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zenodo52/100

Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal

These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms". The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.

opencc-by-4.0Nov 2021View details →

zenodo52/100

Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels

Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS To accompany     Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and     Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change     in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of     Climate. doi: 10.1175/JCLI-D-17-0465.1. We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns. Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich. The format of the files is:      grid_sitenumber_region.txt where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska).  The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.

opencc-by-4.0Feb 2018View details →

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

A novel approach to the detection of unusual mitochondrial protein change suggests hypometabolism of ancestral simians: Supplemental Files

Supplementary Fig. S1: θevo calculated for each analyzed edge for specific OXPHOS complexes. Analyses were performed as in fig. 1F, except that SPCSs calculated from mtDNA-encoded protein positions in Complex I, Complex III, Complex IV, or Complex V were used to generate θevo values. Supplementary Fig. S2: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 2A, except that θevo values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides. Supplementary Fig. S3: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median confidence intervals). Analysis was performed as in (A) fig. 2B or (B) fig. 2C, except that θevo values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins. Supplementary Fig. S4: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 3A, except that θevo values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V subunits. Supplementary Fig. S5: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by lower 90% median confidence limit). Analysis was performed as in fig. 3B, except that θevo values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins. Supplementary Fig. S6: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by upper 90% median confidence limit). Analysis was performed as in fig. 3C, except that θevo values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides. --- Supplementary File 1: All predicted protein substitutions along all edges at positions containing less than 2% gaps across input and ancestral sequences are listed, along with associated taxonomy information, TSS, and branch length. All alignment positions refer to Bos taurus reference sequences. Supplementary File 2: The TSS calculated for each mitochondrial protein alignment position. All alignment positions refer to Bos taurus reference sequences. Supplementary File 3: SPCS and θevo outputs are provided for analyses across all mitochondria-encoded positions, as well as for focused analyses of specific OXPHOS complexes and individual proteins. Supplementary File 4: A GenBank flat file containing RefSeq entries for mammalian mtDNAs, as well as the entry for the reptile Anolis punctatus. Supplementary File 5: A maximum likelihood inferred tree generated by a RAxML-NG analysis of concatenated and aligned protein coding sequences from mammalian and Anolis punctatusmtDNAs. Supplementary File 6: Bootstrap replicates were generated from the alignment of concatenated protein coding sequences. Felsenstein’s Bootstrap Proportions (Felsenstein 1985) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs. Supplementary File 7: Bootstrap replicates were generated using concatenated mammalian mtDNA coding sequences. Transfer Bootstrap Expectations (Lemoine 2018) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs. Supplementary File 8: PAGAN tree output produced using aligned amino acid sequences and the rooted maximum likelihood inferred tree as input.

opencc-by-4.0Aug 2021View details →

zenodo52/100

Supplemental Information to Climate-driven habitat shifts of high-ranked prey species structure Late Upper Paleolithic hunting

The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal Scientific Reports. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x. Below is the legend for the Supplementary Information, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in Scientific Reports. Supplementary 1 – UpperPaleoDietV4.html – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions. Supplementary 2 – Support Information 2.docx – Word document containing supplementary tables 2 and 3. Supplementary 3 – ArchaeoloigcalDataset_v8.csv – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 4 – EuroUpperPaleoFaunas_v6.csv – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 5 – Lupo2016.csv – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1. Supplementary 6 – PushkinaRaia_FaunaWeights.csv – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1. Supplementary 7 – environmental_BG.csv – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1. For more information on the data, methods, and results, please see the main paper.

Find research datasets worth reusing

Dataset results

Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal

Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels

Supplement to: Electron energy partition across interplanetary shocks

A novel approach to the detection of unusual mitochondrial protein change suggests hypometabolism of ancestral simians: Supplemental Files

Supplemental Information to Climate-driven habitat shifts of high-ranked prey species structure Late Upper Paleolithic hunting

Data set supplementing "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study"

The Neanderthal Niche Space of Western Eurasia - Supplemental Material

The Human Niche Space of Post-LGM Late Upper Paleolithic Europe - Supplemental Material

Supplemental Information - Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling

Supplemental data for characterization of alpha and beta interactions using the HeXe setup [Eur. Phys. J. C 82, 361]

Supplement 2 for https://doi.org/10.5194/se-13-793-2022

Supplemented material to "Mycobacteriosis in various pet and wild birds from Germany: pathological findings, coinfections, and characterisation of causative Mycobacteria."

Supplemental Figures for "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction"

Supplemental Material to "Tenacity of Animal Disease Viruses on Wood Surfaces Relevant to Animal Husbandry"

Dataset supplementing Schütz, I. & Einhäuser, W. (2018) Visual awareness in binocular rivalry modulates induced pupil fluctuations.

Survey of participant experience in workshop for testing IGP software setup: supplemental dataset for SimAUD 2018

A Surface-Induced Asymmetric Program Promotes Tissue Colonization by a Human Pathogen - Supplemental data Fig 4A

Supplemental catalogs for "The Sloan Digital Sky Survey Reverberation Mapping Project: Sample Characterization"

Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland

Data supplement for "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century"

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