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247 results for “envelope”
Data for Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
<p>This dataset contains the experimental data used in the figures of the paper "Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes" by E. Hyyppä, A. Vepsäläinen, ..., and J. Heinsoo published in PRX Quantum 5, 030353 (2024): https://doi.org/10.1103/PRXQuantum.5.030353.</p> <p>The data is stored mostly as csv-files, the contents of which are explained in the readme-files. Each subfolder corresponds to one figure of the paper and also contains a Jupyter Notebook for plotting the data. The subfolders S1-S10 correspond to the supplementary figures, i.e., figures 6-15 in the Appendix of the paper.</p> <p>Furthermore, we provide a Jupyter notebook in the folder Code_to_plot_FAST_and_HD_DRAG_pulses/ that provides Python functions for evaluating and plotting the proposed FAST DRAG and HD DRAG pulses in time domain and frequency domain. Please cite our paper if you use the Python code for your published research.</p> <p>The notebooks have been tested using the following Python package versions<br>Python 3.11<br>scipy 1.14.1<br>numpy 2.1.0<br>matplotlib 3.9.2</p>
Data for "Globally widespread and increasing violations of environmental flow envelopes"
<p>Data and code for</p> <p><strong>Globally widespread and increasing violations of environmental flow envelopes</strong></p> <p>Vili Virkki*#, Elina Alanärä#, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu*</p> <p># equal contribution to the article<br> * Correspondence to: Vili Virkki (vili.virkki@aalto.fi), Matti Kummu (matti.kummu@aalto.fi)</p> <p><br> link to published version: https://hess.copernicus.org/articles/26/3315/2022/</p> <p><strong>Please cite the published version of the article when using these data.</strong></p> <p><strong>See readme.txt in data for a detailed description of attached files.</strong></p>
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve
<p>Dataset containing the recorded and simulated data reported in the manuscript "Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve" published in the Journal of the Association for Research in Otolaryngology, JARO (<a href="https://doi.org/10.1007/s10162-019-00721-7">https://doi.org/10.1007/s10162-019-00721-7</a>):</p> <ol> <li>RECORDED Envelope Following Responses (EFR) in normal-hearing (NH) threshold and hearing-impaired (HI) human listeners using deeply (m = 85%) and shallowly (m = 25%) modulated sinusoidally amplitude modulated (SAM) tones.</li> <li>SIMULATED EFRs using the auditory nerve (AN) model by Zilany et al. (2009, 2014).</li> </ol> <p>Files content and structure:</p> <p><strong>Recorded EFRs</strong></p> <p><strong>Fig. 2:</strong></p> <ul> <li><em>fig2__recorded_efr.csv</em>: <br> EFR recordings as a function of stimulus level (EFR magnitude-level functions) for the NH and HI listeners using two modulation depths.</li> </ul> <p>The file containing the recorded EFR data have the following columns:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>m85_ok: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Significant responses (F-test = 1)</li> <li><em>m85_ko: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Non-significant responses (F-test = 0)</li> <li><em>m85_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 85%</li> <li><em>m25_ok: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Significant responses (F-test = 1)</li> <li><em>m25_ko: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Non-significant responses (F-test = 0)</li> <li><em>m25_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 25% </li> <li><em>subj: </em>Listener id</li> <li>hearing: Hearing group (nh | hi) of the listener</li> </ul> <p><strong>Simulated EFRs:</strong></p> <p><strong><em>Files with the simulation results summing across frequency and SR fiber type</em></strong></p> <p><strong>Fig. 4:</strong></p> <ul> <li><em>fig4a__simul_efr__nh_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4a).</li> <li><em>fig4b__simul_efr__nh_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of OHC loss (Fig. 4b).</li> <li><em>fig4c__simul_efr__nh_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of IHC loss (Fig. 4c).</li> <li><em>fig4d__simul_efr__hi_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4e__simul_efr__hi_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of OHC loss (Fig. 4e).</li> <li><em>fig4f__simul_efr__hi_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of IHC loss (Fig. 4f).</li> <li><em>fig4g__simul_efr__hi_slp_thres_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming sloping threshold at EHF and 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4h__simul_efr__hi_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and all of OHC loss (Fig. 4e).</li> <li><em>fig4i__simul_efr__hi_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and and all of IHC loss (Fig. 4f).</li> </ul> <p> </p> <p><strong>Fig. 5:</strong></p> <ul> <li><em>fig5a__simul_efr__nh__cs_ms_ls_100p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming cochlear synaptopathy (CS) of a 100% of loss of only medium- and low-spontaneous rate (SR) AN fibers (Fig. 5a).</li> <li><em>fig5b__simul_efr__nh09_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the NH threshold listener NH09 including CS (Fig. 5b).</li> <li><em>fig5c__simul_efr__hi04_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the HI listener HI04 including CS (Fig. 5c).</li> </ul> <p> </p> <p><strong>Fig. 6:</strong></p> <p>Files with the simulation results for the NH threshold listener in different characteristic frequency (CF) bands and SR fiber types</p> <ul> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m12.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 12%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m25.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 25%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m50.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 50%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m85.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 85%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m100.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 100%.</li> </ul> <p> </p> <p><strong>Fig. 7:</strong></p> <ul> <li><em>fig7a__simul_efr__bw_analys__32oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 3/2-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7a).</li> <li><em>fig7b__simul_efr__bw_analys__1oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7b).</li> <li><em>fig7c__simul_efr__bw_analys__13oct_[20, 40, 60, 80, 100]p.csv</em>:<br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1/3-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7c).</li> </ul> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p>The structure of the .csv files that contain the EFR simulations is:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>mgn_mxxx: </em>Simulated EFR magnitude (a.u. in dB) for each modulation depth (100%, 85%, 50%, 25% and 12%)</li> <li><em>bkg_mxxx: </em>Estimate of the background noise floor (a.u. in dB) for each modulation depth. </li> <li><em>ftest_mxxx: </em>Result of the F-test statistical test (0 or 1) for each modulation depth.</li> </ul> <p> </p> <p>The structure of the .mat files that contain the EFR simulations is:</p> <ul> <li><em>exper_type</em>: Name of the simulated experiment</li> <li><em>species</em>: Species used in the AN model (in this study is always 2: human)</li> <li><em>species_age</em>: (Not used in this work). Age of the animal when species is 4: mouse</li> <li>modulation<em>: </em>Modulation depth of the SAM tone used in the simulation <em>(100%, 85%, 50%, 25% or 12%)</em></li> <li><em>f_on: </em>Center frequency of the on-frequency band (<em>2000 Hz</em>)</li> <li><em>f_off_hf: </em>Center frequency of the first off-frequency band (<em>3000 Hz</em>)</li> <li><em>f_off_vhf: </em>Center frequency of the second off-frequency band (<em>7000 Hz</em>) </li> <li><em>f_off_uvhf: </em>Center frequency of the third off-frequency band (<em>12000 Hz</em>)</li> <li><em>lvl_vect: </em>Stimulus level vector <em>(from 5 to 100 dB SPL, in steps of 5 dB)</em></li> <li><em>simul_efr </em> Structure with the simulated data <ul> <li>The data structure contains many fields which are matricies of size 3x20. The columns are the 20 stimulus levels defined in <em>lvl_vect</em>, and the first row is the simulated EFR, the second row is the estimates background noise floor in the simulation, and the third row is the output of the F-test statistics.</li> <li>The structure fields can be divided in 4 groups: <ul> <li><em>ihc_</em> Responses from the IHC (not shown in the paper)</li> <li><em>an_hs_</em> Responses from the High-SR fibers in the AN</li> <li><em>an_ms_</em> Responses from the Medium-SR fibers in the AN</li> <li><em>an_ls_</em> Responses from the Low-SR fibers in the AN</li> </ul> </li> <li>Each group has 5 responses corresponding to the on-frequency band (<em>_on</em>) and the three off-frequency bands (<em>_off_hf</em>, <em>_off_vhf</em>, <em>_off_uvhf</em>); and the sum across frequencies (<em>_across_f</em>)</li> </ul> </li> </ul>
Perceptions of green facades among residents of buildings with and without a greened envelope – Data from a household survey in Leipzig, Germany
<p>The data set stems from a survey of residents in two neighborhoods of Leipzig, Germany, and was implemented in April and May of 2022. The primary aim of the study was to better understand resident perceptions of green facades, including their (perceived) benefits as well as concerns. Additionally, residents were asked for a number of other perceptions, including heat stress, noise and air pollution. The sample includes both residents of buildings with and without an existing green facade.</p> <p>All variables included in this data publication are described in the codebook. The original German language wording of the survey questions can be found in the questionnaire enclosed with the data set. We include responses to all questions from the survey that were close-ended or had a numerical response. Open-ended questions were excluded from this publication for data privacy reasons. </p>
Two-bubble envelope approximation code and results
<p>This deposit contains the code required to carry out the envelope approximation simulations used in the paper with preprint title <em>Vacuum bubble collisions: from microphysics to gravitational waves</em> by Oliver Gould, Satumaaria Sukuvaara, and David Weir.</p> <p>The method and results are based on the following papers:</p> <ul> <li><em>Gravitational Wave Production by Collisions: More Bubbles</em> by Stephan J. Huber and Thomas Konstandin [<a href="https://arxiv.org/abs/0806.1828">arXiv:0806.1828</a>].</li> <li><em>Gravitational radiation from colliding vacuum bubbles: envelope approximation to many bubble collisions</em> by Arthur Kosowsky and Michael S. Turner [<a href="https://arxiv.org/abs/astro-ph/9211004">arXiv:astro-ph/9211004</a>].</li> <li><em>Gravitational radiation from colliding vacuum bubbles </em>by Arthur Kosowsky, Michael S. Turner and Richard Watkins<em> </em>[<a href="https://inspirehep.net/literature/324187">Inspire</a>].</li> </ul> <p>In the latter paper the envelope approximation is introduced in Appendix C.</p> <p>There are two files:</p> <ul> <li><strong>dweir-envelope-b2842c4827ad.zip</strong> is a snapshot of the <a href="https://bitbucket.org/dweir/envelope/">Bitbucket-hosted Git repository for the simulation code</a>, corresponding to <a href="https://bitbucket.org/dweir/envelope/src/v1.1.0/">the commit tagged <em>v1.1.0</em></a>. Later versions of the code may exist. The snapshot was generated by the Bitbucket service and does not include the Git metadata.</li> <li><strong>two-bubbles-envelope-data.tar.gz </strong>is an archive of the simulation results used to generate the envelope approximation results seen in the preprint, as well as numerical tests and some additional numerical explorations. While the results were generated with a slightly earlier version of the code than the tagged release in this deposit, they have been confirmed to be reproducible with the code provided.</li> </ul> <p>Both archives contain README.md files with further information.</p>
Coarse-grained molecular dynamics simulations of SARS-CoV-2 envelope protein E in the pentameric form
<p>The trajectories of coarse-grained (CG) molecular dynamics (MD) simulations of<br> 1) unmodified (FeigLab_NMR; FeigLab_PentamerNoPTM_POPC_Martini3b: 5 μs; 5 μs); <br> 2) palmitoylated (FeigLab_PentamerCYSP43; PentamerCYSP44_POPC_Martini3b: 5 μs; 5 μs); <br> SARS-CoV-2 E protein pentamer in a POPC bilayer.</p> <p>The trajectory of CG MD of system containing 2 pentamers in the membrane buckled in a single direction (BuckledMembrane_FeigLab_2xPentamerNoPTM_POPC_Martini3b: 1 μs).</p> <p>FeigLab_Pentamer: https://github.com/feiglab/sars-cov-2-proteins/blob/master/Membrane/E_protein.pdb<br> FeigLab_NMR_Pentamer is assembled based on the transmembrane domain determined by NMR (PDB ID: 7K3G) and FeigLab model for the rest.</p>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Identification of a lineage-specific protein network at the trypanosome nuclear envelope
<p>The nuclear envelope (NE) separates translation and transcription and is the location of multiple functions, including chromatin organization, nucleocytoplasmic transport, ribosomal maturation and mRNA processing/quality control. The molecular basis for many of these functions have diverged between different eukaryotic lineages. <em>Trypanosoma brucei</em>, a member of the early branching eukaryotic lineage Discoba, highlight many of these, including a distinct lamina and kinetochore composition. Here we describe a cohort of proteins interacting with both the lamina and NPC, which we term lamina-associated proteins (LAPs). LAPs represent a diverse group of proteins, including two candidate NPC-anchoring pore membrane proteins (POMs) with architecture conserved with <em>S. cerevisiae </em>Pom152 and <em>H. sapiens </em>Nup210, and additional peripheral components of the NPC. While many of the LAPs are specific to Trypanosomatids, we also identified broadly conserved proteins, indicating an amalgam of divergence and conservation within the NE proteome of trypanosomes, highlighting the diversity of nuclear biology across the eukaryotes and increasing our understanding of eukaryotic and NPC evolution.</p>
Human antimicrobial peptide inactivation mechanism of enveloped viruses
<p>This dataset provides the raw data supporting the paper: Human antimicrobial peptide inactivation mechanism of enveloped viruses. <a href="https://doi.org/10.1016/j.jcis.2023.11.055">https://doi.org/10.1016/j.jcis.2023.11.055</a></p><p>It comprises the infectivity data (Figure 1), DLS data (Figure 2 and Figures S1-5), cryo-TEM images (Figure 2), SAXS data (Figure 3), SANS data (Figure 3), and the Zeta-potential measurement (in text).</p><p>Setup and conditions for the experiments are described in the experimental section of the published (open access) manuscript.</p>
Multiple Nuclei HeLa cell ground truth images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.
<p>This is a data set that contains <strong>labelled HeLa cell images</strong>, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background. Similar ground truth have been published for this data set, but in this case, multiple nuclei have been labelled, whilst previous ones only focused on the central cell (https://doi.org/10.5281/zenodo.3874949)</p> <p>Details of the imaging, preparation and segmentation have been published in:</p> <ul> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> <strong>2019</strong>, <em>5</em>(9), 75; <a href="https://doi.org/10.3390/jimaging5090075">https://doi.org/10.3390/jimaging5090075</a></li> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, <strong>2020</strong>; <a href="https://doi.org/10.1371/journal.pone.0230605">https://doi.org/10.1371/journal.pone.0230605</a></li> <li> <p>Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells,<em> J. Imaging</em> <strong>2021</strong>, <em>7</em>(6), 93; <a href="https://doi.org/10.3390/jimaging7060093">https://doi.org/10.3390/jimaging7060093</a></p> </li> </ul> <ul> <li>The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.</li> </ul>
A gap in the double white dwarf separation distribution caused by the common-envelope evolution: astrometric evidence from Gaia
<p>Here we provide a supplementary dataset to our publication <em>A gap in the double white dwarf separation distribution caused by the common-envelope evolution: astrometric evidence from Gaia,</em> <a href="https://arxiv.org/abs/2203.03659">arXiv:2203.03659</a>. The dataset consists of 119 double white dwarf candidates selected in the Gaia Early Data Release 3 (EDR3) based on the sources' astrometric wobble amplitude. For each candidate we provide sky coordinates (RA, DEC), Gaia EDR3 ID, position on the HR diagram (G, BP-RP) and estimated astrometric wobble amplitude (delta a). Note that the astrometric wobble amplitude (delta a) is directly related to the binary’s orbital separation, as detailed in our paper.</p>
Data presented in Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.
<p>This folder contains datasets analyzed in the manuscript:</p> <p>Multi-trial analysis of HIV-1 envelope gp41-reactive antibodies among global recipients of candidate HIV-1 vaccines.</p> <p>Frontiers in Immunology<br> Sec. Vaccines and Molecular Therapeutics<br> doi: 10.3389/fimmu.2022.983313</p>
Dataset for "Imaging of Small-Scale Heterogeneity and Absorption Using Adjoint Envelope Tomography: Results from Laboratory Experiments"
<p>The codes for Monte-Carlo simulation, scripts used to calculate the misfit kernels, and the processed data of the laboratory experiment.</p>
On the use of envelope following responses to estimate peripheral level compression in the auditory system
<p>Dataset containing the envelope following responses (EFR) recordings related to the manuscript "Can envelope following responses be used to estimate level compression in the auditory system?".</p> <p>Files content and structure:</p> <p><strong>EFR:</strong></p> <ul> <li><em>1_data__efr_nh.csv</em>: EFR recordings from the normal-hearing (NH) listeners</li> <li><em>2_data__efr_hi.csv</em>: EFR recordings from the hearing-impaired (HI) listeners</li> <li><em>3_data__efr_nh_repeat.csv</em>: Repeated EFR recordings from the NH listeners</li> </ul> <p>The files regarding the EFR recordings contain:</p> <ul> <li><em>subject</em>: Listener id</li> <li><em>lvl_vect</em>: Stimulation level</li> <li><em>efr_magn_xxxx_hz: </em>EFR magnitude (dB re to 1 µV) for each of the simultaneously recorded frequencies (0.5, 1, 2 and 4 kHz).</li> <li><em>efr_bkg_xxxx_hz: </em>Magnitude (dB re to 1 µV) of an estimate of the background noise floor of the recording for each frequency. </li> <li><em>ftest_xxxx_hz: </em>Ftest (0 or 1) value for each frequency.</li> </ul> <p> </p> <p>Distortion-product otoacoustic emissions (DPOAE) were also recorded in the same listeners although the data was not included in the final manuscript. Details of the methodology used can be given on request.</p> <p><strong>DPOAE:</strong></p> <ul> <li><em>4_data__dpoae_nh.csv</em>: DPOAE recordings from the NH listeners.</li> </ul> <p>The files regarding the DPOAE recordings contain:</p> <ul> <li><em>subject</em>: Listener id</li> <li><em>lvl_vect</em>: Stimulation level</li> <li><em>dpoae_magn_xxxx_hz: </em>DPOAE magnitude (dB SPL) for each of the simultaneously recorded frequencies (0.5, 1, 2 and 4 kHz).</li> <li><em>dpoae_bkg_xxxx_hz: </em>Magnitude (dB SPL) of an estimate of the background noise floor of the recording for each frequency. </li> <li><em>signif_xxxx_hz: </em>Satisfaction of the significance criterion (0 or 1) for each frequency.</li> </ul> <p> </p>
Method to assess the functional role of noisy brain signals by mining envelope dynamics
<p>Preprocessed envelope EEG features based on a spatial filter approach. The features were computed across multiple within-trial SVIPT events for a large hyperparameter space on data of an exemplary subject.</p> <p>The file "components.bsv" contains the preprocessed envelope features of all investigated configurations and provides underlying parameters as well as a relative path for the key ``record_dir'' to additional component information. Specifically, for each configuration the spatial filter, spatial activity pattern and the time-resolved within-trial envelope signal is provided under "records/". </p>
The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</p>
Text-fig. 11. Scanning electron microscope (SEM) images of seeds assigned to the BEG group (a–d) and associated pollen grains (e–i); Torres Vedras locality, Portugal. a) Seed of Tomcatia taylorii showing the four horns formed by extensions of the envelope and the central projection of the envelope that surrounds to the micropylar tube; b, c) Seeds of Quadrispermum parvum in lateral (b) and apical (c) views showing the transverse ribs and the central projection of the envelope that surrounds the micropylar tube; d–f) Seeds of Ephedrispermum lusitanicum showing the four-angled seed envelope (d), the micropylar tube surrounded by the tissues of the integument (e), and ephedroid pollen grains on the seed surface (f); g) Apex of seed of Quadrispermum parvum showing simple in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 11. Scanning electron microscope (SEM) images of seeds assigned to the BEG group (a–d) and associated pollen grains (e–i); Torres Vedras locality, Portugal. a) Seed of Tomcatia taylorii showing the four horns formed by extensions of the envelope and the central projection of the envelope that surrounds to the micropylar tube; b, c) Seeds of Quadrispermum parvum in lateral (b) and apical (c) views showing the transverse ribs and the central projection of the envelope that surrounds the micropylar tube; d–f) Seeds of Ephedrispermum lusitanicum showing the four-angled seed envelope (d), the micropylar tube surrounded by the tissues of the integument (e), and ephedroid pollen grains on the seed surface (f); g) Apex of seed of Quadrispermum parvum showing simple
Wind envelope at 300 hPa in the Southern Hemisphere during austral summer between 1979-2020
<p>The datasets contain wind envelope data at 300 hPa from the Southern hemisphere during austral summer (December-March) between 1979-2020. The envelope was obtained using the methodology of Zimin et al 2003, retaining only the local zonal wavenumbers correspondant to the southern hemisphere transients (Trenberth 1981) using datasets of meridional wind speed at 300 hPa from ERA-5 and NCEP DOE 2 reanalysis .The resolution of both datasets is 2.5º x 2.5º with daily frequency. </p> <p>Both datasets contain a 4 dimensional array in which the first column is the day of the December-March season, such as the first day is the 1st of Decemeber and day 121 is the 31th of March. Second column indicates the season (from 1979-2020, starting at December), and the last two dimensions are the latitude and longitud coordinates.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.