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7,370 results for “supplement”
Gaussian polarizable-ion tight binding (Supplemental Data)
<p><strong>This is a collection of input and output files for the publication below.</strong></p> <p>Abstract:</p> <p>To interpret Ultrafast Dynamics experiments on large molecules, computer simulation is required due to the complex response to the laser field. We present a method capable of efficiently computing the static electronic response of large systems to external electric fields. This is achieved by extending the density-functional tight binding method to include larger basis sets and by multipole expansion of the charge density into electrostatically interacting Gaussian distributions. Polarizabilities for a range of hydrocarbon molecules are computed for a multipole expansion up to quadrupole order, giving excellent agreement with experimental values, with average errors similar to those from density functional theory, but at a small fraction of the cost. We apply the model in conjunction with the polarizable-point-dipoles model to estimate the internal fields in amorphous Poly(3-hexylthiophene-2,5-diyl).</p>
Image database to supplement "Paulus, F.M. et al. Pain empathy but not surprise in response to unexpected action explains arousal related pupil dilation." (VIPER database)
<p>This folder contains the 282 images of the "VIPER" database (visually-induced pain empathy repository) along with ratings of 24 independent raters. Details are described in the following publication:</p> <p>Paulus, F.M., Müller-Pinzler, L., Walper, D., Marx, S., Hamschmidt, L., Rademacher, L., Krach, S., Einhäuser, W. Pain empathy but not surprise in response to unexpected action explains arousal related pupil dilation.</p> <p>The material can be used for scientific purposes, provided this reference is appropriately cited. Please check the download site to get the up-to-date reference at the time of your publication.</p> <p> </p> <p>Conditions are identified by the filename of the image, which consists of the number of the scenario (1-83) and the condition identifier:<br> pain<br> neut(ral)<br> mism(atch)<br> tool<br> Note that the tool and the mismatch condition do not exist for all scenarios.</p> <p>The file ratings_viper.csv contains the ratings. Each image corresponds to a line, the columns are as follows:<br> Column 1: Filename of the image<br> Column 2: Scenario number<br> Column 3: condition<br> Columns 4 through 27: ratings of the 24 individuals (between 0 and 4, NaN if there was no rating recorded)</p> <p>The file thumbnail_viper.jpg provides an overview over all images in the database.</p> <p>For ease of download, the images are available as tar-archive (allImages_viper.tar) and as inidivual files.</p> <p> </p>
Data supplementing the article "Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13."
<p>These data supplement the article Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13.</p> <p>The data can be used freely for academic purposes, provided the aforementioned reference is appropriately cited.</p> <p>The following files are available for experiment 2 of the article:</p> <p>allData.mat</p> <p>Includes the datamatrix allData with the following columns:</p> <p>1) Line used for analysis in the article (0 - no, 1-yes).<br> Possible reasons for exclusion:<br> a. fixation duration smaller than 50ms or larger 1000ms<br> b. fixation adjacent to a blink (preceding or following)<br> c. fixation outside the image</p> <p>2) ID of observer (1-24)</p> <p>3) ID of condition (1:grayscale, 2: reduced luminance, 3: reduced contrast, 4: equalized luminance, 5: equalized contrast, 6: phasenoise)</p> <p>4) ID of image (48 unique numbers between 1 and 135)</p> <p>5) horizontal eye position</p> <p>6) vertical eye position</p> <p>7) fixation duration in ms</p> <p>8) value of empirical map generated from search condition of experiment 1 at fixated location</p> <p>9) value of empirical map generated from preference condition of experiment 1 at fixated location</p> <p>10) value of empirical map generated from memorization condition of experiment 1 at fixated location</p> <p>11) value of empirical map generated from joining memorization and preference condition of experiment 1 at fixated location</p> <p>12) value of empirical map generated from condition 1 at fixated location</p> <p>13) value of empirical map generated from condition 2 at fixated location</p> <p>14) value of empirical map generated from condition 3 at fixated location</p> <p>15) value of empirical map generated from condition 4 at fixated location</p> <p>16) value of empirical map generated from condition 5 at fixated location</p> <p>17) value of empirical map generated from condition 6 at fixated location</p> <p>18) value of empirical map generated from condition 1 at fixated location leaving out the current observer</p> <p>19) value of empirical map generated from condition 2 at fixated location leaving out the current observer</p> <p>20) value of empirical map generated from condition 3 at fixated location leaving out the current observer</p> <p>21) value of empirical map generated from condition 4 at fixated location leaving out the current observer</p> <p>22) value of empirical map generated from condition 5 at fixated location leaving out the current observer</p> <p>23) value of empirical map generated from condition 6 at fixated location leaving out the current observer</p> <p>24) luminance at fixation</p> <p>25) luminance contrast at fixation</p> <p>26) edge density at fixation</p> <p>27) eccentricity of fixation</p> <p> </p> <p>usedData.Rdata</p> <p>- for all lines that are used for analysis (allData(:,1)==1) a field in an R dataframe is created, which contains the following fields (for details, see description of matlab file above):</p> <p>obsNum: the ID of the observer (1-24)</p> <p>condNum: the ID of the condition (1-6)</p> <p>imgNum: the ID of the image (48 unique numbers between 1 and 135)</p> <p>fixDur: fixation duration</p> <p>LUM, LCG, ED, ECC: luminance, contrast, edge density and eccentricity at fixation</p> <p>empMapFromSearch, empMapFromPref, empMapFromMem, empMapFromJoint: values of empirical maps generated from data of experiment 1 (search, preference, memorization task as well as combination of the latter two) at fixation</p> <p>empMapFromC1 through empMapFromC6: value of empirical map generated from condition 1 through 6 at fixated location</p> <p>empMapFromC1loo through empMapFromC6loo - value of empirical map generated from condition 1 through 6 at fixated location leaving out the current observer</p> <p>x,y - coordinates of fixation</p> <p> </p> <p>modelsFigure7.R - computes all models for figure 7 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p>modelsFigure8.R - computes all models for figure 8 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p> </p>
Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.
<p>These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.</p> <p>Use is free for academic purposes, provided the aforementioned article is appropriately cited.</p> <p>The directory contains the following files</p> <p>stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist.</p> <p>ratings.mat contains the variables<br> arousal - mean arousal rating<br> valence - mean valence rating<br> valence2 - squared mean valence rating (after subtracting midpoint)<br> motivationalValue - mean motivation rating<br> motivaionalValue2 - squared mean motivation rating (after subtracting midpoint)</p> <p>All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive)</p> <p><br> Experiment 1</p> <p>fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN.</p> <p><br> boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively.</p> <p><br> figure2.m uses these data to computes figure 2 of the article from these data</p> <p><br> dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object.</p> <p><br> table1.R computes and prints the models for table 1</p> <p> </p> <p>Experiment 2</p> <p>fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial.</p> <p>boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally.</p> <p><br> figure3and4.m generates figures 3 and 4 of the article from these data files.</p> <p>dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively.</p> <p>table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results</p> <p>table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.</p> <p> </p>
Dataset supplementing Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Video S1–3, Model S1, 2 & Figure S1: Oxfordiana motturii gen. et sp. nov. supplemental information
<p><strong>Video S1.</strong> Animation of the isosurface-based false-coloured three-dimensional model for specimen BU 5265.1. Full 360-degree rotation on the x-axis followed by the y-axis. Scale bar = 0.5 cm [MOV format 13MB, AVI format 44MB; 1920x1080 px; 1:04 min]</p> <p><strong>Video S2.</strong> Animation of the isosurface-based false-coloured three-dimensional model for specimen BU 5265.2. Full 360-degree rotation on the x-axis followed by the y-axis. Scale bar = 0.5 cm. [MOV format 20MB, AVI format 44MB; 1920x1080 px; 1:04 min]</p> <p><strong>Video S3.</strong> Tomographic data set showing raw X-ray contrast data as produced by the I12 JEEP beamline at the Diamond Light Source for specimen BU 5265.1. Scale bar = 0.25 mm. [MOV format 42MB, AVI format 53MB; 1004x1002 px; 1:15 min]</p> <p><strong>Model S1.</strong> Zenodo hosted three-dimensional model file of BU 5265.1 isosurface-based false-coloured reconstruction. This model is saved as a ZIP compressed VAXML datasets. VAXML uses one or more STL files to define the geometry of objects that comprise the dataset, together with one VAXML file that provides metadata on the dataset as a whole, and specifies how the STL/PLY files are to be put together. Additionally, a native SPV file has been included for direct viewing in SPIERSview. We therefore recommend the free SPIERS software to view this model format (http://spiers-software.org/). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm. [ZIP/VAXML format 198.4 MB; SPV format 4MB]</p> <p><strong>Model S2.</strong> Zenodo hosted three-dimensional model file of BU 5265.2 isosurface-based false-coloured reconstruction. This model is saved as a ZIP compressed VAXML datasets. VAXML uses one or more STL files to define the geometry of objects that comprise the dataset, together with one VAXML file that provides metadata on the dataset as a whole, and specifies how the STL/PLY files are to be put together. Additionally, a native SPV file has been included for direct viewing in SPIERSview. We therefore recommend the free SPIERS software to view this model format (http://spiers-software.org/). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm. [ZIP/VAXML format 173.6 MB; SPV format 3MB]</p> <p><strong>Figure S1. </strong>High resolution volume rendered images from Drishti showing the anatomy of BU 5265 and BU 5266. A) 16 longitudinal virtual thin-sections through BU 5265.1 at 500 µm spacing; B) 40 transverse virtual thin-sections through BU 5265.1 at 500 µm spacing; C) 28 transverse virtual thin-sections through BU 5265.2 at 500 µm spacing; D) 38 transverse virtual thin-sections through BU 5265.3 at 500 µm spacing; E) Reconstruction of BU 5265.1 showing longitudinal view of whole specimen fragment; F) Reconstruction of thick section slide BU 52665.30<strong>, </strong>a: oblique view of slide looking from the outer surface toward the inside of the ovule near the apex, b: oblique view of the slide looking out from the inside of the ovule, c: top-down view of the slide looking towards the ovule base, d: 3D section virtually cut from the slide showing the orientation of integumentary layers. G) Reconstruction of thick section slide BU 52665.1, a: view of slide looking from the inner surface toward the outside of the ovule, b: view of the slide looking towards the inside of the ovule, c: longitudinal section through the virtual slide. H) Reconstruction of thick section slide BU 52665.38, a: outer anatomy seen looking to the ovule centre from the base, b: inner anatomy seen looking from the ovule centre towards the base, c: longitudinal section through the virtual slide. I) Reconstruction of thick section slide BU 52665.3, a: view of slide looking towards the ovule centre, b: view of slide looking from ovule centre towards the external surface, c: longitudinal section through the virtual slide. J) Reconstruction of thick section slide BU 52665.37, a: view of slide looking away from the ovule centre, b: view of slide looking towards the ovule centre, c: longitudinal section through the virtual slide. Notes: A–D) These images show the two phases of mineralization which have preserved the ovule anatomy, blue = carbonate (calcite), yellow = pyrite, each having differing x-ray attenuation characteristics; E–J) These images highlight just the pyrite within the ovule, in particular that which infills the cells. Scale bars sizes are indicated on the figure. [PNG format 76MB]</p>
Dataset supplementing Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006
<p>These data supplement the publication</p> <p>Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>Note that the image files cannot be provided on this site due to copyright restrictions.</p> <p>The dataset contains the following files:</p> <p>maps_01.mat - maps_72.mat:</p> <p>For each image the 6 maps used in the paper are contained, the maps of experiment 1 are labelled as in the paper (AWS, OOM, nOOM, PVL,UNI), AWS2 is the AWS map for the modified stimuli of experiments 2 and 3.</p> <p>exp?_fixations.mat contains all fixations of the respective experiment.</p> <p>For experiment 1, there are the variables xFix, yFix, durFix, which contain the x position, the y condition, and the fixation duration of each fixation. Dimensions are images x subjects x fixation number, where the first fixation is the 0th (initial) fixation. The variable condition (image x subject) contains the condition in which the respective image was shown to the subject. For the main analysis only the "0" condition was used, refer to the paper's appendix for the other conditions.</p> <p>For experiment 2 and 3, variables are called xFixByImage, yFixByImage, dFixByImage and the dimensions are subject x image x fixation number. In addition tFixByImage contains the start of the fixation relative to trial onset (negative for the 0th fixation).<br> In both cases, empty entries are filled with nans.</p> <p><br> computeROC.m is a helper function called by other functions.</p> <p><br> figure1.m through figure7.m reproduce the figures from the paper to exemplify data usage.</p> <p> </p>
Data supplementing article "Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution" under review at the Journal of Geophysical Research - Biogeoscience
<p>These data supplement the article: Du, J. and J. Shen, Transport of riverine material from multiple rivers in the Chesapeake Bay: important control of estuarine circulation on the material distribution, under review at the Journal Of Geophysical Research: Biogeoscience</p> <p>contact: Jiabi Du, jiabi@vims.edu</p> <p>Below are descriptions of the data files included here:</p> <p>1. Monthly mean tracer output [1985-2014]</p> <p>-netCDF format results for monthly mean tracer concentrations from different sources (Susquehanna, Potomac, Rappahannock, York, James Rivers, and Coastal Ocean)</p> <p>-grid information are also included</p> <p>2. Matlab Scripts For Plotting.zip:</p> <p>-Matlab scripts used to plot the horizontal map, the vertical profile for the along channel section, the vertical profile for cross-channel sections. The script enables users to define the period and section no to plot. </p> <p>3. tracer influx and outflux ratio at 9 cross-section.xls:</p> <p>-an excel file contains the bottom tracer influx ratio and surface tracer outflux ratio for different rivers at different sections. </p>
Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.
<p>Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. <em>Annals of the New York Academy of Sciences. 1339, </em>138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited.</p> <p>Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026</p> <p> </p> <p>The following files are contained in this folder:</p> <p>dataExp1.mat contains the data of experiment 1</p> <p>The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right.</p> <p><br> dataExp2.mat contains the data of experiment 2</p> <p>Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively.</p> <p>Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation)</p> <p>Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period.</p> <p><br> figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper</p>
Dataset supplementing the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19.
<p>These files supplement the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19. The data are free for scientific use, provided this reference is appropriately cited.</p> <p>exp1_data.mat contains all the data of experiment 1 as cell arrays of size 8x16x8 (subject x block x trial) or 8x16 (subject x block). Specifically:<br> xEye: the horizontal eye position in raw (pixel coordinates)<br> gain: the OKN slow phase gain computed from the xEye data as described in the paper; in audio-visual blocks the sign is chosen such that positive gain corresponds to the direction of the grating associated with the low tone; in unambiguous visual blocks (1,16) positive sign corresponds to the direction of the grating.<br> ixLow, ixHigh, ixNone, ixBoth: indices for xEye and gain of the same subject and block for which the button corresponding to the low tone, the high tone, both buttons or no button was pressed.</p> <p>exp2_data.mat and exp3_data.mat contain the data of experiment 2 and experiment 3, respectively, and are organized analogously to exp1_data.mat.</p> <p>figure3.m through figure6.m use these data to plot the respective paper figures to exemplify usage of the data.</p> <p> </p>
Dataset supplementing B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)
<p>Dataset presented in B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)</p>
Dataset supplementing "Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11."
<p>These data supplement the publication</p> <p>Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11.</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>exp1_data.mat contains data of experiment 1</p> <p>exp2_data.mat contains data of experiment 2</p> <p>figure2_3.m and figure4_5.m exemplify usage of the data and reproduce the figures 2-5 of the aforementioned article.</p>
Supplemental dataset for Northern Spotted Owl (<i>Strix occidentalis caurina</i>) genome assembly version 1.0
<p><strong>StrOccCau_1.0_nuc.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the file that we deposited at DDBJ/ENA/GenBank as a Whole Genome Shotgun (WGS) project under accession NIFN00000000. It is is the file that you will most likely want to download if you would like to perform an alignment to this genome assembly. This file is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt and also without the contigs and scaffolds that we identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>StrOccCau_1.0_nuc_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked (hard-masked) assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt and also without the contigs and scaffolds that we identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>StrOccCau_1.0_mito.fa</strong> : This FASTA format file is the mitochondrial-genome-derived scaffold from the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_1.0.gff.bz2</strong> : This gff format file compressed this file with bzip2 contains the gene annotations of StrOccCau_1.0_nuc.fa.</p> <p><strong>StrOccCau_1.0_transcripts.fa.bz2</strong> : This FASTA format file compressed this file with bzip2 contains the sequences of the gene transcript sequences of the genes annotated in StrOccCau_1.0.gff.</p> <p><strong>StrOccCau_1.0_proteins.fa.bz2</strong> : This FASTA format file compressed this file with bzip2 contains the protein sequences of the genes annotated in StrOccCau_1.0.gff.</p> <p><strong>StrOccCau_1.0_RM_homology_includes_LowComplexity.out.bz2</strong> : This file provides the repeat annotations produced by the homology-based masking of StrOccCau_1.0_nuc.fa that included masking of low complexity regions and simple repeats. We compressed this file with bzip2.</p> <p><strong>StrOccCau_1.0_RM_DeNovo_includes_LowComplexity.out</strong> : This file provides the repeat annotations produced by the de novo masking (which followed after first performing homology-based masking) of StrOccCau_1.0_nuc.fa that included masking of low complexity regions and simple repeats.</p> <p><strong>StrOccCau_1.0_RM_homology_no_LowComplexity.out.bz2</strong> : This file provides the repeat annotations produced by the homology-based masking of StrOccCau_1.0_nuc.fa that did not include masking of low complexity regions and simple repeats. We compressed this file with bzip2.</p> <p><strong>StrOccCau_1.0_RM_DeNovo_no_LowComplexity.out</strong> : This file provides the repeat annotations produced by the de novo masking (which followed after first performing homology-based masking) of StrOccCau_1.0_nuc.fa that did not include masking of low complexity regions and simple repeats.</p> <p><strong>StrOccCau_1.0_alignments_of_light_associated_genes.txt</strong> : This file provides alignments of light-associated gene orthologs as well as assemblies of transcriptome sequences in NEXUS format.</p> <p><strong>StrOccCau_1.0_nuc_masked_SpottedBarredOwl_variant_file.vcf.bz2</strong> : This is a raw, unfiltered variant call format file compressed with bzip2 that was generated after aligning both spotted owl and barred owl short read data aligned to StrOccCau_1.0_nuc_masked.fa.</p> <p><strong>StrOccCau_0.1.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_0.1_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012).</p> <p><strong>StrOccCau_0.2.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt.</p> <p><strong>StrOccCau_0.2_masked.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the repeat-masked assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without any contigs and scaffolds less than 1,000 nt.</p> <p><strong>StrOccCau_GapCloser_output_NoContamNoMito.fa.bz2</strong> : This FASTA format file compressed with bzip2 is the assembly output from SOAPdenovo2 toolkit GapCloser version 1.12-r6 (Luo et al. 2012) without the contigs and scaffolds that we later identified either as the mitochondrial genome sequence or as contaminant sequences.</p> <p><strong>Citations</strong> - if you utilize these data, please include these citations:</p> <p>Hanna ZR., Henderson JB., Wall JD., Emerling CA., Fuchs J., Runckel C., Mindell DP., Bowie RCK., DeRisi JL., Dumbacher JP. 2017a. Supplemental dataset for Northern Spotted Owl (<em>Strix occidentalis caurina</em>) genome assembly version 1.0. <em>Zenodo</em>. DOI: 10.5281/zenodo.822859.</p> <p>Hanna ZR., Henderson JB., Wall JD., Emerling CA., Fuchs J., Runckel C., Mindell DP., Bowie RCK., DeRisi JL., Dumbacher JP. 2017b. Northern Spotted Owl (Strix occidentalis caurina) Genome: Divergence with the Barred Owl (<em>Strix varia</em>) and Characterization of Light-Associated Genes. <em>Genome Biology and Evolution</em> 9:2522–2545. DOI: 10.1093/gbe/evx158.</p>
Datasets and supplemental information accompanying the corneal meta-atlas
<p>This repository currently contains datasets and files needed for cPredictor: <a href="https://github.com/Arts-of-coding/cPredictor">https://github.com/Arts-of-coding/cPredictor</a>.</p> <p> </p> <p>Additionally, "cornea_v1_pnas_nexus.h5ad" contains the integrated and pre-processed single-cell object with raw counts only.</p>
GFSI-MRM Supplemental data and extended results
<p>Supplemental data and extended results associated with the article entitled '<strong>Ambitious food system interventions required to mitigate the risk of exceeding Earth’s environmental limits</strong>' (see Hadjikakou et al., 2025, <em>One Earth, </em><a href="https://doi.org/10.1016/j.oneear.2025.101351">https://doi.org/10.1016/j.oneear.2025.101351</a>).</p> <p>This repository contains the following files:</p> <ul> <li>Systematic search results and strings used to identify studies (<strong><em>Systematic_search_details.xlsx</em></strong>)</li> <li>A harmonised input database assembled from systematically selected studies (<strong><em>Harmonised_input_database.xlsx</em></strong>)</li> <li>Mapping of all on-ground actions in the literature to food system interventions (<strong><em>Action-intervention mapping.xlsx</em></strong>)</li> <li>Source data for key figures in the article and SI (<strong><em>Source data for figures.xlsx</em></strong>)</li> <li>Linear mixed model (LMM) predictions in physical units across all environmental indicators for all intervention combinations (<strong><em>Extended_results - LMM_indicator_predictions.zip</em></strong>)</li> <li>Risk estimates across all environmental limits for all intervention combinations (<strong><em>Extended_results - Risk_estimates_across_environmental_limits.zip</em></strong>)</li> </ul> <p>For all code, see the Global Food System Intervention Meta-Regression Model (<a href="https://github.com/MichalisHadjikakou/GFSI-MRM"><strong>GFSI-MRM</strong></a>). </p>
Supplement to "The virtual spot approach: a simple method for image U-Pb carbonate geochronology by high-repetition rate LA-ICP-MS" by Hoareau et al
<p>This repository contains supplementary data, excel datasets and python / R codes as supplement to the publication by Hoareau et al.</p> <p>Files are:</p> <ul> <li>pdf with Supplementary material (S1 to S6)</li> <li>Excel spreadsheet with all image ratio and CPS data (Table S7)</li> <li>Iolite4 python plugin for virtual spot calculation (Iolite4_plugin_virtual_spot.py)</li> <li>Jupyter .ipynb file for U-Pb age calculation (Multiage.ipynb, R language)</li> <li>Jupyter .ipynb file for cp-sat minimum rectangle problem calculation (Rects.ipynb, Python language)</li> </ul>
E-Supplement to the Relocation of the Seismicity in Central Asia using the Global RSTT model
<p><span>The e-Supplement contains the Ground Truth event list in the Central Asia region, as well as the relocated event bulletin in ISF2.0 format.</span></p>
Well-being at the Frontlines: Leadership Strategies for Building Up Strong Resources Among Emergency Responders: Online Supplement
<p>This is the online supplement for a mixed-methods article on resource-oriented leadership in emergency organizations.</p> <p><em>Abstract: </em>Repeated exposure to extreme events and other demanding missions can erode protective resources among emergency responders, causing health problems that, in turn, jeopardize their readiness and performance. Using a mixed-methods approach, this article examines how leaders of emergency responders can systematically maintain and even enhance their subordinates’ well-being. To identify effective leadership strategies, we conducted in-depth interviews with 25 fire chiefs, 25 platoon leaders, and 26 subordinates from the German fire services. Employing the critical incident technique, we explored leadership processes both during emergency situations and during interim phases (training, standby time, etc.). Person-oriented leadership (developing, group-serving, and caring behavior) was described as fostering various resource gains, while task-oriented leadership (directive and problem-solving behavior) was reported to mitigate resource threats. Moreover, the interviews underscore the importance of interim phases in enhancing well-being. A cross-sectional survey with firefighters and members of a technical relief organization (N = 459) supported the qualitative findings. We integrate our results into a framework that links leadership in emergency organizations to subordinates’ well-being and readiness in demanding work contexts. Moreover, we discuss the generalizability of our results to other high-risk work contexts. </p> <p>The study was approved by the ethics committee of the Faculty of Psychology & Sports Science of [institution anonymized for review].</p> <p><br>This online supplement includes</p> <ul> <li>STUDY1_GermanFireServices.pdf: Description of the organizational differences between volunteer and professional firefighters in Germany</li> <li>STUDY1_InterviewGuides.pdf: All interview guides used in Study 1 (English translations)</li> <li>STUDY1_CI-summaries.pdf: Short summaries of all critical incidents collected in Study 1</li> <li>STUDY1_AnalysisDetails.pdf: Supplementary material on the qualitative analysis of the interviews conducted in Study 1</li> <li>STUDY2_Codebook.xlsx: a codebook describing all instructions and items of Study 2</li> <li>rawData_Study2.csv: raw data of Study 2 (anonymised; the raw data contains only the information of persons who were included in the analysis and have agreed to it.)</li> <li>STUDY2_AnalysisScript.R: An analysis script based on R</li> <li>STUDY2_Supplemental-Analyses.pdf: supplemental analyses including comparisons between final sample and dropout, comparisons between subgroups, detailed descriptive analyses</li> </ul>
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
Supplemental dataset to "Cloud sync in response to wave-like large-scale forcings"
<p>This dataset deposits the supplemental materials for the manuscript "Cloud sync in response to wave-like large-scale forcings".</p> <p><a href="https://zenodo.org/api/records/14164798/draft/files/math_note.pdf/content" target="_blank" rel="noopener noreferrer">math_note.pdf</a> A hand-written math derivation note for equations in the appendices. </p> <p><a href="https://zenodo.org/uploads/15304862" target="_blank" rel="noopener noreferrer">movie_wL_006_T24hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=24 hours experiment (the reference CM1 simulation).</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T18hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T18hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=18 hours experiment.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T12hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T12hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=12 hours experiment.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_000.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_000.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio, without large-scale wave-like forcing.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/input_sounding/content" target="_blank" rel="noopener noreferrer">input_sounding</a> The initial sounding for all CM1 simulations. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/namelist.input/content" target="_blank" rel="noopener noreferrer">namelist.input</a> The namelist file for launching all CM1 simulations. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/postprocessing_CM1.zip/content" target="_blank" rel="noopener noreferrer">postprocessing_CM1.zip</a> The postprocessing code of the CM1 simulations, including intermediate output files (.mat) in data postprocessing. </p> <p><a href="https://zenodo.org/api/records/14164798/draft/files/microscopic_model.zip/content" target="_blank" rel="noopener noreferrer">microscopic_model.zip</a> The MATLAB code for the microscopic model. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/cm1.F/content" target="_blank" rel="noopener noreferrer">cm1.F</a> The CM1 script where the large-scale vertical velocity is programmed. You can copy it directly to your CM1/src/ path. </p> <p> </p> <p>Feel free to send an email to Dr. Hao Fu (haofu@nju.edu.cn) if you have any questions!</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.