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FIGURE 198. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 198. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Ventrifossa garmani. BSKU 101604, 41.8 mm HL. (A) View from above; (B) oblique view. [Photos: N. Nakayama]
FIGURE 195. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 195. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Trachonurus villosus. BSKU 43458, 79.7 mm HL. (A) View from above; (B) oblique view. [Photos: N. Nakayama]
FIGURE 162. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 162. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Malacocephalus nipponensis. BSKU 13317, 65.3 mm HL. (A) View from above; (B) oblique view. [Photos: N. Nakayama]
FIGURE 159. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 159. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Lucigadus nigromarginatus. BSKU 98982, 35.2 mm HL. (A) View from above; (B) oblique view. [Photos: N. Nakayama]
FIGURE 149. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 149. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Kumba japonica. NSMT-P 104114, 28.3 mm HL. (A) View from above; (B) oblique view. [Photos: NSMT]
FIGURE 184. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 184. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Pseudocetonurus sp. cf. septifer. ASIZP 61237, 33.2 mm HL. (A) View from above; (B) oblique view. [Photos: NSMT]
FIGURE 182. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 182. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Odontomacrurus murrayi. BSKU 104866, 30.0 mm HL. (A) View from above; (B) oblique view. [Photos: reproduced from Nakayama et al. (2015b: fig. 2) with permission from the Japanese Society of Systematic Zoology]
FIGURE 145. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 145. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Hymenocephalus striatissimus. BSKU 43410, 31.3 mm HL. (A) View from above; (B) oblique view. [Photos: NSMT]
FIGURE 140. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 140. Scanning electron micrographs showing a body scale (from the chest) of Hymenocephalus longibarbis. BSKU 113072, 42.6 mm HL. (A) View from above; (B) oblique view. [Photos: NSMT]
FIGURE 77. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 77. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Coelorinchus nox sp. nov. BSKU 109205, holotype, 99.2 mm HL. (A) View from above; (B) oblique view. [Photos: NSMT]
FIGURE 39. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 39. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Coelorinchus gilberti. NSMT-P 76191, 130 mm HL. (A) View from above; (B) oblique view. [Photos: N. Nakayama]
FIGURE 17. Scanning electron micrographs showing a in Grenadiers (Teleostei: Gadiformes: Macrouridae) of Japan and adjacent waters, a taxonomic monograph
FIGURE 17. Scanning electron micrographs showing a body scale (from the dorsum below the interdorsal space) of Cetonurus globiceps. USNM 76871, paratype of C. robstus, 56.6 mm HL. (A) View from above; (B) oblique view. [Photos: N. Nakayama]
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
Apertif drift scan based compound beam maps (1220-1520 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the second Apertif imaging survey frequency setting (1220-1520 MHz). Apertif imaging observations were take at these frequencies between January 2021 and February 2022. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>All CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <table> <caption>Notes on the individual CB data sets</caption> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td> <p>210205</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz)</p> </td> </tr> <tr> <td> <p>210402</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), no RTC and RTD, spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211103</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211118</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Apertif drift scan based compound beam maps (1130-1430 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the first Apertif imaging survey frequency setting (1130-1430 MHz). Apertif imaging observations were take at these frequencies between July 2019 and January 2021. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>Wit the exception of two datasets (191008 and 191023), all CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <p>Notes on the individual CB data sets:</p> <table> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td>190628</td> <td>Cyg A</td> <td>issue in channel 2 (1.31 GHz) with CBs 15-16,<br> issue in channels 5-6 (1.36 - 1.37 Ghz) and 8-9 (1.4 - 1.41 GHz), spline fit for CBs 01-07 failed for the per antenna maps</td> </tr> <tr> <td>190722</td> <td>Cyg A</td> <td>RFI in chan 6 (1.37 GHz) for CB 1</td> </tr> <tr> <td>190821</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190826</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190912</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190916</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>191008</td> <td>Cyg A</td> <td>CBs 14, 19, 20 and 26 are distorted, no per antenna maps</td> </tr> <tr> <td>191023</td> <td>Cyg A</td> <td>RFI in channel 6 ( 1.37 GHz) for CBs 0, 3-4, 9-10, 15-17, 22, no per antenna maps</td> </tr> <tr> <td>191120</td> <td>Cyg A</td> <td>RFI in channels 8-9 (1.4 - 1.41 GHz)</td> </tr> <tr> <td>200130</td> <td>Cyg A</td> <td>no RT5, RFI in channel 6 (1.37 GHz) for CBs 1, 6-8, 14, 21, 33</td> </tr> <tr> <td>200430</td> <td>Cyg A</td> <td>no RTC and RTD</td> </tr> <tr> <td>200710</td> <td>Cyg A</td> <td>no RTB, CBs 0, 20-39 are affected by RFI</td> </tr> <tr> <td>200819</td> <td>Cyg A</td> <td>issue with CBs 37-39, RFI in channel 6 (1.37 GHz), spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201009</td> <td>Cyg A</td> <td>no RTB, antenna 9 (RTB) files are empty in the antenna based maps</td> </tr> <tr> <td>201028</td> <td>Cas A</td> <td>spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201218</td> <td>Cyg A</td> <td> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Scan files, 3D reconstructions, data spreadsheet and supplementary files for Heterochrony and parallel evolution of echinoderm, hemichordate and cephalochordate internal bars
<p><span>Deuterostomes comprise three phyla with radically different body plans. Phylogenetic bracketing of the living deuterostome clades suggests the latest common ancestor of echinoderms, hemichordates and chordates was a bilaterally symmetrical worm with pharyngeal openings, with these characters lost in echinoderms. Early fossil echinoderms with pharyngeal openings have been described, but their interpretation is highly controversial. Here, we critically evaluate the evidence for pharyngeal structures (gill bars) in the extinct stylophoran echinoderms <em>Lagynocystis pyramidalis</em> and <em>Jaekelocarpus oklahomensis</em> using virtual models based on high-resolution X-ray tomography scans of three-dimensionally preserved fossil specimens. Multivariate analyses of the size, spacing and arrangement of the internal bars in these fossils indicate they are substantially more similar to gill bars in modern enteropneust hemichordates and cephalochordates than to other internal bar-like structures in fossil blastozoan echinoderms. The close similarity between the internal bars of the stylophorans <em>L. pyramidalis</em> and <em>J. oklahomensis</em> and the gill bars of extant chordates and hemichordates is strong evidence for their homology. Differences between these internal bars and bar-like elements of the respiratory systems in blastozoans suggest these structures might have arisen through parallel evolution across deuterostomes, perhaps underpinned by a common developmental genetic mechanism.</span></p>
Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities</em>”</strong> in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a><em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes.</p> <p>For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length.</p> <p>For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step.</p> <p>The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Applicability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 12-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 20-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 18-05-2021.zip</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>Dataset for alignment angle of 2.7 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>Both methods</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>DataProcessing.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines diffusion coefficient from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Waist.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines focus beam waist from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Angle.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines M-scan and B-scan flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Omnidirectional.py</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>This script determines flow profiles for arbitrary scan alignment.</p> </td> </tr> </tbody> </table>
Scanning electron microscope images of Dunaliela tertiolecta and Phaeodactylum tricornutum cultures and scanning electron microscope images and cryogenic electron microscope images of isolated small cellular particles from respective conditioned media
<p>Scanning electron microscope images of cultures of microalgae<em> Dunaliela</em><em> </em><em>tertiolecta</em><em> </em>and <em>Phaeodactylum</em><em> </em><em>tricornutum</em><em> </em>and scanning electron microscope images and cryogenic electron microscope images of isolated small cellular particles from respective conditioned media are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and Anna Romolo, University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics.</p> <p> </p> <p> </p>
Dataset for "In silico Positional Analogue Scanning with Amber GPU-TI"
<p>This repository contains the full data set and analysis scripts to reproduce all results for the manuscript "<a href="https://pubs.acs.org/doi/10.1021/acs.jcim.2c00860"><strong>In silico Positional Analogue Scanning with Amber GPU-TI</strong>, <em>J. Chem. Inf. Model.</em> 2022, 62, 18, 4448–4459</a>".</p> <p><a href="https://doi.org/10.1021/acs.jcim.2c00860">https://doi.org/10.1021/acs.jcim.2c00860</a></p> <p><br> The repository contains the following data:</p> <ul> <li><strong>20_PDB_66_MOL2_input_coordinates_for_PAS.tar.gz (7.7MB)</strong> <ul> <li>input structures of proteins (pdb format), ligands (mol2 format), experimental data and GPU-TI maps for all the 20 scans including Br-Scan, Cl-Scan, F-Scan, HO-Scan, MeO-Scan, Me-Scan, N-Scan.</li> </ul> </li> <li><strong>AMBER18.GPUTI.scripts.tar.gz (597.2 M)</strong> <ul> <li>Scripts and example of CDK8 TI output for GPU TI ddG calculation and cycle closure correlation.</li> </ul> </li> <li><strong>Supporting_Information_Tables_dG_ddG_small_big_change.xlsx (60kB)</strong></li> <li>AMBER18_input_fort files, parameter and topology files, first 500 ps equilibrated restart files, amber TI input files for each TI pair calculations (total 12.9 GB) <ul> <li><strong>AMBER-GPUTI_PAS_input.01.N-Scan.CDK8.tar.gz (736.2MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.02.N-Scan.Tankyrase.tar.gz (266.1 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.03.N-Scan.HCV_NS5B_gt1b.tar.gz (722.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.04.F-Scan.ox1r_antagonist.tar.gz (549.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.05.F-Scan.ox2r_antagonist.tar.gz (768.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.06.F-Scan.KAT6A.tar.gz (407.9 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.07.F-Scan.PDE1B.tar.gz (470.7 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.08.F-Scan.Akt1_kinase.tar.gz (390.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.09.Cl-Scan.PPAR_Gama.tar.gz (412.9 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.10.Cl-Scan.erk12.tar.gz (352.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.11.Cl-Scan.KAT6A.tar.gz (408.2 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.12.Br-Scan.PRMT4.tar.gz (522.1 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.13.Me-Scan.BD1_scaffold_thiophene.tar.gz (187.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.14.Me-Scan.BD1_scaffold_furan.tar.gz (187.3 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.15.Me-Scan.HIV-1.tar.gz (740.2 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.16.Me-Scan.PPAR_Gama.tar.gz (412.8 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.17.Me-Scan.avb6.tar.gz (2.2 GB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.18.MeO-Scan.KAT6A.tar.gz (409.3 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.19.MeO-Scan.ox2r_agonist.tar.gz (1.1 GB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.20.HO-Scan.ox2r_agonist.tar.gz (1.1 GB)</strong><br> </li> </ul> </li> <li><strong>Data Structures inside each files:</strong> <ul> <li><strong>directory_tree.20_PDB_66_MOL2_input_coordinates_for_PAS.txt (5 kB)</strong></li> <li><strong>directory_tree.AMBER18.GPUTI.scripts.txt (91 kB)</strong></li> <li><strong>directory_tree.AMBER18_input_for_PAS_GPUTI_simulations.txt (1.5 MB)</strong></li> </ul> </li> </ul>
IODP Expedition 352 Scanning electron microscope images
<p>Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.</p>
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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.