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Figure 2. from: New evidence shows that Pocillopora 'damicornis-like' corals in Singapore are actually Pocillopora acuta (Scleractinia: Pocilloporidae) - Biodiversity Data Journal 5: e11407 (13 February 2017) https://doi.org/10.3897/BDJ.5.e11407
Figure 2. - Pocillopora specimens previously identified as P. damicornis from the Zoological Reference Collection, Lee Kong Chian Natural History Museum, Singapore (A, B: ZRC.1980.20.133; C, D: ZRC.1991.766; E, F: ZRC.1987.1538; G, H: ZRC.1987.1995; I, J: ZRC.1991.763; K, L: ZRC.1987.1537). A–F, colonies with thick branches; G–L, colonies with thinner branches. Scale bars represent 1 cm.
Figure 3. from: New evidence shows that Pocillopora 'damicornis-like' corals in Singapore are actually Pocillopora acuta (Scleractinia: Pocilloporidae) - Biodiversity Data Journal 5: e11407 (13 February 2017) https://doi.org/10.3897/BDJ.5.e11407
Figure 3. - Maximum likelihood tree of seven Pocillopora species based on the mitochondrial open reading frame. Colonies from Singapore are shown in red. Bootstrap values (≥ 50) and Bayesian posterior probabilities (≥ 0.85) are shown for supported clades.
Figure 3. from: New evidence shows that Pocillopora 'damicornis-like' corals in Singapore are actually Pocillopora acuta (Scleractinia: Pocilloporidae) - Biodiversity Data Journal 5: e11407 (13 February 2017) https://doi.org/10.3897/BDJ.5.e11407
Figure 3. - Maximum likelihood tree of seven Pocillopora species based on the mitochondrial open reading frame. Colonies from Singapore are shown in red. Bootstrap values (≥ 50) and Bayesian posterior probabilities (≥ 0.85) are shown for supported clades.
Figure 1. from: New evidence shows that Pocillopora 'damicornis-like' corals in Singapore are actually Pocillopora acuta (Scleractinia: Pocilloporidae) - Biodiversity Data Journal 5: e11407 (13 February 2017) https://doi.org/10.3897/BDJ.5.e11407
Figure 1. - Pocillopora specimens examined in this study. In situ appearances (A: HD159, D: HD162, G: HD161, J: HD160, M: HD154), with corresponding images of bleached skeletons (B, E, H, K, N). C, live specimen showing brown ring surrounding each oral opening (image by Jenny). F, I, branches from colonies shown in D and G respectively. L, O, calices and septa from colonies shown in J and M respectively. Scale bars represent 1 cm (B, E, H, K, N) and 1 mm (F, I, L, O) respectively.
Figure 2. from: New evidence shows that Pocillopora 'damicornis-like' corals in Singapore are actually Pocillopora acuta (Scleractinia: Pocilloporidae) - Biodiversity Data Journal 5: e11407 (13 February 2017) https://doi.org/10.3897/BDJ.5.e11407
Figure 2. - Pocillopora specimens previously identified as P. damicornis from the Zoological Reference Collection, Lee Kong Chian Natural History Museum, Singapore (A, B: ZRC.1980.20.133; C, D: ZRC.1991.766; E, F: ZRC.1987.1538; G, H: ZRC.1987.1995; I, J: ZRC.1991.763; K, L: ZRC.1987.1537). A–F, colonies with thick branches; G–L, colonies with thinner branches. Scale bars represent 1 cm.
Figure 1. from: New evidence shows that Pocillopora 'damicornis-like' corals in Singapore are actually Pocillopora acuta (Scleractinia: Pocilloporidae) - Biodiversity Data Journal 5: e11407 (13 February 2017) https://doi.org/10.3897/BDJ.5.e11407
Figure 1. - Pocillopora specimens examined in this study. In situ appearances (A: HD159, D: HD162, G: HD161, J: HD160, M: HD154), with corresponding images of bleached skeletons (B, E, H, K, N). C, live specimen showing brown ring surrounding each oral opening (image by Jenny). F, I, branches from colonies shown in D and G respectively. L, O, calices and septa from colonies shown in J and M respectively. Scale bars represent 1 cm (B, E, H, K, N) and 1 mm (F, I, L, O) respectively.
Large-aperture Experiment to Detect the Dark Ages -- February 2016
<p>Initial data release of LEDA characterization data from February 2016 testing campaign. Please see manuscript, "Design and Characterization of a Radiometric Receiver for the Large-Aperture Experiment to Detect the Dark Ages (LEDA)", D. C. Price et. al.</p>
Level A Pan Europe Solar Index for estimation of Potential evaporation February
Solar Index for estimation of Potential evaporation February. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-InverseRelations-OWLNETS</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
CTD+ hydrographic measurement results from Admiralty Bay, Antarctica from February 2022 to February 2023
<p>The dataset contains CTD+ measurement results from Admiralty Bay on King George Island. It consists of data on conductivity, salinity, temperature, pH, turbidity, optical dissolved oxygen (ODO), fluorescent Dissolved Organic Matter (fDOM), chlorophyll A, and Phycoerythrin, measured from February 2022 to February 2023.</p><p>This dataset is a continuation of a larger measurement campaign described in: </p><p>Osińska, M., Wójcik-Długoborska, K. A., & Bialik, R. J. (2023). Annual hydrographic variability in Antarctic coastal waters infused with glacial inflow. <i>Earth System Science Data</i>, <i>15</i>(2). https://doi.org/10.5194/essd-15-607-2023</p><p>which can be found at:</p><p>Osińska, M., Wójcik-Długoborska, K. A., & Bialik, R. J. (2022). Water conductivity, salinity, temperature, turbidity, pH, fluorescent dissolved organic matter (fDOM), optical dissolved oxygen (ODO), chlorophyll a and phycoerythrin measurements in Admiralty Bay, King George Island, from Dec 2018 to Jan 2022. <i>PANGAEA</i>. https://doi.org/https://doi.org/10.1594/PANGAEA.947909</p>
Hourly time series of soil and atmosphere variables at the experimental site of El Cautivo, Tabernas Desert, Almeria, Spain (February 2018 to December 2019)
<p>Measurements were performed along a hypothetical succession of biological soil crusts. Main studied variables were the soil-atmosphere CO2 and water vapor fluxes. This dataset was used by Lopez-Canfin et al. (2022) and Kim and al. (2024) at the time of publication.</p>
Monthly averaged lightning data extracted from 1-year EMAC simulation including LCC-lightning (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).
<p>About Dataset<br> Monthly averaged lightning data extracted from 1-year EMAC simulation (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).</p> <p>Authors: Francisco J. Perez-Invernon, Heidi Huntrieser, Patrick Joeckel and Francisco J. Gordillo-Vazquez</p> <p>Description of the data<br> P_cth.tar: Lightning parameterization based on cloud top height. Scaling factor is applied.<br> L_cth.tar: Lightning parameterization based on cloud top height and modified over the oceans. A scaling factor of 0.571 have to be applied.<br> G_updr: Lightning parameterization based on updraft velocity. Scaling factor is applied.<br> A_prec: Lightning parameterization based on convective precipitation. A scaling factor of 0.76 have to be applied.<br> A_updr: Lightning parameterization based on Updraft strength at 440~hPa. A scaling factor of 0.01618 have to be applied.<br> P_cth + A_prec: Lightning parameterization based on cloud top height and updraft velocity. A scaling factor of 1.13 have to be applied.</p> <p>File format: netcdf</p> <p> Example:<br> <br> netcdf LCC_2017_______20170201_0000_mmlb_PaR_T {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2017-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2017-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point"</p>
LPL5 - February 2021
<p>Measurements of LPL5 (Tejeda) in February 2021</p> <p>Tejeda, Gran Canaria</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
LPL2 - February 2021
<p>Measurements of LPL2 (PN Caldera de Taburiente) in February 2021</p> <p>El Paso, La Palma</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></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.