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7,195 results for “SET”
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
Data set and classification method for low quality web traffic identification in video marketing campaigns
<p>Final outcomes of the InPreVi (AI4Media) project developed in 2022. </p> <p>1. Data set describing the statistics of the video ad marketing campaigns</p> <p>2. Script for web traffic classification</p>
Lake Mendota metabolism model and data set
<p>This is a zipped file that includes the model code, written in R, as well as the input and output data for the model. This publication accompanies the manuscript entitled, Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake</p>
BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding
<p>Data used in the paper: "BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding". The paper has been accepted at <a href="https://sulab-sever.u-aizu.ac.jp/ACNS2023/">ACNS-2023</a>.</p> <p>The available dataset contains a file with a few power consumption curves taken from a Cortex-M4 (STM32F4) on a CW308 board.</p> <p>The file is a numpy array stored using the np.save API.<br> The file can be directly used for running the notebooks provided in the <a href="https://github.com/benoitgerard/sca-bike">publication github</a>.</p>
DA-TCA test data sets (KIAPS-GIST)
<p><strong>About the data sets:</strong></p> <p>The test datasets from SMOS/ASCAT will be utilized to validate the efficacy of SMAP data assimilation in numerical weather forecasting. This project is being conducted in collaboration with the Korea Institute of Atmospheric Prediction Systems (KIAPS) and the Gwangju Institute of Science and Technology (GIST) in South Korea.</p> <p><strong>About the project:</strong></p> <p>Soil moisture assimilation from satellites can significantly improve weather prediction accuracy by providing valuable information about the moisture content in the soil. When integrated into numerical weather forecasting models, satellite-derived soil moisture data enhances the representation of land surface processes and interactions between the atmosphere and the Earth's surface.</p> <p>Here's how soil moisture assimilation benefits weather prediction:</p> <ol> <li> <p>Initialization of Models: Accurate initial conditions are crucial for reliable weather forecasting. Satellite-derived soil moisture data serves as an important source of information for initializing models. By incorporating this data, forecast models can start with more realistic representations of the state of the land surface, enabling a better starting point for predictions.</p> </li> <li> <p>Land-Atmosphere Interaction: Soil moisture plays a significant role in land-atmosphere interactions. It influences the partitioning of energy, the transfer of moisture, and the formation of clouds and precipitation. Assimilating satellite-derived soil moisture data allows forecast models to better capture these interactions and improve the representation of feedback mechanisms between the land surface and the atmosphere.</p> </li> <li> <p>Precipitation Forecasting: Soil moisture assimilation also enhances precipitation forecasting. The availability of accurate soil moisture data helps in understanding soil moisture-atmosphere feedback processes, which impact the formation, intensity, and movement of precipitation systems. By incorporating this information into forecasting models, more accurate precipitation predictions can be made.</p> </li> <li> <p>Drought and Flood Monitoring: Satellite-derived soil moisture data aids in monitoring and predicting droughts and floods. Real-time updates of soil moisture conditions can be integrated into forecasting models, allowing for timely and accurate assessments of soil moisture deficits or surpluses. This information is crucial for managing water resources, agriculture, and mitigating the impacts of extreme weather events.</p> </li> </ol> <p><strong>Related code in Github</strong>:</p> <p>https://github.com/Hyunglok-Kim/HydroAI/blob/main/Ex_in_TCA.ipynb</p>
Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space
<p>The products are the first <strong>regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from remote sensing observations</strong>. They cover three major river basins: the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project (<a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>). The irrigation amounts have been estimated through the <strong>SM-based (Soil-Moisture-based) inversion approach</strong>. The satellite-derived irrigation products referring to the European sites have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. The three irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. Details on the data sets development and on their performance assessment can be found in:</p> <p><strong>Dari, J.</strong>, Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Volden, E. <strong>Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space</strong>. <em>Earth System Science Data, </em>15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023.</p> <p> </p> <p><strong>Novelties in v1.1 with respect to v1.0:</strong></p> <p>v1.1 of irrigation estimates through the SM-based inversion approach are currently available for the Ebro basin and the Po valley only. The novelties with respect to the previous version are: (i) the use of high-resolution (1 km) potential evapotranspiration rates in the algorithm and (ii) temporal extension as now the data sets cover a 6-year period from January 2016 to December 2021.</p> <p><strong>Acknowledgements</strong>:</p> <p>ESA Irrigation+ project, <a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>, (contract n. 4000129870/20/I-NB).</p> <p>ESA 4DMED-Hydrology project, <a href="https://esairrigationplus.org/">https://www.4dmed-hydrology.org/</a>, (contract n. 4000136272/21/I-EF).</p>
NewSOC, supplementary information to WT2.5.4 "Cells with honeycomb structured oxygen electrodes": electrochemical and post-mortem data sets.
<p>These are data set, related to participation of the IEN in project NewSOC. It includes results of the SEM-EDS analysis of the cells with hexagonal current collecting net and electrochemical performance data, both EIS and C-V characteristics. Results are grouped in zip archives, named according to cell design, “infill-net”.</p> <p>Compositions:</p> <p>LNF – LaNi<sub>0.6</sub>Fe<sub>0.4</sub>O<sub>3</sub> (net)</p> <p>LSC – La<sub>0.6</sub>Sr<sub>0.4</sub>CoO<sub>3-</sub><sub>d</sub> (infill)</p> <p>LSF – La<sub>0.5</sub>Sr<sub>0.5</sub>FeO<sub>3-</sub><sub>d</sub> (infill)</p> <p>LSCCF – La<sub>0.6</sub>Sr<sub>0.4</sub>Co<sub>0.15</sub>Cu<sub>0.05</sub>Fe<sub>0.8</sub>O<sub>3-</sub><sub>d </sub>(net)</p> <p>PCM – Pr<sub>0.5</sub>Ca<sub>0.5</sub>MnO<sub>3 </sub>(net)</p> <p>BSCFM – Ba<sub>0.5</sub>Sr<sub>0.5</sub>Co<sub>0.725</sub>Fe<sub>0.2</sub>Mo<sub>0.075</sub>O<sub>3-</sub><sub>d</sub> (infill)</p> <p> </p> <p><strong>Data presentation. </strong></p> <p><em>C-V characteristics:</em></p> <p>This is text files, generated by Zahner galvanostat, with self-decriptional titles.</p> <p>“05iv_650c_100h2+100h2o_500air.txt” – measurement at 650°C, 100 mL/min H<sub>2</sub> and 100 mL/min H<sub>2</sub>O on fuel side, 500 mL/min of air on air side.</p> <p><em>EIS data:</em></p> <p>EIS results were extracted from proprietary binary files, generated by Zahner galvanostat, and raw data is generally meaningless except the owners of such hardware. So, extracted EIS can be found in Excel files, used in analysis, in sheet “Experimental”. Other sheets in xlsx include some metadata (“info”), results of the equivalent circuit fit (“fit”) and some plots. Fit results might not be relevant. </p> <p><em>SEM</em></p> <p>Post-mortem results presented as SEM images (tif or jpg files) and pdf files with results of the EDS analysis.</p> <p><strong>LSC-LNF </strong></p> <p><em>(air flow 1 L/min, current density 0.25 A/cm<sup>2</sup>)</em></p> <p>test_1_07: </p> <p>03_eis20200917142808.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H<sub>2</sub>;</p> <p>05_eis20200917123105.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>08_eis20200917123857.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>10_eis20200917131537.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>test_1_08:</p> <p>03_eis20200917125707.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H<sub>2</sub>;</p> <p>09_eis20200917130019.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.09 H<sub>2</sub>+ 0.21 H<sub>2</sub>O;</p> <p>10_eis20200917130600.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.06 H<sub>2</sub>+ 0.14 H<sub>2</sub>O;</p> <p>12_eis20200917130737.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.12 H<sub>2</sub>+ 0.28 H<sub>2</sub>O;</p> <p>test_3_14:</p> <p>01_eis20210517102042.xlsx– EIS, SOFC, 700°C, Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>; 05_eis20210517102625.xlsx– EIS, SOFC, 700°C, flows L/min: F:0.1 H<sub>2</sub>+ 0.1 H<sub>2</sub>O;</p> <p>07_eis20210517102305.xlsx– EIS, SOEC, 700°C, flows L/min: F:0.06 H<sub>2</sub>+ 0.14 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem after test_1_07)</em></p> <p>ogniwo_310_2020 ****.jpg - surface</p> <p> </p> <p><strong>BSCMF-PCM</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>test_2_14</p> <p>01eis_700c_cc4a_100h2+100n2_500air_eqc20220110112836.xlsx –700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02eis_700c_cc4a_100h2+100h2o_500ai_eqc20220110112724.xlsx –700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04eis_650c_cc3a_100h2+100h2o_500ai_eqc20220110112503.xlsx–650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06eis_650c_cc3a_100h2+100n2_500air_eqc20220110112332.xlsx –650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>07eis_625c_cc3a_100h2+100n2_500air_eqc20220110112214.xlsx –625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>08eis_625c_cc3a_100h2+100h2o_500ai_eqc20220110111834.xlsx–625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>test_1_67</p> <p>01_eqc20220831134628.xlsx–700°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02_eqc20220831134724.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04_eqc20220831135442.xlsx–650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>06_eqc20220831135622.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08_eqc20220831135801.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>09_eqc20220831135933.xlsx –650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>SEM</p> <p><em>(post-mortem of the test_2_14)</em></p> <p>493-2021-1.pdf, 493-2021-1i.pdf, 493-2021-2.pdf, 493-2021-2-2.pdf, 493-2021-2-3.pdf –cross-sections with EDS</p> <p>493_2021_*_**.tif - cross-sections</p> <p> </p> <p><strong>BSCMF-LSCCF</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p> test_2_08</p> <p>01_eis20211104144342.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>03eis_700c__eis20211108102241.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04eis_700c__eis20211108102401.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08eis__eis20211110094642.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>10eis__eis20211110094945.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>11eis__eis20211110101358.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>13eis__eis20211110100837.xlsx- 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p> test_2_26</p> <p>04_eqc20220817114103.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06_eqc20220817121529.xlsx - 700°C, 0.25 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2 </sub></p> <p>07_eqc20220802084119.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1</p> <p>08_eqc20220802084616.xlsx - 650°C, 0.1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>10_eqc20220802150705.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>11_eqc20220802150108.xlsx - 625°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem of the test_2_26)</em></p> <p>661_BSCMF_LSCCF_p.pdf – cross-section with EDS</p> <p>661_BSCMF_LSCCF_PM.pdf - surface with EDS</p> <p>661_BSCMF_LSCCF_p_01.tif, 661_BSCMF_LSCCF_p_02.tif, 661_BSCMF_LSCCF_p_03.tif, 661_BSCMF_LSCCF_p_04.tif - cross-section, infill zone</p> <p>661_BSCMF_LSCCF_p_05.tif, 661_BSCMF_LSCCF_p_06.tif - cross-section, net zone zone</p> <p>661_BSCMF_LSCCF_PM_**.tif - surface</p> <p> </p> <p><strong>LSF-LSCCF</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>test_1_65</p> <p>01_eqc20220816105347.xlsx - 700°C, 0. 1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>02_eqc20220816105941.xlsx - 700°C, 0. 1875 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>04_eqc20220816112311.xlsx - 650°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>06_eqc20220816111640.xlsx - 650°C, 0.125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>07_eqc20220816142436.xlsx- 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>08_eqc20220816142842.xlsx-625°C, 0.625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>SEM</p> <p><em>(post-mortem) </em></p> <p>663_LSF_LSCCF_p.pdf – cross-section with EDS</p> <p>663_LSF_LSCCF_PM.pdf - surface with EDS</p> <p>663_LSF_LSCCF_P_GR_**.tif – cross-section of the cell</p> <p>663_LSF_LSCCF_P_LSCCF_**.tif – surface of the LSCCF grid</p> <p>663_LSF_LSCCF_PM_**.tif - surface of the LSF infill</p> <p> </p> <p> </p> <p><strong>LSF-PCM</strong></p> <p><em>(SOFC, air flow 0.5 L/min)</em></p> <p>tests_1_66</p> <p>01_eqc20220831133210.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>02_eqc20220831133409.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub></p> <p>03_eqc20220831133928.xlsx - 700°C, 0. 125 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>05_eqc20220831134048.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>06_eqc20220831134142.xlsx - 650°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>08_eqc20220831134237.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 H<sub>2</sub>O;</p> <p>10_eqc20220831134412.xlsx - 625°C, 0.0625 A/cm<sup>2</sup> Flows L/min: F:0.1 H<sub>2</sub>+0.1 N<sub>2</sub>;</p> <p>SEM</p> <p><em>(post-mortem)</em></p> <p>658_LSF_PCM_p.pdf – cross-section with EDS</p> <p>658_LSF_PCM_PM.pdf - surface with EDS</p> <p>658_LSF_PCM_P_LSF_**.tif – cross-section, infill zone</p> <p>658_LSF_PCM_P_pcm_**.tif - cross-section, net zone</p> <p>658_LSF_PCM_PM_**.tif - surface</p> <p><strong>description.pdf </strong>- pdf version of this information.</p>
QLKNN7D-edge training set
<p><strong>QLKNN7D-edge training set</strong></p> <p>This dataset contains a large-scale run of ~15 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The dataset is in a parameter regime typical of the L-mode near edge (pedestal forming region). QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/">https://gitlab.com/qualikiz-group/QuaLiKiz/</a>. This dataset was generated with QuaLiKiz 2.8.4, which includes numerical improvements increasing the robustness of strongly driven (high gradient) calculations typical of the L-mode near-edge. See <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4">https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4</a> for the in-repository tag.</p> <p>The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See <a href="https://doi.org/10.1063/1.5134126">https://doi.org/10.1063/1.5134126</a> for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on <a href="https://arxiv.org/abs/1911.05617">arXiv</a> and the older dataset on <a href="https://doi.org/10.5281/zenodo.3497066">Zenodo</a>. For an application example, see <a href="http://https://doi.org/10.1088/1741-4326/ac0d12">Van Mulders et al 2021</a>, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. An additional, larger, QuaLiKiz dataset is found at <a href="https://zenodo.org/record/8017522">https://zenodo.org/record/8017522</a>. Neither the QLKNN10D or QLKNN11D datasets include L-mode near-edge parameters. For any learned surrogates developed for QLKNN7D-edge, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended.</p> <p>Related repositories:</p> <ul> <li><a href="https://qualikiz.com">General QuaLiKiz documentation </a></li> <li><a href="https://qualikiz.com/QuaLiKiz/Input-and-output-variables">QuaLiKiz/QLKNN input/output variables naming scheme </a></li> <li><a href="https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop">Training, plotting, filtering, and auxiliary tools </a></li> <li><a href="https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools">QuaLiKiz related tools </a></li> <li><a href="https://gitlab.com/qualikiz-group/QLKNN-fortran">FORTRAN QLKNN implementation with wrapper for Python and MATLAB </a></li> <li><a href="https://gitlab.com/qualikiz-group/qlknn-hyper">Weights and biases of 'hyperrectangle style' QLKNN </a></li> </ul> <p> </p>
The first 10m resolution thermokarst lake and pond data set in the Lena basin during 2020 thawing season
<p>We present the first 10m resolution thermokarst lake and pond data set in the Lena basin during 2020 thawing season. A mapping workflow was proposed and implemented on the Google Earth Engine (GEE) platform. The accuracy assessment demonstrates a satisfactory overall accuracy of 93.63%, and comparing with several land cover and waterbody products, our results exhibited better consistency with TLPs under real conditions.</p>
CPT-1 whole-proteome feature matrices (EVE set)
<p><strong>Cross-protein transfer learning for variant effect prediction</strong></p> <p>This repository contains the feature matrices for CPT-1 to make variant effect prediction on 3,045 human proteins within the EVE set (<a href="https://www.nature.com/articles/s41586-021-04043-8">Frazer et al., 2021</a>), initially released with the manuscript "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects".</p> <p> </p> <p><strong>Citation</strong></p> <p>Jagota, M.*, Ye, C.*, Albors, C., Rastogi, R., Koehl, A., Ioannidis, N., and Song, Y.S.†<br> "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects", bioRxiv (2022)</p> <p>*These authors contributed equally to this work.<br> †To whom correspondence should be addressed: <a href="mailto:yss@berkeley.edu">yss@berkeley.edu</a></p> <p>DOI: <a href="https://doi.org/10.1101/2022.11.15.516532">https://doi.org/10.1101/2022.11.15.516532</a></p>
zebrafish GSE223922 scRNA data set objects
<p>scRNA data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 (Sur et al. 2023), see a detailed description of the study here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055256/</p> <p>Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 to create a R Seurat object and converted into AnnData (h5ad) file to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Sur et al. 2023.</p>
Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes
<p>Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles:</p> <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> <p>The spatial reference system is EPSG 25830.</p>
UCI and OpenML Data Sets for Ordinal Quantification
<p>These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.</p> <p>With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.</p> <p>We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files <em>app_val_indices.csv</em>, <em>app_tst_indices.csv</em>, <em>app-oq_val_indices.csv</em>, and <em>app-oq_tst_indices.csv</em> represents one sample.</p> <p>Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.</p> <p><strong>Usage</strong></p> <p>You can extract four CSV files through the provided script <em>extract-oq.jl</em>, which is conveniently wrapped in a <em>Makefile</em>. The <em>Project.toml</em> and <em>Manifest.toml</em> specify the Julia package dependencies, similar to a requirements file in Python.</p> <p><strong>Preliminaries:</strong> You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.</p> <p><strong>Data Extraction:</strong> In your terminal, you can call either</p> <pre><code>make</code></pre> <p>(recommended), or</p> <pre><code>julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl</code></pre> <p><strong>Outcome: </strong>The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.</p> <p><strong>Further Reading</strong></p> <p>Implementation of our experiments: <a href="https://github.com/mirkobunse/regularized-oq">https://github.com/mirkobunse/regularized-oq</a></p>
Codes and data set for Cryoconite hole model (CryHo)
<p><strong>Codes and data set for cryoconite hole model (CryHo)</strong> (Onuma et al., 2023, <em>The Cryosphere</em>). The content is as below.</p> <p><strong>- cryho_disclose_v1</strong>: readme, the model codes, parameter files for the model, model outputs and shell scripts for sensitivity tests <br> <strong>- data</strong>: model input data (meteorological conditions), model data of extinction coefficient for ice* and observational data of cryoconite hole depths<br> <strong>- python</strong>: Python scripts for the visualization<br> <strong>- figure</strong>: png files created by the Python scripts<br> <br> *If you have any questions about extinction coefficients for ice, please contact Dr. Teruo Aoki, a co-author in this dataset.</p>
AIT Alert Data Set
<p>This repository contains the AIT Alert Data Set (AIT-ADS), a collection of synthetic alerts suitable for evaluation of alert aggregation, alert correlation, alert filtering, and attack graph generation approaches. The alerts were forensically generated from the <a href="https://zenodo.org/record/5789064">AIT Log Data Set V2 (AIT-LDSv2)</a> and origin from three intrusion detection systems, namely Suricata, Wazuh, and AMiner. The data sets comprise eight scenarios, each of which has been targeted by a multi-step attack with attack steps such as scans, web application exploits, password cracking, remote command execution, privilege escalation, etc. Each scenario and attack chain has certain variations so that attack manifestations and resulting alert sequences vary in each scenario; this means that the data set allows to develop and evaluate approaches that compute similarities of attack chains or merge them into meta-alerts. Since only few benchmark alert data sets are publicly available, the AIT-ADS was developed to address common issues in the research domain of multi-step attack analysis; specifically, the alert data set contains many false positives caused by normal user behavior (e.g., user login attempts or software updates), heterogeneous alert formats (although all alerts are in JSON format, their fields are different for each IDS), repeated executions of attacks according to an attack plan, collection of alerts from diverse log sources (application logs and network traffic) and all components in the network (mail server, web server, DNS, firewall, file share, etc.), and labels for attack phases. For more information on how this alert data set was generated, check out our paper accompanying this data set [1] or our <a href="https://github.com/ait-aecid/alert-data-set">GitHub repository</a>. More information on the original log data set, including a detailed description of scenarios and attacks, can be found in [2].</p> <p>The alert data set contains two files for each of the eight scenarios, and a file for their labels:</p> <ul> <li><em><strong><scenario>_aminer.json</strong> </em>contains alerts from AMiner IDS</li> <li><em><strong><scenario>_wazuh.json</strong> </em>contains alerts from Wazuh IDS and Suricata IDS</li> <li><strong><em>labels.csv</em></strong> contains the start and end times of attack phases in each scenario</li> </ul> <p>Beside false positive alerts, the alerts in the AIT-ADS correspond to the following attacks:</p> <ul> <li>Scans (nmap, WPScan, dirb)</li> <li>Webshell upload (CVE-2020-24186)</li> <li>Password cracking (John the Ripper)</li> <li>Privilege escalation</li> <li>Remote command execution</li> <li>Data exfiltration (DNSteal) and stopped service</li> </ul> <p>The total number of alerts involved in the data set is 2,655,821, of which 2,293,628 origin from Wazuh, 306,635 origin from Suricata, and 55,558 origin from AMiner. The numbers of alerts in each scenario are as follows. fox: 473,104; harrison: 593,948; russellmitchell: 45,544; santos: 130,779; shaw: 70,782; wardbeck: 91,257; wheeler: 616,161; wilson: 634,246.</p> <p>Acknowledgements: Partially funded by the European Defence Fund (EDF) projects AInception (101103385) and NEWSROOM (101121403), and the FFG project PRESENT (FO999899544). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. The European Union cannot be held responsible for them.</p> <p><strong>If you use the AIT-ADS, please cite the following publications:</strong></p> <p>[1] Landauer, M., Skopik, F., Wurzenberger, M. (2024): <a href="https://doi.org/10.1145/3675741.3675748">Introducing a New Alert Data Set for Multi-Step Attack Analysis.</a> Proceedings of the 17th Cyber Security Experimentation and Test Workshop. [<a href="https://dl.acm.org/doi/pdf/10.1145/3675741.3675748">PDF</a>]</p> <p>[2] Landauer M., Skopik F., Frank M., Hotwagner W., Wurzenberger M., Rauber A. (2023): <a href="https://ieeexplore.ieee.org/abstract/document/9866880">Maintainable Log Datasets for Evaluation of Intrusion Detection Systems.</a> IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 3466-3482. [<a href="https://arxiv.org/pdf/2203.08580.pdf">PDF</a>]</p>
Data Sets: The Influence of Synoptic Wind on Land-Sea Breezes
<p>DATA & FILE OVERVIEW</p> <p>This dataset contains the dimensional results of large-eddy simulations in .nc file format conducted over the thirty one simulations detailed in Allouche et al. (2023) (https://doi.org/10.1002/qj.4552) with differing synoptic pressure forcings and alignment angles of the latter with the shoreline. The patterns are altered as to conduct the analysis followed in Allouche et al. (2023) (https://doi.org/10.1002/qj.4552).</p>
UWB Positioning and Tracking Data Set
<p><strong># UWB Positioning and Tracking Data Set</strong></p> <p>UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments.</p> <p> </p> <p><strong># Measurement system</strong></p> <p>The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag.</p> <p>Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies.</p> <p>All tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section.</p> <p> </p> <p><strong># Folder structure</strong></p> <p>Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg.</p> <p>Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously.</p> <p>The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 8 subfolders:</p> <p>- cir_min_max_mean</p> <p>- los_nlos</p> <p>- positioning_wls</p> <p>- range</p> <p>- range_error</p> <p>- range_error_A6</p> <p>- range_error_histograms</p> <p>- rss</p> <p> </p> <p>The organization of the data set is the following:</p> <p>data_set</p> <p>+ location0</p> <p>- anchors.csv</p> <p>- data.json</p> <p>- walking_path.csv</p> <p>+ floorplan</p> <p>- floorplan.dxf</p> <p>- floorplan.png</p> <p>- floorplan_track.jpg</p> <p>- walking_path.csv</p> <p>+ location1</p> <p>- ...</p> <p>+ location2</p> <p>- ...</p> <p>+ location3</p> <p>- ...</p> <p>+ raw_data</p> <p>+ location0</p> <p>+ data</p> <p>+ 1.07_9.37_1.2</p> <p>- ch1_A1.csv</p> <p>- ch7_A8.csv</p> <p>- ...</p> <p>+ 1.37_9.34_1.2</p> <p>- ...</p> <p>+ ...</p> <p>+ location1</p> <p>+ ...</p> <p>+ location2</p> <p>+ ...</p> <p>+ location3</p> <p>+ ...</p> <p>+ technical validation</p> <p>+ cir_min_max_mean</p> <p>+ positioning_wls</p> <p>+ range</p> <p>+ range_error</p> <p>+ range_error_histograms</p> <p>+ rss</p> <p>- LICENSE</p> <p>- README</p> <p> </p> <p><strong># Data format</strong></p> <p>Raw measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are:</p> <p> </p> <p>TAG_ID</p> <p>ANCHOR_ID</p> <p>X_TAG</p> <p>Y_TAG</p> <p>Z_TAG</p> <p>X_ANCHOR</p> <p>Y_ANCHOR</p> <p>Z_ANCHOR</p> <p>NLOS</p> <p>RANGE</p> <p>FP_INDEX</p> <p>RSS</p> <p>RSS_FP</p> <p>FP_POINT1</p> <p>FP_POINT2</p> <p>FP_POINT3</p> <p>STDEV_NOISE</p> <p>CIR_POWER</p> <p>MAX_NOISE</p> <p>RXPACC</p> <p>CHANNEL_NUMBER</p> <p>FRAME_LENGTH</p> <p>PREAMBLE_LENGTH</p> <p>BITRATE</p> <p>PRFR</p> <p>PREAMBLE_CODE</p> <p>CIR (starts with this column; all columns until the end of the line represent the channel impulse response)</p> <p> </p> <p><strong># Availability of CODE</strong></p> <p>Code for data analysis and preprocessing of all data available in this data set is published on GitHub:</p> <p>https://github.com/KlemenBr/uwb_positioning.git</p> <p>The code is licensed under the Apache License 2.0.</p> <p> </p> <p><strong># Authors and License</strong></p> <p>Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si.</p> <p>This work is licensed under a Creative Commons Attribution 4.0 International License.</p> <p> </p> <p><strong># Funding</strong></p> <p>The research leading to the data collection has been partially funded from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116, the Slovenian Research Agency under Grant numbers P2-0016, J2-2507 and bilateral project with Grant number BI-ME/21-22-007.</p> <p> </p>
hiPSC 3D immunofluorescence images, tiny test set
<p>Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a tiny subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal</p> <p>It is a subset of this dataset: https://zenodo.org/record/7057076</p> <p>1 Channel is included:</p> <p>- C01: DAPI, nuclear stain</p> <p> </p> <p>This dataset also contains a small Fractal workflow for standard processing</p> <p> </p> <p>This dataset contains 2 Z levels for 2 field of views for this 1 channel, as well as (manually adjusted) metadata files from the Yokogawa CV7000.</p> <p> </p> <p>The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless). </p>
Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"
<p>In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course.</p> <p>The upload consists of the following .csv files:</p> <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
H2020 Platone German Demonstrator - Use Case Setting Data
<p>This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). The dataset contain information that have been set for Use Cases (UCs) parameterization during the project phase. UC have been parameterized along a grafical user interface (GUI) named "Use Case Selector". Each parameterized UC triggered has been logged in the dataset.</p> <p><strong>Background - Field Test Setup</strong></p> <p>The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single point of common coupling (PCC). i.e., a secondary substation that includes a transformer with senors on the LV busbar, to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity. </p> <p><strong>Definition of data:</strong></p> <p><strong>RequID</strong> - Request ID - Identifier for each triggered UC</p> <p><strong>Alert</strong> - Indicates, whether UC has been executed successfull ( " "and " true" indacates successfull implementation by Energy Management System (EMS); "false" indicates that UC has not been implemented by EMS)</p> <p><strong>Submission -</strong> timestamp of UC submission<strong> </strong></p> <p><strong>Note -</strong> Annotations entered by UseCase operator</p> <p><strong>Priority -</strong> Defines UC priority set by operator (priority: 1 - high , 2 - medium, 3 - low, 4 - very low) (only relevant for UC 2)</p> <p><strong>Status - </strong>Indicates the status of the UC (closed - UC has been executed, cancelled, UC has been has been canceled before or during application)</p> <p><strong>Start</strong> - Point of time set for the beginning of UC</p> <p><strong>End</strong> - Point of time set for the end of UC</p> <p><strong>Type</strong>- Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4)</p> <p><strong>Subtype</strong> - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End;</p> <p><strong>bulkStart</strong> - Point of time of start of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulkEnd</strong> - Point of time of end of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulk Energy</strong> - Amount of energy triggered to be imported or exported as bulk (only relevant for UC 3 and 4)</p> <p><strong>Final_SOF </strong>- State of Charge (SOC) of CBES that should be achieved at end of UC (End)</p> <p><strong>FlexDemand </strong>- Requested power exchange that should be achieved at MV/LV PCC (Only relevant for UC 2)</p> <p><strong>Ptcb - </strong>Measured CBES charging power at poin of time of UC submission </p> <p><strong>Ptei </strong>- Measured power exchange at PCC at point of time of UC submission </p> <p><strong>SoC </strong>- State of Charge of CBES at point of time of UC submission </p> <p><strong>SoE</strong> - State of Energyof CBES at point of time of UC submission </p> <p><strong>ActiveSet </strong>- State of Energyof CBES at point of time of UC submission </p> <p><strong>maxSoC </strong>- Maximum SoC set for CBES at point of time of UC submission </p> <p><strong>minSoc - </strong>MinimumSoC set for CBES at point of time of UC submission </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.