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140 results for “Spy”
Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b
<p>This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (<a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a> and O3, where O3 is split into <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a>). Gravity Spy classified all noise events identified by the <a href="https://doi.org/10.1016/j.softx.2020.100620">Omicron trigger pipeline</a> in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made <a href="https://en.wikipedia.org/wiki/Constant-Q_transform">Omega scans</a> of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.</p> <p>There are <a href="https://doi.org/10.1088/1361-6382/aa5cea">22 classes</a> used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are <a href="https://doi.org/10.1088/1361-6382/ac1ccb">two additional classes</a> used to classify O3 data (while None_of_the_Above was removed).</p> <p>For O1 and O2, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>For O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, <strong>Blip_Low_Frequency</strong>, Chirp, Extremely_Loud, <strong>Fast_Scattering</strong>, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>The data set is described in <a href="https://doi.org/10.1088/1361-6382/acb633"><strong>Glanzer </strong><em>et al</em><strong>. (2023)</strong></a>, which we ask to be cited in any publications using this data release. Example code using the data can be found in this <a href="https://colab.research.google.com/drive/19q_lItODPk7qw_sohlHyWPnAbY0FZyt8?usp=sharing"><strong>Colab notebook</strong></a>.</p> <p>If you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool <a href="https://gwpy.github.io/docs/stable/">GWpy</a>. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command</p> <pre><code class="language-bash">conda create --name gravityspy-py38 -c conda-forge python=3.8 gwpy pandas psycopg2 sqlalchemy</code></pre> <p>After downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.</p> <pre><code class="language-python">from gwpy.table import GravitySpyTable H1_O2 = GravitySpyTable.read('H1_O2.csv') H1_O2[(H1_O2["ml_label"] == "Blip") & (H1_O2["ml_confidence"] > 0.9)] H1_O2[0:4].download(nproc=1)</code></pre> <p>Each of the columns in the CSV files are taken from various different inputs: </p> <p>[‘event_time’, ‘ifo’, ‘peak_time’, ‘peak_time_ns’, ‘start_time’, ‘start_time_ns’, ‘duration’, ‘peak_frequency’, ‘central_freq’, ‘bandwidth’, ‘channel’, ‘amplitude’, ‘snr’, ‘q_value’] contain metadata about the signal from the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron pipeline</a>. </p> <p>[‘gravityspy_id’] is the unique identifier for each glitch in the dataset. </p> <p>[‘1400Ripples’, ‘1080Lines’, ‘Air_Compressor’, ‘Blip’, ‘Chirp’, ‘Extremely_Loud’, ‘Helix’, ‘Koi_Fish’, ‘Light_Modulation’, ‘Low_Frequency_Burst’, ‘Low_Frequency_Lines’, ‘No_Glitch’, ‘None_of_the_Above’, ‘Paired_Doves’, ‘Power_Line’, ‘Repeating_Blips’, ‘Scattered_Light’, ‘Scratchy’, ‘Tomte’, ‘Violin_Mode’, ‘Wandering_Line’, ‘Whistle’] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity). These use the original 22 classes in all cases.</p> <p>[‘ml_label’, ‘ml_confidence’] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification. </p> <p>[‘url1’, ‘url2’, ‘url3’, ‘url4’] are the links to the publicly-available <a href="https://gwdetchar.readthedocs.io/en/stable/omega/">Omega scans</a> for each glitch. ‘url1’ shows the glitch for a duration of 0.5 seconds, ‘url2’ for 1 seconds, ‘url3’ for 2 seconds, and ‘url4’ for 4 seconds.</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo. </p> <p><br> For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo.</p>
Machine learning for Gravity Spy: Glitch classification and dataset
<p>We present the first version of the training set used in the Gravity Spy citizen science project. This training set, discussed in detail <a href="https://www.sciencedirect.com/science/article/pii/S0020025518301634">here</a>, was utilized to train the convolutional neural network employed in the Gravity Spy project. We anticipate moving forward to release more labelled Gravity Spy data sets, including a refined version of this training set which can be found here <a href="https://doi.org/10.5281/zenodo.1476551">10.5281/zenodo.1476551</a>, and data sets containing the annotations provided by our citizen science volunteers.</p> <p><strong>Data Set Information</strong></p> <p>There are three files provided in this data set</p> <ul> <li><strong>trainingset_v1d0_metadata.csv</strong> <ul> <li>This file has three columns, <em>gravityspy_id, label, </em>and <em>sample_type.</em><em> gravityspy_id </em>is the unique 10 character hash given to every Gravity Spy sample. <em>label</em> is the string label of the sample. <em>sample_type </em>indicates whether this sample was used in the paper for testing training or validating the models. This is provided for those who would like to do direct comparisons to the network described in the paper.</li> </ul> </li> <li><strong>trainingsetv1d0.h5</strong> <ul> <li>This file contains the exact arrays used in the paper for every Gravity Spy sample. Each Gravity Spy sample is defined by four different images with varying temporal duration, <em>0.5, 1.0, 2.0, and 4.0</em> second, respectively. This also determines the naming conventions of the PNGs: <em>interferometer_gravityspyid_spectrogram_duration.png (e.g. H1_Fv3p6eROvA_spectrogram_0.5.png, H1_Fv3p6eROvA_spectrogram_1.0.png, H1_Fv3p6eROvA_spectrogram_2.0.png, H1_Fv3p6eROvA_spectrogram_4.0.png</em>).</li> <li>This file contains all the information needed for each sample in the Gravity Spy dataset (i.e. the label, the sample type of the sample, the unique id of the sample, and the image data for that sample. <ul> <li>/1080Lines/validation/xUEyaWr34c Group<br> /1080Lines/validation/xUEyaWr34c/0.5.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/1.0.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/2.0.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/4.0.png Dataset {1, 140, 170}</li> </ul> </li> </ul> </li> <li><strong>trainingsetv1d0.tar.gz</strong> <ul> <li>Contains the raw PNGs of the Gravity Spy training set.</li> <li>The structure of the folder is <em>/"label"/"sample_type"/"pngs"</em></li> </ul> </li> </ul> <p><strong>Data Set Parsing Information</strong></p> <p>To read and crop out the plot axis and labels of the provided PNGs, the following small python code using scikit-image should work.</p> <p>from skimage import io</p> <p>image_data = io.imread("filename_of_image")</p> <p>x=[66, 532]; y=[105, 671]</p> <p>image_data = image_data[x[0]:x[1], y[0]:y[1], :3]</p>
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>
Gravity Spy Training Set
<p>We present an updated version of the training set used in the Gravity Spy citizen science project. This training set, curated further from the one discussed in detail <a href="https://www.sciencedirect.com/science/article/pii/S0020025518301634">here</a> and available here <a href="https://doi.org/10.5281/zenodo.1476156">10.5281/zenodo.1476156</a>, was utilized to train the convolutional neural network employed in the Gravity Spy project. We anticipate moving forward to release more labelled Gravity Spy data sets, including a data set containing the annotations provided by our citizen science volunteers.</p> <p><strong>Data Set Information</strong></p> <p>There are three files provided in this data set</p> <ul> <li><strong>trainingset_v1d1_metadata.csv</strong> <ul> <li>This file has many columns, <em>gravityspy_id, label, </em>and <em>sample_type.</em><em> gravityspy_id </em>is the unique 10 character hash given to every Gravity Spy sample. <em>label</em> is the string label of the sample. <em>sample_type </em>indicates whether this sample was used in the paper for testing training or validating the models. This is provided for those who would like to do direct comparisons to the network described in the paper.</li> <li>Additional columns contain some metadata information about the glitch <ul> <li><em>event_time,ifo,peak_time,peak_time_ns,start_time,start_time_ns,duration,search,process_id,event_id,peak_frequency,central_freq,bandwidth,channel,amplitude,snr,confidence,chisq,chisq_dof,param_one_name,param_one_value,</em></li> </ul> </li> </ul> </li> <li><em><strong>trainingsetv1d1.h5</strong></em> <ul> <li>This file contains the exact arrays used in the paper for every Gravity Spy sample. Each Gravity Spy sample is defined by four different images with varying temporal duration, <em>0.5, 1.0, 2.0, and 4.0</em> second, respectively. This also determines the naming conventions of the PNGs: <em>interferometer_gravityspyid_spectrogram_duration.png (e.g. H1_Fv3p6eROvA_spectrogram_0.5.png, H1_Fv3p6eROvA_spectrogram_1.0.png, H1_Fv3p6eROvA_spectrogram_2.0.png, H1_Fv3p6eROvA_spectrogram_4.0.png</em>).</li> <li>This file contains all the information needed for each sample in the Gravity Spy dataset (i.e. the label, the sample type of the sample, the unique id of the sample, and the image data for that sample. <ul> <li>/1080Lines/validation/xUEyaWr34c Group<br> /1080Lines/validation/xUEyaWr34c/0.5.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/1.0.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/2.0.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/4.0.png Dataset {1, 140, 170}</li> </ul> </li> </ul> </li> <li><strong>trainingsetv1d1.tar.gz</strong> <ul> <li>Contains the raw PNGs of the Gravity Spy training set.</li> <li>The structure of the folder is <em>/"label"/"sample_type"/"pngs"</em></li> </ul> </li> </ul> <p><strong>Data Set Parsing Information</strong></p> <p>To read and crop out the plot axis and labels of the provided PNGs, the following small python code using scikit-image should work.</p> <p>from skimage import io</p> <p>image_data = io.imread("filename_of_image")</p> <p>x=[66, 532]; y=[105, 671]</p> <p>image_data = image_data[x[0]:x[1], y[0]:y[1], :3]</p>
Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions and in Steady-State (dataset)
<p>Database for reproducing the calculations presented in the publication "Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions", submitted to <em>Fusion Engineering and Design</em>.</p> <p>Work carried out within the framework of the EUROfusion Consortium.</p>
Simulation and Observation of GICs in the Portuguese power network SPI substation
<p>We developed an instrumental setup to measure Geomagnetic Induced Currents (GICs), consisting of a Hall effect current sensor LEM HOP 1000-SB with a manufacturer's sensitivity 4 mV/A, and a Raspberry Pi 4 Model B platform with a high resolution 24-bit digitizer board (Waveshare AD/DA). The sensor was installed at the Portuguese power network Paraimo (SPI) substation, about 35 km north of Coimbra, in the TRF6 transformer neutral to Earth connection cable.</p> <p>Here, we present measurements of GICs at SPI, during the 17th September 2021 geomagnetic event. Measurements were compared with estimations, which are also provided in this dataset.</p>
Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b
<p>This dataset contains machine learning and volunteer classifications from the <a href="https://www.zooniverse.org/projects/zooniverse/gravity-spy">Gravity Spy project</a>. It includes glitches from observing runs <a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a>, <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a> that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the <a href="https://ui.adsabs.harvard.edu/abs/2017CQGra..34f4003Z/abstract">Gravity Spy Methods paper</a>. </p> <p>When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are: </p> <ol> <li>A glitches must be classified by at least 2 registered volunteers</li> <li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li> <li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li> </ol> <p>The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep). </p> <p>The dataset can be read in using e.g. Pandas: <br> ```<br> import pandas as pd<br> dataset = pd.read_hdf('<a href="https://zenodo.org/api/files/512bfa79-dfbc-4af0-b563-9fdd06edcb16/retired_fulldata_min2_max50_ret0p9.hdf5?versionId=7f568823-76d9-4452-8553-c1eee5993f81">retired_fulldata_min2_max50_ret0p9.hdf5</a>', key='image_db')<br> ```<br> Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. </p> <p><strong>Description of series in dataframe</strong></p> <ul> <li>['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle'] <ul> <li>Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity</li> </ul> </li> <li>['ml_confidence', 'ml_label'] <ul> <li>Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score</li> </ul> </li> <li>['gravityspy_id', 'id'] <ul> <li>Unique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">full Gravity Spy dataset</a> (which contains GPS times among many other descriptors)</li> </ul> </li> <li>['retired'] <ul> <li>Marks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)</li> </ul> </li> <li>['Nclassifications'] <ul> <li>The total number of classifications performed by registered volunteers on this glitch</li> </ul> </li> <li>['final_score', 'final_label'] <ul> <li>The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch</li> </ul> </li> <li>['tracks'] <ul> <li>Array of classification weights that were added to each glitch category due to each volunteer's classification</li> </ul> </li> </ul> <p> </p> <p>```<br> For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">Gravity Spy Machine Learning Classifications</a> on Zenodo</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo.</p> <p>For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo. </p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based SPI and SPEI data
<p>Standardised Precipitation Index (SPI; McKee et al., 1993) and Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2009) computed from UKCP18 Strand 3 simulations (Met Office Hadley Centre, 2018).</p> <p>This data was produced for the study by Reyniers et al. (<em>in prep</em>) analysing (diferences in) drought projections using these indicators. The methodology used to produce this data can be found there if/when the paper is accepted, however do not hesitate to reach out with any further questions. Please note the RCM data was bias adjusted prior to SPI and SPEI computation. There is one file per ensemble member containing the full simulated period on a monthly time step, using aggregation periods of 1, 3, 6, 12, 24 and 36 months for the computation of SP(E)I.</p> <p><strong>References</strong></p> <p>McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, pp. 179–183, Boston, 1993</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. Centre for Environmental Data Analysis, <em>date of citation</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Reyniers, N., Osborn, T. J., Addor, N., Darch, G.: Projected changes in droughts and extreme droughts in Great<br> Britain are strongly influenced by the choice of drought index. Hydrology and Earth System Sciences, <em>in prep. for HESS</em></p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2009.</p>
Text-fig. 9. Paramblypterus cf. rohani. Scale bars 5 mm. a, b: drawing and photograph of the skull in lateral view, locality Otovice "Stěnava", DP 4529; c, d: photograph and drawing of the skull in lateral view, locality Otovice "Chmelnice", P 64673; e: bones of the skull roof in dorsal view, locality Otovice "Chmelnice", P 64656; f: bones of the skull roof in dorsal view, locality Otovice "Chmelnice", P 30945; g, h: drawing and photograph (whitened) of the bones of the skull roof in dorsal view, locality Otovice "Chmelnice", P 64658. Abbreviations: ap – anterior pit line, Cl – cleithrum, Cor – coronoid, Dhy – dermohyal, Dpt – dermopterotic, Dsph – dermosphenotic, Extl – extrascapular lateral, Extm – extrascapular medial, Fr – frontal, Gul – lateral gular, Gum – medial gular, ioc – infraorbital canal, Ios – infraorbital superior, Ju – jugal, mc – mandibular canal, Md – mandible, Mx – maxila, mp – medial pit line, Na – nasal, Op – operculum, Pa – parietal, Pop – preoperculum, pp – posterior pit line, Psp – postspiracular, Pt – posttemporal, Ptr – postrostral, Qu – quadratum, Rbr – branchiostegaly rays, Sbo – suborbital, soc – supraorbital canal, Sop – suboperculum, Spi – spiracular. in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)
Text-fig. 9. Paramblypterus cf. rohani. Scale bars 5 mm. a, b: drawing and photograph of the skull in lateral view, locality Otovice "Stěnava", DP 4529; c, d: photograph and drawing of the skull in lateral view, locality Otovice "Chmelnice", P 64673; e: bones of the skull roof in dorsal view, locality Otovice "Chmelnice", P 64656; f: bones of the skull roof in dorsal view, locality Otovice "Chmelnice", P 30945; g, h: drawing and photograph (whitened) of the bones of the skull roof in dorsal view, locality Otovice "Chmelnice", P 64658. Abbreviations: ap – anterior pit line, Cl – cleithrum, Cor – coronoid, Dhy – dermohyal, Dpt – dermopterotic, Dsph – dermosphenotic, Extl – extrascapular lateral, Extm – extrascapular medial, Fr – frontal, Gul – lateral gular, Gum – medial gular, ioc – infraorbital canal, Ios – infraorbital superior, Ju – jugal, mc – mandibular canal, Md – mandible, Mx – maxila, mp – medial pit line, Na – nasal, Op – operculum, Pa – parietal, Pop – preoperculum, pp – posterior pit line, Psp – postspiracular, Pt – posttemporal, Ptr – postrostral, Qu – quadratum, Rbr – branchiostegaly rays, Sbo – suborbital, soc – supraorbital canal, Sop – suboperculum, Spi – spiracular.
Text-fig. 6. Paramblypterus vratislaviensis (AGASSIZ, 1833). a: restoration of the body in lateral view, scale bar 10 mm; b: restoration of the skull in lateral view (bones that are not clearly retained on the skull are marked with dashed lines), scale bar 10 mm; c: restoration of the skull in dorsal view, scale bar 10 mm. Abbreviations: Cl – cleithrum, Dhy – dermohyal, Dpt – dermopterotic, Dsph – dermosphenotic, Ext – extrascapular, Fr – frontal, Gul – lateral gular, Ios – infraorbital superior, Ju – jugal, La – lacrymal, Md – mandible, Mx – maxilla, Na – nasal, Op – operculum, Orb – orbit, Pa – parietal, Pmx – premaxilla, Pop – preoperculum, Pt – posttemporal, Ptr – postrostral, Rbr- branchiostegal rays, Scl – supracleithrum, Soant – supraorbital anterior, Sop – suboperculum, Spi – spiracular, sr – sclerotic ring. in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)
Text-fig. 6. Paramblypterus vratislaviensis (AGASSIZ, 1833). a: restoration of the body in lateral view, scale bar 10 mm; b: restoration of the skull in lateral view (bones that are not clearly retained on the skull are marked with dashed lines), scale bar 10 mm; c: restoration of the skull in dorsal view, scale bar 10 mm. Abbreviations: Cl – cleithrum, Dhy – dermohyal, Dpt – dermopterotic, Dsph – dermosphenotic, Ext – extrascapular, Fr – frontal, Gul – lateral gular, Ios – infraorbital superior, Ju – jugal, La – lacrymal, Md – mandible, Mx – maxilla, Na – nasal, Op – operculum, Orb – orbit, Pa – parietal, Pmx – premaxilla, Pop – preoperculum, Pt – posttemporal, Ptr – postrostral, Rbr- branchiostegal rays, Scl – supracleithrum, Soant – supraorbital anterior, Sop – suboperculum, Spi – spiracular, sr – sclerotic ring.
Dataset: SPI Energy Co., Ltd. (SPI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Text-fig. 1. Amblypterus latus AGASSIZ, 1833. Pen and ink drawing (A) and photo (B) of the skull in lateral view. Mb. F. 3809b, scale bar represents 10 mm. Cl – cleithrum; Dent – dentalosplenial; Dhy – dermohyal; Dpt – dermopterotic; Dsph –dermosphenotic; Extl – extrascapular lateral; Fr –frontal; Gu – gular; Infs – infraorbital superior; Ju – jugal; La – lacrimal; Mx – maxilla; Na – nasal; Op – operculum; Pa – parietal; Pmx – premaxillar; Pop – preoperculum; Pp – postparietal; Pt – posttemporal; Rbr – branchiostegal rays; Sbo – suborbital; Scl – supracleithrum; Soant – supraorbital anterior; Sop – suboperculum; Spi – spiracular; sr – sclerotical ring. in New Data On The Osteology Of The Actinopterygian Fish Amblypterus And The Relationship Between Amblypterus And Paramblypterus
Text-fig. 1. Amblypterus latus AGASSIZ, 1833. Pen and ink drawing (A) and photo (B) of the skull in lateral view. Mb. F. 3809b, scale bar represents 10 mm. Cl – cleithrum; Dent – dentalosplenial; Dhy – dermohyal; Dpt – dermopterotic; Dsph –dermosphenotic; Extl – extrascapular lateral; Fr –frontal; Gu – gular; Infs – infraorbital superior; Ju – jugal; La – lacrimal; Mx – maxilla; Na – nasal; Op – operculum; Pa – parietal; Pmx – premaxillar; Pop – preoperculum; Pp – postparietal; Pt – posttemporal; Rbr – branchiostegal rays; Sbo – suborbital; Scl – supracleithrum; Soant – supraorbital anterior; Sop – suboperculum; Spi – spiracular; sr – sclerotical ring.
DeliCS Training+Validation Data - SPI-TGAS-MRF+GRE
<p>This data set consists of raw MRI k-space data from 12 healthy volunteers. The data were acquired on a 3T Premier MRI scanner (GE Healthcare, Waukesha, WI) with a 48-channel head receiver-coil. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. </p> <p>SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>):</p> <p>The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats)</p> <p>GRE (files named <strong>raw_gre.npy</strong>):</p> <p>A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64))</p> <p>Noise estimation (files named <strong>noise.npy</strong>):</p> <p>Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points)</p> <p>To run the processing pipeline presented in [1], please follow the instructions on <a href="http://github.com/SetsompopLab/deli-cs">https://github.com/SetsompopLab/deli-cs</a> and download Zenodo datasets <a href="https://doi.org/10.5281/zenodo.7734431">10.5281/zenodo.7734431</a> and <a href="http://doi.org/10.5281/zenodo.7703200">10.5281/zenodo.7703200</a> with testing data and meta data needed for the reconstruction pipeline.</p> <p> </p> <p>[1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em><a href="https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1">https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1</a></p> <p>[2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:<a href="https://doi.org/10.1002/mrm.29194">10.1002/mrm.29194</a></p>
SPI Trend DCLIM
<p>This dataset is obtained from DCLIM, experience CLIMSEC on the model ARPEGE_RETIC according to scenario A2.</p> <p>Mann Kendall regression was performed on the SPI with the time window of 100 years.</p>
5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor while walking in LOS and NLOS to two antennas through the Esch-Belval Area, Luxembourg (2022-11-10)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul>
5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor while walking in LOS and NLOS to two antennas through the Esch-Belval Area, Luxembourg (2023-02-15)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul>
5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor while driving in LOS and NLOS to several antennas through Luxembourg-Hollerich and Luxembourg Center, Luxembourg (2022-08-25)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>- Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul>
5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor close to a window of a building in LOS to two antennas in the Esch-Belval Area, Luxembourg (2023-02-07)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul>
5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor while walking in LOS and NLOS to two antennas through the Esch-Belval Area, Luxembourg (2022-10-17)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul> <p> </p>
5G-EMIT Project: EMF measurements of 2G/3G/4G/5G frequency bands captured with a MVG EME Spy Evolution sensor while walking in LOS and NLOS to several antennas through Luxembourg Center, Luxembourg (2023-04-06)
<p>During the 5G-EMIT project, EMF measurements of relevant mobile frequency bands have been taken at various sites around known antennas.</p> <p>Focus was in measuring the the following standard frequency bands:</p> <ul> <li>5G 700 <ul> <li>Band 28 FDD down</li> <li>758 - 803 MHz (Luxembourg: 758 - 788 MHz)</li> </ul> </li> <li>LTE 800 <ul> <li>Band 20 FDD down<br> 791 - 821 MHz</li> </ul> </li> <li>GSM 900 <ul> <li>Band 8 FDD down</li> <li>925 - 960 MHz</li> </ul> </li> <li>GSM/LTE 1800 <ul> <li>Band 3 FDD down</li> <li>1805 - 1880 MHz</li> </ul> </li> <li>UMTS 2100 <ul> <li>Band 1 FDD down</li> <li>2110 - 2170 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 38 TDD</li> <li>2570 - 2620 MHz</li> </ul> </li> <li>LTE 2600 <ul> <li>Band 7 FDD down</li> <li>2620 - 2690 MHz</li> </ul> </li> <li>5G 3500 <ul> <li>Band 78 TDD</li> <li>3300 - 3800 MHz (Luxembourg: 3400 - 3800 MHz)</li> </ul> </li> </ul> <p><br> Two sensor have been used: The MVG EME Spy Evolution and a sensor developed by IMEC/University of Ghent.</p> <ul> <li>The MVG Spy Evolution sensor is able to measure all of the requested frequency bands as defined by the standard.</li> <li>The IMEC/University of Ghent sensor is only able to measure the LTE 800, GSM 900, GSM/LTE 1800 bands as defined by the standard.<br> In the 5G 3500 band it is only able to measure the range of 3550-3700 MHz (3465-3785 MHz within a -5db response).</li> </ul> <p>The datasets of the ZIP file has following structure:</p> <ul> <li>measurements: <ul> <li>the files of the measurements as provided by the sensor or application</li> <li>combined file of measurements in case the measurements have been split into several parts</li> <li>potentially analysis files of the measurements </li> </ul> </li> <li>pictures: <ul> <li>pictures of the antennas</li> <li>pictures of the sensor</li> <li>pictures of the location of the sensor</li> </ul> </li> <li>Readme.txt: <ul> <li>Used time-zone in the tables</li> <li>Used units in the tables</li> <li>Calculation of the total values in the tables</li> <li>Used labels of the the frequency bands</li> <li>Used sensor (MVG EME Spy Evolution or IMEC/University of Ghent)</li> <li>Locations of the target antennas</li> <li>Potentially location of the fixed sensor</li> </ul> </li> </ul>
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.