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951 results for “Data release”
Data Release: Aurorasaurus Science Products Inventory & Survey Results (2014-2019)
<p>Aurorasaurus is an eight-year-old project: the first and only citizen science initiative that tracks auroras around the world via reports on our website, mobile apps, and social media. (See Kosar et al., (2018). Aurorasaurus Real-Time Citizen Science Aurora Data (Version v1.0) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1255196">http://doi.org/10.5281/zenodo.1255196</a>.)</p> <p>At the American Geophysical Union Fall Meeting 2019, MacDonald and Brandt (2019) gave a talk entitled "Towards developing appropriate and diverse metrics for citizen science – a case study." In the presentation, they examined our 2015 user survey, used more recent metrics to assess Aurorasaurus' current status, and began to lay a framework for their next round of evaluation. </p> <p>In order to frame the process, MacDonald and Brandt (2019) utilized the Science Products and Data Practices inventories created by Wiggins et al., (2018). The authors constructed lists of items and practices that should be present in citizen science projects. While the Science Products and Data Practices inventories are excellent for quantitative analysis, MacDonald and Brandt (2019) wanted to include qualitative analysis of past evaluation as well. To that end, they informally and retrospectively mapped Aurorasaurus' 2015 survey questions to the BASIK framework.</p> <p>Aurorasaurus is publishing this dataset as a case study for how the Wiggins et al. framework can be applied and combined with other systems.</p>
Data release - A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr
<p>Data release accompanying the manuscript</p> <p> "<strong>A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr</strong>" - <a href="https://arxiv.org/abs/2009.14057">Chen et al. (2020)</a></p> <p>assuming an association between the LIGO-Virgo gravitational wave signal <a href="https://www.gw-openscience.org/eventapi/html/O3_Discovery_Papers/GW190521/">GW190521</a> and the electromagnetic signal ZTF19abanrhr as identified by <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.251102">Graham et al 2020</a>.</p> <p>The posterior samples for the GW analyses are available from <a href="https://doi.org/10.5281/zenodo.4057130">Isi (2020)</a> and <a href="https://dcc.ligo.org/LIGO-P2000158/public">LVC (2020)</a> respectively.</p>
Stealth dark matter confinement transition and gravitational waves --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating the confinement transition of SU(4) stealth dark matter and the possibility that this early-universe transition may produce an observable stochastic background of gravitational waves. See the README for further information.</p> <p> </p>
Tara Pacific 18S-based coral host genetic analysis data release version 1
<p>This dataset contains 4 tables and 3 sets of figures related to the primary analysis of the 18S metabarcoding sequencing output. This dataset is only concerned with the identity of the coral host (i.e. not additional protist diversity). The samples included in this dataset have a 'sample-material_label' value of 'CORAL' and 'sampling-protocol_label' value of 'SEQ-CS4L'. They represent the coral samples collected at all 32 of the islands visited in the Tara Pacific expedition.</p>
Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory -- data release
<p>This submission contains the Markov-Chain Monte Carlo data required to reproduce central results of the paper "Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory" (<a href="https://arxiv.org/abs/2009.14768">https://arxiv.org/abs/2009.14768</a>). </p> <p>The Python code required to read and analyse the data can be found under https://github.com/andreasjuettner/Finite-Size-Scaling-Analysis (the relevant release is attached to 10.5281/zenodo.4290508).</p> <p>For any questions please get in touch: juettner@soton.ac.uk.</p>
baofeng-apm/DATAforAGU: First release of data for Love verification
<p>Love waveform derived from the empirical Green's functions and Ground Truth earthquake in my manuscript submitted to Journal of Geophysical Research: Solid Earth.</p>
Data from: Plant uptake offsets silica release from a large Arctic tundra wildfire
Rapid climate change at high latitudes is projected to increase wildfire extent in tundra ecosystems by up to five-fold by the end of the century. Tundra wildfire could alter terrestrial silica (SiO2) cycling by restructuring surface vegetation and by deepening the seasonally-thawed active layer. These changes could influence the availability of silica in terrestrial permafrost ecosystems and alter lateral exports to downstream marine waters, where silica is often a limiting nutrient. In this context, we investigated the long-term effects of the largest Arctic tundra fire in recent times on plant and peat amorphous silica content and dissolved silica concentration in streams. Ten-years after the fire, vegetation in burned areas had 73% more silica in aboveground biomass compared to adjacent, unburned areas. This increase in plant silica was attributable to significantly higher plant silica concentration in bryophytes and increased prevalence of silica-rich gramminoids in burned areas. Tundra fire redistributed peat silica, with burned areas containing significantly higher amorphous silica concentrations in the O-layer, but 29% less silica in peat overall due to shallower peat depth post burn. Despite these dramatic differences in terrestrial silica dynamics, dissolved silica concentration in tributaries draining burned catchments did not differ from unburned catchments, potentially due to the increased uptake by terrestrial vegetation. Together, these results suggest that tundra wildfire enhances terrestrial availability of silica via permafrost degradation and associated weathering, but that changes in lateral silica export may depend on vegetation uptake during the first decade of post-wildfire succession.
studyforrest-data-phase2: First public release
<p>Extension of the dataset published in Hanke et al. (2014; doi:10.1038/sdata.2014.3) with additional acquisitions for 15 of the original 20 particpants. These additions include: retinotopic mapping, a localizer paradigm for higher visual areas (FFA, EBA, PPA), and another 2h movie recording with 3T full-brain BOLD fMRI with simultaneous 1000 Hz eyetracking.</p> <p>Only open-source software was employed in this study. We thank their respective authors for making it publicly available.</p> <p>Please follow good scientific practice by citing the most appropriate publication(s) describing the aspects of this datasets that were used in a study.</p> <p> </p>
Trophic-meta-analysis: Second release of tritrophic meta-analysis data, code and appendices
<p>Data, R script and appendices for "Interaction strength and the impact of introduced omnivores: A meta-analysis of introduced aquatic invasive species" submitted to Oikos</p>
solar_home_system_data_log: Initial release of data sets and script
<p>In this first release, this repository includes three sets of data (date/time, temperature, current, and voltage) of over 6 months of electricity consumption of three households in an off-grid area in the state of Jharkhand, India. The goal of this data set is to be made open so that the community working in off-grid electricity access can get a sense of electricity consumption patterns and apply various analytical and visualization techniques.</p>
Data used in 'Slumping regime in lock-release turbidity currents'
<p>This repository contains the data used in the paper:</p><blockquote><p><strong>Gadal, C., Mercier, M., Rastello, M., & Lacaze, L. (2023). Slumping regime in lock-release turbidity currents. </strong><i><strong>Journal of Fluid Mechanics,</strong></i><strong> </strong><i><strong>974</strong></i><strong>, A4. doi:10.1017/jfm.2023.762</strong></p></blockquote><p><br>where the slumping regime of turbidity currents is studied with respect to the initial volume fraction, the bottom slope and the particle settling velocity. The folder 'runs' contains 169 netcdf4 files corresponding to each experimental run used in the paper. For each run, the structure of the NetCDF file is the following:</p><ul><li>attributes:<ul><li>particle_type: particle type used (silica sand, glass beads or saline water)</li><li>run_number: NetCDF file name</li><li>expe_type: always lock-release here</li><li>surface_type: can be 'open surface' or 'rigid lid'</li><li>set_up: can be 'set-up 1' or 'set-up 2'</li><li>run_oldID: run name corresponding to the experimental notebook</li></ul></li><li>groups:<ul><li>initial_parameters:<ul><li>dimensions(sizes):</li><li>variables(dimensions):<ul><li>Bottom slope(): bottom slope</li><li>Current density(): initial average (fluid + particle) lock density</li><li>Grain density(): particle density (not measured, estimated)</li><li>Grain diameter(): particle diameter</li><li>Initial Reynolds number(): initial Reynolds number, [rho_0 * u_0 * h_0 / mu]</li><li>Initial Rouse number(): initial Rouse number, [v_s / u_0]</li><li>Initial volume fraction(): initial lock particle volume fraction</li><li>Reduced gravity(): reduced gravity, [g*(rho_0 - rho_f)/rho_f]</li><li>Settling velocity(): particle settling velocity</li><li>Temperature(): water temperature (not measured)</li><li>V0 (lock volume)(): lock suspension volume</li><li>Water density(): water density</li><li>Water dynamic viscosity(): water dynamic viscosity (not measured)</li><li>h0 (lock height)(): suspension height inside lock</li><li>u0 (velocity scale)(): velocity scale, [sqrt(g'*h_0)]</li><li>w0 (tank width)(): lock crosstream width</li><li>x0 (lock length)(): lock streamwise length</li></ul></li></ul></li><li>scalar_variables:<ul><li>dimensions(sizes): tuples(2), x(1181)</li><li>variables(dimensions):<ul><li>Av. shape('x',): current average shape</li><li>Av. shape head volume(): Volume per unit of width of the head part of current average shape</li><li>Av. shape tail volume(): Volume per unit of width of the tail part of current average shape</li><li>Av. shape volume(): Volume per unit of width of current average shape</li><li>Bulk entrainment coefficient(): Bulk entrainment coefficient during slumping</li><li>Current Froude number(): Current Froude number, [u_c/sqrt(g' * h_b)]</li><li>Current Reynolds number(): Current Reynolds number, [rho_0 * u_c * h_b / mu]</li><li>Current Rouse number(): Current Rouse number, [v_s / u_c]</li><li>Current head height (log fit)(): current height h_h coming from log fit</li><li>Current height (benjamin fit)(): current height h_b coming from fit of Benjamin's shape</li><li>Current nose height (benjamin fit)(): current nose height h_n coming from fit of Benjamin's shape</li><li>Current nose height (log fit)(): current nose h_n coming from log fit</li><li>Geometrical Froude number(): Current Geometrical Froude number, [u_c/sqrt(g' * h0)]</li><li>Geometrical Reynolds number(): Current Geometrical Reynolds number [rho_0 * u_c * h0 / mu]</li><li>Times lock opening (tstart, tend)('tuples',): Start and end times of lock opening</li><li>Times slumping regime (tstart, tend)('tuples',): Start and end times of constant velocity regime</li><li>Velocity (slumping regime)(): Current velocity during slumping</li><li>time_series:</li><li>dimensions(sizes): time(3071)</li><li>variables(dimensions):</li><li>Volume('time',): current volume per unit of width</li><li>contour time series (x)('time',): x coordinate time series of the current contours</li><li>contour time series (y)('time',): y coordinate time series of the current contours</li><li>position('time',): front position</li><li>time('time',): time vector</li><li>velocity('time',): front velocity</li></ul></li></ul></li></ul></li></ul><p><br>Most variables possess the following attributes:</p><ul><li>unit: corresponding unit</li><li>std: error(s) on the given quantity, calculated by error propagation from measurement uncertainties using the `uncertainties` module (https://pythonhosted.org/uncertainties/) in Python.</li><li>comments: comments on the given quantity (definition, formulas, etc ..)</li></ul><p>Note that all variables related to the current shape are not available for experimental runs carried out in set-up 2.<br>The script ReadPlotData.py shows how to display the structure of a NetCDF file, and gives examples of how to load some variables and plot them by reproducing some of the paper's figures.<br>The CSV file 'dataset_summary.csv' offers a summary of all runs and corresponding experimental parameters, allowing for easier access for testing purposes. *Note that errors are not given in this file.*</p><p>OpenData License: licence-ouverte-v2.0</p><p>If you use this open data in your work (research or other), please cite in your bibliography the following reference doi:https://doi.org/10.1017/jfm.2023.762</p>
Harvey-Lab-UW/Radcliffe_etal_2024_FEM: Release of data for Radcliffe et al. 2024 FEM
<p>This is the latest release of data for reproducing the analyses in the manuscript 'How are long-term stand structure, fuel profiles, and potential fire behavior affected by fuel treatment type and intensity in Interior Pacific Northwest forests?' by Radcliffe, Bakker, Churchill, Alvarado, Peterson, Laughlin, and Harvey, published in Forest Ecology and Management. See the main text of the manuscript for complete description of data collection, processing and analyses.</p><p>Shield: CC BY 4.0</p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p>CC BY 4.0</p><p>Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.</p>
ROSSyndicate Cameron Peak Fire (CPF) reservoir water quality data: Latest Release: 2021- 11/2023 Dataset
<p><strong>Data Description:</strong> The majority of this dataset is water chemistry grab sample data collected post-Cameron Peak Fire in the Cache la Poudre Watershed between the years of 2021 and 2023. This dataset also includes historical data collected pre Cameron Peak Fire by the Rhoades lab at the US Forest Service Rocky Mountain Research Station. These data are focused on basic water quality parameters, as well as cations and anions. Data were collected at various reservoirs in the Cache la Poudre watershed as well as the mainstem of the Cache la Poudre River. This project is ongoing and additional data will be released as it is analyzed.</p> <p><strong>Background Information:</strong> The 2020 Cameron Peak wildfire (CPF) was the largest wildfire in Colorado history at over 200,000 acres. The CPF burned a large proportion of the Cache la Poudre watershed, in particular areas surrounding high elevation reservoirs. This work is funded to support ongoing source water protection programs by the City of Fort Collins, Greeley, Thornton and Northern Water. In collaboration with the Rocky Mountain Research Station (USFS, RMRS), we are sampling various reservoir, tributary, and mainstem sites of the Cache la Poudre watershed. This field campaign allows us to analyze trends in water quality focusing on nutrients and other key constituents mobilized post-fire. The goal of this project is to understand how these nutrients affect algal growth in reservoirs and how those changes are propagated downstream. The reservoirs studied are the following: Barnes Meadow Reservoir, Chambers Lake, Comanche Reservoir, Hourglass Reservoir, Joe Wright Reservoir, Long Draw Reservoir, and Peterson Lake. Historical data (prior to 2021) was collected by the Rhoades Lab at the USFS' Rocky Mountain Research Station.</p> <p><strong>The primary data file is data/cleaned/CPF_reservoir_chemistry_up_to_202301027.csv.</strong> Column definitions and units are defined in the file <em>metadata/Units_Cam_Peak.xlsx</em>. Methods used to collect these data are outline below or in <em>metadata/rmrs_procedures.png</em></p> <p>Location metadata file is <em>data/metadata/cpf_sites.csv</em>. A basic map showing all sampling locations is available at cpf_sites_map.html.</p> <p>Code is housed in the <em>scripts</em> folder and contains the following files:</p> <p>- <em>00_analysis_setup.R</em> provides loads packages and metadata files to be collated in <em>01_chem_prep.qmd</em>.</p> <p>- <em>01_chem_prep.qmd</em> adds metadata to most recent .csv of water chemistry data supplied by RMRS lab.</p> <p>- <em> distance_finder.R</em> uses NHDflowlines to calculate distances from furthest downstream site, PBD.</p> <p>- <em>cpf_sites_map.R</em> uses location metadata to create <em>cpf_sites_map.html</em></p> <p>- <em>demo.R</em> provides an example of how to download data from Zenodo directly in RStudio</p> <p><strong>Data are housed in the data folder and it contains the following:</strong></p> <p>- cleaned: This folder contains the most recently available dataset and has associated burn severity and location data added to the chemistry data. The addition of the metadata was accomplished using the `01_chem_prep.qmd` R script.</p> <p>- cleaned_archive: This folder contains an archive of previously cleaned data. <strong>Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv`</strong> in the `cleaned` directory.</p> <p>- raw: These data were directly received by the ROSSyndicate from RMRS lab managers. Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv` in the `cleaned` directory.</p> <p>- metadata: this contains location data, parameter/column name definitions, units, and methods used at the RMRS Lab. The `README` file in this folder explains burn severity classifications used in the files `sbs_watershed.csv`,`sbs_watershed.csv` and `cpf_sites.csv`</p> <p><strong>Sample Collection</strong></p> <p>Field measurements were taken using a Thermo Orion Star with RDO Optical and Conductivity probes. Time data, when present, are listed in MST. Samples were collected and processed using the Rocky Mountain Research Station's Biogeochemistry Lab, overseen by Timothy Fegel and Charles Rhoades, according to the methods described in rmrs_procedures.png</p> <p><strong>Version: v2023.12.13</strong></p>
Data from: Male lake char release taurocholic acid as part of a mating pheromone
<p>The evolutionary origins of sexual preferences for chemical signals remain poorly understood, due, in part, to scant information on the molecules involved. In the current study, we identified a male pheromone in lake char (<em>Salvelinus namaycush</em>) to evaluate the hypothesis that it exploits a nonsexual preference for juvenile odour. In anadromous char species, the odour of stream-resident juveniles guides migratory adults into spawning streams. Lake char are also attracted to juvenile odour but have lost the anadromous phenotype and spawn on nearshore reefs, where juvenile odour does not persist long enough to act as a cue for spawning site selection by adults. Previous behavioural data raised the possibility that males release a pheromone that includes components of juvenile odour. Using metabolomics, we found that the most abundant molecule released by males was also released by juveniles but not females. Tandem mass spectrometry and nuclear magnetic resonance were used to identify the molecule as taurocholic acid (TCA), which was previously implicated as a component of juvenile odour. Additional chemical analyses revealed that males release TCA at high rates via their urine during the spawning season. Finally, picomolar concentrations of TCA attracted prespawning and spawning females but not males. Taken together, our results indicate male lake char release TCA, a mating pheromone, and support the hypothesis that the pheromone is a partial match of juvenile odour.</p> <p><span> </span></p>
Regional Assessment of buildings' Material Intensities (RASMI): Version 20230905: first public release B - data only
<p><strong>Version 20230905: first public release of RASMI (Regional Assessment of buildings' Material Intensities).</strong></p> <p>This Zenodo version contains two files:</p> <ul> <li><code>MI_ranges_20230905.xlsx</code> is the dataset of the estimated MI ranges. <em><strong>This is probably the file you're looking for.</strong></em></li> <li><code>MI_data_20230905.xlsx</code> is the raw pools of MI used to create the MI ranges. This is mostly for reproducability.</li> </ul> <p>Please refer to the GitHub readme.md in <a href="https://github.com/TomerFishman/MaterialIntensityEstimator">https://github.com/TomerFishman/MaterialIntensityEstimator</a> for details and how to use.</p> <p>Please cite both the Data Descriptor and the specific data version used:</p> <p>Data Descriptor: Tomer Fishman, Alessio Mastrucci, Yoav Peled, Shoshanna Saxe, Bas van Ruijven. <em>RASMI: Global Ranges of Building Material Intensities Differentiated by Region, Structure, and Function</em>. Scientific Data 2024, 11 (1), 418. <a href="https://doi.org/10.1038/s41597-024-03190-7" rel="nofollow">https://doi.org/10.1038/s41597-024-03190-7</a>.</p> <p>Data version: preferably use the DOI of the Zeonodo release. Refer to the release number (on the right)</p> <p>This work was conducted with support by the IIASA-Israel program, and by the Israel Science Foundation project RUSTY (grant no. 2706/19). Funding was also provided by the Horizon Europe research and innovation programme under grant agreement no. 101056868 (CIRCOMOD) for TF and grant agreement No 101056810 (CircEUlar) for AM. Opinions are those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for this. BvR and AM have been supported by the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is an initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and the International Institute for Applied Systems Analysis (IIASA), and funded by the Ministry of Economy, Trade, and Industry (METI), Japan. SS was supported by the Canada Research Chair in Sustainable Infrastructure, Grant Number: 232970.</p>
Data release accompanying JGR publication "Electron-induced radiolysis of water ice and the buildup of oxygen" by Tinner et al.
Open the record for dataset details and reuse information.
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory---Data Release
<p>This release contains the analysis workflow used to prepare the publication <a href="https://arxiv.org/abs/2311.14663" target="_blank" rel="noopener">Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory</a>.</p> <p>A Python code for performing the analysis and generating the plots and tables is <a href="https://doi.org/10.5281/zenodo.10929539" target="_blank" rel="noopener">uploaded to Zenodo</a>. See the README therein for details on running the code.</p> <p>For details on the data formats, see the relevant README.md files.</p> <h2>Content of directories and files:</h2> <ul> <li>README.md: This contains general information on the content of the release.</li> <li>raw_data.zip: This compressed file contains all the raw data utilized in the research outlined in arXiv:2311.14663. These data were crucial in generating the results showcased in the paper.</li> <li><span>data.h5: An HDF5 file housing the correlators derived from the raw data through the processing code, <code>generate/transform_h5.py</code>.</span></li> <li><span>metadata.zip: This archive furnishes essential metadata such as ensemble information, fitting intervals, and smearing parameters crucial for extracting masses.</span></li> <li><span>F_meson.csv: Presents the fundamental meson masses extracted via the <code>analysis/analysis_F.py</code> script.</span></li> <li><span>AS_meson.csv: Presents the antisymmetric meson masses extracted via the <code>analysis/analysis_AS.py</code> script.</span></li> <li><span>CB_mass.csv: Presents the chimera baryon masses extracted via the <code>analysis/analysis_CB.py</code> script.</span></li> <li><span>FIT_mass.csv: Offers the AIC scan results conducted through the <code>analysis/analysis_AIC.py</code> script.</span></li> <li><span>FIT_cross_fixAS.csv and FIT_cross_fixF.csv: These files provide cross-check results computed by the</span> <span><code>analysis/analysis_cross.py</code> script, specifically for fixing antisymmetric and fundamental masses, respectively.</span></li> </ul>
Harvey-Lab-UW/Buonanduci_etal_2024_Ecosphere: Release of data and code for Buonanduci et al. 2024 Ecosphere
<p>This is the latest release of data and code for reproducing the analyses in the manuscript 'Few large or many small fires: Using spatial scaling of severe fire to quantify effects of fire-size distribution shifts' by Buonanduci, Donato, Halofsky, Kennedy, and Harvey, published in Ecosphere. See the main text of the manuscript for complete descriptions of how data were processed and analyzed.</p> <p>This information is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.</p>
GBM Targeted Search Data Release for GRB 241112B
<div> <p>The following Files include:</p> <ul> <li>The summed GBM NaI detector lightcurve,</li> <li>The individual GBM NaI detector lightcurves,</li> <li>The BGO detector lightcurves, </li> <li>The GBM NaI detector lightcurves sliced by energy.</li> <li>The waterfall plot (all energy) showing the most significant timescale of the event,</li> <li>The waterfall plot with the three different spectral models (soft, normal and hard, see below).</li> <li>The localization of GRB 241112B, showing the 3 and 1-sigma contours.</li> <li>The healpix map for "Event 1", which is the event found by the Targeted Search and shown in these plots. </li> </ul> <p><br>The definitions for soft, normal and hard spectra are:<br><br>"soft" spectrum (Band function with Epeak = 70 keV, alpha = -1.9, beta = -3.7) for a GRB.<br>"normal" spectrum (Band function with Epeak = 230 keV, alpha = -1.0, beta = -2.3) for a GRB.<br>"hard" spectrum (Comptonized function with Epeak = 1500 keV, alpha = -0.5) for a GRB. </p> <p> </p> </div>
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.