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2,113 results for “High resolution”
High resolution images for 'Identification of "BRAF-positive" cases based on whole-slide image analysis'
<p>This archive contains high resolution images to accompany the article 'Identification of “BRAF-positive” cases based on whole-slide image analysis' by V. Popovici, A. Krenek and E. Budinska.</p>
Uribe-Rivera et al 2017 DataSet: High resolution bioclimatic layers for southwest of South America for three recent past periods (1970, 1990 and 2010)
<p>These files were generated as part of the article "Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin’s frog" (Uribe-Rivera et al. 2017; accepted in Ecological Applications)</p> <p>We used point data of meteorological stations between 34°-48°S and 70°-75°W, to generate new climatic surfaces for three recent past periods (1970; 1990; 2010). Meteorological data encompassed 293 weather stations, and were extracted from three databases: Dirección Meteorológica de Chile (DMC); Dirección General de Aguas de Chile (DGA); and the FAOClim-NET Agroclimatic database management system (FAO 2001), recording monthly records of mean daily minimum temperature, mean daily maximum temperature and total rainfall for 5-year periods (1965-1969 for 1970 climatic conditions; 1985-1989 for 1990 climatic conditions; and 2005-2009 for 2010 climatic conditions). For each period monthly mean values of each climatic variable were interpolated to generate surfaces using Anusplin v.4.4 (Hutchinson and Xu 2006), which applies the same algorithm used to derive the WorldClim bioclimatic surfaces (Hijmans et al. 2005). Interpolations were fitted following Pliscoff et al. (2014) at a ~1x1 Km resolution, with elevation as an independent variable using the GTOPO30 global digital elevation model (USGS, 1996). Because some weather stations do not have information for every month, we used the option of non-data of Anusplin. The quality of interpolations of climatic data was assessed calculating the Pearson correlation coefficient at the cell level between the monthly climatic values from the CRU-TS v3.10.01 Historic Climate Database for GIS (Climatic Research Unit - Time Series, 2012), and the monthly climatic values from the new climatic layers. Finally, surfaces of 19 bioclimatic variables were generated using the dismo package in R (Hijmans et al. 2014).</p> <p>All bioclimatic layers were uploaded in a single compressed ZIP file. Individual layers can be found inside it as georeferenced ASCII raster files, and nominated primarily by time period, and secundarily by the number of bioclimatic layer, following the worldclim nomenclature (http://www.worldclim.org/bioclim).</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 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
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>
High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"
<p>High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"</p>
Evaluation of Mesoscale Convective Systems in High Resolution E3SMv2
<p>This is the open data resource for the paper "Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations" that submitted to the Journal of Geophysical Research: Atmospheres. </p>
High-resolution mapping of soil carbon stocks in the western Amazon
<h2>Dear Researchers and Interested Parties,</h2> <p> It is with great enthusiasm that we share our page on Zenodo, where we provide <strong>detailed maps</strong> (30 m resolution) of <strong>soil carbon stocks in Rondônia, Brazil</strong>. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region.</p> <h2>Available resources:</h2> <h3>High Resolution Maps:</h3> <p>We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha<sup>-1</sup>), quantiles (Mg ha<sup>-1</sup>) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (<a href="https://epsg.io/102015">EPSG :102015</a>).</p> <p>The entire process was conducted in open source (R Language). The codes and database used <strong>can be found in the <a href="https://github.com/moquedace/ro_soil_carbon_stock" target="_blank" rel="noopener">GitHub repository</a></strong>, and more information about the methodology is available in the following publication:</p> <p>Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. <em>Geoderma Regional</em>, v. 36, p. e00773, 2024. DOI: <a href="https://doi.org/10.1016/j.geodrs.2024.e00773">10.1016/j.geodrs.2024.e00773</a></p> <h2>Availability objectives:</h2> <h3>Promote scientific collaborations:</h3> <p>We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change.</p> <h3>Enhance environmental understanding:</h3> <p>By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies.</p> <h3>Stimulating innovation:</h3> <p>We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon.</p> <h2>Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.</h2> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
High-resolution environmental and host-related factors impacting questing Ixodes scapularis at their northern range edge
<p>The geographic range of tick populations has expanded in Canada due to climate warming and the associated poleward range shifts of their vertebrate hosts. Abiotic factors, such as temperature, precipitation, and snow, are known to directly affect tick abundance. Yet, biotic factors, such as the abundance and diversity of mammal hosts, may also alter tick abundance and consequent tick-borne disease risk. Here, we incorporated host surveillance data with high-resolution environmental data to evaluate the combined impact of abiotic and biotic factors on questing <em>Ixodes scapularis </em>abundance in Ontario and Quebec, Canada. High-resolution abiotic factors were derived from remote sensing satellites and meteorological towers, while biotic factors related to mammal hosts were derived from active surveillance data that we collected in the field. Generalized additive models were used to determine the relative importance of abiotic and biotic factors on questing <em>I. scapularis</em> abundance. Combinations of abiotic and biotic factors were identified as important drivers of abundances of questing <em>I. scapularis</em>. Positive and negative linear relationships were found for questing <em>I. scapularis </em>abundance with precipitation and accumulated snow, but no effect was found for the relative abundance of white-footed mice. Positive relationships were also identified between questing <em>I. scapularis </em>abundance with monthly mean precipitation and mammal species richness. Therefore, future studies that assess <em>I. scapularis </em>should incorporate host surveillance data with high-resolution environmental factors to determine the key drivers impacting the<em> </em>abundance and geographic spread of tick populations and tick-borne pathogens.</p>
Supplemental data files for: A potential mode of the lithospheric growth and delamination of orogenic belts: Evidence from high-resolution magnetotelluric data
<p>Data files for a 2-D electrical resistivity model in the Greater Xing'an Range, including the observed MT data, the data fitting, the preferred resistivity model, and the raw data in impedance format.</p>
Roll-to-roll, high-resolution 3D printing of shape-specific particles
<p>Particle fabrication has attracted recent attention due to its diverse applications in bioengineering, drug and vaccine delivery, microfluidics, granular systems, self-assembly, microelectronics, and abrasives. Herein we introduce a scalable, high-resolution, 3D printing technique for the fabrication of shape-specific particles based on roll-to-roll continuous liquid interface production (r2rCLIP). We demonstrate r2rCLIP using single-digit, micron-resolution optics in combination with a continuous roll of film (in lieu of a static platform), enabling the rapidly permutable fabrication and harvesting of shape-specific particles from a variety of materials and complex geometries, including geometries not possible to achieve with advanced mould-based techniques. We demonstrate r2rCLIP production of mouldable and non-mouldable shapes with voxel sizes as small as 2.0 × 2.0 μm<sup>2</sup> in the print plane and 1.1 ± 0.3 μm unsupported thickness, at speeds of up to 1,000,000 particles per day. Such microscopic particles with permutable, intricate designs enable direct integration within biomedical, analytical and advanced materials applications.</p>
Dataset of 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations'
<p>Dataset of the article 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations', recently published in Open Research Europe (DOI 10.12688/openreseurope.17388.1). The code which implements the physics-informed neural network can be found at https://github.com/AlvaroMS90/PINNs-for-high-resolution-weather-reconstruction-from-sparse-weather-stations.</p> <p>Funded by the European Union under action HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships, call HORIZON-MSCA-2021-PF-01 (project number 101059984 with acronym PERSEVERE). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <pre> </pre>
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.