. Locations quality is provided in terms of a unique numeric normalized value, named quality factor, varying between qf=0 (best quality location) and qf=1 (worst quality location). Then locations have been assigned to a quality class depending on the qf parameter value according to the following scheme: A-class (0 < qf ≤ 0.25), B-class (0.25 < qf ≤ 0.50), C-class (0.50 < qf ≤ 0.75), and D-class (0.75 < qf < 1.00). The earthquake locations are distributed between the quality classes as A-30.6%, B-31.4%, C-18.6%, and D-19.4% (details in Spallarossa et al., 2020).</p> <p>We accompanied the catalogue with the 30 events with M>3.5 missed by our procedure (bring the total number of events to 440,727), including the first Amatrice mainshock (M<sub>W</sub>6.0; see Spallarossa et al., 2020). These 30 missing events recognisable by the ID starting with ISI), have been taken from INGV bulletin (<a href="http://terremoti.ingv.it">http://terremoti.ingv.it</a>; ISIDe Working Group., 2007), manually generated. These additional events report INGV locations and magnitude parameters while are missing related quality factors and quality class, being generated by a different procedure.</p> <p>We added to the larger events, the available moment magnitudes (M<sub>W</sub>) from Time Domain Moment Tensor catalogue (<a href="http://terremoti.ingv.it/tdmt">http://terremoti.ingv.it/tdmt</a>; Scognamiglio et al., 2006).</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-event – ID</li> <li>Latitude (°) expressed in decimal degrees - LAT</li> <li>Longitude (°) expressed in decimal degrees - LON</li> <li>Depth(km) hypocentral depth expressed in kilometres - DEP</li> <li>Year of origin time in the format yyyy - YR</li> <li>Month of origin time in the format mo - MON</li> <li>Day of origin time in the format dd - DY</li> <li>Hour of origin time in the format hh - HR</li> <li>Minute of origin time in the format mi - MIN</li> <li>Second of origin time in the format XX.XXX s - SEC</li> <li>Local Magnitude - ML</li> <li>Standard deviation of the Local Magnitude – STD</li> <li>Moment Magnitude – Mw (from TDMT)</li> <li>Horizontal Error (from NLL output) (km) expressed in kilometres - ERH</li> <li>Vertical Error (from NLL output) (km) expressed in kilometres - ERZ</li> <li>RMS (from NLL output) (s) expressed in seconds - RMS</li> <li>Number of Phases – NPHS</li> <li>Stations Azimuthal GAP (°) expressed in decimal degrees - GAP</li> <li>Quality factor - Qf</li> <li>Quality class - Qc</li> </ul> <p> </p> <p>De Luca G., M. Cattaneo, G. Monachesi and A, Amato (2009). Seismicity in the Umbria-Marche region from the integration of national and regional seismic networks. Tectonophysics, 476(1), 219-231. doi: 10.1016/j.tecto.2008.11.032.</p> <p>ISIDe Working Group. (2007). Italian Seismological Instrumental and Parametric Database (ISIDe). Istituto Nazionale di Geofisica e Vulcanologia (INGV); https://doi.org/10.13127/ISIDE.</p> <p>Lomax, A., J. Virieux, P. Volant, and C. Berge-Thierry (2000). Probabilistic earthquake location in 3D and layered models: introduction of a Metropolis–Gibbs method and comparison with linear locations. In: Advances in seismic event location, ed. C. H. Thurber and N. Rabinowitz, 101–134. Dordrecht and Boston: Kluwer Academic Publishers.</p> <p>Michele, M., Latorre, D., Emolo, A. (2019). An Empirical Formula to Classify the Quality of Earthquake Locations. Bulletin of the Seismological Society of America. Vol. 109, No. 6, pp. 2755–2761, December 2019, doi: 10.1785/0120190144.</p> <p>Scafidi, D., Viganò A., Ferretti G., and Spallarossa D. (2018). Robust picking and accurate location with RSNI-Picker2: real-time automatic monitoring of earthquakes and non-tectonic events, Seismol. Res. Lett, Vol. 89 (4), pp. 1478-1487, doi: 10.1785/0220170206.</p> <p>Scafidi D, Spallarossa D, Ferretti G, Barani S, Castello B, Margheriti L (2019). A complete automatic procedure to compile reliable seismic catalogs and travel-time and strong-motion parameters datasets. Seismol Res Lett 90(3):1308–1317.</p> <p>Scognamiglio, L., Tinti, E., Quintiliani, M. (2006). Time Domain Moment Tensor [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/TDMT.</p> <p>Spallarossa, D., G. Ferretti, D. Scafidi, C. Turino, and M. Pasta (2014). Performance of the RSNI-Picker, Seismol. Res. Lett. 85, 1243–1254.</p> <p>Spallarossa D., Cattaneo M., Scafidi D., Michele M., Chiaraluce L., Segou M. and I. G. Main (2020). An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times. Geophys. J. Int. doi: 10.1093/gji/ggaa604.</p>
Montreal high-resolution climate data
<p>This proof-of-concept study couples machine learning and physical modelling paradigms to develop a computationally efficient simulator-emulator framework for generating super-resolution (< 250 m) urban climate information, that is required by many sectors. The temperature and dew point fields for 2019 and 2020 and the geophysical fields (geophys.rar) for the study domain, at 2.5 km (LR) and 250 m (HR) resolutions, which are used to train and validate the proposed super-resolution deep learning (DL) model/emulator are provided. </p>
Figure 6. High-resolution x in Two new catfish species of typically Amazonian lineages in the Upper Rio Paraguay (Aspredinidae: Hoplomyzontinae and Trichomycteridae: Vandelliinae), with a biogeographic discussion
Figure 6. High-resolution x-ray computerized microtomography (HRXCT) of Ernstichthys taquari, MZUSP 125825, holotype, 22.8 mm SL. (a) Dorsal view, (b) lateral view of left side, (c) ventral view. acf: anterior cranial fontanel; ach: anterior ceratohyal; ang: anguloarticular; bp: basipterygium; br: branchiostegal rays; cl: cleithrum; co: coracoid; cv: complex vertebra; den: dentary; ds: dorsal shield element; eap: expanded first anal-fin pterygiophore (= second ventral shield); ehs: expanded hemal spine (= first ventral shield); fr: frontal; hyo: hyomandibula; io1: first infraorbital; iop: interopercle; let: lateral ethmoid; lp: lateral plate element (= expanded lateral-line ossicle); mes: mesethmoid; mnp: middle nuchal plate; mx: maxilla; op: opercle; pa: parasphenoid; pal: palatine; pch: posterior ceratohyal; pfr: pelvic-fin rays; pfs: pectoral-fin spine; pmx: premaxilla; pnp: posterior nuchal plate (= first dorsal shield); pso: parietosupraoccipital; pto: pterotic; pv5: parapophysis of fifth vertebra; qu: quadrate; sc: posttemporosupracleithrum; spo: sphenotic; vh: ventral hypohyal; vs: ventral shield element.
High-resolution air temperature observations near the surface using fiber-optic distributed temperature sensing
<p>Time-lapse animation of air temperature observations near the surface, highlighting wave-like motion in opposite direction of the mean wind. </p> <p> </p>
Globular Cluster Abundances from High-Resolution, Integrated-Light Spectroscopy. II. Expanding the Metallicity Range for Old Clusters and Updated Analysis Techniques
<p>Data from:</p> <p> Globular Cluster Abundances from High-Resolution, Integrated-Light Spectroscopy.<br> II. Expanding the Metallicity Range for Old Clusters and Updated Analysis Techniques (Astrophysical Journal)</p> <p> J. E. Colucci, R. A. Bernstein, A. McWilliam, Observatories of the Carnegie Institution for Science</p> <p>This repository contains reduced globular cluster integrated light echelle spectra in IRAF readable format. <br> NOTE: Spectra are *not* flux calibrated or doppler corrected. Sky/Background emission and absorption lines <br> are present. See reference paper for data reduction details.</p> <p>For each globular cluster:<br> <br> 1. *Approximately* normalized spectra are found in files ending with "ils_normalized.fits." The echelle<br> blaze function normalization was performed with an order by order fit to spectra of a reference G-type star.</p> <p> 2. Unnormalized spectra are found in files ending with "ils.fits." These spectra are not flux calibrated so do not<br> use the count values in each order for science purposes. </p> <p><br> Spectra for the globular clusters NGC 104, NGC 362, NGC 2808, NGC 6093, NGC 6397, NGC 6752 were <br> taken with the DuPont telescope. A reference star spectrum associated with the DuPont data is included : hr914_std.fits</p> <p>Spectra for the globular clusters NGC 6388, NGC 6440, NGC 6441, NGC 6528, NGC 6553 were taken with the <br> MIKE spectrograph on Magellan Clay. A reference star spectrum associated with this data is included: ltt9239_std.fits</p> <p>Spectra for the globular cluster Fornax 3 was taken with the MIKE spectrograph on Magellan Clay on a different run. <br> A reference star spectrum associated with this data is included: hd033771_std.fits</p> <p>This research was supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1302710.</p>
High-resolution tracking of microbial colonization in Fecal Microbiota Transplantation experiments via metagenome-assembled genomes
<p>This project contains anvi'o profiles and contigs databases that is used and/or referenced from the Lee STM and Khan SA, <em>et al.</em> study titled "<strong>High-resolution tracking of microbial colonization in Fecal Microbiota Transplantation experiments via metagenome-assembled genomes</strong>". The pre-print of this study is available via http://dx.doi.org/10.1101/090993.</p> <p>To be able to work with the data files you will need anvi'o <strong>v2.1.0</strong> to be installed on your system. For installation instructions, or to have access to a Docker image for anvi'o, please visit this URL: http://merenlab.org/software/anvio</p> <p>Public data:</p> <ul> <li><strong>ANVIO-FMT-D-R01-R02-QUICK-VISUALIZATION.tar.gz</strong>: Data files for a quick visualization of the 97 MAGs and their distribution across the two FMT recipients. A run script in the archive explains how to use this data.<br> </li> <li><strong>ANVIO-FMT-D-R01-R02-MERGED-PROFILE.tar.gz</strong>: The merged anvi'o profile for the entire data, which also contains a collection of 97 MAGs identified in the donor. The profile database contains no hierarchical clustering of contigs, however, individual MAGs can be displayed via the following notation since the collection 'MAGs' describe the organization of contigs in each MAG referenced from the dataset `ANVIO-FMT-D-R01-R02-QUICK-VISUALIZATION`, as well as from the paper: "anvi-refine -c CONTIGS.db -p PROFILE.db -C MAGs -b <em>FMT-Donor_MAG_00054</em>". All MAG names are in the supplementary tables in our paper.<br> </li> <li><strong>ANVIO-FMT-D-R01-R02-MAGs-SUMMARY.tar.gz</strong>: A static HTML website that contains FASTA files for each MAG, and TAB-delimited matrices for coverage and detection values, and others. After unpacking, you can double-click the index.html file. </li> </ul>
Raw data sets for: A simple calculation algorithm to seperate high-resolution CH4 flux measurements into ebullition- and diffusion derived components (AMT)
<p>Raw data sets for the research article "A simple calculation algorithm to seperate high-resolution CH4 flux measurements into ebullition- and diffusion derived components", published in "Atmospheric Measurment Techniques" (AMT). Data sets include raw data sets for the field and laboratory study, as well as calculated CH4 fluxes (field).</p>
Deep Learning with Satellite Images Enables High-Resolution Income Estimation: a Case Study of Buenos Aires
<p>This repository contains the datasets required for replicating the results in Abbate et al (forthcoming). The datasets also include per capita income estimates at a 50x50 meter resolution for the years 2013, 2018, and 2022, using satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census+survey data. The model, based on the EfficientnetV2 architecture, achieved high accuracy in predicting household incomes (R2=0.878), surpassing existing methods in spatial resolution and performance. </p> <p>Inside the Replication Package folder, the user can replicate the main results from the paper. This includes:</p> <ol> <li> <p><strong>Small Area Estimation (SAE) Replication:</strong></p> <ul> <li> <p><strong>Argentina Household Survey Data (EPH):</strong> Processed microdata for 2010, 2013, 2018, and 2022 (ARG_*_EPHC-S2_*.dta).</p> </li> <li> <p><strong>Argentina Census Microdata:</strong> Raw 2010 census microdata (censo2010_fullraw_p.dta).</p> </li> <li> <p><strong>Census Tract Map:</strong> Shapefile of 2010 census tracts (radios_eph_with_link.shp).</p> </li> <li> <p><strong>SAE Output:</strong> The final small_area_estimates.parquet file containing census tract-level population and estimated income, which serves as labels for the CNN model.</p> </li> </ul> </li> <li> <p><strong>CNN-based Income Prediction Replication (Paper Results):</strong></p> <ul> <li> <p><strong>CNN Model Income Predictions:</strong> Gridded 50x50m income estimates for Buenos Aires for 2013, 2018, and 2022 (income_estimates_*.shp).</p> </li> <li> <p><strong>Normalization Scalars:</strong> A CSV file (scalars_ln_pred_inc_mean_trimTrue.csv) to convert the model's log-scale outputs into real income values (2010 PPP-adjusted Argentinian pesos).</p> </li> <li> <p><strong>World Settlement Footprint (WSF):</strong> Satellite-based data (WSF2015_v2_-60_-36.tif) used to mask predictions in uninhabited areas.</p> </li> </ul> </li> </ol> <p>Key prediction datasets are published in shapefile format, while input data for SAE and other auxiliary files are in formats like .dta, .parquet, .csv, and .tif.</p> <p>Results can be replicated by connecting these datasets with the scripts available at the GitHub repo linked below.</p> <p>For researchers who wish to replicate the full analysis pipeline starting from the original source imagery, the data must be acquired commercially. The proprietary Pleiades and Pleiades NEO satellite imagery is owned by Airbus and can be purchased through their data portal: https://space-solutions.airbus.com/imagery/. To facilitate this process, we provide the unique product identifiers for each scene used in this study. These identifiers can be used to query the Airbus archive and purchase the exact scenes.</p> <ul> <li><strong>Pléiades</strong>: for 2013 imagery the IDs are DS_PHR1A_201302051411520_FR1_PX_W059S35_0807_03124, DS_PHR1A_201302071357305_FR1_PX_W059S35_0410_06105 and DS_PHR1A_201302071357509_FR1_PX_W059S35_0609_05426, and for 2018, DS_PHR1A_201803251356358_FR1_PX_W059S35_0909_03875, DS_PHR1A_201808021356574_FR1_PX_W059S35_0509_06938 and DS_PHR1A_201808021357186_FR1_PX_W059S35_0706_06104.</li> <li><strong>Pleiades NEO</strong>: for 2022 imagery the IDs used are 000047717_1_22_STD_A, 000047717_1_24_STD_A, 000047717_1_25_STD_A, 000047717_1_26_STD_A, 000058605_1_3_STD_A, 000058605_1_4_STD_A, 000058605_1_7_STD_A, and 000058608_1_2_STD_A.</li> </ul> <p><strong>Important Usage Note:</strong> Since the predictions for each 50x50m cell individually present some random variation, we recommend that the results are used by averaging out the estimations for each area of interest (e.g., municipalities, neighborhoods, sections, or census tracts) and not at an individual cell level. As detailed throughout the paper, the aggregated results, even in small areas such as census tracts, predict household incomes with precision.</p> <p>Furthermore, inside this repository, it is possible to access and use the model’s trained parameters to make predictions about different satellite images.</p> <p>Data can be visualized by accessing: <a href="https://ingresoamba.netlify.app">https://ingresoamba.netlify.app</a></p> <p> </p> <p> </p>
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.
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>
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>
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>
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
Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
<p>Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we applied a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion were aligned with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program hubs were highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of immediate and long-term consequences of such perturbations, including identification of compensatory programs that will inform improved combinatorial therapies.</p>
Frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery
<p>Results of high-resolution (pixel size 10–15 m) visible satellite imagery analysis, described in Bradtke and Herman 2023 "Spatial characteristics of frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery" (https://tc.copernicus.org/articles/17/2073/2023/).</p> <p>The source data for analysis came from three satellite sensors: ALI (Advanced Land Imager), OLI (Operational Land Imager), and MSI (Multispectral Instrument). </p> <p>The dataset includes:</p> <ol> <li>results of frazil streaks detection in polynya, ice_water (ice =1, water=2, NoData=0)</li> <li>maps of spatially averaged characteristics of ice in polynya (NoData = -100): <ul> <li>ice concentration, Cfs (-),</li> <li>frazil streaks orientation, Thetafs (degrees clockwise from the north)</li> <li>width of frazil streaks, Wfs (m)</li> </ul> </li> <li>maps of spatially averaged wind-wave characteristics obtained from the Fourier analysis (NoData = -100):<br> <ul> <li>peak wave length, Lpeak (m)</li> <li>peak wave direction, Thetapeak (degrees clockwise from the north)</li> </ul> </li> </ol> <p>Data are provided in WGS 1984 / UTM Zone 58S projection (EPSG:32758), in raster grid with 10m resolution, in GeoTIFFformat.</p> <p>File name convention is: sensor_YYYYMMDD_variable.tif</p> <p> </p>
ScienceDex guides
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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.