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10,016 results for “Observation”
Fig. 7. The 14 in On Papulifères, putative ciliate cysts of diverse morphologies, with new observations from the plankton of the Chukchi Sea (Arctic Ocean)
Fig. 7. The 14 spherical/ovoid 'Sphaeropsis' papuliferid cyst forms found in Chukchi Sea plankton net tow material. All the specimens shown are from the 2022 sample station 16 (sample VII in Table 2), except the one shown in Fig. F. Scale bars all represent 50 µm.
Atmospheric observations of SF6, HFC-32, HFC-125, and HFC-134a at Sottens, Switzerland
<p>Atmospheric measurement data (mole fractions) for SF<sub>6</sub> (sulfur hexafluoride), HFC-32 (difluoromethane), HFC-125 (1,1,1,2,2-pentafluoroethane), and HFC-134a (1,1,1,2-tetrafluoroethane). Observations were made at Sottens, Switzerland, in 2021. Measurements were conducted using a Medusa pre-concentration unit coupled to gas chromatography and mass spectrometry (GC-MS), as is used within the global AGAGE network.</p>
Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation
<p>These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data</p>
Kivalina subsidence observations
<p>Updated on 2023-10-4 to correct scaling error. The original estimates were a factor of two too large.</p> <p>Corrected version. The original estimates (v1.0) were a factor of two too large due to a coding error.</p> <p>Thaw-season subsidence in [m] near Kivalina, Northwestern Alaska, derived from Sentinel-1 observations.</p> <p>For each year yyyy from 2017-2019:</p> <ul> <li>yyyy_de_dl.tif contains the early-mid-season (10 June - 10 August) and late-season (10 August - 10 September) subsidence estimated from the spline reconstruction</li> <li>yyyy_timeseries.tif contains the regularly sampled, unconstrained subsidence</li> </ul> <p>The dates are:</p> <p>2017: ['20170604T173314', '20170616T173315', '20170628T173316', '20170710T173316', '20170722T173317', '20170803T173318', '20170815T173318', '20170827T173319', '20170908T173319']<br>2018: ['20180611T173321', '20180623T173322', '20180717T173323', '20180729T173324', '20180810T173325', '20180822T173325', '20180903T173326']<br>2019: ['20190606T173327', '20190618T173327', '20190630T173328', '20190712T173329', '20190724T173330', '20190805T173330', '20190817T173331', '20190829T173332', '20190910T173332']</p>
Figure 3. After extracting a in Observations Of Novel Feeding Tactics In Guira Cuckoo Guira Guira (Aves: Cuculidae)
Figure 3. After extracting a larva the cuckoos descended to the ground to soften it up before consumption.
Figure 2 in Observations Of Novel Feeding Tactics In Guira Cuckoo Guira Guira (Aves: Cuculidae)
Figure 2. Guira Cuckoo Guira guira inserting its bill into the silk nest of Brassolis sophorae to extract a larva.
Binned dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) concentration observations in the ocean
<p>Here we provided binned dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) concentration observations in the ocean used for manuscript "Global patterns of surface ocean dissolved organic matter stoichiometry " submitted to Global Biogeochemical Cycles.</p> <p>DOC and DON concentrations observations are from a compilation of DOM data obtained from global ocean observations from 1994 to 2021 (Hansell et al., 2021, https://doi.org/10.25921/s4f4-ye35)</p> <p>DOP concentration observations are from the DOPv2021 database (Liang et al., 2022, https://doi.org/10.1038/s41597-022-01873-7)</p> <p>We binned the data into the OCIM2 grid with a resolution of 2˚x2˚ with 24 vertical layers. More info about OCIM2 grid can be found on <a href="https://tdevries.eri.ucsb.edu/models-and-data-products/">https://tdevries.eri.ucsb.edu/models-and-data-products/</a></p>
Data and scripts for GRL article: Author's reply to Comment by Greaves et al. on ``Phosphine in the Venusian Atmosphere: A Strict Upper Limit from SOFIA GREAT Observations''
<p>Python scripts and raw SOFIA Data to reproduce the figures found in GRL article: Author's reply to Comment by Greaves et al. on ``Phosphine in the Venusian Atmosphere: A Strict Upper Limit from SOFIA GREAT Observations''</p>
Observation of an isotope effect in state-selective mutual neutralization of lithium with hydrogen
<p>The data files found here contain the data as obtained and displayed in : "Observation of an isotope effect in state-selective mutual neutralization of lithium with hydrogen" published in Physical Review A (2023). Each file contains an explanatory header. Header lines start with #.</p> <p> </p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data behind the figures
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><br> <strong>Data behind the figures</strong></p> <p>This page contains the data behind various paper figures. The material for each figure is contained in a tar file. A short description can be found below. Figures not included here are associated with one of the other GWTC-3 data releases.</p> <p> </p> <p><strong>Figure 1</strong></p> <ul> <li>Figure01.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 1. This shows the number of candidates with probability of astrophysical origin > 50% as a function of surveyed time–volume.</p> <p>The dates for the first observing run (<a href="https://doi.org/10.1103/PhysRevX.6.041015">O1</a>) and second observing run (<a href="https://doi.org/10.1103/PhysRevX.9.031040">O2</a>) candidates are hard-coded into the script, and the dates for third observing run (<a href="https://arxiv.org/abs/2108.01045">O3a</a> and O3b) candidates are included in two text files. The effective binary neutron star time–volume (BNS-VT) for each observing run is stored in separate .csv files.</p> <p>Each .csv file contains two columns, the first is the GPS time and the second is the cumulative effective BNS VT in Mpc<sup>3</sup> kyr (this is converted to Gpc<sup>3</sup> yr in the included script).</p> <p>The included script reproduces Figure 1 from GWTC-3 using the supplied data.</p> <p> </p> <p><strong>Figure 2</strong></p> <ul> <li>Figure02.tar.gz</li> </ul> <p>Data and plotting scripts for GWTC-3: Figure 2. The figure shows sensitivity curves for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script reads the .txt files containing strain data for Hanford, Livingston, and Virgo and saves figures as PDF files.</p> <p>The sensitivity curves are representative of performance during O3b. Further examples of sensitivity curves across observing runs can be found from the <a href="https://www.gw-openscience.org/detector_status/">Gravitational Wave Open Science Center</a>.</p> <p> </p> <p><strong>Figure 3</strong></p> <ul> <li>Figure03.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 3. The left panel shows the <a href="https://doi.org/10.1088/1361-6382/abd594">binary neutron star inspiral range</a> of LIGO Hanford, LIGO Livingston, and Virgo versus time. The right panel shows histograms of the binary neutron star ranges for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script (figure_3.py) reads the range.txt files and the histogram.txt files to produce each panel and saves them as PDF files.</p> <p>Further summary information about the O3b run can be obtained from the <a href="https://www.gw-openscience.org/detector_status/O3b/">Gravitational Wave open Science Center</a>.</p> <p> </p> <p><strong>Figure 4</strong></p> <ul> <li>Figure04.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 4. This plot shows the rate of <a href="https://doi.org/10.1088/1361-6382/abfd85">non-Gaussian noise transients (glitches)</a> in the LIGO Hanford, LIGO Livingston and Virgo data across O3b. The recorded glitches are identified by the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron</a> pipeline with signal-to-noise ratio of > 6.5. There is a reduction in the LIGO glitch rate after the introduction of <a href="https://doi.org/10.1088/1361-6382/abc906">reaction chain (RC) tracking</a>, which reduced the incidence of scattered light (slow scattering) glitches.</p> <p>The script glitch_rates_GWTC-3_Fig_4.py produces Figure 4 of GWTC-3 making use of the glitch rates stored in the glitch_rates_GWTC-3_Fig_4.h5 file. Run the script within an <a href="https://computing.docs.ligo.org/conda/environments/igwn-py37/">igwn-py37</a> or <a href="https://computing.docs.ligo.org/conda/environments/igwn-py38/">igwn-py38</a> Conda environment, paying attention to having the hdf5 file glitch_rates_GWTC-3_Fig_4.h5 in the same directory of the script. Pass the argument -v or --verbose for additional info about the rates.</p> <p> </p> <p><strong>Figure 5</strong></p> <ul> <li>Figure05.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 5. This figure illustrates the time–frequency structure of two common types of glitch seen in O3: <a href="https://doi.org/10.1088/1361-6382/abc906">slow scattering</a> and <a href="https://doi.org/10.1088/1361-6382/ac1ccb">fast scattering</a>. Both are caused by light scattering within the LIGO detectors.</p> <p>The Python script scattering_GWTC-3_Fig_5.py produces Figure 5 in the GWTC-3 Catalog paper using <a href="https://www.gw-openscience.org/O3/">open data</a>. The script saves the plot as a PDF file namely, scattering_GWTC-3_Fig_5.pdf and the data used to generate the plot in the files data_fast_scattering.txt and data_slow_scattering.txt.</p> <p>For further examples of the time–frequency structure of glitches, the community-science project <a href="https://gravityspy.org/">Gravity Spy</a> catalogs visualizations of glitches in gravitational-wave data.</p> <p> </p> <p><strong>Figure 12</strong></p> <ul> <li>Figure12.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 12. This plots results of the waveform consistency test (as does Figure 13), plotting the match between waveform templates and minimally modeled reconstructions. The on-source results are for the candidate signals, while the off-source results are for simulated signals with compatible properties.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The Python script (figure_12.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB to produce the corresponding match–match plots (PDF files).</p> <p> </p> <p><strong>Figure 13</strong></p> <ul> <li>Figure13.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 13. This plots results of the waveform consistency test (as does Figure 12), plotting the p-values for the minimally modeled waveform reconstructions. The p-values are plotted in increasing order.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The script (figure_13.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB (the same files used to produce Figure 12) to produce the corresponding p-value plots (PDF files).</p> <p> </p> <p><strong>Figure 14</strong></p> <ul> <li>Figure14.tar.gz</li> </ul> <p>Data and script to produce GWTC-3 Figure 14. This figure shows representative noise amplitude spectral densities for LIGO Hanford, LIGO Livingston, and Virgo during Observing Run 2 and Observing Run 3b.</p> <p>The script (figure_14.py) reads data from the .txt files included in the release to reproduce Figure 14 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 15</strong></p> <ul> <li>Figure15.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 15. This shows differences in the data used to analyze <a href="https://doi.org/10.7935/b024-1886">GW200115_042309</a>, with and without glitch subtraction. A low frequency cut (illustrated by the dotted white line) was used to remove the glitch in the <a href="https://doi.org/10.3847/2041-8213/ac082e">first analysis</a> of this candidate, whereas glitch subtraction is now used when performing parameter estimation. The curving orange line shows the approximate signal track.</p> <p>The script reads in the deglitched frame L-L1_HOFT_CLEAN_SUB60HZ_C01_T1700406_v4-1263095808-4096.gwf, downloaded from <a href="http://doi.org/10.5281/zenodo.5546679">an associated data release</a>, query raw <a href="https://www.gw-openscience.org/O3/">public LIGO Livingston data</a>, and reproduce Figure 15 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 16</strong></p> <ul> <li>Figure16.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 16. This figure illustrates the <a href="https://gwpy.github.io/docs/latest/examples/timeseries/qscan/">time–frequency structure</a> of data containing three O3 candidates identified only by <a href="https://gwburst.gitlab.io/">cWB</a> (the same as shown in Figure 17). Each shows evidence of instrumental origin. </p> <p>The script queries <a href="http://www.gw-openscience.org/O3/">public LIGO data</a> and reproduce Figure 16 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 17</strong></p> <ul> <li>Figure17.tar.gz</li> </ul> <p>Data and script for GWTC-3: Figure 17. This figure illustrates the <a href="https://doi.org/10.1088/1742-6596/363/1/012032">time–frequency structure</a> of candidate signals as reconstructed by <a href="https://gwburst.gitlab.io/">cWB</a> for three O3 candidates identified only by cWB (the same as shown in Figure 16). Each shows evidence of instrumental origin. For a compact binary coalescence signal, we would expect the signal to have a chirp structure, sweeping up from low to high frequencies.</p> <p>The script figs.py reads data (the reconstruction from cWB) from the .txt files to produce the corresponding time–frequency plots (PDF files). The script must be run three times to produce the panels of Figure 17: the event names are hardcoded into the script, which must be edited to produce the desired panel.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5571766 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data analysis, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
Observational Study of Convalescent Plasma for Treatment of Veterans With COVID-19
ClinicalTrials.gov study NCT04545047. IPD Sharing: NO. Countries: 1. Publications: 1.
An Observational Study of Presentation, Treatment Patterns, and Outcomes in Multiple Myeloma Participants
ClinicalTrials.gov study NCT02761187. IPD Sharing: YES. Countries: 15. Publications: 5.
COVID-19 Observational Research Collaboratory
ClinicalTrials.gov study NCT05394025. IPD Sharing: YES. Countries: 1. Publications: 21.
A Study to Observe Vedolizumab and Anti-tumour Necrosis Factors (Anti-TNFs) Outcomes in Real-world Biologic Ulcerative Colitis (UC) and Crohn's Disease (CD) Participants
ClinicalTrials.gov study NCT03710486. IPD Sharing: YES. Countries: 2. Publications: 1.
Local physical oceanographic and oxygen isotopic observations at Palmyra Atoll of the 2014-15 and 2015-16 El Nino events
Open the record for dataset details and reuse information.
Experimental data: Inferring strategies from observations in long iterated prisoner’s dilemma experiments
Open the record for dataset details and reuse information.
ASRC Rooftop CO observations, NYCMA observed and simulated ΔCO, and relative NYC surface influence
Open the record for dataset details and reuse information.
Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition
Open the record for dataset details and reuse information.
Data for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
Open the record for dataset details and reuse information.
A hierarchical dependent double-observer method for estimating waterfowl breeding pairs abundance from helicopters
Open the record for dataset details and reuse information.
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.