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10,016 results for “Observation”
Observation of an Exotic Insulator to Insulator Transition upon Electron Doping the Mott Insulator CeMnAsO
<p>VASP input and output for the computational part of the paper "Observation of an Exotic Insulator to Insulator Transition upon Electron Doping the Mott Insulator CeMnAsO".</p><p>stoichiometric.tar.gz: Data for stoichiometric CeMnAsO<br>defective.tar.gz: Data for CeMnAsO0.94F0.06</p>
Model and observational datasets used for evaluating CHASER simulated formaldehyde (HCHO) abundances in 2019 and 2020.
<p>The dataset entails the model simulation results and the observational data (satellite, aircraft, and ground-based MAX-DOAS) used for the study titled " Evaluating CHASER V.40 global formaldehyde (HCHO) simulations using satellite aircraft and ground-based remote sensing observations", submitted for peer-review in JGR: Atmospheres. </p>
Observational Bias and Young Massive Cluster Characterisation II. Can Gaia accurately observe young clusters and associations?
<p>Field-of-View for synthetic Gaia observations of clusters Orion-type-3, Orion-type-5.5 and Wd2-type presented in Buckner et al. (2023).</p><p>Files contain both simulation and field stars along the Line-of-Sight (l = 270^o, b = 0^o) for the clusters when placed at 500pc, 2500pc and 4300pc distances.</p><p>The original simulation files are included for reference.</p><p>Included README files provide more detailed descriptions of the files.</p>
Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023
<p>Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023</p>
Primary Sea Ice Edge from Satellite Passive Microwave Observations
<p>These are matlab output files with smoothed and unsmoothed primary ice edges around Antarctica. The primary ice edge is defined as the northernmost contour of 15% sea ice concentration, and defines the outer boundary of sea ice extent. The brightness data come from three satellites; SSM/I, AMSR-E, and AMSR2 and are converted to sea ice concentrations with the NASA Team 2 algorithm and the ARTIST algorithm.</p>
Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach
<p>Notebook and data to accompany the (unpublished) paper titled "Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach". This work expands a previous study, relating particulate matter concentrations and short-term biometric features across multiple participants. </p><p>Github link: https://github.com/mi3nts/DUEDARE_multiple_participants</p>
FIG. 5 in Savoirs, observations et expériences sur l'animal exotique captif ( - siècle)
FIG. 5. — Homme nourrissant une autruche avec des clous et des fers à cheval. Psautier, dit « Queen Mary Psalter », 1310-1320. London, British Library, Ms Royal II B VII, f. 113v.
FIG. 2. — L in Savoirs, observations et expériences sur l'animal exotique captif ( - siècle)
FIG. 2. — L'éléphant du roi Henri III. Mathieu Paris, Liber additamentorum, c. 1255. London, British Library, Cotton M Nero D I, f. 169v.
FIG. 4 in Savoirs, observations et expériences sur l'animal exotique captif ( - siècle)
FIG. 4. — Autruche portant un fer à cheval dans son bec et surveillant l'éclosion de ses oeufs. Franz von Retz, Defensorium inviolatae virginitatis Mariae, Cologne, c. 1490? Dublin, Irish National Library, ms. 32,513, f. 30r.
FIG. 3 in Savoirs, observations et expériences sur l'animal exotique captif ( - siècle)
FIG. 3. — Lion captif ressuscitant ses petits par son souffle. Franz von Retz, Defensorium inviolatae virginitatis Mariae, Cologne, c. 1490? Dublin, Irish National Library, ms. 32,513, f. 31r.
FIG. 1. — L in Savoirs, observations et expériences sur l'animal exotique captif ( - siècle)
FIG. 1. — L'éléphant du roi Henri III et son gardien. Mathieu Paris, Cronica maiora, 2e moitié du XIIIe siècle. Cambridge, Corpus Christi College, Parker Library, CCC Ms 16, f. iir.
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
Dataset for publication: Investigation of Hydrostatic Imbalance with Field Observations
<p>Here is the data set for figure 8 of the publication "Investigation of Hydrostatic Imbalance with Field Observations" by Sun et al., 2023</p><p>Data collected from the Halo Doppler lidars (DL) and Eddy-Covariance station (EC) (EC south, EC-S) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany (lafo.uni-hohenheim.de, Späth et al., 2023, doi: 10.5194/gi-12-25-2023). The data cover the temporal period of 23 August 2022 as presented in figure 8 in Sun et al., 2023, doi: ***.</p>
Juno Plasma Wave Observations at Europa
<p>This is the supporting data set for the paper by the same title published in AGU JGR Space Physics.</p><p><strong>Abstract</strong><br>Juno passed by Europa at an altitude of 355 km on 29 September, day 272, 2022. As one of Juno's in situ science instruments, the Waves instrument obtained observations of plasma waves that are essential contributors to Europa's interaction with its environment. Juno observed chorus, a band at the upper hybrid frequency providing the local plasma density, and electrostatic solitary structures in the wake. In addition, impulses due to micron-sized dust impacts on Juno were recorded with a local maximum very close to Europa. The peak electron density near Europa was ∼ 330 cm−3 while the surrounding magnetospheric density was in the range of 50 to 150 cm−3. There was a significant separation between the Europa flyby and Juno's crossing of Jupiter's magnetic equator, enabling a unique identification of effects associated with the moon as opposed to magnetospheric phenomena normally occurring at the magnetic equator near 10 Jovian radii.</p>
Stellar properties of observed stars stripped in binaries in the Magellanic Clouds - Observations and Results
<p>This Zenodo repository is one of three Zenodo repositories related to the article "Stellar properties of observed stars stripped in binaries in the Magellanic Clouds" by Y. Götberg, M.R. Drout, A.P. Ji, J.H. Groh, B.A. Ludwig, P.A. Crowther, N. Smith, A. de Koter, and S.E. de Mink. In the article, we analyze the optical spectra of ten stars and measure their stellar properties using spectral fitting. This repository contains observational data and the resulting measurements for the stellar properties of the stars analyzed in the article, along with best fit spectral models and spectral models used to estimate the wind mass loss rate of the stars. Below, we describe the content in more detail:</p> <ul> <li><strong>0_ReadMe.txt</strong>: A text file where we describe some more details regarding the content.</li> <li><strong>S2_stacked_stellar_spectra.tar.gz (4.6 MB):</strong> The spectra we use for obtaining stellar properties for the observed stars.</li> <li><strong>Table2.txt</strong>: the apparent AB magnitudes with associated errors for the stars we analyze in the paper.</li> <li><strong>Table3.txt</strong>: the stellar properties for the stars we analyze in the paper. These parameters are obtained by spectral fitting.</li> <li><strong>S5_spectra_best_fit_models.tar.gz (14 MB):</strong> The spectral energy distributions and normalized spectra for the best-fit models, recomputed such that the radius and bolometric luminosity also matches. This tarball contains best-fit spectra for all of the stars in text-files labeled with the format SED_StarX_xxx.txt (~70 MB when inflated). The stellar parameters are presented in Section 5.</li> <li><strong>S5_full_best_fit_models.tar.gz (1.9 GB):</strong> The full CMFGEN version of the best-fit models for each individual star. This tarball contains a tarball for each star, which when inflated becomes ~250-550 MB each. </li> <li><strong>S7_spectra_mdot_models (31 MB): </strong>The spectral energy distributions and normalized spectra for the models used in the mass-loss analysis that we present in Section 7.</li> <li><strong>S7_full_mdot_models (3.5 GB):</strong> The full CMFGEN version of the spectral models used for the mass-loss rate variation presented in section 7 (excluding the best-fit models, which are provided separately).</li> </ul>
Dataset for A Statistical Survey of E-region Anomalous Electron Heating Using Poker Flat Incoherent Scatter Radar Observations
<p>This archive contains the complete list of anomalous electron heating (AEH) events in PFISR data between 2010 and 2023 identified by Zhang and Varney (2024), along with the code necessary to reproduce the results. The main list of AEH events is in the file AEH_event_list.csv, and the rest of this archive is supporting information for reproducibility.</p> <p>The files contained are:</p> <p>algo1.ipynb: Python notebook implementing algorithm 1.</p> <p>algo2.py: Python script implementing algorithm 2.</p> <p>algo3.ipynb: Python notebook implementing algorithm 3.</p> <p>algo4.ipynb: Python notebook implementing algorithm 4.</p> <p>cal_velo.py: Python function to calculate ion velocity.</p> <p>io_utils.py: Python functions for manipulating AMISR hdf5 files.</p> <p>Fig1.ipynb: Python notebook to recreate figure 1.</p> <p>Fig2,5.ipynb: Python notebook to recreate figures 2 and 5.</p> <p>Fig3,11.ipynb: Python notebook to recreate figures 3 and 11.</p> <p>Fig4.ipynb: Python notebook to recreate figure 4.</p> <p>Fig6.ipynb: Python notebook to recreate figure 6.</p> <p>Fig7,8,9,10.ipynb: Python notebook to recreate figures 7, 8, 9, and 10.</p> <p>PFISR_Data_Quality_Checker.ipynb: Python notebook with data preprocessing and quality checking.</p> <p>Table1.ipynb: Python notebook to extract the beamcode information needed for table 1.</p> <p>AEH_events_list.csv: Complete list of AEH events identified by algorithms 1, 3, and 4. The first column indicates the UT time of the start of the event, and 1 or 0 in the three columns denote whether the event was or was not detected by the algorithm, respectively.</p> <p>AEH_in_2010&2011.csv: Spreadsheet to facilitate direct comparisons with previous work on AEH in 2010 and 2011.</p> <p>f107.json: Smoothed F10.7 data used in this study.</p> <p>AEH_Detection_Outputs.zip: Archive of all of the raw output of the python scripts running the detection algorithms.</p> <p>AE&PAE.zip: Archive of all AE data used in this study.</p>
Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition
<p>This dataset can be used to reproduce the figures in the corresponding manuscript, in preparation for publication in the Journal of Chemical Physics. As described in the manuscript, the (mostly statistical) quantities presented here were computed from ensembles of simulated disordered solids (harmonic sphere packings, diluted spring networks, and packing derived networks). The individual simulations involve the minimization of the system's energy which is computed from positional degrees of freedom (of particles in a packing or nodes in a network). Macroscopic quantities (such as shear modulus or contact number/excess connectivity) are then computed for each sample. The displacement response feilds are computed by solving a linear matrix equation involving the Hessian of 2D packings of harmonic spheres. See the manuscript for more details.</p>
STEVE images observed on Sept 3rd, 2022, at Athabasca, Canada
<p>These images show the Strong Thermal Emission Velocity Enhancement (STEVE) observed on Sept 3<sup>rd</sup>, 2022, at Athabasca, Canada, using two Nikon D610 cameras with an all-sky fish-eye lens. The two cameras were located at AUGO-I (54.71°N, 246.69°E, C002) and AUGO-II (54.60°N, 246.36°E, C001), respectively. The format of the filename is C00X_YYDDMMhhmmss_0030.jpg.</p>
ASRC Rooftop CO observations, NYCMA observed and simulated ΔCO, and relative NYC surface influence
<p>Carbon monoxide (CO) observations from the Advanced Science Research Center (ASRC) Rooftop Observatory in Hamilton Heights, West Harlem, Manhattan, calculated background CO, observed, and simulated CO enhancements (ΔCO) for the New York City metropolitan area (NYCMA), and relative NYC surface influence from HRRR-STILT (the Stochastic Time-Inverted Lagrangian Transport model coupled to High-Resolution Rapid Refresh meteorology). All values reported hourly for January-May 2019-2022, when available. Observations and analyzed quantities are as described in "Multi-year observations of variable incomplete combustion in the New York megacity" by Luke D. Schiferl, Cong Cao, Bronte Dalton, Andrew Hallward-Driemeier, Ricardo Toledo-Crow, and Róisín Commane.</p>
An unusual pulse shape change event in PSR J1713+0747 observed with the Green Bank Telescope and CHIME: Profile data and figure reproduction scripts
<p>This Zenodo entry contains data used in the paper "An unusual pulse shape change event in PSR J1713+0747 observed with the Green Bank Telescope and CHIME", to be published in The Astrophysical Journal. This includes minimally processed profile data in PSRFITS format, as well as pulsar ephemerides (.par files), time-of-arrival estimates (.tim files), and scripts which can be used to reproduce the figures and tables in the paper. For more information, see README.md.</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.