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11 results for “observational constraints”
SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints
<p>This dataset contains an initial set of curated and processed oceanographic observations collected as part of a joint effort between the EU SO-CHIC project and a UKRI Future Leaders Fellowship. It is partly intended to be used as a set of observational constraints for a Weddell Gyre region state estimate, although it can be used for more general analysis purposes as well. It has been used as part of an unsupervised clustering analysis [see Jones (2022) for software and Jones and Zhou (2022) for labelled dataset, see references]. </p> <p><strong>Overall spatial and temporal coverage</strong></p> <ul> <li>Latitude: 85°S-30°S</li> <li>Longitude: 65°W-80°E</li> <li>Time: 1974-2020</li> </ul> <p><strong>Contents</strong></p> <ul> <li>CPOM_SSH: sea-ice corrected sea surface height </li> <li>CTD: temperature and salinity profiles from ship-based CTD casts </li> <li>FLOATS: temperature and salinity profiles from Argo floats </li> <li>SEALS: temperature and salinity profiles from seal-mounted profilers</li> <li>Stress_and_EKE: sea-ice corrected surface stress and EKE </li> <li>XBT: temperature and salinity profiles from expendable bathythermographs (XBTs)</li> </ul> <p><strong>Profile quality control</strong></p> <p>We only consider profiles with good position and time flags, as well as good temperature, salinity, and pressure measurements with good flags. Duplicated profiles are identified when multiple profiles are found within 24 hours over the same 2 km x 2 km grid cell, and only one profile within the spatio-temporal window is used. We then used the MITprof toolbox (Forget, G., 2017) to pre-process the selected profiles, re-gridding them onto 72 standard pressure levels; the vertical interval varies from 20 dbar at the surface to 100 dbar in the deep ocean. </p> <p><strong>SSH processing</strong></p> <p>SSH data is sea-ice corrected version provided by the Centre for Polar Observation and Modelling (CPOM) in the UK. It is composed by two satellite missions, Envisat (2004/05-2012/03) and Cryosat-2 (2010/07-2020/04). The data is available in montly along-track format. A gaussian 300km filter, ±3 std outliner removal and 0.5x0.25 deg interpolation is applied to grid the data. Intersatellite offset is removed using the overlapped period between two missions using the mean difference map. SSH is referenced to EIGEN6C4 geoid to obtain the dynamic ocean topography feild for the computation of geostrophic velocity. See the README in the Stress_and_EKE directory for more information. </p> <p><strong>Sources</strong></p> <ul> <li>Argo floats: <a href="http://argo.ucsd.edu">http://argo.ucsd.edu</a></li> <li>World Ocean Database: <a href="https://www.ncei.noaa.gov/products/world-ocean-database">https://www.ncei.noaa.gov/products/world-ocean-database</a></li> <li>MEOP-CTD Database (seal profilers): <a href="https://www.meop.net/">https://www.meop.net/</a></li> <li>CDRv4 available via NSIDC: <a href="https://nsidc.org/data/G02202">https://nsidc.org/data/G02202</a></li> <li>Polar Pathfinder sea ice drift data via NSIDC: <a href="https://nsidc.org/data/nsidc-0116">https://nsidc.org/data/nsidc-0116</a></li> </ul> <p><strong>Version</strong></p> <p>This is a pre-production version, in that it has not yet been used with a state estimate. </p>
Data and simulation files for "Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations"
<p>In this repository, we provide data files in connection to our paper “Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations” accepted for publication in the Astrophysical Journals and soon available on Arxiv.</p> <p>In the publication, we perform a joint analysis of observations of five blazars with the Fermi Large Area Telescope (LAT) and the High Energy Stereoscopic System (H.E.S.S.) in order to search for signatures of a gamma-ray halo around these sources. The non-detection of such extended emission allows us to place lower limits on the intergalactic magnetic field (IGMF).</p> <p>In this repository, we provide our data analysis products of both H.E.S.S. and LAT data for the case when a template for the halo flux is <em>not</em> included in the data. Furthermore, we provide files that contain the log likelihood profiles as functions of the IGMF in case the halo emission <em>is</em> included. Lastly, we also provide our template files for the halo, generated with <a href="https://crpropa.github.io/CRPropa3/">CRPropa 3</a>.</p> <p>Below, we provide minimal code examples to demonstrate how to read in the specific files.</p> <p><strong>H.E.S.S. observational results</strong></p> <p>We provide the best-fit spectral parameters as well as the flux points (spectral energy distribution; SED) for the H.E.S.S. observations of the five blazars under consideration. The corresponding files are:</p> <ul> <li>hess_fit_result_*.fits which contain the best-fit parameters,</li> <li>hess_sed_file_*.fits which contain the flux points.</li> </ul> <p>In the file names above, the '*' should be replaced with a the corresponding source name, e.g. 1ES0229+200. The files can be read in using astropy:</p> <pre><code class="language-python">from astropy.table import Table src = "1ES0229+200" best_fit_pars = Table.read("hess_fit_result_1ES0229+200.fits") sed = Table.read("hess_sed_file_1ES0229+200.fits")</code></pre> <p><strong>Fermi observational results</strong></p> <p>For Fermi-LAT, we provide the SED files as well as the best-fit models for the region of interests. These files are called:</p> <ul> <li>fermi_avg_file_*.npy provides the best-fit ROI model</li> <li>fermi_sed_file_*.npy provides the SED.</li> </ul> <p>Both of these files are generated with <a href="https://fermipy.readthedocs.io/en/latest/">fermipy</a> and can be read-in the following way:</p> <pre><code class="language-python">import numpy as np # first a little helper function since the # fermipy analysis was run under python 2.7 def convert(data): if isinstance(data, bytes): return data.decode('ascii') if isinstance(data, dict): return dict(map(convert, data.items())) if isinstance(data, tuple): return map(convert, data) return data # Load the ROI fit roi_fit_file = "fermi_avg_file_1ES0229+200.npy" roi_fit = np.load(avg_file, allow_pickle=True, encoding="latin1").flat[0] # if you want to inspect the dictionaries in python 3, you need to run the convert function. # For example, to inspect the central source of the ROI # you would first get the source name src_fgl_name = roi_fit['config']['selection']['target'] # and then you can get the dictionary for the central source src_dict = convert(roi_fit['sources'])[src_fgl_name] # Load the SED sed_file = "fermi_sed_file_1ES0229+200.npy" sed = np.load(sed_file, allow_pickle=True, encoding='latin1').flat[0] # to plot the SED, you can use the SEDPlotter class from fermipy from fermipy.plotting import SEDPlotter SEDPlotter.plot_sed(sed)</code></pre> <p><strong>Likelihood profiles</strong></p> <p>The likelihood profiles as function of the IGMF strengths are provided in the files logl_profile_*_*yr.npz. Their are provided for all five sources and all tested blazar activity times of 10, 10<sup>4</sup>, and 10<sup>7</sup> years. They can be read in with the following code snippet:</p> <pre><code class="language-python">import numpy as np logl = dict(np.load("logl_profile_1ES0229+200_1.0e+07yr.npz")) b_fields = np.array([1.00000e-16, 3.16228e-16, 1.00000e-15, 3.16228e-15, 1.00000e-14, 3.16228e-14, 1.00000e-13]) for k, v in logl.items(): print(k,v)</code></pre> <p>As the print command shows, the python dictionary contains 3 entries: "fermi_only" are the likelihood values for the Fermi data as a function of magnetic field, "combined" are the likelihood values from Fermi and H.E.S.S. combined, and "ps" is the likelihood value of the Fit without halo to the H.E.S.S. data only.</p> <p><strong>Halo simulations</strong></p> <p>Lastly, we also provide the output simulations files from CRPropa. For details how the simulations were run, please consult the accompanying paper, in particular Section 3.1 and Appendix C. For each source redshift, a tar file is provided, which in itself contains 7 hdf5 files with the simulation outputs for each tested magnetic field strength. The name of the files is casc_file_z*.tar.gz. After unpacking the files, they can be read in with your favorite hdf5 library; in python you would need to install h5py. We recommend that you check out <a href="https://github.com/me-manu/simCRpropa">this github repository</a> which provides an advanced python wrapper for CRPropa and functions to read in the files. In particular, you can use <a href="https://github.com/me-manu/simCRpropa/blob/b3f39b5c77c6b97d19f7db387427d857690444d2/simCRpropa/cascmaps.py#L28">this function</a> to read in the files. It also writes a new hdf5 file with parallel transport applied. The written data is also returned together with the configuration dictionary.</p> <pre><code class="language-python">from simCRpropa.cascmaps import stack_results_lso data, config = stack_results_lso("casc_file_z0.140_B1.00e-16.hdf5", "casc_file_z0.140_B1.00e-16_theta_obs0.0.hdf5" )</code></pre> <p>You can provide arbitrary angles between the observer and the jet angles using the theta_obs keyword. Note, however, that the simulations used a jet opening angle of 3 degrees and going beyond that value will return zero halo photons.</p>
Observational constraints of fire, environmental and anthropogenic on pantropical tree cover - Data
<p>Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes.</p> <p>Land surface properties:</p> <ul> <li><strong>TreeCover </strong>- Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from <sup>1</sup>, regridded as per <sup>2</sup>.</li> <li><strong>urban </strong>cover from the History Database of the Global Environment, Version 3.1 (HYDE) <sup>3,4</sup></li> <li><strong>crop </strong>cover (from HYDE)</li> </ul> <ul> <li><strong>pas - Pasture </strong>Cover (from HYDE)<strong>PopDen </strong>(population density from HYDE)</li> <li><strong>BurntArea_xxxxx </strong>- Burnt area with xxxx denoting different products, provided by fireMIP <sup>5–7</sup>: <ul> <li>GFED_four: Global Fire Emissions Database, Version 4 (GFED4) <sup>8</sup></li> <li>GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) <sup>9</sup></li> <li>MCD_forty_five: MCD45 <sup>10</sup></li> <li>Meris: Fire_CCI4.0 <sup>11</sup></li> <li>MODIS: Fire_CCI5.1 <sup>12</sup></li> </ul> </li> </ul> <p>Climate:</p> <ul> <li><strong>MAP_xxx </strong>- Mean annual precipitation where xxx denotes data source: <ul> <li><strong>CMORPH </strong><sup>13,14</sup></li> <li><strong>CRU </strong>from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) <sup>15</sup></li> <li><strong>GPCC: </strong><sup>16</sup></li> <li><strong>MSWEP: </strong><sup>17</sup></li> </ul> </li> <li><strong>MAT </strong>- Mean annual temperature from CRU)</li> <li><strong>MConc_xxx </strong>– Mean annual concentration of rainfall as defined by <sup>18</sup>, where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MADD_xxx</strong>- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MDDM_xxx </strong>– Mean fractional dry days of the driest month.</li> <li><strong>MADM_xxx – </strong>Mean annual precipitation of the driest month<strong>.</strong></li> <li><strong>MTWM </strong>- Mean Maximum Temperature of the warmest month from CRU</li> <li><strong>MTCM </strong>- Mean minimum temperature of the coldest month from CRU</li> <li><strong>SW1 </strong>- direct downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>SW2 </strong>- diffuse downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>MaxWind </strong>(Mean Max Windspeed from CRU-(National Centers for Environmental Prediction <sup>15</sup></li> </ul> <p>‘output_summary’ contains framework output. There are several directories for different experiments, each containing a netcdf file. Along with standard latitude and longitude,each file contains ‘model_level_number’ dimension, with each layer representing the 1, 5, 10, 25, 50, 75, 90, 95 and 99% quantiles of the model posterior. The folder represents the experiment:</p> <ul> <li>Control – standard full model reconstruction</li> <li>noHumans – without human influence (from crop, pasture, population density or urban influence)</li> <li>noMortality – without disturbance stress (burnt area, wind, heat stress, rainfall seasonality</li> <li>noMAP – without mean annual precip influence.</li> <li>noNoneMAT – without mean annual temperature influence.</li> <li>noFire – tree cover without the influence of fire</li> <li>noDrought – without the influence of rainfall distribution</li> <li>noTasMort – without mortality from heat stress</li> <li>noWind – without influence from max. windspeed</li> <li>noPas – without exclusion from pasture</li> <li>noCrop – without exclusion from crop</li> <li>noPop – without reduction from population density</li> <li>noUrban – without exclusion from urban</li> <li>firePlus1pc – tree cover with burnt area was 1% higher.</li> </ul> <p> </p> <p><strong>References</strong></p> <p> </p> <p>1. Dimiceli, C. & Others. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). Preprint at (2015).</p> <p>2. Kelley, D. I. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9</strong>, 690–696 (2019).</p> <p>3. Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. <em>Glob. Ecol. Biogeogr.</em> <strong>20</strong>, 73–86 (2010).</p> <p>4. Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> vol. 109 117–161 Preprint at https://doi.org/10.1007/s10584-011-0153-2 (2011).</p> <p>5. Hantson, S., Arneth, A., Harrison, S. P. & Kelley, D. I. The status and challenge of global fire modelling. (2016).</p> <p>6. Hantson, S. <em>et al.</em> Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. <em>Geoscientific Model Development</em> vol. 13 3299–3318 Preprint at https://doi.org/10.5194/gmd-13-3299-2020 (2020).</p> <p>7. Rabin, S. S., Melton, J. R. & Lasslop, G. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. <em>Geoscientific Model</em> (2017).</p> <p>8. Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). <em>J. Geophys. Res. Biogeosci.</em> <strong>118</strong>, 317–328 (2013).</p> <p>9. van der Werf, G. R. <em>et al.</em> Global fire emissions estimates during 1997–2016. <em>Earth Syst. Sci. Data</em> <strong>9</strong>, 697–720 (2017).</p> <p>10. Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. <em>Remote Sensing of Environment</em> vol. 112 3690–3707 Preprint at https://doi.org/10.1016/j.rse.2008.05.013 (2008).</p> <p>11. Alonso-Canas, I. & Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. <em>Remote Sens. Environ.</em> <strong>163</strong>, 140–152 (2015).</p> <p>12. Chuvieco, E. <em>et al.</em> Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. <em>Earth System Science Data</em> vol. 10 2015–2031 Preprint at https://doi.org/10.5194/essd-10-2015-2018 (2018).</p> <p>13. Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xie, P. CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. <em>J. Hydrometeorol.</em> <strong>5</strong>, 487–503 (2004).</p> <p>14. Marthews, T. R., Blyth, E. M., Martínez-de la Torre, A. & Veldkamp, T. I. E. A global-scale evaluation of extreme event uncertainty in the eartH2Observe project. <em>Hydrol. Earth Syst. Sci.</em> <strong>24</strong>, 75–92 (2020).</p> <p>15. Harris, I. C. & Jones, P. D. CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). (2019) doi:10.5285/10D3E3640F004C578403419AAC167D82.</p> <p>16. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.5◦: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historical Data. <em>Deutscher Wetterdienst: Offenbach am Main, Germany</em> (2018).</p> <p>17. Beck, H. E., Van Dijk, A. & Levizzani, V. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. <em>Hydrol. Earth Syst. Sci.</em> (2017).</p> <p>18. Kelley, D. I., Harrison, S. P., Wang, H. & Simard, M. A comprehensive benchmarking system for evaluating global vegetation models. (2013).</p>
Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.
<p>Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload.</p> <p>Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output. </p> <p>Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.</p>
Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter : Weighted Monte Carlo samples for neutron star observables
<p>This data release contains weighted Monte Carlo samples associated with</p> <p>Legred, Chatziioannou, Essick, Han, and Landry, 2021</p> <p>"Impact of PSR J0740+6620 radius constraint on the properties of high-density matter"</p> <p>Phys. Rev. D 104, 063003;</p> <p>doi:10.1103/PhysRevD.104.063003</p> <p> </p> <p> </p> <p> </p> <p> </p>
Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"
<p>Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders:</p> <p>MethodA_proj/</p> <p>MethodB_proj/</p> <p>MethodC_proj/</p> <p>MethodD_proj/</p> <p>MethodE_proj/</p> <p> </p> <p>Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details):</p> <p>FUTUREverif/</p>
Tracer and Observationally Derived Constraints on Diapycnal Diffusivities in an Ocean State Estimate
<p>Data used to generate figures in Trossman et al. (2022) in Ocean Science</p>
Dataset for JGR Atmospheres manuscript " An Observational Constraint of VOC Emissions for Air Quality Modeling Study in the Pearl River Delta Region"
<p>The file includes hourly observations of ground-level ozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>) concentrations at 56 environmental monitoring stations in the Pearl River Delta (PRD) region in China during June 2018. The units are in μg/m<sup>3</sup>.</p>
Constraint-Induced Movement Therapy and Action Observation Training in Children With Unilateral Cerebral Palsy
ClinicalTrials.gov study NCT03256357. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
The Origin of Magnetofossil Coercivity Components: Constraints from Coupled Experimental Observations and Micromagnetic Calculations
Open the record for dataset details and reuse information.
Tracer and Observationally-Derived Constraints on Diapycnal Diffusivities in an Ocean State Estimate
<p>Data used to generate the figures in Trossman et al. (2022) in Ocean Science, an EGU journal</p>
ScienceDex guides
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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.