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10,016 results for “Observation”
Figure 4 in Bird observations from Pulicate Lake Bird Sanctuary and the adjoining areas in India
Figure 4. Order-wise break-up of bird species reported from the study area.
Figure 1 in Bird observations from Pulicate Lake Bird Sanctuary and the adjoining areas in India
Figure 1. Map of the study area showing point locations.
Text-fig. 15. Schema of observed rays of Manilkaroxylon sp. (sample DR2). in New Fossil Woods From The Paleogene Of Doupovské Hory And České Středohoří Mts. (Bohemian Massif, Czech Republic)
Text-fig. 15. Schema of observed rays of Manilkaroxylon sp. (sample DR2).
Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance
<div>This data corresponds to the following paper:</div> <div> </div> <div>Title: Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance</div> <div>Journal: Analytical Chemistry</div> <div>Authors: Josh P. Peters, Charbel Assaf, Farhad Haj Mohamad, Eric Beitz, Sanjay Tiwari, Konrad Aden, Jan-Bernd Hövener and Andrey N. Pravdivtsev</div> <div> </div> <div>The data is organized with respect to the subfigures in figure 2 to 6.</div> <div>A description of acquisition parameters is provided in the "Acquisition parameters.xlsx" file for each dataset.</div> <div> </div> <div>Figure 2:</div> <div>- Overview of pyruvate metabolism in yeast cells using hyperpolarized and 13C labeled [1-13C]pyruvate. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included. </div> <div> </div> <div>Figure 3:</div> <div>- Overview of pyruvate metabolism in yeast cells using co-hyperpolarized [1-13C] and [2-13C]pyruvate at an n.a. of 13C. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 4:</div> <div>- Overview of fumarate metabolism in yeast cells using hyperpolarized and 13C labeled [1,4-13C2]fumarate</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 5:</div> <div>- The fitted conversion exchange rate constants as a function of the yeast concentration. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 6:</div> <div>- Metabolic data from pyruvate metabolism in yeast depending on the position of the yeast in the NMR tube. We </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <p> </p>
Energy and source of a natural ball lightning based on observational facts
<p><span>The origin data about all figures </span><span>of this work (Energy and source of a natural ball lightning based on observational facts).</span></p>
Raw data, processing, and simulation scripts for "Observation of Dynamic Nuclear Polarization Echoes"
<p>Data files and processing/plotting scripts for the first observation of "dynamic nuclear polarization echoes". Also a simulation script for a semi-quantitative quantum mechanical simulation of the spin dynamics.</p>
Fig. 1 in Form and formation of flares and parabolae based on new observations of the internal shell structure in lytoceratid and perisphinctid ammonoids
Fig. 1. Different expressions of flares (A) and parabolae (B).
Replication data for: "Revisiting the 'East African Paradox': CMIP6 models also struggle to reproduce strong observed Long Rain drying trends."
<h3><strong>Intro</strong></h3> <p>This repository contains replication data for Schwarzwald, Kevin and Richard Seager (2024), "'Revisiting the “East African Paradox': CMIP6 models also struggle to reproduce strong observed MAM long rain drying trends." (under revision at Journal of Climate). This includes the data necessary to replicate all main text figures and most figures in Supplementary Materials. Additional figures in Supplementary Materials require raw precipitation time series, detailed below. This repository also includes a copy of the code necessary to replicate the entire study; the latest version of the code, in addition to instructions on how to use it, is kept at <a href="https://github.com/ks905383/gha_trends">this GitHub archive</a>. </p> <h3><strong>Structure</strong></h3> <p>The repository is structured as follows: </p> <ul> <li><code>code</code>: Static / stable version of reproduction code for Schwarzwald and Seager (2024) that uses and creates these data (see <a href="https://github.com/ks905383/gha_trends">here</a> for more detailed instructions)</li> <li><code>figures</code>: Static / stable versions of main and supplemental figures for Schwarzwald and Seager (2024).</li> <li><code>climate_raw</code>: "raw" (often pre-processed) climate data files; only some files are included, see below for more details</li> <li><code>climate_proc</code>: Processed climate data files upon which the analysis is based, used by main and supplemental figure code</li> <li><code>aux_data</code>: Certain auxiliary data files (fonts, critical values) and intermediate files for long code processes. Created and used by the replication code. </li> </ul> <h3><strong>Notes on raw climate data</strong></h3> <p>Due to space limitations (and a desire to not create yet another cloud copy of CMIP6 data), this repository only contains processed CMIP6 data: the calculated linear trends of rainfall, sea surface temperatures, and 500 hPa geopotential height used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://aims2.llnl.gov/search/cmip6/">ESGF</a>, and consists of every available CMIP6 monthly rainfall, sea surface temperature (SST), and geopotential height file on the archive at the time of processing for the experiments detailed in the manuscript. For precipitation, only a bounding box (-3 S to 12.5 N, 32 E to 55 E) around East Africa was downloaded, and saved with the file suffix "_HoAfrica" (see replication code README for more details). One example precipitation file (one ensemble member of ACCESS-CM2 historical precipitation) is included in this repository for reference. </p> <p>Similarly, this repository only contains processed NMME data: calculated linear trends of rainfall used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/">IRI Data Library</a> and consists of every available monthly rainfall hindcast / forecast file from the NMME archive for the models used (see manuscript Table S1). As above, only a bounding box around East Africa was downloaded, and files were standardized to a partial CMIP* file format (one file per variable, with CMIP file and variable name conventions, but with forecast lead time as an additional dimension). Preprocessing is a bit more extensive than for CMIP6 models: first, hindcasts and forecasts were concatenated into a single file (since hindcasts are saved in the archive only up to the time when the model was operationalized), and then reindexed such that the "time" variable refers to the time <em>for which the forecast is made</em>, and not the time <em>at</em> which the forecast is made. One example precipitation file (CanCM4i rainfall hindcasts/forecasts) is included in this repository for reference. </p> <p>This repository does, however, contain preprocessed copies of the "raw" gridded observational rainfall data products used (in addition to the processed trends), since harmonizing and standardizing the data into a format easily compatible with the CMIP6 data was a nontrivial amount of work that would be tedious to replicate. For the ten gridded observational data products used, monthly rainfall was brought into the CMIP* file format (one file per variable, with CMIP file and variable name conventions), with one notable exception: all observational rainfall is saved in units of mm/day. </p> <p>Reanalysis and gridded ocean observations can be downloaded from each product's respective repositories. As before, the code assumes the data have been preprocessed into something akin to the CMIP* file format. </p> <h3><strong>Notes on processed climate data</strong></h3> <p>Note that the repository includes a file (<code>aux_data/pr_doyavg_CHIRPS_historical_seasstats_dunning_19810101-20141231_HoAfrica.nc</code>) containing CHIRPS seasonal characteristics in East Africa, created as part of Schwarzwald et al., 2023, <em>Climate Dynamics</em>. This file is primarily used to set the boundaries of the study region, see the manuscript for details of how it was calculated. </p> <h3><strong>Licensing, citing, questions</strong></h3> <p>These data are offered under a CC 4.0 license, which allows redistribution and reuse as long as they are correctly cited; note that for much of these data (especially for "raw" data), this requires citations to the original creators. </p> <p>For questions, please feel free to reach out to corresponding author Kevin Schwarzwald. </p>
Mushroom Observer: Mushroom Observer (16) XML
Purpose: The purpose of this site is to record observations about mushrooms, help people identify mushrooms they aren__t familiar with, and expand the community around the scientific exploration of mushrooms (mycology). Some have asked what counts as a mushroom. This site takes a very broad view. While the emphasis is on the large fleshy fungi, other fungi such as lichens, rust and molds as well as fungus-like organisms such as slime-molds are all welcome. Ultimately, I hope this site will become a valuable resource for both amateur and professional mycologists. I like to think of it as a living field guide for mushrooms or a collaborative mushroom field journal. <p></p>https://mushroomobserver.org/<p></p>Mushroom Observer is a collaborative mycology-related website. The community supporting Mushroom Observer is committed to sharing and expanding people__s knowledge of Fungi at all levels from beginners to world authorities. Users provide content to the site in the form of observations, species descriptions, discussions, voting and expert review. All copyrighted content on the Mushroom Observer is available under one of the Creative Commons licenses and all source code for the site is open source. <p></p>http://mushroomobserver.org/
Mushroom Observer: test
Purpose: The purpose of this site is to record observations about mushrooms, help people identify mushrooms they aren__t familiar with, and expand the community around the scientific exploration of mushrooms (mycology). Some have asked what counts as a mushroom. This site takes a very broad view. While the emphasis is on the large fleshy fungi, other fungi such as lichens, rust and molds as well as fungus-like organisms such as slime-molds are all welcome. Ultimately, I hope this site will become a valuable resource for both amateur and professional mycologists. I like to think of it as a living field guide for mushrooms or a collaborative mushroom field journal. <p></p>https://mushroomobserver.org/<p></p>direct from Nathan Wilson
A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area
<p>This dataset (in ArcGIS project) contains the results of geomorphological spatial analysis of water-related features at the Zhurong landing area in Southern Utopia on Mars. It includes vector data of the Zhurong landing site, crater maps, ghost craters, AMA craters, various geomorphological features (cones, etched flows, troughs, pancake craters, rampart craters), and newly defined geological units. Other supporting data and basemaps are also included.</p> <p>This dataset is related to the following paper currently under review in Scientific Reports:</p> <p>Wu B., Dong J., Wang Y., Rao W., Sun Z., Krasilnikov S., Li Z., Tan Z., Chen Z., Wang C., Ivanov M., Zhu J., Liu W.C., Chen L., Li H. 2024. A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area. </p>
Line intensities from IRAM observations: C/2021 A1 (Leonard) and C/2022 E3 (ZTF)
<p>The following tables provide the line integrated intensities in K km s −1 corrected for main beam efficiency and Doppler shifts, when detected. Data in Tables 1 and 2 are average of several lines (aligned according to doppler velocity scale) of a given molecule, for lines of similar energy levels when possible.</p> <p>Data in Tables 3 and 4 are provided for individual lines, averaged over one day or several days. Integrated intensities and rms are weighted average when covered by several backends, and Doppler shift measured with the backend providing the highest spectral resolution.</p>
Observed and simulated MCSs in northwestern South America
<p>Dataset used to generate the results and plot the figures of the investigation entitled "Spatio-temporal representation of Mesoscale Convective Systems in convection-permitting simulations over Northwestern South America". Each file contains the results of running the ATRACKCS algorithm (Ramírez-Cardona et al., 2022) in different periods for the WRF model and observations.</p>
Spatiotemporal dominance of afterslip and viscoelastic relaxation revealed by four decades of post-1973 Luhuo earthquake observations
<p>The datasets include: (1) post-processed fault-crossing short baseline data; (2) triangulation data; (3) coseismic slip models of the Zhuwo and Luhuo earthquakes; and (4) afterslip model of the Luhuo earthquake.</p>
The identified bow shock crossings based on Venus Express observations
<div>Bow shock locations and upstream parameters observed by Venus Express.</div> <div> </div> <div>The columns are structured as follows:</div> <div> 1: time of bow shock crossings dd-mmm-yyyy HH:MM:SS</div> <div> 2: X-VSE [Rv]</div> <div> 3: Y-VSE [Rv]</div> <div> 4: Z-VSE [Rv]</div> <div>5: Solar wind magnetosonic Mach number</div> <div> 6: EUV [mW/m^2]</div> <div> 7: IMF Bx in VSE [nT]</div> <div>8: IMF By in VSE [nT]</div> <div> 9: IMF intensity |B| [nT]</div> <div> 10: IMF cone angle [degree]</div> <div>11: Solar wind dynamic pressure [nPa]</div> <div>12: The angle between the IMF and the shock normal, thetaBn [degree]</div> <div> </div> <div>Please contact xuqi01@nssc.ac.cn if you have questions.</div>
At-a-station hydraulic geometry exponent b derived from Landsat river width and discharge observation
<h2>Description</h2> <p>Global at-a-station hydraulic geometry exponent b dataset derived from Landsat river width (Feng et al., 2022) and discharge observation (Lin et al., 2019).</p> <p> </p> <p>For more details, please refer to:</p> <div> </div> <p><span>Zimin Yuan, Peirong Lin, Xiwei Guo, Kai Zhang, Hylke E. Beck. Revisiting At-a-Station Hydraulic Geometry Using Discharge Observations and Satellite-Derived River Widths. <em>J Remote Sens.</em> 2024;4:0271. DOI:<a href="https://doi.org/10.34133/remotesensing.0271">10.34133/remotesensing.0271</a></span></p> <p> </p> <h2>Contacts</h2> <ul> <li>Zimin Yuan, <a href="mailto:ziminyuan@pku.edu.cn">ziminyuan@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Anthropogenic carbon monoxide emissions during 2014-2020 in China constrained by in-situ observations
<p><strong>The description of the NetCDF files (12×200×350):</strong></p> <ol> <li> <p>The first dimension represents the months, the second represents latitude, and the third represents longitude.</p> </li> <li> <p>The latitude ranges from 15.1°N to 54.9°N, and the longitude ranges from 66.1°E to 135.9°E, with a uniform grid spacing of 0.2° for both.</p> </li> </ol> <p><strong>The units for all files are as follows:</strong></p> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Format</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>All emission data.zip</p> </td> <td> <p>netcdf</p> </td> <td> <p>kg·m<sup>-2</sup>·s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Emissions in seven regions.csv</p> </td> <td> <p>csv</p> </td> <td> <p>10<sup>3</sup> kt</p> </td> </tr> <tr> <td> <p>Simulated CO concentrations.zip</p> </td> <td> <p>txt</p> </td> <td> <p>μg·m<sup>-3</sup> </p> </td> </tr> </tbody> </table>
Live Magnetic Observation of Parahydrogen Hyperpolarization Dynamics
<p>The entry contains numerical data files, processing code (Wolfram Mathematica) and simulation code (Wolfram Mathematica running the 'SpinDynamica' packages) to generate Figures 1 to 6 of the manuscript titled above, arXiv preprint: <a href="https://arxiv.org/abs/2402.10766">https://arxiv.org/abs/2402.10766</a>. A PDF export of the Mathematica code is also provided.</p> <p><strong>Authors: </strong><a href="https://arxiv.org/search/physics?searchtype=author&query=Eills,+J">James Eills</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Mitchell,+M+W">Morgan W. Mitchell</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Rius,+I+M">Irene Marco Rius</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Tayler,+M+C+D">Michael C. D. Tayler</a></p> <p><strong>Abstract:</strong> Hyperpolarized nuclear spins in molecules exhibit high magnetization that is unachievable by classical polarization techniques, making them widely used as sensors in physics, chemistry, and medicine. The state of a hyperpolarized material, however, is typically only studied indirectly and with partial destruction of magnetization, due to the nature of conventional detection by resonant-pickup nuclear magnetic resonance spectroscopy or imaging. Here we establish atomic magnetometers with sub-pT sensitivity as an use an alternative modality to detect <em>in real time</em> the complex dynamics of hyperpolarized materials without disturbing or interrupting the magnetogenesis process. As an example of dynamics that are impossible to detect in real time by conventional means, we examine parahydrogen-induced 1H and 13C magnetization during adiabatic eigenbasis transformations at μT-field avoided crossings. Continuous but nondestructive magnetometry reveals previously unseen spin dynamics, fidelity limits, and magnetization back-action effects. As a second example, we apply magnetometry to observe the chemical-exchange-driven 13C hyperpolarization of [1-13C]-pyruvate — the most important spin tracer for clinical metabolic imaging. The approach can be readily combined with other high-sensitivity magnetometers and is applicable to a broader range of general observation scenarios involving production, transport and systems interaction of hyperpolarized compounds.</p>
Data set for "Multi-frequency SuperDARN HF radar observations of the ionospheric response to the October 2023 annular solar eclipse"
<p>This data set contains rawACF and sounding mode data files from the Christmas Valley East (CVE) SuperDARN radar and manually scaled ionogram parameters from the Boulder (BC840) Digisonde used in the paper "Multi-frequency SuperDARN HF radar observations of the ionospheric response to the October 2023 annular solar eclipse" submitted to Geophysical Research Letters.</p>
Nares Strait Optical Imagery Sea Ice Observations
<p>Optical satellite imagery from acquired in the Nares Strait between from May to August over a 14-year period for Modis and a seven-year period for Sentinel-2. Clear days after the ice breakup within the Nares Strait were selected to ensure individual fragments could be identified. Images were collected between May and August 2016-2023.</p>
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.