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7,877 results for “supplementary material”
Supplementary Material to article "Molecular Diversity of Mycobacterium avium subsp. paratuberculosis in Four Dairy Goat Herds from Thuringia (Germany)"
<p>These data (supplementary material) belong to the publication "Molecular Diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> in Four Dairy Goat Herds from Thuringia (Germany)". The study determined the diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> (MAP) isolated from four goat herds affected by paratuberculosis in Thuringia (Germany), as well as the detailed distribution of MAP genotypes among the animals and their environment in one herd (herd 1). A combination of three methods was used to genotype isolates from fecal samples of infected goats, from various intestinal and other tissues of clinically affected goats, and from environmental samples. The six MAP-C genotypes identified could be assigned to five different phylogenetic subgroups. The results suggest individual infection strains within each herd. In herd 1, one predominant strain was found, and two strains occurred sporadically. The identified genotypes were not goat specific.</p>
Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (supplementary material)
<p>Supplementary material for Sawi et al., 2023, <i>Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms </i>(The Seismic Record). Catalog of repeating earthquakes in sequences on a 10-km long segment of the San Andreas Fault in California from 1984-2019. </p><p> </p><p><strong>Catalog Header</strong></p><p>YR/MO/DY...........Date of event</p><p>HR/MN/SC...........Time of event</p><p>LAT/LON/DEP........Location of event</p><p>EX/EY/EZ...........Relative location uncertainty (in m)</p><p>MAG................NCSN magnitude</p><p>evID.................NCSN event ID</p><p>seqID................Repeating earthquake sequence ID</p><p>isRESp............Is quasi-periodic RES (bool)</p><p> </p><p><strong>References: </strong></p><p>Sawi T., Waldhauser F., Holtzman B. K., Groebner, N. (2023) Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms. The Seismic Record. </p><p>Waldhauser, F., and Schaff, D. P. (2021). A Comprehensive Search for Repeating Earthquakes in Northern California: Implications for Fault Creep, Slip Rates, Slip Partitioning, and Transient Stress. J Geophys Res B Solid Earth, 126(11), 1–22. <a href="https://doi.org/10.1029/2021JB022495">https://doi.org/10.1029/2021JB022495</a></p>
Microbial narrow-escape is facilitated by wall interactions: Simulation Supplementary material
<p>Simulation codes and simulation results for the paper "Microbial narrow-escape is facilitated by wall interactions".</p>
Updating Möller – Teil 2 (supplementary material)
<p>Supplementary material for blog post <a href="http://aku.hypotheses.org/412">Updating Möller – Teil 2</a>. It contains a CSV file with a concordance of sigla used in Möller, Georg. 1927. <em>Hieratische Paläographie. Die aegyptische Buchschrift in ihrer Entwicklung von der fünften Dynastie bis zur römischen Kaiserzeit: II. Von der Zeit Thutmosis’ III bis zum Ende der einundzwanzigsten Dynastie</em>. 2. Aufl. Leipzig: Hinrichs. The column names of the header are “Spalte”, “Sigle” and “Identifizierung“.</p>
Updating Möller – Teil 1 (supplementary material)
<p>Supplementary material for blog post <a href="https://aku.hypotheses.org/64">Updating Möller – Teil 1</a>. It contains a CSV file with a concordance of sigla used in Möller, Georg. 1927. Hieratische Paläographie. Die aegyptische Buchschrift in ihrer Entwicklung von der fünften Dynastie bis zur römischen Kaiserzeit I. Bis zum Beginn der Achtzehnten Dynastie. 2. Aufl. Leipzig: Hinrichs. The column names of the header are “Spalte”, “Sigle” and “Identifizierung“.</p> <p><strong>Version 1.2.0</strong><strong>:</strong> Updated sigla in column “Sinuhe”.</p>
Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations
<p><strong>Description of the Supplementary Data</strong></p> <p>This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “<i>A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations</i>,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor.</p> <p>This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code><strong>[CHANNEL]</strong></code> refers to the annihilation channel used, i.e. <i>e<sup>+</sup> e<sup>-</sup></i>, <i>μ<sup>+</sup> μ<sup>-</sup></i>, <i>τ<sup>+</sup> τ<sup>-</sup></i>, <i>b b̄</i>, <i>c c̄</i>, <i>t t̄</i>, <i>g g</i>, <i>W<sup>+</sup> W<sup>-</sup></i>, and <i>Z Z</i>. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files.</p> <ul> <li>One-dimensional limits on <i><σ v></i>. The files <code>oneD_frequentist_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass <i>m<sub>χ</sub></i> tabulated there. The files <code>oneD_Bayesian_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass <i>m<sub>χ</sub></i> tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of <i>m<sub>χ</sub></i> and <i><σ v></i>. The files <code>twoD_posterior_probability_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <i><σ v></i>) together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. The tabulated values of <i>m<sub>χ</sub></i> and <i><σ v></i> correspond to the centres of the respective bins in <i>m<sub>χ</sub></i> and <i><σ v></i> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> <p>Please contact the authors if you require different data or have any questions regarding this data set.</p>
Superposition for Lambda-Free Higher-Order Logic — Supplementary Material
<p>We provide the following supplementary material for our <a href="http://matryoshka.gforge.inria.fr/pubs/lfhosup_paper.pdf">paper</a>. The longer version of our paper can be found here: <a href="http://matryoshka.gforge.inria.fr/pubs/lfhosup_report.pdf">report</a>.</p> <p><strong>Compiling Zipperposition</strong></p> <p>Compilation instructions for Zipperposition, in particular instructions for compilation for <a href="https://www.starexec.org">StarExec</a>, can also be found in the <a href="https://github.com/c-cube/zipperposition/#starexec">Zipperposition readme</a>.</p> <p>In general, follow the following steps:</p> <ul> <li>Install <a href="https://opam.ocaml.org/doc/Install.html">OPAM</a></li> <li>Zipperposition requires OCaml >= 4.03.0, e.g. use <pre>opam switch 4.05.0+flambda eval `opam config env`</pre> </li> <li>You might have to install gmplib on your system, e.g. <pre>sudo apt-get install libgmp3-dev</pre> </li> <li>Zipperposition requires the following dependencies. <pre>opam install zarith containers sequence oasis msat menhir jbuilder</pre> </li> <li>Clone Zipperposition and compile it (we used commit 7fe2ebeb0500e350fd0f1dd4c4ad402ba9cf99b6): <pre>git clone https://github.com/c-cube/zipperposition.git --branch dev cd zipperposition make build</pre> This will create the binary zipperposition.native.</li> <li>Compile the applicative encoder if needed: <pre>jbuilder build ./src/tools/app_encode.exe</pre> This will create the binary ./_build/default/src/tools/app_encode.exe.</li> </ul> <p><strong>Problems</strong></p> <p>We used the following first-order (TFF) and the higher-order (THF) <a href="http://www.tptp.org/">TPTP (v7.0.0) problems</a> for the evaluation:<br> <a href="/record/3975512/files/list_TFF.txt">TFF problem list </a><br> <a href="/record/3975512/files/list_THF.txt">THF problem list </a></p> <p>The selection criteria for the problems are described in our paper. We used these scripts to create these problems lists and to generate an XML file to link these problems into a seperate space in Starexec: <a href="/record/3975512/files/starexec_xml.tar.gz"> Problem selection & StarExec XML generation scripts </a></p> <p><strong>Run scripts</strong></p> <p>We used the following run scripts on starexec. This archive also contains the Zipperposition binary and the applicative encoder binary, compiled for Starexec: <a href="/record/3975512/files/run_scripts.tar.gz"> StarExec run scripts </a></p> <p>The scripts use the following command-line options for Zipperposition:</p> <ul> <li>First-order mode: <pre>./zipperposition.native -i tptp -o tptp --no-ho --no-avatar --no-induction --no-unif-pattern --simultaneous-sup false --no-max-vars --no-fool</pre> </li> <li>Intensional purifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --ho-purify int --ho-no-ext-pos --ho-no-ext-neg --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> <li>Intensional nonpurifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --sup-at-vars --ho-no-ext-pos --ho-no-ext-neg --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> <li>Extensional purifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --ho-purify ext --ho-ext-axiom --restrict-hidden-sup-at-vars --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> <li>Extensional nonpurifying calculus: <pre>./zipperposition.native -i tptp -o tptp --no-avatar --ho --force-ho --no-ho-elim-pred-var --ho-general-ext-pos --no-ho-unif --no-induction --no-unif-pattern --simultaneous-sup false --sup-at-vars --restrict-hidden-sup-at-vars --ho-ext-axiom --ho-prim-enum none --no-max-vars --dont-select-ho-var-lits --no-fool</pre> </li> </ul> <p>Additionally, we provided the problem's filename as the first argument, the order (<code>--ord rpo6</code> or <code>--ord kbo</code>), the timeout (<code>--timeout 600</code>) and the memory limit (<code>--mem-limit 128000</code>). We used <code>--timeout 600</code> and Starexec's Wallclock timeout was set to 600s, but Starexec's CPU timeout was set to 300s. So effectively we allowed only 300s of calculation time.</p> <p>For the applicative encoder app_encode.exe, we provide the problem's filename as the first argument. The option <code>-o tptp</code> should be used to ensure that the output is also in TPTP format. To add the extensionality axiom to the applicative encoding, we use <code>--app-encode-extensional</code>.</p> <p><strong>Results</strong></p> <p>Download the raw output of the evaluation and the .csv files created by StarExec here: <a href="/record/3975512/files/output.tar.gz"> Evaluation output </a></p> <p>We used our own scripts to statistically analyse the results instead of using StarExec's statistics: <a href="/record/3975512/files/eval.tar.gz"> Evaluation scripts</a>. Set the location of the xml-file and the output files produced by StarExec in config.py and run stats.py to obtain the statistics displayed in the paper.</p> <p><strong>Examples</strong></p> <p>We tested the examples given in our paper in Zipperposition. Here are the problem files we used. Some are in TPTP format (.p) and some are in Zipperposition format (.zf).</p> <ul> <li><a href="/record/3975512/files/example_varcond.zf">Example "Justification for the weaker variable condition in the intensional variant" </a></li> <li><a href="/record/3975512/files/example_posext.zf">Example "Justification PosExt" (only in report) </a></li> <li><a href="/record/3975512/files/example_supatvars_rpo.zf">Example "Justification for purification and SUP inferences at variables" (only in report) </a></li> <li><a href="/record/3975512/files/example_add_equiv_defs.p">Example "Addition of Peano numbers" </a></li> <li><a href="/record/3975512/files/example_add_equiv_defs_app.p">Example "Addition of Peano numbers" (applicative encoding) </a></li> </ul>
Supplementary Materials: A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education
<p>Example data sets, syntax files and macros for the tutorials in: Balloo, K., & Winstone, N. E. (2021). A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education.<em> Frontline Learning Research</em>. <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">https://doi.org/10.14786/flr.v9i2</a><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">.675</a> </p> <p><strong>The data for all examples are fictional, and have only been designed to simulate the possible behaviour of institutional data for the purposes of demonstrating the analytical approaches in the primer. No inferences or conclusions should be drawn from the findings of these examples, because the results are not real. </strong></p> <p>We anticipate that readers can use the example data sets as templates and substitute in their own data.</p>
Supplementary Materials for "On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective"
<p>Supplementary materials with all used benchmark scripts, plot scripts, benchmark results and PIConGPU example data for the submission to "The 1st International Workshop on Data Reduction for Big Scientific Data (DRBSD-1)" held in conjunction with ISC 2017 in Frankfurt, Germany.</p>
Supplementary Materials for "Accelerating data sharing and re-use in volume electron microscopy"
<p>The deposition contains supporting materials for "Accelerating data sharing and re-use in volume electron microscopy" Comment</p> <ul> <li>Sample preparation protocol for cell monolayers optimized for serial block face scanning electron microscopy</li> <li>Supporting movies showing models of biological specimens imaged using volume electron microscopy</li> </ul>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
Supplementary material to: Long-term (bio)deterioration of Fe-containing and Fe-depleted sandstones: An experimental insight into biotic and abiotic interactions.
<p>This dataset includes: micorphotographs, scanning electron microscope images and related EDS spectra, thermal analysis (DSC-TG), grain size distribution. Abbreviations used in the supplementary file names refer to: GMB (growth medium inoculated with the bacteria, Pseudomonas fluorescens), GM (sterile growth medium), ARE (artificial root exudates), H2O (water), NR (Sample Nowa Ruda), Z (Sample Żerkowice ŻR).</p>
Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood – Supplementary material
<p>Supplementary material to the article "Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood"</p>
Supplementary material to: Bioweathering of Egyptian Nubian sandstone and Theban limestone: three months insight by experimental incubation
<p>This dataset includes: microscope images, scanning electron microscope images and related EDS spectra, thermal analysis (DSC-TG), grain size parameters, XRD data. Abbreviations used in the supplementary file names refer to: S3: Nubian sandstone sample S3, S6: Nubian sandstone sample S6, TL: Theban limestone sample, SEM afterEXP: scanning electron microscope observations made after termination of the experiment, SEM: scanning electron microscope, XRD: X-ray powder diffraction.</p>
HTSinfer: Supplementary materials
<p>This repository contains data used for the testing of <a href="https://github.com/zavolanlab/htsinfer">HTSinfer</a>, a Python-based tool to infer metadata directly from bulk RNA-sequencing data, generated on Illumina platforms. </p> <p>The repository includes the sample tables containing metadata from the 72 self-generated and publicly shared RNA-Seq samples, the 720 samples collected from the SRA database, and the aggregated tables, describing the results of the inference. </p> <p>For testing purposes, HTSinfer was installed from <a href="https://anaconda.org/bioconda/htsinfer">Bioconda</a> and executed with default settings, except for the <code>--records</code> parameter, which was adjusted in the case of the 720 RNA-Seq samples set. HTSinfer was tested with the <code>--records</code> argument set to different values (10 thousand, 100 thousand, 1 million, 10 million, and 0 – indicating all records) to evaluate performance and runtimes.</p> <p>A supplementary table comparing the functionality of HTSinfer with other available metadata inference tools is also available in the repository.</p>
Supplementary Materials for 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction'
<p><strong>Supplementary materials for the article 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction' in <em>Linguistic Typology </em>(Vol. and No. TBD).</strong></p> <p>Abstract:</p> <p>We provide a discussion of some of the challenges in using statistical methods to investigate the morphology-syntax distinction cross-linguistically. The paper is structured around three problems related to the morphology-syntax distinction; (i) the boundary strength problem; (ii) the composition problem; (iii) the architectural problem.<br> The boundary strength problem refers to the possibility that languages vary in terms of how distinct morphology and syntax are or the degree to which morphology is autonomous. The composition problem refers to the possibility that languages vary in terms of how they distinguish morphology and syntax: what types of properties distinguish the two systems. The architecture problem refers to the possibility that languages vary in terms of whether a global distinction between morphology and syntax is motivated at all and the possibility that languages might partition phenomena in different ways.<br> This paper is concerned with providing an overarching review of the methodological problems involved in addressing these three issues. We illustrate the problems using three statistical methods: correlation matrices, random forests with different choices for the dependent variable, and hierarchical clustering with validation techniques.</p> <p> </p> <p>Overview of materials:</p> <ul> <li>SM1: csv with the data</li> <li>SM2: code and pdf for generating the correlation matrices</li> <li>SM3: code and pdf for the random forest analyses</li> <li>SM4: code and pdf for the clustering and cluster validation analyses</li> </ul>
Supplementary materials to the paper: Automatic Parameters Tuning of Late Reverberation Algorithms for Audio Augmented Reality
<p>Supplementary materials to the paper:</p> <blockquote> <p>Riccardo Bona, Davide Fantini, Giorgio Presti, Marco Tiraboschi, Isaac Engel and Federico Avanzini. 2022. Automatic Parameters Tuning of Late Reverberation Algorithms for Audio Augmented Reality. In <em>Proceedings of International Conference on Audio Mostly</em>.</p> </blockquote> <p>The supplementary materials include the reverberated audio stimuli employed in the MUSHRA listening test reported in the paper. For each type of audio stimuli (Drums, Sax and Speech) the version reverberated with each of the six target Room Impulse Responses (RIRs) is provided along with the versions reverberated using the reverb matching method proposed in the paper (two different artificial reverberators have been considered: FDN and Freeverb).</p> <p>Further, the reverberation times (<span class="math-tex">\(T_{20}\)</span>) per octave band for each considered RIR are provided.</p>
Supplementary Material for Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics
<p>This dataset accompanies the article "Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics" (Lahnakoski & Bennett et al., submitted). </p> <p>It includes the Neo-Assyrian text corpus that is the basis for the word embeddings, a list of the Akkadian emotion and body words of interest for this study, and the scripts, toolboxes, and data used to generate the heat maps of the body.</p> <p>There is an additional folder containing the high resolution figures included in the article.</p> <p>A detailed ReadMe (README.txt) provides an overview of the folders.</p>
Introduction to bulk RNAseq analysis: supplementary material
<p><strong>Vampirium setup</strong></p><p>This archive contains materials (datasets, exercises and slides, etc) used for the Introduction to bulk RNAseq analysis workshop taught at the University of Copenhagen by the Center for Health Data Science (HeaDS). The course repo can be found on <a href="https://github.com/hds-sandbox/bulk_RNAseq_course">Github</a>:</p><p>Assignments.zip contains exercises for the preprocessing part of the course, like fastqc and multiqc examples of bulk RNAseq experiments</p><p>Data.zip contains count matrices (both traditional counts and salmon pseudocounts), as well as sample metadata (samplesheet.csv) and backup results from the preprocessing pipeline.</p><p>Notes.zip contains supplementary materials such as extra pdfs for more information on bulk RNAseq technology.</p><p>Slides.zip contains all the slides used in the workshop.</p><p>raw_reads.zip contains the raw reads from the bulk RNAseq experiment (<a href="https://doi.org/10.1016/j.celrep.2014.10.054">10.1016/j.celrep.2014.10.054</a>) used in this course.</p>
Supplementary material for 'Revealing patterns of nocturnal migration using the European weather radar network'
<p>This package contains data, filters and visualizations from <a href="https://doi.org/10.1111/ecog.04003">Nilsson and Dokter et al. (2019)</a>.</p> <p><strong>Files</strong></p> <p><strong>radar_metadata.csv</strong>: Metadata for the 84 European radars considered for this study. Includes radar code (<code>odim_code</code> = <code>country</code> + <code>odim_code_3char</code> and alternative radar code <code>vp_radar</code>), radar site location (<code>location</code>, <code>latitude</code>, <code>longitude</code>), radar site elevation (<code>site_altitude_asl</code> in meters above sea level) and radar altitude range used in this study (<code>min_height_cut_asl</code> and <code>max_height_cut_asl</code> in meters above sea level).</p> <p><strong>vp.zip</strong>: Vertical profiles of birds (vp) data, processed from the radar volume data following procedures described by Dokter et al. (2011), using the vol2bird algorithm in the R package bioRad. Zip file includes vp data for the 84 European radars considered for this study from September 19 to October 9, 2016 (21 days). This time period is characterized by strong passerine migration throughout Europe. Files are organized in radar (= <code>odim_code</code>), date and hour directories and follow the <a href="https://github.com/adokter/vol2bird/wiki/ODIM-bird-profile-format-specification">ODIM bird profile format specification</a>. Data can be read with the <a href="https://github.com/adokter/bioRad/">R package bioRad</a>.</p> <p><strong>vp_processing_settings.yaml</strong>: Data selection setting for this study, based on data quality criteria. File lists for each radar the altitudes to include (<code>include_heights</code>), time periods to exclude (<code>exclude_datetimes</code>) and reasons for exclusion (comments). 70 of the 84 radars were retained after filtering.</p> <p><strong>vp_processed_70_radars_20160919_20161009.csv</strong>: Processed vp data for 70 radars. Is the result of processing <code>vp.zip</code> with <code>vp_processing_settings.yaml</code> and <code>radar_metadata.csv</code> using <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Note: includes all timestamps: day and night & those marked for exclusion (marked in <code>exclusion_reason</code>). This data file forms the basis for analysis in the study.</p> <p>Headers are:</p> <ul> <li><code>radar_id</code>: odim_code of the radar</li> <li><code>datetime</code>: timestamp</li> <li><code>HGHT</code>: lower altitude of altitude bin (m above sea level)</li> <li><code>u</code>: bird ground speed towards east (m/s)</li> <li><code>v</code>: bird ground speed towards north (m/s)</li> <li><code>dens</code>: bird density (birds/km3)</li> <li><code>dd</code>: bird flight direction (degrees from north)</li> <li><code>ff</code>: bird ground speed (m/s)</li> <li><code>DBZH</code>: reflectivity factor (dBZ) in horizontal polarisation</li> <li><code>mtr</code>: migration traffic rate (birds/km/h)</li> <li><code>day_night</code>: timestamp occurs during <code>day</code> or <code>night</code> (based on sunrise/sunset)</li> <li><code>date_of_sunset</code>: date at sunset, with night timestamps between midnight and sunrise belonging to the previous date</li> <li><code>exclusion_reason</code>: reason timestamp is excluded in vp_processing_settings.yaml (if applicable). Excluded timestamps have <code>NA</code> values for <code>u</code>, <code>v</code>, <code>dens</code>, <code>dd</code>, <code>ff</code>, <code>DBZH</code>, and <code>mtr</code>.</li> </ul> <p><strong>vp_flowviz.csv:</strong> Input data for visualizations. Is the result of processing <code>vp_processed_70_radars_20160919_20161009.csv</code> using <code>vp-to-flowviz.Rmd</code> in <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Aggregates data in hourly bins for 200-2000m (<code>altitude_band</code> = 1) and above (<code>altitude_band</code> = 2). Only altitude band 1 is used in visualizations.</p> <p><strong>flowviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized with <a href="https://doi.org/10.5281/zenodo.57472">Bird migration flow visualization v2</a> (Desmet et al. 2016, Shamoun-Baranes et al. 2016). The visualization extrapolates the migration over the entire sampling range (cropped in the screencast due to technical limitations and thus excluding the Bulgarian radar), not taking topography or water bodies into account, and shows the ground speed (length of arrows) and direction of migration over time. Note that density is not shown: low density movements can therefore appear as strong as high density movements when ground speeds are similar.</p> <p><strong>cartoviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized as an interactive map with <a href="https://carto.com">CARTO</a>. Visualization shows migration density (size of circles) and mean direction (colour) over time. The interactive map is available at <a href="https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed">https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed</a>.</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.