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10,016 results for “Observation”
Final results from McCoy et al., 'Global Observations of Submesoscale Coherent Vortices in the Ocean', submitted to Progress in Oceanography.
<p>Submesoscale coherent vortices (SCVs) are small-scale, subsurface eddies that are ubiquitous in the ocean. Observations suggest that they efficiently trap and transport water, nutrients, and other properties thousands of kilometers away from their formation regions. However, the weak sea-surface signature restricts SCV observations to mostly chance encounters with shipboard subsurface instrumentation. Thus, the global occurrence, properties, and generation frequency of SCVs remain poorly constrained. Here we present results from a new algorithm used to identify SCVs from Argo float data, applied to roughly 2 million profiles conducted globally from August 1997 to January 2020.</p> <p>After application of the SCV detection algorithm to the global Argo array, we identify 2501 casts piercing spicy-core SCVs (those with anomalously hot and salty water mass characteristics), and 1583 casts piercing minty-core SCVs (anomalously cold and fresh cores) over more than 20 years of available data. The Matlab file 'final_individual_scvs.mat' contains various data for each SCV identified.</p> <p>By grouping detections from consecutive Argo casts, we are also able to record 383 spicy-core SCV time-series and 169 minty-core SCV time-series. The Matlab file 'final_timeseries_scvs.mat' contains the data for these time-series. </p> <p>For a more detailed description of each Matlab file, please see 'README.rtf'. </p> <p>Reach out to Daniel McCoy (dmccoy801@gmail.com) or Daniele Bianchi (dbianchi@atmos.ucla.edu) for inquiries. </p>
Conjunctions between ICON-MIGHTI and 4 meteor radars, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to meteor radars" by Harding et al. (2020, Submitted)
<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are los_wind (the line of sight wind profiles observed by ICON-MIGHTI) and los_wind_r (the wind profiles observed by the meteor radar, interpolated in time and altitude to the MIGHTI sample, and projected onto the MIGHTI line of sight). Dimensions are "time" and "row" (which refers to the row of the MIGHTI CCD, roughly equivalent to altitude. Velocity units are m/s, distances are km, and lat/lon are in degrees. More information can be found in the paper.</pre>
Basic Considerations for the Observability of Kinematically Offset Binary AGN
<p>KDE resampled massive black-hole (MBH) binary population used for the analysis in Kelley 2020, <em>Considerations for the Observability of Kinematically Offset Binary AGN</em> (2005.10255).</p> <p>We start with a population of MBH binaries derived from the Illustris cosmological simulations, and evolved using semi-analytic post-processing models. These methods are described in Kelley et al. 2016 (1606.01900) and Kelley et al. 2017a,b (1702.02180 and 1711.00075). We then use the kernel density estimation (KDE) technique to resample this population from the Illustris volume, into a sample of binaries from a full, simulated light-cone of observations. The binaries are resampled in bins of observed orbital period. The number of resampled binaries in a given period bin are restricted to 500,000. When this occurs, the 'weight' of those binaries is assigned a value greater than one, while the 'weight' of binaries in unrestricted period bins is set to unity. For example, if the 'true' number of binaries in a bin should have been 1e6, the number of samples would be restricted to 5e5, and the weight of each of those binaries would be set to 2.0. If the number of binaries in a bin were instead 2e5, which is less than the limit, then each of those binaries is given a weight of 1.0. Only a single realization of this sample is included, but additional realizations can be produced if needed and on request.</p> <p>The data are presented in hdf5 format and contain the following arrays:</p> <pre><code># Meta-Data `periods`: shape: (201,) units: seconds descr: the edges of the bins in observed orbital period from which the population was sampled. `counts`: shape: (200,) units: None descr: The number of binaries produced in each period bin. This number for each bin is capped at 500,000. The sum of these values equals the total number of samples (70622013). # Data In this particular realization of data, the number of sampled binaries is `S = 70622013`. `mtot`: shape: (S,) units: grams descr: the total mass of each binary (`M = M1 + M2`). `mrat`: shape: (S,) units: None descr: The mass ratio of each binary, `q = M2/M1 <= 1.0`, where M2 is the mass of the secondary (lower mass) component. `pobs`: shape: (S,) units: seconds descr: The orbital period of each binary in the observer's rest frame (at redshift, `z = 0`). `redz`: shape: (S,) units: None descr: The redshift of each binary. `weights`: shape: (S,) units: None descr: The number of 'true' binaries (i.e. the number that are expected to occur in this realization of an observed universe) represented by this individual 'sample', and having similar binary parameters.</code></pre> <p> </p>
Precipitating Solar Wind Hydrogen at Mars: Improved Calculations of the Backscatter and Albedo with MAVEN Observations
<p>These files contain the derived data products used in the paper, including the penetrating and backscatter energy spectra and directional fluxes. See Readme.txt for a description of the data that is stored in each file.</p>
Observation and modeling of high-7Be events in Northern Europe associated with the instability of the Arctic polar vortex in early 2003
<p>Data repository for paper: "Observation and modeling of high-7Be events in Northern Europe associated with the instability of the Arctic polar vortex in early 2003"</p> <p>Erika Brattich; Hongyu Liu; Bo Zhang; Miguel Angel Hernandez-Ceballos; Jussi Paatero; Darko Sarvan; Vladimir Djurdjevic; Laura Tositti; Jelena Ajtić, submitted to Atmospheric Chemistry and Physics, October 2020</p> <p>Created by Erika Brattich (erika.brattich@unibo.it), October 2020</p> <p>-> Data</p> <p>- Pb210_2sites.csv contains daily 210Pb observations at Helsinki and Sodankyla in the period January-March 2003. Dates are in "m/d/yyyy" format and 210Pb measurements are in µBq/m³.</p> <p>Please refer to Mattson et al. (1996) for details.</p> <p>-> Model output</p> <p>- trac_avg.merra2_2x25_RnPbBe.200301-200303-monthly-means.bpch.gz: contains model output of monthly mean 210Pb and 7Be concentrations for January, February, and March 2003.</p> <p>- ts_1h.200301.bpch.tar: hourly model output for January 2003</p> <p>- ts_1h.200302.bpch.tar: hourly model output for February 2003</p> <p>- ts_1h.200303.bpch.tar: hourly model output for March 2003</p> <p>- diaginfo.dat, tracerinfo.dat: information data files needed for the GAMAP package (in IDL) used to read and process the model output. GAMAP is publicly available at: <a href="http://acmg.seas.harvard.edu/gamap/">http://acmg.seas.harvard.edu/gamap/</a> </p> <p>References:</p> <p>Mattsson, R., Paatero, J., and Hatakka, J.: Automatic Alpha/Beta Analyser for Air Filter Samples - Absolute Determination of Radon Progeny by Pseudo-coincidence Techniques, Radiat. Prot. Dosim., 63, 133-139, doi:10.1093/oxfordjournals.rpd.a031520, 1996.</p> <p> </p> <p> </p>
Very high rate GPS observables from UCOE, UJUR, TNGF and OXUM continously operating stations, 17-19 Feb 2020. Dataset for the study "Assesment of Morelian meteoroid impact on Mexican environment" Part 3
<p>Archived data is Trimble raw T02 format from Trimble NetR9 receivers. Complete metadata for the stations can be obtained at the TLALOCNet archive at http://tlalocnet.udg.mx.</p> <p>This dataset includes high rate (5 and 50hz) versions of the UCOE, UJUR, TNGF and OXUM continous GPS station observables that is otherwise available as a 15 sec recording on the TLALOCNet archive (http://tlalocnet.udg.mx).</p> <p>Any use of this dataset or parts of it must give the proper acknowledgement , including the Zenodo assigned DOI as follows:</p> <p>This material is based on GPS data provided by the Trans-boundary, Land and Atmosphere Long-term Observational and Collaborative Network (TLALOCNet; Cabral-Cano et al., 2018) jointly operated by Servicio de Geodesia Satelital (SGS) at the Instituto de Geofísica-Universidad Nacional Autónoma de México in collaboration with and the GAGE facility operated by UNAVCO Inc. We gratefully acknowledge Armando Carrillo-Vargas and all the personnel from SGS and UNAVCO Inc. for station maintenance, data acquisition, IT support and data curation and distribution. TLALOCNet and related GPS operations at SGS are supported by the Consejo Nacional de Ciencia y Tecnología (CONACyT) projects 253760, 256012 and 2017-01-5955, UNAM-Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT) projects IN104213, IN111509, IN109315-3, IN104818-3, NSF grant 2025104 and supplemental support from UNAM-Instituto de Geofísica. UNAVCO's initial support for TLALOCNet (now part of NOTA) was performed under EAR-1338091 and is currently supported by the National Science Foundation and the National Aeronautics and Space Adminsitration under NSF Cooperative Agreement EAR-1724794. </p>
Dataset for "Toward closure between predicted and observed particle viscosity over a wide range temperature and relative humidity"
<p>Raw data and processing scripts associated with the paper "Toward closure between predicted and observed particle viscosity over a wide range temperature and relative humidity". Please see README.md for details. </p>
Data and code for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities
<p>Raw and preprocessed data for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities accepted for publication in the journal Atmospheric Measurement Techniques.</p> <p>The dataset includes total losses (transmitted - received power levels) of commercial microwave links and observations of rainfall, air temperature, and air relative humidity. In addition, theoretical gaseous attenuation calculated from air temperature and relative humidity is provided.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. Raw data contain not regular time series, preprocessed data contain regular time series at 1-min and 5-min temporal resolution. Metadata are stored in textfiles.</p> <p>Dataset contains also R code for processing and analyzing the data as presented in the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities. The code is in the form of R Markdown files and interactive html notebooks.</p>
Vertical Wind and Temperature Gravity Wave Perturbations Derived from Na Lidar Observations
<p>The gravity wave perturbations associated with vertical wind and temperature in the mesopause region for heat flux calculations. </p>
Electron density and altitude of the main ionospheric peak of Mars as observed by Mars Express instruments. Archived data for the paper "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", submitted to JGR-Planets
<p>This repository contains archived data for the manuscript "Seasonal and geographical variability of the Martian ionosphere from Mars Express observations", published in Journal of Geophysical Research-Planets. Details about the methods to generate the data can be found in the paper.</p> <p>5 data files plus 2 readme text files are included.</p> <p>The file MEx_ionpeak.dat (described in the readme file README_ionpeak.txt) contains the peak electron densities and peak altitudes resulting from 34539 observations. Each record includes 14 columns. The content of each column is:</p> <p>Column 1: Instrument providing the observation (MARSIS or MaRS)<br> Column 2: Mars Year at which the observation was obtained (from MY27 to MY33)<br> Column 3: Solar Longitude (Ls) of the observation (unit: degrees)<br> Column 4: Latitude of the observation (unit: degrees)<br> Column 5: Longitude of the observation (unit: degrees)<br> Column 6: Solar Zenith Angle (SZA) of the observation (unit: degrees)<br> Column 7: F10.7 solar proxy index at 1 Astronomic Unit (unit: solar flux units)<br> Column 8: Peak electron density measured by the instrument (unit: cm-3)<br> Column 9: Peak electron density at the subsolar point, i.e., corrected for the SZA variation (unit: cm-3)<br> Column 10: Peak electron density at the subsolar point and at F10.7 (1AU)=100, i.e., corrected for the SZA and the solar radiation output variations (unit: cm-3)<br> Column 11: Peak electron density at the subsolar point, at F10.7 (1AU)=100 and corrected for the seasonal variation (unit: cm-3)<br> Column 12: Peak altitude measured by the instrument (unit: km)<br> Column 13: Peak altitude at the subsolar point, i.e. corrected for the SZA variation (unit: km)<br> Column 14: Peak altitude at the subsolar point and corrected for the seasonal variation (unit: km)</p> <p> </p> <p>The files eprofiles_MaRS.dat, eprofiles_MARSIS_prof1.dat, eprofiles_MARSIS_prof2.dat and eprofiles_MARSIS_prof3.dat contain 4 electron density profiles. They are described in the file README_eprofiles.txt. Each file includes 2 columns, the first one being the altitude (unit: km) and the second one the electron density (unit: cm-3).</p> <p> </p> <p>Contact: Francisco Gonzalez-Galindo, ggalindo@iaa.es<br> </p>
Observed and model postseismic time series at GPS sites due to the 2012 Craig and 2013 Haida Gwaii earthquakes
<p><strong>Files with the observed and model displacements, along with predicted model time series</strong>, which derive from the paper of '<em>Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure'</em>.</p> <p><strong>SITE.obs files:</strong> observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes</p> <p><strong>SITE.mod files:</strong> Model postseismic displacements, along with predicted time series. Detailed explanations please see <strong>readme.txt</strong>.</p> <p><strong>GPS site names</strong> are the same with the study of Tian et al. (2021). </p> <p> </p>
Data Files for Observations and simulations of dropout events and flux decays in October 2013
<p>This webpage provides access to diffusion coefficients and simulated electron distributions used in the article "Observations and simulations of dropout events and flux decays in October 2013: Comparing MEO equatorial with LEO polar orbit" by Pierrard V., J.-F. Ripoll, G. Cunningham, E. Botek, O. Santolik, S. Thaller, B. Kurth, M. Cosmides<span>.</span></p>
Observed and model postseismic time series at GPS sites due to the 2012 Craig and 2013 Haida Gwaii earthquakes
<p><strong>Files with the observed and model displacements, along with predicted model time series</strong>, which derive from the paper of '<em>Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure' </em><strong>JGR: Soild Earth (2021), </strong><a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p> <p><strong>SITE.obs files:</strong> observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes</p> <p><strong>SITE.mod files:</strong> Model postseismic displacements, along with predicted time series. Detailed explanations please see <strong>readme.txt</strong>.</p> <p><strong>GPS site names</strong> are the same with the study of Tian et al. (2021). <a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p>
Figure 2 Observational study 2016 in Mites associated with egg masses of the viburnum leaf beetle Pyrrhalta viburni (Paykull) on Viburnum tinus L.
Figure 2 Observational study 2016. (a) abundance and (b) diversity of mites foundV. tinus on twigs depending onP. viburni infestation: twigs
Figs 21–22 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites
Figs 21–22. Single large egg in a female Termitotrox icarus sp. nov. (21 – dorsal viewT 22 – lateral view).
Figs 18–19 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites
Figs 18–19. Carrying behavior by host termite Odontotermes proformosanus Ahmad, 1965 to Termitotrox icarus sp. nov. (observed in laboratory).
Figs 14–17 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites
Figs 14–17. Habitat of Termitotrox icarus sp. nov. 14–15 – termite mound of host termite Odontotermes proformosanus Ahmad, 1965T 16 – a fungus garden chamber of host termiteT 17 – alive specimen of Termitotrox icarus sp. nov. walking on the wall of fungus garden chamber.
Figs 9–13 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites
Figs 9–13. Body parts of Termitotrox icarus sp. nov. (paratype male). 9 – epipharynx, ventral viewT 10 – elytra, dorsal viewT 11 – abdomen, ventral viewT 12 – pygidium, postero-lateral viewT 13 – aedeagus, dorsal view. Scale bars: 0.05 mm (Fig. 9)T 0.20 mm (Fig. 10)T 0.10 mm (Figs 11–13).
Figs 3–8 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites
Figs 3–8. Habitus of Termitotrox icarus sp. nov. (holotype and paratypes). 3 – holotype male, dorsal viewT 4 – paratype male, ventral viewT 5 – paratype female, dorsal viewT 6 – ditto, ventral viewT 7 – holotype male, antero-lateral viewT 8 – paratype male, dorsal view (elytra removed). Scale bar = 1.0 mm.
Figs 1–2 in Termitotrox icarus sp. nov. (Coleoptera: Scarabaeidae): a new termitophilous beetle from Myanmar with observations of carrying behavior by host termites
Figs 1–2. Terminology used in species description. Head: CFS – clypeofrontal suture, CO – clypeal outline, CF – clypeofrons, GE – gena, AN – antenna, VE – vertex. Pronotum: ALL – anterolateral lobe, MC – median costa, SLC – sublateral costa, CD – central depression, PMC – paramedian costa, LC – lateral costa, MGC – marginal costa, BMS – basomedian section, BP – base of pronotum. Elytra: IS – interstria, ES – elytral stria, is1–is9 – interstriae 1 to 9, AS – apicosutural area, EPI – epipleuron. Ventral structures of thorax: PR – propectus, PLAP – posterolateral areas of propectus, PPS – postprosternal surface, MSV – mesoventrite, MTV – metaventrite. Abdomen: av1–av5 – abdominal ventrite 1 to 5, Py – pygidium. Legs: PF – profemur, PRT – protrochanter, PT – protibia, PRTA – protarsus, AC – anterior claw.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.