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Design values of the detectors developed in the project PrimA-LTD
<p>Design values and expected energy resolution at <em>T</em> = 20 mK of all detectors developed for PrimA-LTD. The detectors are designed to have a signal rise time of 1 μs and a signal decay time of 1 ms. A SQUID white noise level of 0.15 μPhi0 ∕√Hz and SQUID 1/f noise level at 1 Hz of 3 μPhi0 ∕√Hz is assumed.</p>
GEOLAB - Transnational Access project SHARP - Soil Heterogeneity for soil Amelioration in Road Projects
<p><span>The SHARP - Soil Heterogeneity for soil Amelioration in Road Projects experiment proposes a novel approach to subgrade improvement: the strategic placement of gravel or other recycled aggregates in a layered configuration. This methodology aims to achieve a sufficient enhancement of the subgrade's mechanical properties, potentially eliminating the need for more resource-intensive and environmentally impactful techniques. The current research phase focuses on the evaluation of performance improvement in silt through the introduction of gravel layers via large-scale triaxial testing. Numerical simulations have already yielded promising results, highlighting the potential of this approach. However, the inherent size disparity between gravel and silt particles necessitates the use of large-scale testing equipment to accurately capture their composite behavior and avoid scale issues. Conventional triaxial equipment, typically limited to 10 cm diameter specimens, is inadequate for this purpose. The utilization of a large-scale triaxial apparatus facilitates the acquisition of reliable data directly applicable to real-world road and railway construction practices.</span></p> <p><span>The traditional Terzaghi method for analyzing consolidation (settling) in soils is not accurate for layered soils with different compressibility characteristics. This is a well-established fact (Schiffman & Stein, 1970; Lee et al., 1992). Previous research (Huang & Griffiths, 2010) has also shown that using the Terzaghi method for layered soils in finite element modeling can lead to inaccurate results due to issues with flow continuity at the interfaces between layers. This highlights the importance of studying consolidation in layered soils, both for practical reasons (ensuring the proposed solution is viable) and to gain a deeper theoretical understanding (the heterogeneity effect on consolidating silty soil).</span></p> <p><span>While numerical simulations have suggested that adding gravel layers to silty soil can significantly improve slope stability (Bossi et al., 2016), this needs experimental validation. Smaller lab specimens are not suitable due to the size difference between gravel and silt.</span></p> <p><span>The test performed within the SHARP project aimed to investigate the effectiveness of a "patchy" gravel-in-silt mix on volumetric changes and shear strength. This approach aims to be practical for construction crews by allowing the gravel to be spread in lenses within the existing subgrade. This would simplify construction and ensure a minimum level of performance for the improved subgrade.</span></p> <p><span>Ultimately, the goal is to use the data from these tests to assess the validity of using an "average friction angle" approach (Elkateb, 2003) for layered soils, considering the impact of the spatial distribution of the gravel layers. Numerical modeling can help answer these questions, but data for validation are necessary.</span></p>
Data for "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model"
<p>Data to accompany:</p> <p>O’Neill, J.F., Edwards, T.L., Martin, D.F., Shafer, C., Cornford, S.L., Seroussi, H.L., Nowicki, S., Adhikari, M., Gregoire, L.J.. (2024). "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere". <em>The Cryosphere</em>. DOI: 10.5194/egusphere-2024-441 (preprint)</p> <p>Zipped directories called ismip6_<em>expname</em>_8km containing NetCDFs of output data from each experiment, on an 8 km EPSG3031 polar stereographic common grid for ISMIP6. Variable names are the same as those used for ISMIP6 i.e: land ice mass (lim), land ice mass above floatation (limnsw), floating area (iareaf), grounded area (iareag), thickness (lithk), x component of mean velocity (xvelmean), y component of mean velocity (yvelmean), basal mass flux (libmassbffl), acabf (surface mass balance), sftflf (floating ice mask), sftgrf (grounded ice mask), sftgif (ice mask), dlithkdt (ice thickness imbalance), base (elevation at base of ice sheet) and orog (surface elevation of ice sheet). These latter two are only included for the experiments plotted in Figure 11 in "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere".</p> <p> </p> <p>Also included are csv data for summary variables, masked regionally, and by sectors detailed in the main paper. Please contact J ONeill with any questions or requests. </p>
Projected surface mass balance of the Juneau Icefield, Southeast Alaska (2030 to 2060, RCP8.5)
<p>This dataset contains the modelled projected surface mass balance of the Juneau Icefield in Southeast Alaska for two global climate models (GFDL-CM3 and NCAR-CCSM4) for the RCP8.5 emissions scenario from 2030 to 2060. The simulated surface mass balance was produced using COSIPY - a coupled snowpack and ice surface energy and mass balance model. The input climate data was originally dynamically downscaled in Lader et al., (2020, <em>J. Appl. Meteor. Climatol.)</em></p> <p>This data supplements the manuscript "<em>Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century</em>" submitted to the Journal of Glaciology. </p>
Scenario database of the SHAPE project
<p>The scenario data from the SHAPE project are available for interactive visualisation and download in the SHAPE Scenario Explorer: <a href="https://shape.apps.ece.iiasa.ac.at/">https://shape.apps.ece.iiasa.ac.at/</a> . Please refer to the Explorer webpage for documentation, license and suggested references when using the data.</p> <p>This data release accompanies the following publication: </p> <p>B. Soergel, S. Rauner, V. Daioglou, et al., Multiple pathways towards sustainable development goals and climate targets, Environmental Research Letters, 2024, <a href="https://doi.org/10.1088/1748-9326/ad80af">https://doi.org/10.1088/1748-9326/ad80af</a></p> <p>The SHAPE project investigated interactions between options to mitigate climate change and the broader agenda of the United Nations' Sustainable Development Goals (SDGs). It modelled and analysed Sustainable Development Pathways that make as much progress towards the SDGs as possible by 2030, and maintain sustainable development thereafter, while meeting the climate targets set out in the Paris agreement. The project was funded by the <a href="https://jpi-climate.eu/programme/axis/">ERA-NET AXIS Call</a> initiated by the JPI Climate, and funded by FORMAS (SE), FFG/BMBWF (AT), DLR/BMBF (DE), NWO (NL) and RCN (NO) with co-funding by the European Union.</p>
MULTIPLIERS_WP5_Science learning project on Forest use vs. forest protection_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Forest use vs. forest protection</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with OSC members, and student groups (</span><span>Pseudo-/Anonymised)</span></li> </ul>
MULTIPLIERS_WP5_Science learning project on Biodiversity and agriculture_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Biodiversity and agriculture</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with teachers, and OSC members (</span><span>Pseudo-/Anonymised)</span></li> </ul>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 1: Operational scenario with 6.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 1. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p> <p> </p> <p> </p> <p> </p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 2: Operational scenario with 4.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 2. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 8: Damaged scenario with 1.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 8. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 7: Damaged scenario with 3.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 7. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 5: Damaged scenario with 6.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 5. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 6: Damaged scenario with 4.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 6. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 4: Operational scenario with 1.5 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 4. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 3: Operational scenario with 3.0 m significant wave height
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 3. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Fossil Fuel CO₂ Emissions for the OCO2 Model Intercomparison Project (MIP)
<p>These are fossil CO<sub>2</sub> fluxes updated through August 2024 for atmospheric CO<sub>2</sub> modeling. They were constructed primarily to be used for the OCO2 Model Intercomparison Project (MIP).</p> <ul> <li>For 2000-2022, they're based on <a href="https://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2023.html">ODIAC 2023</a>, which in turn uses BP's energy use statistics for 2021 and 2022.</li> <li>ODIAC monthly emissions have been disaggregated to hourly using the TIMES emission factors for day of week and time of day (<a href="https://urldefense.us/v3/__https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196__;!!PvBDto6Hs4WbVuu7!YsQP_T-Vf3Fv83toql-90HY0NO5e92fR0D9kAi10tTzUd0Ugum9d3CTUMBp22qA0M-vYoU_fvd4$">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196</a>).</li> <li>For 2023 onwards, ODIAC's 2022 emissions have been scaled by the ratio of that month to 2022 emissions reported by <a href="https://www.nature.com/articles/s41597-020-00708-7">Carbon Monitor</a>, downloaded on October 15, 2024 from <a href="https://carbonmonitor.org">https://carbonmonitor.org/</a>. <ul> <li>ODIAC does not have sectoral decomposition to the degree provided by Carbon Monitor, so total ODIAC emissions for each region have been scaled by the total emission change between 2022 and each extended year reported by Carbon Monitor, i.e., power, ground transport, etc. have <strong>not</strong> been separately scaled.</li> <li>Carbon Monitor data are daily, but ODIAC emissions are monthly. So Carbon Monitor data have been aggregated to monthly totals before deriving scaling factors between 2022 and the extended years.</li> <li>Carbon Monitor reports international aviation emissions by country of origin, while ODIAC reports aviation emissions on a grid. Since there is no way to derive the points of emission for Carbon Monitor aviation emissions , all Carbon Monitor international aviation was aggregated to create a single number for each month, then that number was used to scale ODIAC's bunker fuel for each month in 2023-2024.</li> <li>CarbonMonitor data used for deriving 2023 and later emissions are now included in this dataset for convenience as netcdf files (converted from original CSV files).</li> </ul> </li> <li>Hourly global totals are given in the files as a check, in case you want to verify your units and file reading.</li> </ul> <p>These files can be downloaded from the browser, or from the command line following guides such as <a href="https://ict.ipbes.net/ipbes-ict-guide/data-and-knowledge-management/technical-guidelines/zenodo#b.-programmatically-using-r" target="_blank" rel="noopener">this</a>.</p>
Long-range axonal projections analyses of the mouse brain
<p>Accompanying data and analyses of the article <a href="https://doi.org/10.1101/2024.10.04.616605">"Generating brain-wide connectome using synthetic axonal morphologies"</a>. The code to reproduce the figures is available at <a href="https://github.com/Remy2506/axon_projection_figures">this repository</a>.</p> <p>Main contents:</p> <ul> <li><strong>SEU_morphs.zip, peng_2021_morphs.zip, ML_morphs.zip: </strong>input morphologies from various sources (novel morphologies collected by H. Peng, Southeast University, <a href="https://www.nature.com/articles/s41586-021-03941-1">Peng et al. 2021</a>, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754285/">Winnubst et al. 2019</a>) after <a href="https://github.com/BlueBrain/morphology-workflows"><em>morphology-workflows</em> <em>Repair</em></a> post-processing steps.</li> <li><strong>atlas</strong>: atlas of the mouse brain used (enhanced version of Allen Brain CCFv3)</li> <li><strong>out_a_p</strong>: output of the <a href="https://github.com/BlueBrain/axon-projection">axon projection</a> anaylsis and clustering on the 3601 morphologies of the dataset. <ul> <li>axon_lengths_12.csv, axon_terminals_12.csv: the lengths of axons in each subregion where they terminate, and terminals, at hierarchy level 12 from the brain hierarchy.</li> <li>clustering_output.csv: output of the GMM clustering.</li> <li>config_a_p.cfg: configuration file used to produce this analysis, with running the axonal projection code.</li> </ul> </li> <li><strong>circuit</strong>: contains files describing the circuit synthesized with Blue Brain's <em>circuit-build*.</em> <ul> <li>bioname: parameters used to synthesize the circuit (regions to synthesize, cell densities per region, location of axons to graft...).</li> <li>sonata: files that contain nodes and edges of the circuit.</li> <li>auxiliary: various cell collections from the synthesized cells, filtered by region. Cell collections are to be read with <a href="https://github.com/BlueBrain/voxcell/tree/main">Voxcell.</a></li> <li>conn_mat.h5: the connectivity matrices obtained for the case where MOp5 long-range axons were synthesized, computed with <a href="https://github.com/BlueBrain/ConnectomeUtilities/tree/main">ConnectomeUtilities.</a>Contains connectivity matrices for the synthesized LRAs, biological LRAs, and grafted local axons.</li> </ul> </li> <li><strong>local_axons</strong>: biological local axons that are grafted to the synthesized dendrites that do not have synthesized long-range axons.</li> <li><strong>synthesized_MOp5_LRAs</strong>: 1695 synthesized cells with long-range axons of the MOp5 region, in the atlas reference frame. </li> <li><strong>out_a_p_synth_MOp5</strong>: axonal projection analysis of the synthesized MOp5 axons.</li> <li><strong>synthesized_isocortex_cells</strong>: all synthesized cells of the isocortex region, except MOp5 cells. They are in h5 format, which takes less space than asc and swc. The h5 format can be read and converted for instance with <a href="https://github.com/BlueBrain/MorphIO">MorphIO</a>.</li> <li><strong>synthesized_isocortex_LRAs</strong>: 21680 synthesized cells with long-range axons of the isocortex regions for which a GMM cluster was created. </li> </ul> <p>Additional files:</p> <ul> <li>flatmap_both.nrrd: file used to generate a flat map visualization of the mouse isocortex, shown in the article.</li> <li>config_a_s.cfg: configuration file used to synthesize the long-range axons with the <a href="https://github.com/BlueBrain/axon-synthesis">axon-synthesis</a> code.</li> <li>target_pts: tufts common ancestors for the synthesized MOp5 axons.</li> <li>brain_atlas.zip: a lightweight version of the mouse brain atlas for the synthesis code example.</li> </ul> <p>*<em>These softwares might not be open-source at the time of publication of this data, but a public link will be provided as soon as they are.</em></p>
MULTIPLIERS_WP3/4/5_Science learning project on Anti-microbial resistance_Public data_UCY_20241018_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Anti-microbial resistance</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with teachers and students (</span><span>Pseudo-/Anonymised)</span></li> <li>Summary<span> of transcripts from focus group discussions with students (</span><span>Pseudo-/Anonymised)</span></li> </ul>
MULTIPLIERS_WP3/4/5_Science learning project on Clean water and sanitation_Public data_IREN_20241021_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Clean water and sanitation</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts, quotes and translations, from interviews with teachers, OSC members, and students (</span><span>Pseudo-/Anonymised)</span></li> <li>Summary<span> of transcripts, quotes and translations, from focus group discussions with students (</span><span>Pseudo-/Anonymised)</span></li> </ul>
Linked collectors and determiners for: Swedish Malaise Trap Project (SMTP) - Strepsiptera.
Natural history specimen data linked to collectors and determiners held within, "Swedish Malaise Trap Project (SMTP) - Strepsiptera". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e73886a6-15e6-462d-b81e-bafcd858b07e">https://bionomia.net/dataset/e73886a6-15e6-462d-b81e-bafcd858b07e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e73886a6-15e6-462d-b81e-bafcd858b07e">https://gbif.org/dataset/e73886a6-15e6-462d-b81e-bafcd858b07e</a>. Formatted as a Frictionless Data package.
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.