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951 results for “Data release”
mvaziri/PredictingActions_RawData: Second release of the Predicting Actions raw data
<p>Raw data for the manuscript titled "Predicting actions from subtle preparatory movements" in the journal "Cognition".</p>
Data Release for Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model
<p>CESM2 and CISM2 data files for figures in "Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model" (Sommers et al., 2021, Paleoceanography and Paleoclimatology)</p>
Data from: Acetylcholine waves and dopamine release in the striatum
<p>Striatal dopamine encodes reward, with recent work showing that dopamine release occurs in spatiotemporal waves. However, the mechanism of dopamine waves is unknown. Here we report that acetylcholine release in mouse striatum also exhibits wave activity, and that the spatial scale of striatal dopamine release is extended by nicotinic acetylcholine receptors. Based on these findings, and on our demonstration that single cholinergic interneurons can induce dopamine release, we hypothesized that the local reciprocal interaction between cholinergic interneurons and dopamine axons suffices to drive endogenous traveling waves. We show that the morphological and physiological properties of cholinergic interneuron – dopamine axon interactions can be modeled as a reaction-diffusion system that gives rise to traveling waves. Analytically-tractable versions of the model show that the structure and the nature of propagation of acetylcholine and dopamine traveling waves depend on their coupling, and that traveling waves can give rise to empirically observed correlations between these signals. Thus, our study provides evidence for striatal acetylcholine waves <em>in vivo</em>, and proposes a testable theoretical framework that predicts that the observed dopamine and acetylcholine waves are strongly coupled phenomena.</p>
Data from: Using controlled subsurface releases to investigate the effect of leak variation on above-ground natural gas detection
<p class="MsoNormal"><span>Pipelines transport natural gas (NG) in all stages between production and the end user. The NG composition, pipeline depth, and pressure vary significantly between extraction and consumption. As methane (CH<sub>4</sub>), the primary component of NG is both explosive and a potent greenhouse gas, NG leaks from underground pipelines pose both a safety and environmental threat. Leaks are typically found when an observer detects a CH<sub>4</sub> enhancement as they pass through the downwind above-ground NG plume. The likelihood of detecting a plume depends, in part, on the size of the plume, which is contingent on both environmental conditions and intrinsic characteristics of the leak. To investigate the effects of leak characteristics, this study uses controlled NG release experiments to observe how the above-ground plume width changes with changes in the gas composition of the NG, leak rate, and depth of the subsurface emission. Results show that plume width generally decreases when heavier hydrocarbons are present, the leak rate is reduced, and as leak depth decreases from 0.9 to 0.6 m. The above surface CH<sub>4</sub> plume is undetectable when leaks are 1.8 m deep. As most survey methods typically prioritize leaks based on the leak size, this study shows that the effect of NG density on above-ground plume width is only 4%, equivalent to the effect of leak rate. This suggests that reported leaks in areas with heavier hydrocarbons could currently be missed or underestimated. Furthermore, this study shows that leaks from pipelines laid in covers meeting minimum depth requirements of 0.9 m could be easier to detect compared to those buried shallower. Overall, this study illustrates that leak survey protocols for flowlines and gathering lines should be different from distribution pipelines and tailored to the compositions of the transported NG to report emissions accurately.</span></p>
Data Release for "Revisiting the evidence for precession in GW200129 with machine learning noise mitigation"
<p>Cleaned gravitational-wave data frame for the Livingston interferometer around GW200129 using NLSub, a machine-learning algorithm. For more details, see the publication on ArXiv: <a href="https://arxiv.org/abs/2311.09921">https://arxiv.org/abs/2311.09921</a>.</p><p>The data frame can be loaded in Python using e.g. GWPy TimeSeries class:</p><blockquote><p>from gwpy.timeseries import TimeSeries<br>tseries = TimeSeries.read('L-L1_DCS-CALIB_STRAIN_CLEAN_C01_NLSUB_P2300358_v4-1264314077-4078.hdf5')</p></blockquote><p> </p>
Data from: A maximum of two readily releasable vesicles per docking site at a cerebellar single active zone synapse
<p>Recent research suggests that in central mammalian synapses, active zones contain several docking sites acting in parallel. Before release, one or several synaptic vesicles (SVs) are thought to bind to each docking site, forming the readily releasable pool (RRP). Determining the RRP size per docking site has important implications for short-term synaptic plasticity. Here, we take advantage of recently developed methods to count the number of released SVs at single glutamatergic synapses in response to trains of action potentials. In each recording, the number of docking sites was determined by fitting with a binomial model the number of released SVs in response to individual action potentials. After normalization with respect to the number of docking sites, the summed number of released SVs following a train of action potentials was used to estimate of the RRP size per docking site. To improve this estimate, various steps were taken to maximize the release probability of docked SVs, the occupancy of docking sites, as well as the extent of synaptic depression. Under these conditions, the RRP size reached a maximum value close to two SVs per docking site. The results indicate that each docking site contains two distinct SV binding sites that can simultaneously accommodate up to one SV each. They further suggest that under special experimental conditions, as both sites are close to full occupancy, a maximal RRP size of two SVs per docking site can be reached. More generally, the results validate a sequential two-step docking model previously proposed at this preparation.</p>
Data from: Comparison of dopamine release and uptake parameters across sex, species and striatal subregions
<p><span>Dopamine in the striatum strongly regulates behavioral output in a heterogenous across the various striatal subregions. Moreover, dopamine dynamics not only displays heterogeneity across brain structures but also within males and females. The purpose of this dataset was to evaluate the dopamine dynamics in male and female mice and rats across five subregions: the dorsolateral caudate, ventromedial caudate, nucleus accumbens core, nucleus accumbens lateral shell, and the nucleus accumbens medial shell. Fast scan cyclic voltammetry (FSCV) was employed to measure dopamine release and uptake following a single pulse electrical stimulation in each of these subregions within a single brain slice. The dopamine dynamics were also observed across a variety of stimulation amplitudes. The goal of this dataset was to produce systematic FSCV measurements of dopamine across the rodent striatum using FSCV which would be available as a </span><span>resource for further investigation of DA terminal function.</span></p>
Data and Codes for Experimentally Validated Inverse design of Multi Property Fe-Co-Ni alloys: Data and codes release v1.0.1
<p>Data and Codes for Experimentally Validated Inverse design of Multi-Property Fe-Co-Ni alloys</p>
Data from: "Dead birds flying": Can North American rehabilitated raptors released into the wild mitigate anthropogenic mortality?
<p>As the human footprint expands to meet societal energy needs, so do the impacts on wildlife. Raptors in particular are highly susceptible to anthropogenic caused mortality. Industry sectors are encouraged to offset these causes of mortality. Several options to mitigate these losses have been proposed, including raptor rehabilitation. However, its role as a conservation tool is untested. Currently, no peer-reviewed demographic analyses exist using post-release data from rehabilitated raptors to evaluate its effectiveness at continental scales. Our objectives were to estimate annual survival of rehabilitated and wild raptors, and then use those estimates in demographic models to assess potential effects at individual and population levels. We hypothesized that rehabilitated raptors would survive similarly to their wild counterparts after an acclimation period, and that longer-lived species (<em>K-</em>selected) would benefit most from these releases. We used U.S. Geological Survey Bird Banding Lab band-recovery data (1974 – 2018) from 20 raptor species for modeling survival of rehabilitated individuals (<em>n </em>= 125,740) in comparison to wild birds (<em>n </em>= 1,913,352). Results from 17 species with adequate recovery data indicated that 5 species rehabilitated ≠ wild survival, 2 species had uncertain estimates, and 10 species rehabilitated ≈ wild survival by years 2 and 3 post-release. We acquired admission (<em>n </em>= 69,707) and release (<em>n = </em>25,740) data from 24 rehabilitation centers across the U.S. (2012-2021). We integrated survival, fecundity, and numbers of releases into demographic models. These models quantified the extent to which rehabilitated raptors may contribute to broader conservation efforts, especially in the context of individual take. All but two species had measurable numbers of individuals added to the population regardless of the number of releases. The general pattern was for <em>K</em>-selected species to yield larger benefits from rehabilitated supplementation to the population. These results provide evidence that rehabilitation may serve as a mitigation tool to offset incidental take.</p>
On the Landau gauge ghost-gluon-vertex close to and in the conformal window - data release
<h4>On the Landau gauge ghost-gluon-vertex close to and in the conformal window</h4><p>This repository contains the data presented in "On the Landau gauge ghost-gluon-vertex close to and in the conformal window". See the file README.md for more information.</p>
Data and codes for "Chapter 2: Particle-laden gravity currents: the lock-release slumping regime at the laboratory scale"
<div> <div> <h1>PALAGRAM Monograph</h1> </div> <p>This repository contains the data used in the book chapter:</p> <blockquote> <p>Gadal, C., Schneider, J., Bonamy, C., Chauchat, J., Dossmann, Y., Kiesgen de Richter, S., Mercier, M.J., Naaim-Bouvet, F., Rastello, M. and Lacaze, L. (2025). <strong>Particle-laden Gravity Currents: The Lock-release Slumping Regime at the Laboratory Scale.</strong> In Particulate Gravity Currents. <a href="https://doi.org/10.1002/9781394216727.ch2">10.1002/9781394216727.ch2</a></p> </blockquote> <div> <h2>Repository organization</h2> </div> <div> <pre><code> palagram_monograph │ └───data: data are stored here │ └───input_data: input data as sent by everyone │ └─── ... : NETCDF files │ └───output_data: processed data output by analysis.py (also contains input_data) │ └─── ... : NETCDF files └───analysis: └───analysis.py: analysis code, that reads input_data and writes output_data └───paper: contains source files for article │ └───figures: contains source figures │ └─── ... : PDF files │ └─── figure_scripts: contains figure scripts that reads data in data/output_data and writes figures in paper/figures │ └─── *.py : python scripts for figures │ └─── ... : various files (.tex, .bib, ...) │ └─── main.pdf : article preprint </code></pre> <div> </div> </div> <div> <h2>Data organization</h2> </div> <p>The CSV file <code>dataset_summary.csv</code> offers a summary of all runs and corresponding experimental parameters, allowing for easier access to the data.</p> <p>The folder <code>data/output_data</code> contains 287 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> <p>attributes:</p> <ul> <li>particle_type: particle type used (silica sand, glass beads, etc..)</li> <li>label: filename</li> <li>lab: lab where this run has been performed</li> <li>run_oldID: Old filename, corresponding to the experimental notebook</li> <li>author: author(s) that acquired this run</li> <li>setup: setup used to acquire the data. See article.</li> <li>dataset: Dataset classification of this run, See paper.</li> </ul> </li> <li> <p>dimensions(sizes): time(n)</p> </li> <li> <p>variables(dimensions):</p> <ul> <li>At(): Atwood number</li> <li>Fr(): Froude number (adi. initial current velocity)</li> <li>H0(): initial heavy fluid height inside the lock</li> <li>H_a(): ambient fluid height outside the lock</li> <li>L0(): streamwise lock length</li> <li>L_1(): streamwise tank length after the lock</li> <li>Re(): Reynolds number</li> <li>S(): Settling number</li> <li>St(): Stokes number</li> <li>T_a(): ambient temperature</li> <li>T_f(): heavy fluid temperature inside the lock</li> <li>W0(): crossstream lock width</li> <li>a(): lock aspect ratio</li> <li>alpha(): bottom slope</li> <li>d(): particle diameter</li> <li>gprime(): specific gravity</li> <li>lamb(): adi. attenuation parameter</li> <li>nu_a(): ambient viscosity</li> <li>nu_f(): heavy fluid lock viscosity</li> <li>phi(): initial particle volume fraction inside the lock</li> <li>rho_a(): ambient fluid density</li> <li>rho_c(): heavy fluid mix density inside the lock</li> <li>rho_f():</li> <li>rho_p(): particle density</li> <li>t('time',): time vector</li> <li>t0(): characteristic timescale, t0 = L0/u0</li> <li>u0(): characteristic velocity scale, u0 = sqrt(gprime*H0)</li> <li>vs(): particle Stokes velocity</li> <li>x_front('time',): front position vector</li> </ul> </li> </ul> <p>Variables can sometimes 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 <code>uncertainties</code> module (<a href="https://pythonhosted.org/uncertainties/" rel="nofollow">https://pythonhosted.org/uncertainties/</a>) in Python.</li> <li>comments: comments on the given quantity (definition, formulas, etc ..)</li> </ul> <div> <h2>Related works</h2> </div> <ul> <li> <p>Gadal, C., Schneider, J., Bonamy, C., Chauchat, J., Dossmann, Y., Kiesgen de Richter, S., Mercier, M.J., Naaim-Bouvet, F., Rastello, M. and Lacaze, L. (2025). Particle-laden Gravity Currents: The Lock-release Slumping Regime at the Laboratory Scale. In Particulate Gravity Currents. <a href="https://doi.org/10.1002/9781394216727.ch2">10.1002/9781394216727.ch2</a></p> </li> <li>Gadal, C., Mercier, M. J., Rastello, M., & Lacaze, L. (2023). Slumping regime in lock-release turbidity currents. <em>Journal of Fluid Mechanics</em>, 974, A4. <a href="https://doi.org/10.1017/jfm.2023.762" rel="nofollow">doi:10.1017/jfm.2023.762</a></li> <li> <p>Gadal, C., Mercier, M., Rastello, M., & Lacaze, L. (2023). Data used in 'Slumping regime in lock-release turbidity currents' [Data set]. In Journal of Fluid Mechanics (Vol. 974, p. A4). <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.10058946" rel="nofollow">https://doi.org/10.5281/zenodo.10058946</a></p> </li> <li> <p>Schneider, J., Dossmann, Y., Farges, O. et al. Investigation of particle laden gravity currents using the light attenuation technique. <em>Exp Fluids</em>, 64, 23 (2023). <a href="https://doi.org/10.1007/s00348-022-03562-y" rel="nofollow">doi:10.1007/s00348-022-03562-y</a></p> </li> <li> <p>Chauchat, J., Cheng, Z., Nagel, T., Bonamy, C., and Hsu, T.-J. (2017) SedFoam-2.0: a 3-D two-phase flow numerical model for sediment transport, <em>Geosci. Model Dev.</em>, 10, 4367-4392, <a href="https://doi.org/10.5194/gmd-10-4367-2017" rel="nofollow">doi:10.5194/gmd-10-4367-2017</a> and <a href="https://github.com/sedfoam/sedfoam">github</a></p> </li> </ul> </div>
CEDS v_2024_04_01 Release Emission Data
<p>Emissions data files by emission species (SO2, NOx, BC, OC, NH3, NMVOC, CO, CO2, CH4, N2O), country, and sector produced by the April-01-2024 release of CEDS. </p> <p>See the <a href="https://github.com/JGCRI/CEDS/">CEDS GitHub</a> site for details including journal paper reference information and any known issues with this data.</p> <p>The three file bundles are:</p> <p>CEDS_v_2024_04_01_aggregate.zip (global by sector, global by fuel, country total, country and sector)<br>CEDS_v_2024_04_01_detailed.zip (country, sector, and fuel)<br>CEDS_v_2024_04_01_supplementary_bunkers.zip (Additional detail for aviation and shipping by country)<br><br>(Metadata for this record is still under construction...)</p>
Observation of Gravitational Waves from the Coalescence of a 2.5-4.5 Msun Compact Object and a Neutron Star --- Data Release
<p>This data release contains the analysis results and data behind the figures of the GW230529 discovery paper (<a href="https://urldefense.com/v3/__https://dcc.ligo.org/LIGO-P2300352/public/__;!!Dq0X2DkFhyF93HkjWTBQKhk!W4i4x3JfGgemcFsnnEYP5qxiknddvrG1LWpTLjs_JGK907kTrEBkS8o6i5T6RUFMX0v04jCPhtTq9K2SLcv_4g$" target="_blank" rel="nofollow noreferrer noopener">https://dcc.ligo.org/LIGO-P2300352/public/</a>). Strain data for this event (the L1:GDS-CALIB_STRAIN_CLEAN_AR channel) can be downloaded on GWOSC (<a href="https://doi.org/10.7935/6k89-7q62" target="_blank" rel="noopener">https://doi.org/10.7935/6k89-7q62</a>).</p> <p>The PESummary metafile containing the parameter estimation posterior samples for all analyses performed in the paper (<strong>posterior_samples.h5</strong>) and skymap fits file (<strong>skymap_combined_PHM_high_spin.fits</strong>) for the preferred parameter estimation analysis (high-spin, combined samples using binary black hole waveforms) can be downloaded directly as individual files.</p> <p>The other analysis results are grouped by type: rates, populations, searches, and tidal. The <strong>figure_scripts.tar.gz</strong> file contains all the paper figures in jpeg format along with a Jupyter notebook to reproduce them and additional required helper scripts. Example code for working with the individual result files is given in the <strong>PaperPlots.ipynb</strong> notebook included in this tar file.</p> <p>In brief, the <strong>rates.tar.gz</strong> file contains two files that each include a subset of the rates probability distributions shown in Fig. 3 of the paper. The <strong>populations.tar.gz</strong> file contains all the data behind Figs. 4-8, with subdirectories for each of the three population analyses considered in the paper: Binned Gaussian Process, NSBH-pop, and Power-Law + Dip + Break. In addition to the data behind the figures, the Power-Law + Dip + Break subdirectory additionally includes two *result.json files for the hyper-parameter posterior samples. These files have the same format as the corresponding NSBH-pop *result.json files and can be manipulated in the same way, as shown in the figures notebook.</p> <p>The <strong>searches.tar.gz</strong> file contains the data behind Figs. 9-11 for each of the three search pipelines whose results are included in the paper. Finally, the <strong>tidal.tar.gz</strong> file contains the four probability distributions plotted in Fig. 14. All other figures are produced only using the posterior_samples.h5 file.</p>
Data from: Release from aboveground enemies increases seedling survival in grasslands
<p>Plant enemies can influence plant community assembly and structure. However, it is unclear how insect herbivores and fungal pathogens affect seedling recruitment. Complex interactions with competition and resource availability make it difficult to isolate the effect of enemies. This uncertainty can impede understanding of community assembly drivers, species coexistence, and trophic interactions; and limits hypothesis testing such as the enemy release hypothesis, a key hypothesis in invasion biology. Using a novel species-specific approach, we examine how enemies affect seedling survival and recruitment of 16 grassland species.</p> <p>We planted seedlings of 16 native species from two functional groups (C4 grasses and non-legume forbs) into two grassland sites (early and mid succession). We hand-painted 1,548 individual seedlings with pesticides (insecticide and fungicide) over the course of one growing season to enforce aboveground species-specific release from enemies, and tested whether it enhanced survival relative to untreated controls. Applying treatments to individuals allowed us to test for the effect of enemies on plant performance while controlling for contexts such as competition from the resident community and resource levels.</p> <p>Release from insects increased seedling survival by 56% on average, with no additional benefit of release from fungal pathogens. This effect was observed for both forbs and C4 grasses across both sites, and was strongest in resource-acquisitive species. There was no effect of the mean phylogenetic distance between our target species and the resident plant community, or of light availability or soil moisture.</p> <p><em>Synthesis</em>. The significant positive effect of release from insect herbivores on survival early in colonisation – a trend that held across functional groups and types of resident communities – suggests insects play an important regulatory role in community assembly, especially for resource-acquisitive species. As variation between species could be explained by traits but not by phylogeny, we emphasise the importance of trait-based approaches to plant community ecology. Our results also support the key mechanism (increased performance following release from enemies) underlying the enemy release hypothesis. Enemy release may therefore aid initial recruitment of plants during the invasion process.</p>
Age Dependence of the Occurrence and Architecture of Ultra-Short-Period Planet Systems (public data release)
<p>This file includes key data and column definitions for generating figures in the manuscript. The data were collected and processed from publicly available datasets, including Kepler DR25 and the planetary systems table (https://exoplanetarchive.ipac.caltech.edu/), LAMOST DR9 (https://www.lamost.org/), Gaia DR3 (https://gea.esac.esa.int/archive/), and The California-Kepler Survey (https://california-planet-search.github.io/cks-website/). Additional data required for the manuscript can also be obtained from these publicly available datasets.</p>
WikiPathways Sept 2021 Release - RDF data
<p>Archive of the WikiPathways September 2021 RDF data for <em>Homo sapiens</em> as GPMLRDF and WPRDF (.ttl format). The data is licenced under the <a href="https://creativecommons.org/share-your-work/public-domain/cc0/">CCZero waiver</a>.</p>
Data release: Understanding binary neutron star collisions with hypermodels
<pre># Data release for "Understanding binary neutron star collisions with hypermodels" ## Summary For each event, we include sub-directories of the studies performed. In each directory, we provide the `bilby_pipe` configuration (`.ini`) files, `bilby` result file (`.json`). We also provide figures, samples (in the form of `.csv` files), and summary statistics where they are relevant. ## Hypermodel script All results obtained using the *hypermodel* technique use the script `multiwaveform.py`. We include this script at the top-level of this directory. To reproduce results, in each configuration file, replace the `analysis-executable` with the path to this script. ## Software versions All results obtained using * bilby_pipe=1.0.3: (CLEAN) 1220709 2021-05-14 06:28:48 -0700 * bilby=1.1.2: (CLEAN) e5481028 2021-07-05 16:45:01 +0100</pre>
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)
<p>Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p>SVP1 data missing for June 17-18 (2021) and July 5-7 (2021).</p> <p><br> Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) June 9-11 - 2021</p> <p>AIS recorded data not available after June 11, 2021</p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
Data release for "The T2K Neutrino Flux Prediction" (2013)
<p>This data release is superseded by the<a href="https://zenodo.org/record/5734267#.YaYEsFOnxsE"> flux release in 2016</a>.<br><br>The files in the following archive file contain the neutrino beam flux predictions for the T2K ND280 (near) and Super-Kamiokande (far) detectors. The description of the flux predictions is published in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.87.012001">Phys. Rev. D.87.012001</a> (<a href="https://arxiv.org/abs/1211.0469">arxiv.org:1211.0469 [hep-ex]</a>). The provided flux predictions include no neutrino oscillations. This tar file contains the flux predictions as well as a README.pdf file with detailed information on the included files.</p>
docker-compose for neo4j with paradise papers data loaded: Release v0.1-43
<p><code>docker-compose</code> for neo4j with paradise papers data loaded</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.