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zenodo52/100

Supplement to: Electron energy partition across interplanetary shocks

Quick Summary: The three files herein comprise supplemental information and standalone datasets for a three-part study of Electron energy partition across interplanetary shocks that describe the modeling of solar wind electron velocity distribution functions (VDFs) near interplanetary shocks observed by the Wind spacecraft.  Part I of the study (published in the The Astrophysical Journal Supplement Series on July 3, 2019 doi:10.3847/1538-4365/ab22bd) describes the methodology and how the two ASCII files (i.e., those stored here) were created and their contents.  Part I also explains the nuances of the analysis, the limitations of the dataset, and how to use the data within the two ASCII files.  Parts II and III (in preparation) present the statistical results and the detailed analysis of these results in the context of the dependence on relevant interplanetary shock parameters.  Below are the descriptions of each data product starting with the PDF supplemental file to the three-part study and then the associated ASCII files.  First we provide some background/definitions of jargon and terms used in each. Solar Wind Electrons: The solar wind electron VDF below ~1 keV is comprised of cold, dense core (subscript c or ec) population with thermal energies typically in the ~5-15 eV range, a hot, tenuous halo (subscript h or eh) population with thermal energies typically >20-30 eV, and an anti-sunward, field-aligned beam called the strahl or beam/strahl (subscript b or eb) population with thermal energies typically ~few 10s of eV.  Most previous work modeled the core as a bi-Maxwellian and the halo and beam/strahl as bi-kappa VDFs.  The work described in Part I (and the PDF supplement stored here) show that the core is more accurately described by a self-similar model VDF, which reduces to a bi-Maxwellian under appropriate conditions/limits and deviation from Maxwellian quantifies inelasticity in the plasma collisions.  That is, if the plasma were controlled by elastic Coulomb particle-particle collisions (e.g., in the low corona or chromosphere or photosphere), the VDF would relax to a Maxwellian in the absence of other forces.  When the plasma particles undergo inelastic collisions, the VDF profile changes from a Gaussian to something more like a "flattop" or box-like shape. Wind Spacecraft: The Wind spacecraft (<a href="http://wind.nasa.gov">https://wind.nasa.gov</a>) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun.  It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo.  The instruments used in this study and these datasets are the fluxgate magnetometer (MFI), the radio receivers (WAVES), ion Faraday cups (SWE), and the electron and ion electrostatic analyzers (3DP).  The MFI measures 3-vector Bo at ~11 samples per second (sps); the SWE measures reduced VDFs of the thermal proton and alpha-particle populations from which velocity moments are derived and used herein; WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density; and 3DP observes full 4π steradian VDFs of electrons and ions from a few eV to ~30 keV which provide both ion velocity moments and the electron VDFs modeled herein. PDF Supplement Description: The PDF document contains descriptions and definitions of relevant interplanetary shock parameters and shock analysis techniques used by the Harvard Smithsonian Center for Astrophysics' Wind shock database at <a href="https://www.cfa.harvard.edu/shocks/wi_data/">https://www.cfa.harvard.edu/shocks/wi_data/</a>.  It describes the details of the symbols/parameters used on the database website and their translation to plasma parameters or shock parameters.  The PDF also defines the shock normal finding techniques listed as two-four character inputs on the database website.  The PDF file lists the shocks analyzed and their relevant parameters in two tables, with the second listing the relevant critical Mach numbers.  Next the PDF provides some extra statistics of the analysis performed in the three-part study on Electron energy partition across interplanetary shocks in the form of histograms comparing differences for different selection criteria (e.g., low versus high Mach number shocks).  Finally, there are detailed descriptions and definitions of the model functions used to fit to the solar wind electron VDFs. Both ASCII files have detailed headers outlining and defining the parameters contained therein.  They also provide column headings where the labels/names of each are defined and/or described in the header.  The headers also provide links to the analysis software used to perform the model fits to the VDFs.  We will first describe the contents of the file labeled Wind_ip_shock_3dp_fit_constraints_electrons.txt (FCONSTS for brevity) and then the file labeled Wind_ip_shock_3dp_fit_results_electrons.txt (FRESULTS for brevity).  Below use the following definitions: <ul> <li>\(N_{s}\) = number density of species s [cm-3] (s = ec for core, eh for halo, eb for beam/strahl, p for proton, etc.)</li> <li>\(B_{o, j}\)= jth component (GSE coordinate basis) of quasi-static magnetic field vector [nT]</li> <li>\(V_{Ts, j}\) = jth component (relative to Bo) of thermal speed of species s [km/s] <ul> <li>\(V_{Ts,j} = \sqrt{{2 k_{B} T_{s,j} \over m_{s}}}\), where \(T_{s, j}\) is the jth component (relative to Bo) of the temperature of species s [eV]</li> </ul> </li> <li>\(V_{os, j}\) = jth component (relative to Bo) of drift speed of species s [km/s] in ion rest frame</li> <li>\(V_{s, j}\) = jth component (GSE coordinate basis) bulk velocity of species s [km/s] in spacecraft frame</li> <li>\(T_{s, tot} = {1 \over 3} (T_{s, \parallel} + 2 \ T_{s, \perp})\), where \(\parallel(\perp)\) is the parallel(perpendicular) component relative to Bo</li> <li>\(s_{es}\) = exponent for the symmetric self-similar model VDF of species s</li> <li>\(\kappa_{es}\) = kappa value for the bi-kappa VDF of species s</li> <li>\(p_{es}(q_{es})\) = parallel(perpendicular) exponent for the asymmetric self-similar model VDF of species s</li> <li>\(\chi_{s}^{2}\) = least chi-squared of fit to species s</li> <li>\(\phi_{sc}\) = spacecraft electric potential [eV]</li> <li>\(\delta R = \lvert 1 - Median(f^{data}/f^{model}) \rvert\) = excess median deviation of fit [%]</li> </ul> FCONSTS File Description: The FCONSTS file contains all the pertinent information used during the fit process for all VDFs that were analyzed including the fit results.  The columns are organized by electron component from core to halo to beam/strahl, in that order, sorted by the time stamp (UTC) of the observed VDF (very first column).  The first column in each set of electron component groups is a numerical indicator of the fit status for that component of the ith VDF.  This is followed by 30 columns consisting of 5 sets of 6 numbers.  Each model function has six fit parameters:  \(N_{s}\) [0], \(V_{Ts, \parallel}\) [1],  \(V_{Ts, \perp}\) [2],  \(V_{os, \parallel}\) [3],  \(V_{os, \perp}\) [4] (or \(p_{es}\) for asymmetric self-similar model VDF), and exponent of fit (i.e., \(s_{es}\), \(\kappa_{es}\), or \(q_{es}\)).  Thus, there are six columns for each of the following for each of the three components (i.e., 18 columns for each of the following in total):  initial guess values, returned fit values, lower limit constraints, upper limit constraints, and a logical value indicating whether the ith fit value sits on the lower (-1) or upper (+1) limit or neither (0).  These columns are followed by four more containing the number of iterations necessary to find the fit values, the least chi-squared value of the fit, the degrees of freedom in the fit process, and a two-letter designator of the model fit function used (defined in the ASCII file header). FRESULTS File Description: The FRESULTS file contains the fit results used in the three-part study.  Again, the first column starts each row with the time stamp (UTC) of the observed VDF.  In the following, all parameters listed with subscript j will correspond to three columns (one for each component) except the drift velocities which only have two for \(\parallel(\perp)\).  That is followed by:  \(N_{p}\) (SWE), \(N_{\alpha}\) (SWE), \(N_{i}\) (3DP), \(T_{p, j}\) (SWE), \(T_{\alpha, j}\) (SWE), \(T_{i, j}\) (3DP), \(B_{o, j}\) (MFI), \(V_{p, j}\) (SWE), \(V_{\alpha, j}\) (SWE), \(V_{i, j}\) (3DP), \(\phi_{sc}\) (multiple instruments), \(\delta R\) (3DP),  \(N_{ec}\) (fit), \(T_{ec, j}\) (fit), \(V_{oec, j}\) (fit), \(\kappa_{ec}\) (fit), \(s_{es}\) (fit), \(p_{es}\) (fit), \(q_{es}\) (fit), reduced \(\chi_{ec}^{2}\) (fit), core fit status, and repeats for the halo and beam/strahl fits.  The last four columns contain, in the following order, the total reduced chi-squared of the model fit of all components combined and fit flags (0 = worst, 10 = best) for each electron component.  Note that all possible exponents are provided for each component but only the one that is not set as a fill value corresponds to the functional form used to model that electron component (e.g., if \(s_{ec}\) is the only non-fill exponent for the core, then the core was modeled as a symmetric self-similar VDF).

opencc-by-4.0May 2019View details →

zenodo52/100

Ancient mitogenomes reveal the evolutionary history and biogeography of sloths

Supplementary Material for: Delsuc F., Kuch M., Gibb G.C., Karpinski E., Hackenberger D., Szpak P., Martínez J.G., Mead J.I., McDonald H.G., MacPhee R.D.E., Billet G., Hautier L., and Poinar H.N. (2019). Ancient mitogenomes reveal the evolutionary history and biogeography of sloths. Current Biology. doi:10.1016/j.cub.2019.05.043.   Delsuc-CurrBiol-2019_capture_baits.fasta: Sequence baits designed from living xenarthran mitogenomes and reconstructed ancestral sequences used to capture ancient sloth mitogenomes.   Delsuc-CurrBiol-2019_dataset.fasta: Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in fasta format. Delsuc-CurrBiol-2019_dataset.phylip: Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in phylip format. Delsuc-CurrBiol-2019_dataset_partitions.nex: Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in nexus format with partitions. Delsuc-CurrBiol-2019_FigS2_RAxML_MLtree_100BP_nexus_for_FigTree.tree: Maximum likelihood mitogenomic tree inferred under the best-fitting partitioned model using RAxML. Related to Figure 1. Maximum-likelihood bootstrap percentages are indicating at nodes (100 replicates). Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree. Delsuc-CurrBiol-2019_FigS3_IQ-TREE_MLtree_100BP_nexus_for_FigTree.tree: Maximum likelihood mitogenomic tree inferred under the best-fitting partitioned model using IQ-TREE. Related to Figure 1. Maximum-likelihood bootstrap percentages are indicating at nodes (100 replicates). Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree. Delsuc-CurrBiol-2019_FigS4_MrBayes_consensus_nexus_for_FigTree.tree: Bayesian consensus mitogenomic tree inferred under the best-fitting partitioned model using MrBayes. Related to Figure 1. Clade posterior probabilities (PP) are indicated at nodes. Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree.  Delsuc-CurrBiol-2019_FigS5_PhyloBayes_consensus_nexus_for_FigTree.tree: Bayesian consensus mitogenomic tree inferred under the CAT-GTR+G4 mixture model using PhyloBayes. Related to Figure 1. Clade posterior probabilities (PP) are indicated at nodes. Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree. Delsuc-CurrBiol-2019_FigS6_PhyloBayes_chronogram_nexus_for_FigTree.tree: Bayesian mitogenomic chronogram. Related to Figure 2. This chronogram was inferred under the CAT-GTR+G4 mixture model and an autocorrelated lognormal model of clock relaxation using PhyloBayes. Tree in nexus format viewable with FigTree. Delsuc-CurrBiol-2019_Megatherium_bone_extraction_protocol.pdf: Detailed protocol for Megatherium americanum MAPB4R 3965 bone sample preparation. Delsuc-CurrBiol-2019_ML_ancestral_reconstruction_MOL_constraint.pdf: Maximum likelihood ancestral character state reconstruction. Related to Figure 3. Maximum likelihood estimation of ancestral states for six dental characters from Varela et al. (2019) under the Mk model on the maximum likelihood topology obtained using the molecular topology as a backbone constraint.  Delsuc-CurrBiol-2019_ML_ancestral_reconstruction_MORPH_constraint.pdf: Maximum likelihood ancestral character state reconstruction. Related to Figure 3. Maximum likelihood estimation of ancestral states for six dental characters from Varela et al. (2019) under the Mk model on the maximum likelihood topology obtained using the same topological constraint that these authors used in their Bayesian phylogenetic reconstructions.  Delsuc-CurrBiol-2019_MP_ancestral_reconstruction_MOL_constraint.pdf: Maximum parsimony ancestral character state reconstruction. Related to Figure 3. Maximum parsimony estimation of ancestral states for six dental characters from Varela et al. obtained using the molecular topology as a backbone constraint. Delsuc-CurrBiol-2019_MP_ancestral_reconstruction_MORPHO_constraint.pdf: Maximum parsimony ancestral character state reconstruction. Related to Figure 3. Maximum parsimony estimation of ancestral states for six dental characters from Varela et al. (2019) on the maximum parsimony topology obtained using the same topological constraint that these authors used in their Bayesian phylogenetic reconstructions.  Delsuc-CurrBiol-2019_TableS1_PartitionFinder_RAxML_best_partition_scheme.txt: Detailed results of the PartitionFinder analysis for RAxML. Delsuc-CurrBiol-2019_TableS2_ModelFinder_IQ-TREE_best_partition_scheme.txt: Detailed results of the ModelFinder analysis for IQ-TREE. Delsuc-CurrBiol-2019_TableS3_PartitionFinder_MrBayes_best_partition_scheme.txt: Detailed results of the PartitionFinder analysis for MrBayes.

opencc-by-4.0Jun 2019View details →

zenodo52/100

Sub-micron aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured sub-micrometer aerosol particles with two scanning mobility particle spectrometers (SMPSs) between 11 and 400 nm (file name ACESPACE_submicron_aerosol_particle_size_distribution.csv) in 100 bins, and 11 and 181 nm in 77 bins - so no data entry in the remaining 23 bins - (ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). Particles in this size range are important for cloud formation because a sub-set of them can act as cloud condensation nuclei (CCN). The time series of the size distribution shows that the particle population over the Southern Ocean can be quite variable featuring three dominant modes: a new particle formation mode (11 – 30 nm); an Aitken mode (20 – 70 nm); and an accumulation mode (> 70 nm). Often a concentration minimum between the Aitken and accumulation mode can be observed. It is known as Hoppel minimum (Hoppel and Frick, 1990; 10.1016/0960-1686(90)90020-N). Typically, particles larger than this minimum act as CCN. The variability of the particle size spectrum is a result of particle sources and atmospheric processes. Sea spray generation adds larger particles likely with a peak in the mode around 200 nm. Trace gas emissions from microbial communities in the ocean, such as dimethylsulfide (DMS) will be oxidized to either sulphuric acid or methanesulfonic acid in the atmosphere which condense onto pre-existing particles, hence growing those. Sulphuric acid can also form new particles (new particle formation mode). Rain and snow will remove particles larger than the Hoppel minimum. The data set can be used to explore the variability of the particle size distribution in three different oceans around Antarctica (Indian, Pacific, Atlantic Oceans) and from Cape Town to Europe in relation to weather patterns, air mass trajectories, microbial activity etc. It is best used in combination with CCN data to explore the importance of particles for cloud formation. This data set cannot be used to unambiguously determine sources of particles over the southern ocean or to trace anthropogenic impact in the region. The data have been cleaned from the influence of the exhaust of the research vessel. We give five-minute average data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/64. To derive the total particle number concentration between 11 and 400 nm one has to integrate over the diameter range taking into account the normalization by dlog(dp). Temporal coverage is from December 20, 2016 to April 10, 2017. The file “ACESPACE_submicron_aerosol_particle_size_distribution.csv” covers the entire time period except between 9 and 14 January 2017 due to instrument issues. The file “ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv” contains data for the period between 9 and 14 January 2017 and can be used to fill the above gap. The second data file stems from another SMPS with a smaller differential mobility analyser, hence the smaller diameter coverage. Dataset contents The data set contains two files with the size distribution of sub-micrometer aerosol particles. The rows are indexed by the time stamp, which is the end of the 5-minutes averaging interval. The columns are the normalized concentrations of particles in the respective size bin. See the data abstract for details. <ul> <li>ACESPACE_submicron_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv, data file, comma-separated values</li> <li>ACESPACE_particle_diameter_bins.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values in a complete row denote missing values because of e.g., calibration periods, ship exhaust contamination, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This sub-micron aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jun 2019View details →

zenodo52/100

Equivalent black carbon aerosol measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured equivalent black carbon (eBC) with an aethalometer (model AE33, Magee Scientific) at a time resolution of one second during the Antarctic Circumnavigation Expedition (ACE). We report five-minute averaged data, cleaned from exhaust gas influence. Temporal coverage is from December 20, 2016 to April 10, 2017. The mass concentration of eBC, reported in ng m-3, reflects how far fossil fuel combustion or biomass burning contribute to the aerosol population over the Southern Ocean and between South Africa and Europe. Over the Southern Ocean there are no sources of eBC, except for ship emissions and (sub-)Antarctic station emissions, and hence an enhancement of eBC points towards long-range influence from Africa, Australia, New Zealand and South America. When plotted against latitude, eBC concentrations drop south of 60°S, indicating a more pristine environment. Elevated concentration around the equator are likely influenced by biomass burning in tropical Africa. Dataset contents <ul> <li>ACESPACE_equivalent_black_carbon_aerosol.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> The data file listed above contains five-minute averaged values of equivalent black carbon (eBC) measured during the Antarctic Circumnavigation Expedition. Timestamps are the end of the five-minute period over which the eBC values were averaged. Latitude and longitude are average values of the position of the measurement during the five-minute interval. NaN values of eBC denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure or signal noise levels exceeding 200 ng/m3. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This equivalent black carbon dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jun 2019View details →

zenodo52/100

Coarse mode aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured coarse mode aerosol particle size distributions with an aerodynamic particle sizer (APS, model TSI 3321) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). The diameter range is 0.7 to 19 µm. Particles in this size range are indicative of primary sea spray aerosol, biological particles and potentially long-range transported mineral dust. These particles are also important for cloud formation as they act as cloud condensation nuclei or ice nucleating particles, the latter especially in the case of biological particles and mineral dust. Typically the instrument reports data starting from particles with a diameter greater than 500 nm, however, particle number concentrations in the channels below 723 nm were overestimated, which is a common artefact with this instrument. The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from December 20, 2016 to April 10, 2017. We give five-minute averaged data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/32. To derive the total particle number concentration one has to integrate over the diameter range taking into account the normalization by dlog(dp). Dataset contents <ul> <li>ACESPACE_coarse_mode_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values in a complete row denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This coarse mode aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jun 2019View details →

zenodo52/100

Aerosol particle number concentration measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured aerosol particle number concentration with a condensation particle counter CPC model TSI 3022 at a time resolution of 10 seconds during the Antarctic Circumnavigation Expedition (ACE). We report five-minute averaged data cleaned from exhaust gas influence. The lower cut-off of the CPC is 7 nm. Temporal coverage of the dataset is from December 20, 2016 to April 10, 2017. The total particle number concentration reflects aerosol particles from a variety of sources and processes. The concentrations include for example sea spray aerosol, long-range transported particles, newly formed particles and others. The variability in the concentration reflects processes such as wet removal through precipitation, new particle formation or sea spray formation. Dataset contents <ul> <li>ACESPACE_aerosol_particle_concentration.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values of aerosol particle number concentration denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This aerosol particle number concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jun 2019View details →

zenodo52/100

Fine Fuel Moisture Code - ERA-Interim

The Fine Fuel Moisture Code (FFMC) is a numeric rating of the moisture content of litter and other cured fine fuels. This code is an indicator of the relative ease of ignition and the flammability of fine fuel. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.   Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md).  This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).  Details:  <ul> <li> File format: netcdf4  </li> <li> Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).  </li> <li> Longitude range: [-180, +180]  </li> <li> Latitude range: [-90, +90]  </li> <li> Temporal resolution: 1 day  </li> </ul> <ul> <li> Spatial resolution: 0.7 degrees (~80 Km)  </li> <li> Spatial coverage: Global  </li> <li> Time span: from 1980-01-01 to 2018-12-31  </li> </ul>

opencc-by-4.0Jun 2019View details →

zenodo52/100

Build Up Index - ERA-Interim

The Build Up Index (BUI) is a numeric rating of the total amount of fuel available for combustion. It combines the DMC and the DC. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.   Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md).  This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).  Details:  <ul> <li> File format: netcdf4  </li> <li> Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).  </li> <li> Longitude range: [-180, +180]  </li> <li> Latitude range: [-90, +90]  </li> <li> Temporal resolution: 1 day  </li> </ul> <ul> <li> Spatial resolution: 0.7 degrees (~80 Km)  </li> <li> Spatial coverage: Global  </li> <li> Time span: from 1980-01-01 to 2018-12-31  </li> </ul>

opencc-by-4.0Jun 2019View details →

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Initial Spread Index - ERA-Interim

The Initial Spread Index (ISI) is a numeric rating of the expected rate of fire spread. It combines the effects of wind and the FFMC on rate of spread without the influence of variable quantities of fuel. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.   Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md).  This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).  Details:  <ul> <li> File format: netcdf4  </li> <li> Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).  </li> <li> Longitude range: [-180, +180]  </li> <li> Latitude range: [-90, +90]  </li> <li> Temporal resolution: 1 day  </li> </ul> <ul> <li> Spatial resolution: 0.7 degrees (~80 Km)  </li> <li> Spatial coverage: Global  </li> <li> Time span: from 1980-01-01 to 2018-12-31  </li> </ul>

opencc-by-4.0Jun 2019View details →

zenodo52/100

Daily Severity Rating - ERA-Interim

The Daily Severity Rating (DSR) is a numeric rating of the difficulty of controlling fires. It is based on the Fire Weather Index but more accurately reflects the expected efforts required for fire suppression. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md).  This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). Details: <ul> <li> File format: netcdf4  </li> <li> Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).  </li> <li> Longitude range: [-180, +180]  </li> <li> Latitude range: [-90, +90]  </li> <li> Temporal resolution: 1 day  </li> <li> Spatial resolution: 0.7 degrees (~80 Km)  </li> <li> Spatial coverage: Global  </li> <li> Time span: from 1980-01-01 to 2018-12-31 </li> </ul>

opencc-by-4.0Jun 2019View details →

zenodo52/100

Duff Moisture Code - ERA-Interim

The Duff Moisture Code (DMC) is a numeric rating of the average moisture content of loosely compacted organic layers of moderate depth. This code gives an indication of fuel consumption in moderate duff layers and medium-size woody material. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.   Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md).  This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).  Details:  <ul> <li> File format: netcdf4  </li> <li> Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).  </li> <li> Longitude range: [-180, +180]  </li> <li> Latitude range: [-90, +90]  </li> <li> Temporal resolution: 1 day  </li> </ul> <ul> <li> Spatial resolution: 0.7 degrees (~80 Km)  </li> <li> Spatial coverage: Global  </li> <li> Time span: from 1980-01-01 to 2018-12-31  </li> </ul>

opencc-by-4.0Jun 2019View details →

zenodo52/100

Drought Code - ERA-Interim

The Drought Code (DC) is a numeric rating of the average moisture content of deep, compact organic layers. This code is a useful indicator of seasonal drought effects on forest fuels and the amount of smoldering in deep duff layers and large logs. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.   Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md).  This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).  Details:  <ul> <li> File format: netcdf4  </li> <li> Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326).  </li> <li> Longitude range: [-180, +180]  </li> <li> Latitude range: [-90, +90]  </li> <li> Temporal resolution: 1 day  </li> </ul> <ul> <li> Spatial resolution: 0.7 degrees (~80 Km)  </li> <li> Spatial coverage: Global  </li> <li> Time span: from 1980-01-01 to 2018-12-31  </li> </ul>

opencc-by-4.0Jun 2019View details →

zenodo52/100

Lab513/Landscape of Gene Expression Dataset

Datasets from the article: A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies. by Zoran S Marinkovic1,2,3, Clément Vulin1,4,5, Mislav Acman1,3, Xiaohu Song2, Jean Marc Di Meglio1, Ariel B. Lindner*,2,3, Pascal Hersen*,1 eLife 2019;8:e47951 DOI: <a href="https://doi.org/10.7554/eLife.47951">10.7554/eLife.47951</a> first versions on BioRxiv : <a href="https://www.biorxiv.org/content/10.1101/527846v2">https://www.biorxiv.org/content/10.1101/527846v2</a>

opencc-by-nc-sa-4.0Feb 2019View details →

zenodo52/100

IMRPhenomD_NRTidal_128s

Contains data for linear and quadratic parts of the ROQ. Parameter ranges are specified in the params.dat file. B_linear: EIM "B matrix" for the linear roq of the likelihood function B_quadratic: EIM "B matrix" for the quadratic roq of the likelihood function fnodes_linear: EIM frequency nodes for the linear roq of the likelihood function fnodes_quadratic: EIM frequency nodes for the quadratic roq of the likelihood function

opencc-by-4.0Jun 2019View details →

zenodo52/100

Data of European University Association (EUA) Open Access Survey 2017-2018

This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html">https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html</a>. The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available: <ul> <li>Questionnaire</li> <li>Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv</li> <li>Codebook: includes information on all the variables and their coding.</li> </ul>

opencc-by-4.0Jul 2019View details →

zenodo52/100

Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark

This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_X_(T)", where X denotes the case label and T refers to the temperature value. Each file "Case_X_(T).zip" contains two folders "Case_X_(T)_1" and "Case_X_(T)_2", wherein the test results from the two sensor layouts are stored.

opencc-by-4.0May 2019View details →

zenodo52/100

Fire Weather Index - ERA5 HRES

The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread.  This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (Hersbach et al., 2019), and replaces the homonymous indices based on ERA-Interim (Vitolo et al., 2019). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs.  The dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately on Zenodo.   Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). The caliver R package (Vitolo et al. 2017, 2018) contains useful functions to process this dataset.  Details:  <ul> <li>File format: netcdf4</li> <li>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326)</li> <li>Longitude range: [-180, +180]</li> <li>Latitude range: [-90, +90]</li> <li>Temporal resolution: 1 day (at 12 local noon)</li> <li>Spatial resolution: 0.28 degrees (~31 Km)</li> <li>Spatial coverage: Global</li> <li>Time span: from 1980-01-01 to 2019-06-30</li> <li>Stream: Deterministic forecasts</li> </ul>

opencc-by-4.0Jul 2019View details →

zenodo52/100

Database_Citizensurvey_KAP_HOMEEU.csv

Data Base of the Knowledge, Attitudes and Practices (KAP) Studies within the HOME_EU Project (www.home_eu.org), with Citizens of the European Union on Homelessness..

opencc-by-4.0Jul 2019View details →

zenodo52/100

PsPM-DoxMem1: SCR, ECG and respiration measurements in an RCT using doxycycline/placebo during delay fear conditioning and retention.

This dataset includes skin conductance response (SCR), electrocardiogram (ECG), respiration and electromyogramm (EMG, only relevant for retention phase) measurements. Also included are CS and US information, keypress responses and keypress response times for 78 healthy participants (40 males and 38 females aged 23.3+/-3.6 years) under either doxycycline or placebo during a classical (Pavlovian) discriminant delay fear conditioning task. CS were a red and a blue screen. US consisted of 0.5 s square electric pulses with participant-specific duration and 500 Hz frequency. SOA between the CS onset and US was 3.5 s. CS and US co-terminated. Before the fear conditioning task, participants completed an N-back task and several questionnaires. During the retention/extinction phase one week after learning and without drug, an auditory startle probe (ST) and no US was delivered 3.5 s after CS onset via headphones (100 dB, 50 ms duration with 2ms on- and offset ramp). In an immediately following re-learning phase, the ST was omitted and the CS reinforced with the same schedule as during acquisition. The ITI was randomly determined on each trial to be 7, 9, or 11 s.

opencc-by-4.0Dec 2018View details →

zenodo52/100

CMS 2011A Simulation | Pythia 6 QCD1800-inf | pT > 375 GeV | MOD HDF5 Format

Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.CX2X.J3KW">Simulated QCD 1800-\(\infty\) Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have \(p_T^\text{jet}>375\) GeV, where \(p_T^\text{jet}\) includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files. For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have \(|\eta^\text{jet}|<1.9\) to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets. The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>. For reference, the other corresponding datasets of simulated jets available on Zenodo are: <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-\(\infty\) GeV</a></li> </ul> There is an associated dataset of jets recorded by the CMS detector available on Zenodo: <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>

opencc-by-4.0Aug 2019View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record