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10,016 results for “Observation”
Data for "Observation and quantification of the pseudogap in unitary Fermi gases"
<p>This dataset is for research article "Observation and quantification of the pseudogap in unitary Fermi gases".</p>
Reproduction package for 'Low-frequency radio observations of recurrent nova RS Ophiuchi with MeerKAT and LOFAR'
<p>This is a basic reproduction package for the paper "Low-frequency radio observations of recurrent nova RS Ophiuchi with MeerKAT and LOFAR".</p><p> </p><p> </p>
Supplementary Data for Manuscript 'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques'
<p>The attached file contains supplementary documents offered in the Quaternary Geochronology manuscript 'Observing<strong> </strong>impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques' for the LED2023 special issue. </p>
Model outputs for "Modeling the day-night temperature variations of ultra-hot Jupiters: confronting non-grey general circulation models and observations"
<p>SPARC/MITgcm general circulation model outputs, including temperature structure, atomic hydrogen mixing ratio, winds, and time-resolved spectra produced by PICASO, for the paper "<strong>Modeling the day-night temperature variations of ultra-hot Jupiters: confronting non-grey general circulation models and observations" </strong>by Tan et al. (2023). </p><p>Plotting scripts in Matlab for almost all figures in the paper are provided. These scripts can be modified to read out and plot any outputs that are not shown in the paper. </p>
Dataset: Very-small-aperture 3-D infrasonic array for volcanic jet observation at Stromboli Volcano, Geophysical Journal International, Volume 229, Issue 1, Pages 459–471, https://doi.org/10.1093/gji/ggab487
<p>This is the dataset of the infrasound observation of Yamakawa et al. (GJI, 2022).</p>
Data for: Observing dynamical phases of BCS superconductors in a cavity QED simulator
<p>In conventional Bardeen-Cooper-Schrieffer (BCS) superconductors, electrons with opposite momenta bind into Cooper pairs due to an attractive interaction mediated by phonons in the material. While superconductivity naturally emerges at thermal equilibrium, it can also emerge out of equilibrium when the system's parameters are abruptly changed. The resulting out-of-equilibrium phases are predicted to occur in real materials and ultracold fermionic atoms but have not yet all been directly observed. Here we realise an alternate way to generate the proposed dynamical phases using cavity quantum electrodynamics (cavity QED). Our system encodes the presence or absence of a Cooper pair in a long-lived electronic transition in <sup>88</sup>Sr atoms coupled to an optical cavity and represents interactions between electrons as photon-mediated interactions through the cavity. To fully explore the phase diagram, we manipulate the ratio between the single-particle dispersion and the interactions after a quench and perform real-time tracking of subsequent dynamics of the superconducting order parameter using non-destructive measurements. We observe regimes where the order parameter decays to zero (phase I), assumes a non-equilibrium steady-state value (phase II), or exhibits persistent oscillations (phase III). This opens up exciting prospects for quantum simulation, including the potential to engineer unconventional superconductors and to probe beyond mean-field effects like the spectral form factor, and for increasing coherence time for quantum sensing.</p>
Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach
<p>Data and code in form of Jupyter Notebook to accompany an unpublished paper with the title " Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach". This work makes use of biometric variables of a participant to estimate the inhaled carbon dioxide in microenvironments and understand various physiological and cognitive responses. </p><p>Github link: <a href="https://github.com/mi3nts/Estimate-CO2">mi3nts/Estimate-CO2: Data and code to estimate inhaled CO2 (github.com)</a></p>
Results of long-slit spectral observations of Arp263
<p>Long-slit spectral data of Arp263 along 3 position angles (PA=256, 289, 318) taken with BTA SAO RAS. The folders names correspond to the position angle of the spectral cut. Each folder contains reduced spectra (sum_abs.fts), error files (err_sum_abs.fts) and the map which corresponds to the slit situated vertically in the image in the pixel given as 'SLITPOS' in the header of the firs-file. </p>
Weather forecasts from multiple models and observations at Norwegian synop stations
<p>The data consists of temperature (2m) and wind speed (10m) observations at 183 Norwegian synop stations and corresponding weather forecasts generated by the following models</p><ul><li>Pangu-Weather</li><li>ECMWF HRES</li><li>ECMWF ENS reforecast</li><li>MEPS</li><li>ECMWF ENS reforecast control member</li><li>MEPS control member</li></ul><p>The data was created for the article <a href="https://arxiv.org/abs/2309.01247"><i>Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations</i> </a>with <a href="https://github.com/jbbremnes/pangu-asr">source codes</a> available in GitHub. The data is stored as a single JLD2 (HDF5) file containing a vector of six data frames.</p>
Subset of Project FeederWatch data demonstrating observer shift
<p>This is a subset of data from the Project FeederWatch dataset, <a href="https://feederwatch.org/explore/raw-dataset-requests/">available on the their website</a>. This dataset is restricted to a 15 km radius around Washington, D.C., and includes observations from the most consistent observers. My GitHub repository, titled <a href="https://github.com/GatesDupont/observer_shift">observer_shift</a>, includes R scripts that processes the raw Project FeederWatch dataset and generates this data subset.</p>
Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"
<p>This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.</p>
Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations
<p>Dataset and analysis for:</p> <p>Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations.<br>Arnab Mukherjee, Sharmistha Mishra, Vijaya Kumar Murty, Swetaprovo Chaudhuri<br> </p> <p>For any questions please contact the first author at: arnab.mukherjee@mail.utoronto.ca</p> <p><strong>Contents:</strong></p> <ol> <li><strong>school_active_cases_ON.zip:</strong> Contains datasets for number of COVID-19 infections reported by public schools in Ontario on ten different dates. The data files have been created based on the raw data in the file named 'covidtesting.csv' that has also been shared.</li> <li><strong>school_active_cases_pdf.m:</strong> Matlab code to obtain PDF of secondary infections in schools for a particular date based on the datasets in 'school_active_cases_ON.zip'. To obtain PDF for different dates, the appropriate dataset needs to be loaded. Created in MATLAB R2021b.</li> <li><strong>U_jet2.m:</strong><em> </em>User-defined Matlab function that is required to run the code 'gZ_code.m'. The function simulates the evolution of a simple jet/puff. Created in MATLAB R2021b.</li> <li><strong>gZ_code.m:</strong> Matlab code to obtain the analytical PDF of secondary infections due to long-range transmission, near-field transmission, or both. Created in MATLAB R2021b.</li> <li><strong>covidtesting.zip: </strong>Contains the data file 'covidtesting.csv' that reports the breakdown of COVID-19 infections in different public schools in Ontario on a daily basis. Data obtained from 'https://data.ontario.ca/dataset/summary-of-cases-in-schools/resource/dc5c8788-792f-4f91-a400-036cdf28cfe8'. Contains information licensed under the Open Government License – Ontario.</li> <li><strong>schoolrecentcovid2021_2022.zip:</strong> Contains the data file 'schoolrecentcovid2021_2022.csv<strong>' </strong>that reports the status of COVID-19 cases in Ontario, obtained from 'https://data.ontario.ca/en/dataset/status-of-covid-19-cases-in-ontario/resource/ed270bb8-340b-41f9-a7c6-e8ef587e6d11'. Contains information licensed under the Open Government License – Ontario.</li> </ol> <p> </p> <p> </p> <p> </p>
Accumulation of 3-aminopropylphosphonate in the ex vivo brain observed by phosphorus-31 nuclear magnetic resonance
<p>This dataset contains primary data for DOI: 10.1002/nbm.4721</p><p>NMR in Biomedicine. 2022; 35(8):e4721</p><p>Title: Accumulation of 3-aminopropylphosphonate in the ex vivo brain observed by phosphorus-31 nuclear magnetic resonance</p><p>Authors: David Shaul, Benjamin Grieb, Naama Lev-Cohain, Jacob Sosna, J. Moshe Gomori, Rachel Katz-Brull</p><p> </p><p>These primary datasets contain data presented in the above publication and consist of 31P-NMR spectra. </p><p> </p><p>Please consult the Archive Guide.</p>
Data supporting "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables"
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Research data supporting ""Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables""
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Feeding observations on the Grizzled Giant Squirrel at Chinnar Wildlife Sanctuary, Southern Western Ghats, India
<p>Animal dietary choices help us understand a species' feeding behaviour and are particularly relevant in conservation management. In this study, we aimed to gather knowledge on food niche and the foraging ecology of the Grizzled Giant Squirrel (<em>Ratufa macroura</em>) in Chinnar Wildlife Sanctuary, Southern Western Ghats, Kerala, India. Specifically, the objectives were to identify food composition, the seasonal variation in food choice and feeding technique. Through an observational sampling method, focal animal sampling, the Grizzled Giant Squirrel in Chinnar Wildlife Sanctuary was found to feed on 30 plant species belonging to 18 families. The most utilized plant family was Fabaceae, with eight species, followed by Moraceae (four species) and Anacardiaceae (two species). The consumed species included 22 trees, four climbers, one liana, one paraphyte, one shrub and one succulent species. The squirrel spent the most time feeding on <em> Bauhinia racemosa</em> (19.79%), followed by<em> Tamarindus indica </em>(14.08%) and <em>Nothopegia beddomei</em> (9.89%). The squirrel's choice of diet was primarily affected by the availability of food tree species and food items rather than the season and non-tree species also have been reported from the diet of Grizzled Giant Squirrel. Even though the Grizzled Giant Squirrel shows plasticity in its food choices, the availability of mature trees and plants as food sources appears important for its conservation in the fragmented riparian forest in the Western Ghats, southern India.</p>
Data presented in figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters and potential precursor molecules present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)"
<p>This dataset includes the data shown in the figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)". Read me files containing information on the reported data can be found in the different folders. </p>
Data related to "Free infragravity waves on the inner shelf: Observations and Parameterizations at two Southern California beaches"
<p><strong>Abstract: </strong></p> <p>Co-located pressure and velocity observations in 10-15m depth are used to estimate the relative contribution of bound and free infragravity (IG) wave energy to the IG wave field. Shoreward and seaward going IG waves are analyzed separately. At the Southern California sites, shoreward propagating IG waves are dominated by free waves, with the bound wave energy fraction <30% for moderate energy incident sea-swell and <10% for low energy incident sea-swell. Only the 5% of records with energetic long swell show primarily bound waves. Consistent with bound IG wave theory, the energy scales as the square (frequency integrated) sea-swell energy, with a higher correlation with swell than sea energy. Seaward and shoreward free IG energy is strongly tidally modulated. The ratio of free seaward to shoreward propagating IG energy suggests between 50-100% of the energy radiated offshore is trapped on the shelf seaward of 10-15m and redirected shoreward. Remote sources of IG energy are small.<br><br>The observed linear dependency of free seaward and shoreward IG energy on local sea-swell wave energy and tide are parameterized with good skill (R2 ~ 0.90). Free (random phase) and bound (phase-coupled) IG waves are included in numerically simulated timeseries for shoreward IG waves that are used to initialize (~ 10m depth) the numerical nonlinear wave transformation SWASH. On the low slope study beach, wave runup is only weakly influenced by free shoreward propagating waves observed at the offshore boundary (foreshore slope = 0.02). <br> </p> <p><strong>Plain Language Summary:</strong></p> <p>Infragravity (IG) waves are long-period (every 25 sec to 2.5 min) waves that contribute to coastal flooding and beach erosion. IG waves, generated near the shoreline by short-period sea-swell (SS) wave groups (known by surfers as "sets"), have long wavelengths (100s of m) and do not curl and break like ordinary sea and swell waves. Instead, they can bounce off the beach face and propagate seaward. Our study concerns IG waves on the inner shelf (10-15m depth, ~ 500-700m offshore), seaward of the region of IG generation. Similar to previous observations in Hawai'i and North Carolina, we find most of the bounced, seaward going IG energy cannot reach deep water and is trapped on the continental shelf. We develop an observation-based estimate IG wave energy on the inner shelf as a function of SS wave energy and tide level. Finally. we show with a numerical model that IG wave runup at the shoreline is influenced only weakly by IG waves on the inner shelf.<br> </p> <p><strong>About the data: </strong></p> <p>The PUV MATLAB data structure is saved in the .mat file provided. Data is aggregated from numerous deployments of Nortek Vectors (PUV) in San Diego County between 2019 and 2022, at Torrey Pines State Beach and Cardiff State Beach. This data was collected by the <a href="https://siocpg.ucsd.edu/">Coastal Processes Group</a> at the Scripps Institution of Oceanography. </p> <p>2488 3 hour at 2Hz timeseries of pressure, cross-shore and alongshore velocity are provided. </p> <ul> <li><em>time</em> is given in UTC time as a MATLAB datetime.</li> <li><em>MOP</em> is the location of the instrument for a specific timeseries given as a MOP number (corresponding to the MOP lines given in O'Reilly et al. (2016) and used by the Coastal Data Information Program (CDIP). The MOP number can be used to obtain shorenormal angle and location of the sensor from <a href="https://cdip.ucsd.edu/mops/">https://cdip.ucsd.edu/mops/</a>.</li> <li><em>depth</em> is given as the mean depth over the 3h pressure records in meters and includes the mean tidal elevation.</li> <li><em>P</em> is the detrended pressure data in meters, detided and the mean depth removed.</li> <li><em>U</em> is the detrended cross-shore velocity data in meters/second, with the main tidal constituents M2, S2 and K1 and the mean depth removed, and rotated to be shorenormal according the the MOP angle (+x is onshore).</li> <li><em>V </em>is the detrended alongshore velocity data in meters/second, with the main tidal constituents M2, S2 and K1 and the mean depth removed, and rotated to be shorenormal according the the MOP angle (+y is north).</li> <li><em>sensor_offsets</em> includes both the sensor offset from the sea floor in meters of the pressure sensor and the current meter. These can be used to surface correct the timeseries.</li> </ul> <p>The timeseries are grouped by instrument.</p>
ABodyBuilder2 predicted structures of paired antibody sequences from Observed Antibody Space.
<p>We used ABodyBuilder2 (https://doi.org/10.1038/s42003-023-04927-7) to model ~1.5M paired antibody structures from paired antibody sequences in Observed Antibody Space (https://opig.stats.ox.ac.uk/webapps/oas/oas_paired/). We have save the structures in folders and sub folders that correspond to the OAS files they came from. Parent folders are named according to study. Within each parent folder are sub folders names according to the files (named by SRA ID) containing sequences. Each structure is then named with the parent file followed by the row number from this file.</p>
Value of water level class observations for parameter set selection in hydrological modelling
<p>Data corresponding to the study "<span>Value of water level class observations for parameter set selection in hydrological modelling</span>", published in the Hydrological Sciences Journal.</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.