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245 results for “geophysical”
Multi-technique surface geophysical surveys over Devon Ice Cap, Canadian Arctic
<p>This dataset was acquired during a multi-technique surface geophysical campaign in May 2022 over Devon Ice Cap, Canadian Arctic.</p> <p><strong>Description of data:</strong></p> <p><strong>Seismic</strong><br> 9 km of active source seismic reflection data<br> raw segy files for line A and line B<br> seismic observation log<br> matlab script for plotting a raw stack of line A and line B<br> coordinates of each seismic spread, with the ice surface elevation and estimated bed elevation</p> <p><strong>Transient electromagnetic (TEM) </strong><br> 7 large loop TEM soundings <br> with 500 x 500 m loop with receiver 250 m outside the loop (away from the transmitter)<br> USF files for each sounding<br> coordinates for each TEM sounding<br> TEM observation log </p> <p><strong>Magnetotelluric (MT)</strong><br> 17 MT stations<br> raw, unprocessed EDI files <br> coordinates for each MT station <br> MT observation log</p> <p>Time series data (~80GB) can be found at:<br> https://drive.google.com/drive/folders/1OyCIP_B3VUJ4-ULSp8YOAPuNEMHuNcN-?usp=share_link</p> <p><strong>Acknowledgments</strong></p> <p>We thanks the Polar Continental Shelf Program for logistical support throughout the field season; Rob Harris at Geonics for his support and help with the TEM method; Zoe Vestrum at the University of Alberta for her MT support during deployment to the field; </p> <p><strong>Funding</strong></p> <p>This work was funded by the Weston Family Foundation. The aircraft hours were funded by the Polar Continental Survey Program (PCSP) and ArcticNet. MT survey was supported by a NSERC Discovery Grant to Martyn Unsworth and the Future Energy Systems program at the University of Alberta. </p> <p><strong>Corresponding Author</strong></p> <p>Siobhan Killingbeck skillin1@ualberta.ca</p>
Local ground-based geophysical observation data (Borehole tilt, broadband seismic, and infrasound) accompanying the 2018 phreatic eruption at Kusatsu-Shirane volcano (Motoshirane)
<p>A geophysical observation dataset (borehole tilt, broadband seismic, infrasound, and GNSS displacement) accompanying the 2018 phreatic eruption at the Motoshirane cone of the Kusatsu-Shirane volcano. The data was obtained by the local geophysical observation network operated by Kusatsu-Shirane Volcano Observatory, Tokyo Institute of Technology. All time is JST (UTC+9). Terada et al. (2021), Yamada et al. (2021), and Yamada et al. (submitted) describe station locations and instrumentations.</p> <p> </p> <p>Tilt (text files: 20190123_stn_1_Hz.txt)</p> <p>format: yy/mm/dd hour:min sec NS tilt EW tilt</p> <p> </p> <p>Broadband seismic and infrasound waveforms (sac files)</p> <p>(ex: 1801230950_stn_cmp.s)</p> <p>*stn: station name</p> <p>*cmp: component</p> <p> </p> <p>GNSS displacement (pos file)</p> <p>(ex: stn1030_0591.pos)</p> <p>*stn: station name</p> <p>Displacements are calculated by kinematic analysis using 960591 (a GNSS station operated by Geospatial Information Authority of Japan) as a base station. The kinematic analysis was performed on RTKLIB (ver. 2.4.2, Takasu, 2013). See the header for each file for detail.</p> <p> </p> <p> </p> <p>References</p> <p> </p> <p>Terada, A., Kanda, W., Ogawa, Y., Yamada, T., Yamamoto, M., Ohkura, T., et al. (2021). The 2018 phreatic eruption at Mt . Motoshirane of Kusatsu – Shirane volcano, Japan: Eruption and intrusion of hydrothermal fluid observed by a borehole tiltmeter network. Earth, Planets and Space, 73. <a href="https://doi.org/10.1186/s40623-021-01475-4">https://doi.org/10.1186/s40623-021-01475-4</a></p> <p> </p> <p>Takasu, T. (2013), RTKLIB: An Open Source Program Package for GNSS Positioning.</p> <p> </p> <p>Yamada, T., Kurokawa, A. K., Terada, A., Kanda, W., Ueda, H., Aoyama, H., et al. (2021). Locating hydrothermal fluid injection of the 2018 phreatic eruption at Kusatsu-Shirane volcano with volcanic tremor amplitude. Earth, Planets and Space, 73(1), 1–15. https://doi.org/10.1186/s40623-020-01349-1</p>
Datasets for "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", Journal of Geophysical Research: Solid Earth
<p>The following files were used in the analysis from "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", <em>Journal of Geophysical Research: Solid Earth</em>:</p> <p><strong>alos2_csk/alos2_sm1_dsc_20180504_20180713/:</strong> Includes DEM used in processing of the ALOS-2 SM1 descending interferogram spanning 4 May 2018–13 July 2018 (dem.2alks_2rlks.crop.*); geocoded SAR offsets in pixels (range resolution=1.43 m/pixel; azimuth resolution=2.01 m/pixel; denseOffsets.bil.2alks_2rlks.geo*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.2alks_2rlks.geo.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.2alks_2rlks.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.2alks_2rlks.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_sm3_asc_20180114_20180701/:</strong> Includes DEM used in processing of the ALOS-2 SM3 ascending interferogram spanning 14 January 2018–1 July 2018 (dem.crop.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_wd1_dsc_147_180518_180629/</strong>: Includes DEM used in processing of the ALOS-2 WD1 descending interferogram spanning 18 May 2018–29 June 2018 (crop.dem.*); geocoded, unwrapped interferometric phase (filt_180629-180518_2rlks_14alks.unw.geo.*) ; geocoded incidence and heading angle for the interferogram (180629-180518_2rlks_14alks.los.geo.*); geocoded coherence for the interferogram (180629-180518_2rlks_14alks.cor.geo.*); geocoded mask for the interferogram (filt_topophase.unw.masked.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180617_20180703/:</strong> Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 17 June 2018–3 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180703_20180719/</strong>: Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 3 July 2018–19 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180618_20180704/:</strong> Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 18 June 2018–4 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180704_20180720/: </strong>Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 4 July 2018–20 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>S1.zip</strong>: Unwrapped, geocoded interferometric phase in meters for Sentinel-1 ascending and descending interferograms, spanning time periods of interest. </p> <p><strong>S1_20180630_20180706_asc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 ascending interferogram spanning 30 June 2018–6 July 2018.</p> <p><strong>S1_20180701_20180707_desc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 descending interferogram spanning 1 July 2018–7 July 2018.</p> <p><strong>tandemx12m_crop.grd</strong>: TanDEM-X 12 meter DEM in meters.</p> <p><strong>pleaides_tandemx12m_diff.grd</strong>: Difference between the TanDEM-X 12 meter DEM and the Pléiades-derived DEM, computed from images on 29 October 2018 and 6 December 2019.</p> <p><strong>trapdoorFaultSlip.zip</strong>: Discretized trapdoor fault patch dip-slip modeled to fit deformation from Sentinel-1 ascending interferograms, estimated using the Classic Slip Inversion software.</p> <p><strong>trapdoorFaultTraces.zip</strong>: Caldera and trapdoor fault traces, derived from <em>Bell et al. 2021</em>.</p> <p><strong>SN14_tilt_10s_2018-19.txt</strong>: Text filt containing date-time (sampled at 10 s, in matplotlib date-time number format), N-S tilt and E-W tilt. Tilt values can be converted to microradians by multiplying by a factor of 0.00129. Tilt data obtained from authors of <em>Bell et al. 2021</em>. For use of this dataset, please cite <a href="https://doi.org/10.1038/s41467-021-21596-4">https://doi.org/10.1038/s41467-021-21596-4</a>.</p>
Comparing thermal regime stages along a small Yakutian fluvial valley with point scale measurements, thermal modeling and near surface geophysics
<ul> <li>Dataset associated with the paper :</li> </ul> <p>"<em>Comparing thermal regime stages along a small Yakutian fluvial valley with point scale measurements, thermal modeling and near surface geophysics</em>"</p> <p>Emmanuel Léger 1 , Albane Saintenoy 1 ,Christophe Grenier 2,† , Antoine Séjourné1 , Eric<br> Pohl2,3 , Frédéric Bouchard4, Marc Pessel1 , Kirill Bazhin5 , Kencheeri Danilov5, François<br> Costard1 , Claude Mugler2 , Alexander Fedorov5 , and Ivan Khristoforov5 and Pavel<br> Konstantinov5<br> 1 Laboratoire Geosciences Paris-Saclay, Université Paris-Saclay, CNRS, GEOPS, 91405, Orsay, France.<br> 2 Laboratoire des Sciences du Climat et de L’Environnement (LSCE), CEA CNRS UVSQ, Université<br> Paris-Saclay, Gif-sur-Yvette, France<br> 3 Department of Geosciences, University of Fribourg, Fribourg, Switzerland<br> 4 Universite de Sherbrooke Département de géomatique appliquée, Sherbrooke, QC, CA<br> 5 Melnikov Permafrost Institute, Yakutsk, Yakutia.</p>
Data for the paper "Two small volcanoes, one inside the other: geophysical and drilling investigation of Bažina maar in western Eger Rift"
<p>Data for the paper "Two small volcanoes, one inside the other: geophysical and drilling investigation of Bažina maar in western Eger Rift"</p> <p>Pavla Hrubcová, Tomáš Fischer, Vladislav Rapprich, Jan Valenta, Petr Tábořík, Jan Mrlina, Torsten Dahm, Tomáš Vylita, Roman Beránek, Radek Klanica, Veronika Turjaková</p> <p> </p> <p>This repository contains following data:</p> <p>1. Gravity data <br> • file: Gravity_Topo.txt (measured field data after topographic correction)<br> <br> 2. Magnetic data <br> • file: bazina_maar_magnetics.xlsx (measured field data corrected for variations)<br> • file: bazina_maar_magnetics_RTP.grd (magnetic field data reduced to pole, Golden <br> Software Surfer grid file)<br> <br> 3. Ground resistivity data using the multielectrode method (ERT data)<br> • folder: ERT_data – raw data for inversion in BERT format (https://gitlab.com/resistivity-net/bert) <br> - profiles<br> - 3d compilation<br> - ESRI ShapeFiles with coordinates<br> • folder: ERT_results – inverted results <br> - profiles<br> - 3d compilation (3d data cube)<br> The data are in VTK file format (The Visualization Toolkit)<br> Geographic coordinates used for the 3d data processing are in the S-JTSK (Krovak’s projection <br> – Cartesian coordinates used by the Czech Office for Surveying, Mapping and Cadastre)<br> <br> • file: Bazina_resistivity.xls (each sheet contains one file with data for the inversion) <br> The format is for the BERT software (https://gitlab.com/resistivity-net/bert) <br> all profiles, 3d compilation, with coordinates </p> <p>4. Lithology and logging data from the two boreholes S4 and S4b <br> • file: S4_data_EN.LAS (logging data for S4 borehole)<br> • file: S4b_data_EN.LAS (logging data for S4b borehole)<br> • file: Lithology_and_Logging_S4_S4b.pdf (lithology and logging data for S4 and S4b boreholes)<br> </p>
Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth
<p>Supplementary data for the study "Unveiling the rheological control of magmatic systems on volcano deformation: the interplay of poroviscoelastic magma-mush and thermo-viscoelastic crust ", submitted in the Journal of Geophysical Research: Solid Earth</p>
Data from: Forecasting landslides using community detection on geophysical satellite data
<p>As a result of extreme weather conditions, such as heavy precipitation, natural hillslopes can fail dramatically; these slope failures can occur on a dry day due to time lags between rainfall and pore-water pressure change at depth, or even after days to years of slow-motion. While the pre-failure deformation is sometimes apparent in retrospect, it remains challenging to predict the sudden transition from gradual deformation (creep) to runaway failure. We use a network science method–multilayer modularity optimization–to investigate the spatiotemporal patterns of deformation in a region near the 2017 Mud Creek, California landslide. We transform satellite radar data from the study site into a spatially-embedded network in which the nodes are patches of ground and the edges connect the nearest neighbors, with a series of layers representing consecutive transits of the satellite. Each edge is weighted by the product of the local slope (susceptibility to failure) measured from a digital elevation model and ground surface deformation (current rheological state) from interferometric synthetic aperture radar (InSAR). We use multilayer modularity optimization to identify strongly-connected clusters of nodes (communities) and are able to identify both the location of Mud Creek and nearby creeping landslides which have not yet failed. We develop a metric, community persistence, to quantify patterns of ground deformation leading up to failure, and find that this metric increases from a baseline value in the weeks leading up to Mud Creek's failure. These methods promise as a technique for highlighting regions at risk of catastrophic failure.</p>
Simulation data for "Connection between Chorus Wave Amplitude and Background Magnetic Field Inhomogeneity: A Parametric Study" which will be submitted to Geophysical Research Letters
<p>Simulation data for "Connection between Chorus Wave Amplitude and Background Magnetic Field Inhomogeneity: A Parametric Study" which will be submitted to Geophysical Research Letters.</p> <p>Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data at certain locations (|MLAT| = 3 deg).</p>
Supplementary Data to: "Organic Phases in Bivalve (Arctica islandica) Shells: Their Bulk and Amino Acid Nitrogen Stable Isotope Compositions" in Geochemistry, Geophysics, Geosystems
<p>This dataset contains the data generated for publication "Organic Phases in Bivalve (<em>Arctica islandica</em>) Shells: Their Bulk and Amino Acid Nitrogen Stable Isotope Compositions", including the following files:</p> <p>Specimen_ID_shell_morphology_measurement: Description of locality of collection, shell ID, shell working ID and the shell morphology measurement which can be referred to Section 2.1 of the manuscript.</p> <p>EA_IRMS_raw_error_propagate: This file contains data showing the m/z 28 signal intensities of all measurements and the error propagation that mentioned in Section 2.3 of the manuscript. It also includes the calculation of analytical accuracy and precision. </p> <p>Statistics: The file contains the summary of statistical analyses performed in this manuscript. Refer to Section 2.7 and 3.3.</p> <p>Recovery: It contains the recovery of the total nitrogen contents or amino acid concentrations after different treatments and this was mentioned in Section 4.1 of the manucript.</p> <p>Mass_balance: The computation procedure that showed in Section 3.4 and discussed in Section 4.3 can be traced in this file.</p> <p>GC-C-IRMS_long_term_sd: The file shows the long-term precision of GC-C-IRMS computed from the mixed standard of amino acids </p> <p>BSIA_d15N: The file contains all the bulk d15N data that used in this manuscript.</p> <p>CSIA_d15N: The file contains all the amino acid d15N data that used in this manuscript.</p> <p>AA_Composition: The file shows AA percent data that used in this manuscript.</p> <p><br> For details see main text of the manuscript.</p> <p><br> </p>
Data from: Forecasting landslides using community detection on geophysical satellite data
Open the record for dataset details and reuse information.
Supporting datasets used in the Geophysical Research - Oceans entitled "Variability of dissolved oxygen in the bottom layer of the southern Senegalese shelf"
<p>This archive contains text files corresponding to datasets used in the</p> <p>Geophysical Research-Oceans paper entitled "Variability of dissolved oxygen in the bottom layer of the southern Senegalese shelf"</p> <p>by Abdoul Wahab Tall, Eric Machu, Vincent Echevin, Xavier Capet, Alice Pietri, Khassoum Corréa, Alban Lazar</p> <p>The data are in standard text file format, which are readily readable using standard text tools.</p> <p>Variables names/content/dimensions and units are described in the text metadata.</p>
Dataset to reproduce the figures in "Revisiting AMOC Transport Estimates from Observations and Models" in Geophysical Research Letters
<p>Reference level assumptions used to calculate the Atlantic meridional overturning circulation transports at the RAPID and MOVE observing arrays are revisited in an eddying ocean model. Observational transport calculation methods are complemented by several alternative approaches. At RAPID, the model transports from the observational method and the model truth (based on the actual model velocities) agree well in their mean and variability. There are substantial differences among the transport estimates obtained with various methods at the MOVE site. These differences result from relatively large and time-varying reference velocities at depth in the model, not supporting a level-of-no-motion. The methods that account for these reference velocities properly at MOVE produce transports that are in good agreement with the model truth. In contrast with the observational estimates, the model transport trends at MOVE and RAPID largely agree with each other on pentadal to multi-decadal time scales. The datasets listed here are output fields from the Meridional ovErTurning ciRculation diagnostIC (METRIC) package which enables consistent calculations of AMOC estimates at the MOVE and RAPID sections from observations and models. The METRIC package is available on GitHub at https://github.com/NCAR/metric. Citation for the code: Castruccio F. S., 2021: NCAR/metric: metric v0.1. doi/10.5281/zenodo.4708277 Citation for the method: Danabasoglu et al. (2021). Revisiting AMOC Transport Estimates from Observations and Models. Geophysical Research Letters.</p>
Data of Fig.3 in Okazaki and Katayama, 2015, Geophysical Research Letters
<p>This dataset is raw data of Figure 3 in Okazaki and Katayama, 2015, Geophysical Research Letters (doi:10.1002/ 2014GL062735). The figure shows the duration of normal/slow stick-slip events as a function of the stress drop in the log-log space. Another dataset is from same mechanical data but recalculated for futher publications.</p>
Hydraulic Tomography Estimates Improved by Zonal Information from the Clustering of Geophysical Survey Data
<p>The spreadsheet contains the information of hydraulic conductivity fields, drawdown calibration and validation data sets, and tracer data sets used to accomplish the research article with the same title.</p>
Dataset for paper "Southward shift and intensification of the intertropical convergence zone in the North Pacific across the mid-Pleistocene transition" submittted to Geophysical Research Letters
<p>Data of SST and SSS from <i>G. ruber</i> 𝛿18O and Mg/Ca of core ODP871 (5°33′N, 172°21′E, water depth 1255 m) in the central equatorial Pacific.</p>
Breathing Phenomenon of Soil Desiccation Cracking: Insights from Novel Geophysical Observations
<p>Jin-Jian Xu, Chao-Sheng Tang*, Yaowen Yang, Lin Li, Hao Zhang, Qing Cheng, Xi-Ying Zhang, Bo Liu, Bin Shi</p>
Alignment of geophysical fields: a differential geometry perspective
<p>This is the document for the code mentioned in "Alignment of geophysical fields: a differential geometry perspective".</p> <p> </p> <p>(To save time, steps 1,2,3,4 can be skipped since the data is already saved in the corresponding filefolder)</p> <p> </p> <p><strong>1,GENERATE ENSEMBLE MEMBERS</strong> : run getens_doublevortex_256.py</p> <p><strong>2,GENERATE THE TRUTH </strong> : run gettruth_doublevortex_256.py</p> <p> </p> <p><strong>3,BEFORE MORPHING</strong> : run exp/get_dataens.py and exp/get_dataobs.py</p> <p><strong>4,MORPH THE ENSEMBLE </strong> : run exp/get_morphed.py</p> <p> </p> <p><strong>5,BEFORE DA </strong> : run exp/get_ens_LR.py and exp/get_truth_LR.py</p> <p><strong>6,DA </strong> : run exp/EnKF_direct.py and exp/EnKF_morphed.py</p> <p><strong>7,PLOT DA RESULTS </strong> : run exp/plt_comparemean.py and exp/plt_compare_member.py</p> <p> </p> <p><strong>8,MORPH WITH PLAIN T#(10000 steps) </strong> : run exp/get_morphed_noDiffGeo.py</p> <p><strong>9,MORPH WITH PLAIN T#(6000 steps) </strong> : run exp/get_morphed_noDiffGeo2.py</p> <p> </p> <p><strong>10,PLOT MORPHED ENSEMBLE MEMBERS </strong> : run exp/ensemble_morphed/figs/plt_main.py</p> <p><strong>11,PLOT MORPHED ENSEMBLE MEMBER WITH PLAIN T# </strong>: run exp/ensemble_morphed_noDiffGEo/figs/plt_main.py and exp/ensemble_morphed_noDiffGEo2/figs/plt_main.py</p>
Multichannel seismic reflection data used in a geophysical survey at the northern continental margin of the South China Sea
<p>This archive includes the stacked multichannel seismic reflection waveform data. The CDP interval is 12.5 m. This raw data was collected by the Guangzhou Marine Geological Survey (GMGS) between July and September 2020. </p>
Dataset for Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera). Tectonics
<p>Dataset for "Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera)" Tectonics<br><br>File "hypoDD_10_300.reloc.txt" compile all the relocated seismicity in the study area.<br>Files "810_.NEU, 811_.NEU, 812_.NEU, 813_.NEU, 814_.NEU, 815_.NEU, and 816_.NEU" presents the time series data of GNSS sites of the Zafarraya network.</p>
Codes for "Summer westerly wind intensification weakens Southern Ocean seasonal cycle under global warming" - submitted to Geophysical Research Letters
<pre>This repository contains the NCAR Command Language (NCL) codes (*.ncl files) used to produce the related contents of Zhang et al. (2024). For queries about this repository and its contents, please contact me (zhangyiwen2024@gmail.com). Figure 1.ncl: For plotting main Fig.1. Figure 2.ncl: For plotting main Fig.2. Figure 3.ncl: For plotting main Fig.3. Figure 4.ncl: For plotting main Fig.4.<br>Figure 5.ncl: For plotting main Fig.5. References: Zhang et al. Summer westerly wind intensification weakens Southern Ocean seasonal cycle under global warming. Geophysical Research Letters. (2024).</pre>
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.