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8,924 results for “formation”
Classifying the generation and formation channels of dynamically-formed gravitational-wave events
<p>This dataset contains all the simulations of dynamically-formed binaries performed with the software <a href="https://github.com/Kkritos/Rapster">rapster</a>, together with trained machine-learning classification models from (Antonelli, Kritos et al, in prep.), see <a href="https://github.com/aantonelli94/TheBHClassifier">the public codes online</a>.</p> <p>All items starting with "mergers_*" are simulations of clusters and they follow the structure reported in the documentation of <a href="https://github.com/Kkritos/Rapster">rapster</a>. The simulations differ in the choice of the hyperparameters for the distribution of the cluster mass, half-mass radius and initial spin distribution for the binaries.</p> <p>All items starting from "RFClassifier_*" are machine-learning classification models that use a Random Forest Classifier and that are trained with the simulations above. The models ending with "*_gen" predict the generation of the black holes, those with "*_form" predict their formation channels. </p> <p> </p>
Section 5.1 "Task Area 1: Image (meta)data formats and standardization)" Figure 8
<p>Figure 8. A FAIR-IO bundle combines the necessary acquisition and provenance metadata together with multi-resolution, chunked binary data in a single cloud-compatible format for simplified sharing and re-use.</p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.
<p>Region: 298 coastal counties of the conterminous USA</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p>
CoastSeg: Shoreline data at 30-m spatial resolution for 2001 coastal provinces or regions of the world, in geoJSON format.
<p>Region: 2001 coastal provinces or regions of the world</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): province_files_bounds.json</p>
BIGG universal model in COBRAPY format
<p>This provides the BIGG universal model in COBRAPY JSON format. The notebook for conversion is provided along with the converted model as well.</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format.
<p><em><strong>CoastSeg: global 30-m shoreline in 5x5 degree chunks</strong></em></p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): global_5x5grid.geojson</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format. Version 2.
<p><em><strong>CoastSeg: global 30-m shoreline in 5x5 degree chunks</strong></em></p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): global_5x5grid.geojson</p>
SDF format of Crystallography Open Database - April 2023
<p>This is a dump & transform to SDF of the crystallography open database that I computed & published in the context of a blog post. You can use that as alternative to extract angle, dihedral or distance statistics from freely & openly accessible crystal structures. </p>
The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex - Minimal Dataset
<p>Minimal Dataset for "The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex", published at <a href="https://www.nature.com/nsmb/">NSMB</a>.</p>
Anypodetus dichotomous, pathway key in SDD-format
<p>Dichotomous, pathway key to species of genus Anypodetus (Diptera: Asilidae) developed with Lucid Builder v4 in XML Structure of Descriptive Data (SDD) format.</p>
Formation and cycling data for Na-ion batteries from high-throughput synthesis, coating, and assembly
<p>Formation and cycling data from a combinatorial/high-throughput upscaling process for the production and characterization of sodium-ion batteries. The process involves batch synthesis, screen printing of electrodes, robotic cell assembly, and battery cycling. The goal of this study was to test how fast a new chemistry (to the group) could be introduced into the workflow and if we are able to enhance efficiency, accuracy, and reproducibility. The cathode material, Na0.9[Cu0.22Fe0.30Mn0.48]O2, was synthesized through a solid-state reaction (Na2CO3 (purity 99.5 %), CuO (purity 99.7 %), Fe2O3 (purity 99.9 %) and Mn2O3 (purity 98 %) at 850°C for 15h) in a pressed pellet (10 MPa) that was ground up again to make a slurry. The electrodes were prepared using screen printing, which offers simplicity, low cost, and quick coating of large areas in a reproducible manner. The binder was sodium carboxymethyl cellulose to make the electrodes water processable in air. The assembled batteries utilized the synthesized cathode material and hard carbon as the anode, with a glass fiber separator and a 1M NaPF6 EC:EMC 3:7 with 2 wt% FEC electrolyte.</p>
Dataset for Measurement report: Ion clusters as indicator for local new particle formation
<p>Data for Measurement report: Ion clusters as indicator for local new particle formation. There are two files, negative_ion_concentrations.csv and positive_ion_concentrations.csv. The former (latter) includes absolute number concentrations for 1.87, 2.16, 2.49, and 2.88 nm negative (positive) ions. The unit for these concentrations is #/cm<sup>-3</sup>.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Processed data supporting figures in Yang et al. 2023: Oceanic eddies induce a rapid formation of an internal wave continuum
<p>This data repository supports a manuscript by Luwei Yang, Roy Barkan, Kaushik Srinivasan, James C. McWilliams, Callum J. Shakespeare, and Angus H. Gibson, submitted to <em>Communications Earth & Environment</em>. This repository contains the processed data that support the figures in the manuscript. </p>
Data for the publication "Future warming exacerbated by aged soot effect on cloud formation"
<p>This repository contains the data for the paper:</p> <p>"Ulrike Lohmann, Franz Friebel, Zamin A. Kanji, Fabian Mahrt, Amewu A. Mensah and David Neubauer: Future warming exacerbated by aged soot effect on cloud formation. <em>Nat. Geosci.</em> <strong>13, </strong>674–680 (2020). https://doi.org/10.1038/s41561-020-0631-0"</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3457878)</p> <p>Also note that Extended Data Fig. 1 is identical to Supplementary Fig. S4 as well as that Extended Data Table 1 is identical to Supplementary Table S2.</p>
Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"
<p>This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.</p>
TUK-FFDat - Data scheme and data format for transferable force fields for molecular simulation
<p>Online repository to suplement the following publication:</p> <p>G. Kanagalingam, S. Schmitt, F. Fleckenstein, S. Stephan: Data scheme and data format for transferable force fields for molecular simulation, Scientific Data, accepted (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>
CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.
<p><strong>CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.</strong></p> <p>This is a shoreline atlas of California at 30m resolution, to support analysis of CoastSat/CoastSeg-derived shoreline time-series and other shoreline data, and miscellaneous analyses of coastal shoreline data. The dataset consists of a GeoJSON files containing a 30-m shoreline estimate for California, based on an analysis of 2014 Landsat imagery (Sayre et al., 2019). This shoreline vector has been attributed with the following fields that may be useful in analyses of shoreline patterns and regional variability:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT (m)</li> <li>TIDAL_RANGE (m)</li> <li>CHLOROPHYLL (mg/L)</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE (descriptive)</li> <li>EMU_PHYSICAL (descriptive)</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE (%)</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY (descriptive)</li> <li>LENGTH_GEO</li> <li>ch_label (descriptive)</li> <li>river_label (descriptive)</li> <li>sinuosity_label (descriptive)</li> <li>slope_label (descriptive)</li> <li>tidal_label (descriptive)</li> <li>turbid_label (descriptive)</li> <li>wave_label (descriptive)</li> <li>CSU_Descriptor (descriptive)</li> <li>CSU_ID</li> <li>elevation (m)</li> <li>aspect (degrees N)</li> <li>slope (degrees)</li> </ol> <p>Fields 1 to 21 inclusive originally come from raw data https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk, which is described in Sayre et al (2019)</p> <p>Fields 22 and 24 come from raw data originally in the U.S. Geological Survey Elevation Derivatives for National Applications (EDNA) database (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-elevation-derivatives-national), accessed through Earth Explorer and processed in QGIS.</p> <p>The figure shows distributions of selected quantities. A python script to reproduce this plot is provided</p> <p>A subset of numeric-only variables and descriptive-only variables has also been prepared and made available. A CSV version of the full dataset is also provided</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></li> <li><a href="https://doi.org/10.5066/F7TD9VTQ">Elevation Derivatives for National Applications (EDNA) Seamless Three-Dimensional Hydrologic Database Digital Object Identifier (DOI) number: /10.5066/F7TD9VTQ</a></li> </ol> <p> </p>
Efficiency of the formation of acid-resistant Calcium-oxalate layers on limestone
<p><strong>Scientific background:</strong></p> <p>Carbonate-based stone monuments and buildings are susceptible to weathering in acidic environments. To combat surface corrosion and slow down material deterioration, protective coatings that inhibit calcite dissolution have been proposed. The efficiency and integrity of the coatings was studied by measuring sulfur distribution along the treated surface. Such experiment cannot be efficiently performed with standard SSD PIXE detectors due to high overlap between the strong Ca K x-ray escape peaks and S Kα. For that purpose, a new parallel-beam wavelength dispersive (PB-WDS) X-ray emission spectrometer at JSI have been used which achieves high energy resolution in the eV range and is able to measure S distribution on the surface of treated marble samples.</p> <p><strong>Measurements performed within TNA project:</strong></p> <p>The new PB-WDS X-ray emission spectrometer at J. Stefan Institute (Ljubljana, Slovenia) was used to map the presence of Sulphur on the surface of 13 marble samples treated with different coatings and after exposure to 2% sulfuric acid. Ge(111) crystal analyzer was used in the spectrometer to record the S Ka signal, the overall scan size was 5 × 5 mm<sup>2</sup>.</p> <p><strong>Data files:</strong></p> <p>We are sharing the files produced during measurements. The signal from the detector preamplifier was processed with the XIA DXP-XMAP digital pulse processor. The files are two main formats:</p> <ol> <li>Files containing mapping data. The spectrometer was set to the Bragg angle corresponding to the energy of the S Ka emission line. In a .zip folder, with 4 .mca files for every measured point (extension: _0-Si(Li) detector, _2- PB-WDS spectrometer)</li> <li>High energy resolution spectra recorded at selected points on the sample surface.</li> </ol> <p> </p> <p> </p> <table> <thead> <tr> <th> <p>#</p> </th> <th> <p>Filename</p> </th> <th> <p> </p> </th> </tr> </thead> <tbody> <tr> <td> <p>1</p> </td> <td> <p>VES_A1_5.zip</p> </td> <td> <p>Map of S on VES_A1_5 sample. Ge 111, 100X100 points, 50μm step, 4s/point</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S_X80_Y85.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_5 sample. Point position x = 80px, y = 85px</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>VES_A3_5.zip</p> </td> <td> <p>Map of S on VES_A3_5 sample. Ge 111, 100X100 points, 50μm step, 4s/point</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S_X95_Y33.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A3_5 sample. Point position x = 95px, y = 33px</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>VES_A1_12.zip</p> </td> <td> <p>Map of S on VES_A1_12 sample. Ge 111, 40x40 points, 125μm step, 5s/point</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S_X3_Y3.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 3px, y = 3px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S_X20_Y20.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 20px, y = 20px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>S_X35_Y20.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 35px, y = 20px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>CAR_A1_12.zip</p> </td> <td> <p>Map of S on VES_A1_12 sample. Ge 111, 80x80 points, 65μm step, 4s/point</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>CAR_A1_12_back_side.txt</p> </td> <td> <p>Scan over S Ka and Kb peak on the back surface of CAR_A1_12 sample.</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>VES_A1_12_Washed.zip</p> </td> <td> <p>Sample washed under running water. 2250 eV - 2350 eV; stepsize = 1.00eV; 6s/point or 10s/point for back</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>VES_A2_12.zip</p> </td> <td> <p>Line map of S on VES_A2_12. 10x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>CAR_A3_5.zip</p> </td> <td> <p>Line map of S on CAR_A3_5. 10x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>CAR_A1_5.zip</p> </td> <td> <p>Line map of S on CAR_A1_5. 10x1 points, 1mm stepsize, 10s/point1</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>VES_A3_12.zip</p> </td> <td> <p>Line map of S on VES_A3_12. 10x1 points, 1mm stepsize, 10s/point + Map of S on VES_A3_12 sample. Ge 111, 50x25 points, 200μm step, 3s/point</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>VES_A2_5.zip</p> </td> <td> <p>Line map of S on VES_A2_5. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>CAR_A2_5.zip</p> </td> <td> <p>Line map of S on CAR_A2_5. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>CAR_A2_12.zip</p> </td> <td> <p>Line map of S on CAR_A2_12. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>19</p> </td> <td> <p>CAR_A3_12.zip</p> </td> <td> <p>Line map of S on CAR_A3_12. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> </tbody> </table> <p> </p>
Proof-of-concept AO telemetry data using the AOT standard format
<p>Dataset containing a demonstration of AO telemetry data using the AOT standard format. Contains data from 5 different systems (CIAO, ERIS, GALACSI, NAOMI and PAPYRUS).</p> <p>The data is as follows:</p> <ul> <li>4 CIAO files "CIAO#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 ERIS files, one in LGS (LTAO) mode and another in NGS (SCAO) mode.</li> <li>1 GALACSI file.</li> <li>4 NAOMI files "NAOMI#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 PAPYRUS files, one using a Shack-Hartmann wavefront sensor (SHWFS) and another using a Pyramid wavefront sensor (PWFS).</li> </ul> <p>GALACSI, CIAO, NAOMI and ERIS data were gathered under ESO's program IDs 60.A-9278(B), 60.A-9278(C), 60.A-9278(D) and 60.A-9278(E) respectively. The data was translated via the functions provided in the Python package <em>aotpy</em> (<a href="https://doi.org/10.5281/zenodo.8187230">10.5281/zenodo.8187230</a>).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004719 (OPTICON–RadioNet Pilot).</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.