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12,346 results for “derivatives”
CLDF dataset derived from Sims' "Diachrony of Tone in Proto-Rma" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sims, Nathanial A. (2020): Reconsidering the diachrony of tone in Rma. Journal of the Southeast Asian Linguistics Society 13.1. 53-85.</p> </blockquote>
CLDF dataset derived from Zhou's "Proto-Bizic" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zhou, Yulou (2020): Proto-Bizic. A study of Tujia historical phonology. Bachelor Thesis. Stanford University.</p> </blockquote>
CLDF dataset derived from Seifart and Echeverri's "Proto Bora-Muinane" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Seifart, Frank and Echeverri, Juán Álvaro. 2015. Proto Bora-Muinane. LIAMES 15(2): 279-311.</p> </blockquote>
CLDF dataset derived from So-Hartmann's "Notes on the Southern Chin Languages" from 1988
<p>Cite the source of the dataset as:</p> <blockquote> <p>So-Hartmann, Helga (1988): Notes on the Southern Chin Languages. Linguistics of the Tibeto-Burman Area 11.2: 98-119.</p> </blockquote>
CLDF dataset derived from Nurse and Philippson's "Tanzania Language Survey" from 1975
<p>Cite the source of the dataset as:</p> <blockquote> <p>Nurse, Derek and Gérard Philippson (1975). The Tanzanian Language Survey. Department of Foreign Languages and Linguistics of the University of Dar es Salaam: Dar es Salaam.</p> </blockquote>
CLDF dataset derived from Robinson and Holton's "Internal Classification of the Alor-Pantar Language Family" from 2012
<p>Cite the source of the dataset as:</p> <blockquote> <p>Robinson, Laura C. and Holton, Gary (2012): Internal Classification of the Alor-Pantar Language Family Using Computational Methods Applied to the Lexicon. Language Dynamics and Change 2.2. 123-149.</p> </blockquote>
CLDF dataset derived from Z'graggen's "Madang Comparative Wordlists" from 1980
<p>Cite the source of the dataset as:</p> <blockquote> <p>Z'graggen, J A. (1980) A comparative word list of the Northern Adelbert Range Languages, Madang Province, Papua New Guinea. Canberra: Pacific Linguistics.</p> </blockquote>
CLDF dataset derived from Zhao's "Investigations of Zhaozhuang Bai" from 2006
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zhao, Yanzhen (2006): Zhàozhuāng Báiyǔ miáoxiě yánjiū 趙莊白語描寫研究 [Investigations of Zhaozhuang Bai]. Běijīng: Zhōngyāng Mínzú Dàxué.</p> </blockquote>
CLDF Dataset derived from the Bahnaric data in Sidwell's "Austroasiatic dataset for phylogenetic analysis" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sidwell, Paul. 2015. Austroasiatic dataset for phylogenetic analysis: 2015 version. Mon-Khmer Studies (Notes, Reviews, Data-Papers) 44. lxviii-ccclvii.</p> </blockquote>
CLDF dataset derived from Sagart et al.'s "Sino-Tibetan Database of Lexical Cognates" from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Laurent Sagart, Jacques, Guillaume, Yunfan Lai, and Johann-Mattis List (2019): Sino-Tibetan Database of Lexical Cognates. Jena: Max Planck Institute for the Science of Human History.</p> </blockquote>
SPI, KUH, and MCM derived lidar T datasets
<p>These datasets are used within Dawkins <em>et al.</em>, 'Validation of SABER v2.0 Operational Temperature Data with Ground-based Lidars in the Mesosphere-Lower Thermosphere Region (75-105 km).'The datasets correspond to the derived Spitsbergen, Kühlungsborn, and McMurdo temperature (T, units: K) datasets presented within the paper. Each text file contains header information necessary for interpreting the data, with each following a standard format.<br> <br> For each season, the dataset comprises of 31 rows, and 5 columns.<br> All rows correspond to altitudes: 75-105 km, at 1 km increments.<br> Column 1 = lidar mean for season [Units=K]<br> Column 2 = lidar standard deviation of mean for season [Units=K]<br> Column 3 = combined SABER and lidar error for season [Units=K]<br> Column 4 = standard deviation of individual (SABER-lidar) colocated pairs. [Units=K]<br> Column 5 = T difference of seasonal means (SABER-lidar) [Units=K]<br> <br> Also contains dataset presenting Figure 12 (n rows, 2 columns). Column 1: all SABER T data for all altitudes. Column 2: corresponding lidar T data. n rows corresponds to number of SABER-lidar T data pairs. All units: K.<br> </p> <ul> <li>DJF = December-January-February. </li> <li>MAM = March-April-May. </li> <li>JJA = June-July-August. </li> <li>SON = September-October-November.</li> </ul>
Identification and Functional Characterization of an Alternative Cancer-derived PD-L1 Isoform (supplemental data)
<p>The enclosed files contain all of the supplemental data from: Identification and Functional Characterization of an Alternative Cancer-derived PD-L1 Isoform. The files include the complete tables in CSV-formatted files.</p>
Global monthly discharge dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution discharge dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre>
Global derived datasets for use in k-NN machine learning prediction of global seafloor total organic carbon
<p>This dataset includes 663 predictor grids used for k-NN global prediction of seafloor total organic carbon.</p> <p>663 predictor grids available in netCDF4 HDF5 file format. Grids are cell-centered sized 4320 x 2160. File names adhere to the naming conventions discussed below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension.</p> <p>Possible interfaces from the top – down:</p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p>Appropriate reference naming marker (bold), original data source, and date of last access:</p> <p><strong>Becker</strong></p> <p>Becker, J. J., Wood, W. T., & Martin, K. M. (2014). <em>Global crustal heat flow using random decision forest prediction</em>, Abstract NG31A-3788 presented at 2014 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 06/23/2015.</p> <p><strong>CRUST1</strong> </p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). <em>LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints</em>, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p><strong>CRUST1_NOAA</strong></p> <p> As the NOAA sediment thickness database is globally not complete, data gaps in the NOAA grid with this have been supplemented by the CRUST1 sediment thickness (see above citation).</p> <p>Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., & Leitchenkov, G. (2013) Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, <em>Geochemistry, Geophysics, Geosystems. </em>https://doi.org/10.1002/ggge.2018.<em> </em>Last access: 09/02/2018.</p> <p><strong>GVP</strong></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p><strong>ETOPO2v2</strong></p> <p>National Geophysical Data Center (2006). 2-minute Gridded Global Relief Data (ETOPO2) v2. National Geophysical Data Center, NOAA. DOI: 10.7289/V5J1012Q. Last access: 02/06/2013.</p> <p><strong>PLATES</strong></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p><strong>ONRL</strong></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), <em>ISLSCP Initiative II Collection</em>. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p><strong>Muller</strong></p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust, <em>Geochemistry, Geophysics, Geosystems</em>, 9(4), Q04006. https://doi.org/10.1029/2007GC001743. Last accessed: 07/19/2011.</p> <p><strong>Woa13x</strong></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), <em>NOAA Atlas NESDIS 72, Technical Ed</em>. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p><strong>KIM</strong></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, <em>Geophysical Journal International</em>, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p><strong>HYCOM</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p><strong>NCEDC</strong></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p><strong>Wei2010</strong></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. <em>PLoS ONE</em>,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p><strong>NGA_egm2008</strong></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). <em>The</em> <em>EGM2008 Global Gravitational Model</em>, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p><strong>WAVEWATCH3</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p>Updated global seafloor porosity grid using our k-nearest neighbors algorithm using 5 nearest neighbors. Observed data used for prediction from Martin et al. (2015). </p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 10640. https://doi.org/10.1002/2015GL065279</p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), longitude (and derivatives), Coriolis, coast_is_1.0, and the random noise grids. </p> <p>Units referenced are as follows:</p> <p>KGM3 - kilogram per cubic meter<br> MS - meters per second<br> KM - kilometer<br> M_ASL - meters above sea level (i.e. meters referenced to sea level)<br> MWM2 - milliwatt per square meter<br> TGCYR - terragram of carbon per year<br> TGYR - terragram per year<br> MA - megaannum<br> M - meters<br> MGCM2 - milligram of carbon per square meter<br> DEG - degree<br> S - seconds</p> <p>Statistics grids are calculated within a given radius (e.g. 10km, 50km, 125km, 250km, 500km, 1000km) of the respective cell-centered value. The statistics grids include mean (.men), average absolute deviation from the mean (.aad), and the common logarithm (.log) of the absolute value of the mean (.mlg). Additionally, some grids are a weighted count for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p>The grid pitch for this dataset is uniformly at 5-arc minute denoted by “.5m”. Additionally, the extension used (netCDF4) is denoted by “.nc”.</p>
Derived data and analysis code accompanying Deines et al. 2019, Environmental Research Letters
<p>This codebase accompanies the paper:</p> <p>Deines, JM, AD Kendall, JJ Butler, Jr., & DW Hyndman. 2019. Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environmental Research Letters. DOI: <a href="https://doi.org/10.1088/1748-9326/aafe39">https://doi.org/10.1088/1748-9326/aafe39</a></p> <p>Data and code at time of publication.</p>
Derivation of Hemispheric Ionospheric Current Functions From Ground-Level Magnetic Fields
<p>These files provide the input and output data for the Figures shown in the paper "Derivation of<br> Hemispheric Ionospheric Current Functions From Ground-Level Magnetic Fields" by Daniel Weimer, published in the Journal of Geophysical Research, Space Physics, paper number 2018JA026191, doi:10.1029/2018JA026191</p> <p>Two IDL program files and the original, PDF versions of the figures are included.</p> <p>The data are provided as IDL "SAVE" files, readable in IDL with the "RESTORE" command. NetCDF<br> versions are included, readable with any NetCDF software library. These NetCDF files have the same<br> names as the ".xdr" files, except they have the extension ".nc". Scalar variables (length 1) are put<br> into the Global Attributes in these files.</p> <p>The files in this archive are:</p> <p>Figures:<br> Figure_1.PDF<br> Figure_2.PDF<br> Figure_3.PDF<br> Figure_4.PDF<br> Figure_5.PDF<br> Figure_6.PDF</p> <p>IDL Programs:<br> spherical_cap_90_fits.pro : Routines for fitting magnetic field data on a hemispheric cap (90<br> degrees), using spherical harmonics having both internal and external sources (or external alone),<br> and functions for evaluating the results as equivalent currents, or the magnetic field components.</p> <p> AllLegendre.pro : Required by spherical_cap_90_fits.pro, provides computations of Associated<br> Legendre Polynominals as arrays, for all combinations of l and m, up to Lmax and Mmax, as well as first derivatives.</p> <p>Data Files:</p> <p> Figures_1_2_3_4_dB_ModelData.xdr : Magnetic field values (output from the 2013 empirical model),<br> shown in Figure 1, and used to calculate the coefficients used to make Figures 2, 3, and 4.<br> Contents:<br> ALLLATS FLOAT = Array[8640] , array of latitude values, degrees<br> ALLMLTS FLOAT = Array[8640] , array of Magnetic Local Time (MLT) values, hours<br> DBNS FLOAT = Array[8640] , array of northward magnetic field values<br> DBES FLOAT = Array[8640] , array of eastward magnetic field values<br> DBVS FLOAT = Array[8640] , array of vertical (downward) magnetic field values<br> NLATS FLOAT = 180.000<br> NMLTS INT = 48<br> empirical model inputs:<br> BT FLOAT = 10.0000 , magnitude of the IMF<br> ANGLE FLOAT = 180.000 , IMF clock angle<br> F107 FLOAT = 120.000 , F10.7 solar index<br> SWVEL FLOAT = 400.000 , solar wind velocity<br> TILTA FLOAT = 0.00000 , dipole tilt angle</p> <p> Figures_2_3_4_SCHA90FitResults.xdr : The coefficients obtained from the fits, used to generate<br> Figures 2, 3, and 4.<br> Contents:<br> SPHCE DOUBLE = Array[115] , external spherical harmonic coefficients<br> SPHCI DOUBLE = Array[115] , interal spherical harmonic coefficients<br> NOINT_SPHCE DOUBLE = Array[115] , external spherical harmonic coefficients,<br> derived without using the internal terms in the fitting of the magnetic potential<br> The following variables are documented in the IDL program spherical_cap_90_fits.pro:<br> MAXL INT = 34<br> MAXM INT = 3<br> ODD INT = 1<br> EVEN INT = 0<br> CSIZE INT = 115<br> LS INT = Array[115]<br> MS INT = Array[115]<br> AB BYTE = Array[115]</p> <p> Figures_3_4_dB_ModelData-Dst.xdr : Magnetic field values, after subtraction of the ring current<br> magnetic field, used to calculate the coefficients to make Figures 3 and 4.<br> Contents: Same variables as in file Figures_1_2_3_4_dB_ModelData.xdr</p> <p> Figures_3_4_SCHA90FitResults-Dst.xdr : The coefficients obtained from fitting the magnetic field<br> that had the ring current subtracted, used to make Figures 3, and 4.<br> Contents: Same variables as in file Figures_2_3_4_SCHA90FitResults.xdr</p> <p> Figure_5_dB_ModelDataTilt3x-Dst.xdr : eight sets of magnetic field values, after subtraction of the<br> ring current magnetic field, used to calculate the coefficients to generate Figure 5.<br> Contents: Similar variables as in file Figures_1_2_3_4_dB_ModelData.xdr, except that TILTA is<br> replaced by TILTS, an array with the eight dipole tilt angles. The magnetic field values are<br> replaced by these arrays:<br> DBN3X FLOAT = Array[180, 48, 3] , northward magnetic field, eight sets<br> DBE3X FLOAT = Array[180, 48, 3] , eastward magnetic field, eight sets<br> DBV3X FLOAT = Array[180, 48, 3] , vertical magnetic field, eight sets</p> <p> Figure_5_SCHA90FitResultsTilt3x-Dst.xdr : eight sets of coefficients obtained from fitting the<br> magnetic field, used to make Figure 5.<br> Contents: Same variables as in file Figures_2_3_4_SCHA90FitResults.xdr, except for these arrays:<br> SPHCE3X DOUBLE = Array[115, 3] , external spherical harmonic coefficients, eight sets<br> SPHCI3X DOUBLE = Array[115, 3] , internal spherical harmonic coefficients, eight sets</p> <p> Figure_6_dB_ModelDataClock8x-Dst.xdr : Eight sets of magnetic field values, after subtraction of<br> the ring current magnetic field, used to calculate the coefficients to generate Figure 6.<br> Contents: Similar variables as in file Figures_1_2_3_4_dB_ModelData.xdr, except that ANGLE is<br> replaced by ANGLES, an array with the eight IMF clock angles. The magnetic field values are<br> replaced by these arrays:<br> DBN8X FLOAT = Array[180, 48, 8] , northward magnetic field, eight sets<br> DBE8X FLOAT = Array[180, 48, 8] , eastward magnetic field, eight sets<br> DBV8X FLOAT = Array[180, 48, 8] , vertical magnetic field, eight sets</p> <p> Figure_6_SCHAFitResultsClock8x-Dst.xdr : Eight sets of coefficients obtained from fitting the<br> magnetic field, used to make Figure 6.<br> Contents: Same variables as in file Figures_2_3_4_SCHA90FitResults.xdr, except for these arrays:<br> SPHCE8X DOUBLE = Array[115, 8] , external spherical harmonic coefficients, eight sets<br> SPHCI8X DOUBLE = Array[115, 8] , internal spherical harmonic coefficients, eight sets<br> </p>
Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution
<p>Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054247">NRCS Soil Survey Laboratory Methods Manual</a>), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <a href="https://doi.org/10.5281/zenodo.2609113"><strong>here</strong></a>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included.</p> <p>To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>available.water.capacity = available water capacity in mm,</li> <li>usda.mm = determination method: Water Retention Difference in mm,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</p>
CLDF dataset derived from Lionnet's "Relaciones Internas de la Rama Sonorense" from 1985
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lionnet, André (1985): Relaciones internas de la rama sonorense. Amerindia 10.</p> </blockquote>
CLDF Dataset derived from List's "Sample Size and Cognate Detection" from 2014
<p>Cite the source of the dataset as:</p> <blockquote> <p>List, Johann-Mattis (2014): Investigating the impact of sample size on cognate detection. Journal of Language Relationship. 11. 91-102. DOI: https://doi.org/10.31826/jlr-2014-110111</p> </blockquote>
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.