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649 results for “bundles”
Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles
<p>This is the supporting dataset of the publication "Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles".</p> <p><a href="https://doi.org/10.1021/acs.jpcb.4c04562">https://doi.org/10.1021/acs.jpcb.4c04562</a></p> <p>The description of the dataset can be found in the file README.txt</p>
Implementation of Frailty Care Bundle (FCB) for older people in acute care settings
<p>A study aimed to implement a Frailty Care Bundle (FCB) for orthopaedic trauma patients to increase mobilisation, nutrition and cognitive well-being in order to reduce hospital associated decline risk.</p>
Data bundle for "Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative <br> electron diffraction and X-ray spectroscopy, in the scanning electron microscope' <br> https://doi.org/10.1016/j.ultramic.2020.112944</p> <p>The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.</p> <p>Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and <br> .mat results files are contained within. These are organised by Figure number in the paper.</p> <p>The provided results are divided into two major sections:<br> (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.<br> RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. <br> (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps. <br> </p>
Data bundle for "Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning' </p> <p>The raw data is given as 'yprime.h5' - this contains patterns and metadata in the Bruker-exported format.</p> <p>Scripts for dataset decomposition into latent factors are given in 'Scripts'.</p> <p>Our spherical analysis code is included in 'SphericalAngleDF'.</p> <p>Outputs of our analysis code are contained in 'Analysis'.</p> <p>Figures for the paper are included in 'Figures'.</p> <p> </p>
Reference data bundle for PacificBiosciences/HiFi-human-WGS-WDL
<p>Static input files to support alignment, variant calling, filtering, and annotation for human HiFi WGS using the GRCh38 reference.</p> <p>https://github.com/PacificBiosciences/HiFi-human-WGS-WDL</p> <p><code>hifi-wdl-resources-v3.1.0</code><br><code>├── GRCh38</code><br><code>│ ├── annotation</code><br><code>│ │ ├── GRCh38.oddRegions.bed.gz</code><br><code>│ │ ├── GRCh38.oddRegions.bed.gz.tbi</code><br><code>│ │ ├── GRCh38.repeats.bed.gz</code><br><code>│ │ ├── GRCh38.repeats.bed.gz.tbi</code><br><code>│ │ ├── GRCh38.segdups.bed.gz</code><br><code>│ │ ├── GRCh38.segdups.bed.gz.tbi</code><br><code>│ │ └── README.md</code><br><code>│ ├── ensembl.GRCh38.101.reformatted.gff3.gz</code><br><code>│ ├── human_GRCh38_no_alt_analysis_set.fasta</code><br><code>│ ├── human_GRCh38_no_alt_analysis_set.fasta.fai</code><br><code>│ ├── methbat</code><br><code>│ │ ├── cpgIslandExt.sorted.hg38.tsv</code><br><code>│ │ └── README.md</code><br><code>│ ├── pharmcat</code><br><code>│ │ ├── pharmcat_positions_2.15.4.vcf.bgz</code><br><code>│ │ ├── pharmcat_positions_2.15.4.vcf.bgz.csi</code><br><code>│ │ └── README.md</code><br><code>│ ├── README</code><br><code>│ ├── sawfish</code><br><code>│ │ ├── annotation_and_common_cnv.hg38.bed.gz</code><br><code>│ │ ├── annotation_and_common_cnv.hg38.bed.gz.tbi</code><br><code>│ │ ├── expected_cn.hg38.XX.bed</code><br><code>│ │ ├── expected_cn.hg38.XY.bed</code><br><code>│ │ └── README</code><br><code>│ ├── slivar_gnotate</code><br><code>│ │ ├── buildGnomad_v4</code><br><code>│ │ ├── CoLoRSdb.GRCh38.v1.2.0.deepvariant.glnexus.zip</code><br><code>│ │ ├── gnomad.hg38.v4.1.custom.v1.zip</code><br><code>│ │ └── README.md</code><br><code>│ ├── sv_pop_vcfs</code><br><code>│ │ ├── CoLoRSdb.GRCh38.v1.2.0.pbsv.jasmine.vcf.gz</code><br><code>│ │ ├── CoLoRSdb.GRCh38.v1.2.0.pbsv.jasmine.vcf.gz.tbi</code><br><code>│ │ ├── gnomad.v4.1.sv.sites.pass.vcf.gz</code><br><code>│ │ ├── gnomad.v4.1.sv.sites.pass.vcf.gz.tbi</code><br><code>│ │ └── README.md</code><br><code>│ └── trgt</code><br><code>│ ├── adotto_strchive_20250827.hg38.bed.gz</code><br><code>│ └── README.md</code><br><code>├── GRCh38.ref_map.v3p1p0.template.tsv</code><br><code>├── GRCh38.tertiary_map.v3p1p0.template.tsv</code><br><code>└── slivar</code><br><code> ├── clinvar_gene_desc.20250618T144412.txt</code><br><code> ├── get_lof_gnomadv4.sh</code><br><code> ├── lof.gnomadv4p1.lookup</code><br><code> ├── README</code><br><code> ├── README.lof.md</code><br><code> └── slivar-functions.v0.2.8.js</code></p> <p><code>9 directories, 40 files</code></p>
Reference data bundle for CoLoRSdb
<p>Static input files to support alignment, quality control, variant calling, and ancestry estimation using the GRCh38 and CHM13 references in the CoLoRSdb workflow.</p><p><a href="https://github.com/juniper-lake/CoLoRSdb">https://github.com/juniper-lake/CoLoRSdb</a></p><p><a href="https://colorsdb.org/">https://colorsdb.org/</a></p><p> </p><p>colorsdb_resources/</p><p>├── CHM13</p><p>│ ├── human_chm13v2.0_maskedY_rCRS.fasta</p><p>│ ├── human_chm13v2.0_maskedY_rCRS.fasta.fai</p><p>│ ├── human_chm13v2.0_maskedY_rCRS.trf.bed</p><p>│ ├── somalier.sites.chm13v2.T2T.vcf.gz</p><p>│ └── vcfparser.CHM13.ploidy.txt</p><p>└── GRCh38</p><p> ├── hificnv.cnv.excluded_regions.hg38.bed.gz</p><p> ├── hificnv.cnv.excluded_regions.hg38.bed.gz.tbi</p><p> ├── hificnv.female_expected_cn.hg38.bed</p><p> ├── hificnv.male_expected_cn.hg38.bed</p><p> ├── human_GRCh38_no_alt_analysis_set.fasta</p><p> ├── human_GRCh38_no_alt_analysis_set.fasta.fai</p><p> ├── human_GRCh38_no_alt_analysis_set.trf.bed</p><p> ├── peddy.GRCH38.sites</p><p> ├── peddy.GRCH38.sites.bin.gz</p><p> ├── somalier.sites.hg38.vcf.gz</p><p> ├── trgt.adotto_repeats.hg38.bed</p><p> ├── trgt.pathogenic_repeats.hg38.bed</p><p> ├── trgt.repeat_catalog.hg38.bed</p><p> └── vcfparser.GRCh38.ploidy.txt</p><p>2 directories, 19 files</p><p> </p>
IGV Bundle for Rhizophagus irregularis DAOM-197198
<p>Use these files to build your own genome browser for the "Rhiir3" <em>Rhizophagus irregularis</em> DAOM-197198 chromosome-scale genome assembly (PRJNA885267). Tracks available:</p> <p><strong>Gene annotation, based on Illumina and Nanopore RNA-Seq reads. </strong><br> Gene models were curated by excluding genes with InterPro domains related to transposable elements.<br> File: Rhiir3_PRJNA885267_genes.gff3</p> <p><strong>Repeat annotation. </strong><br> The repeat library was made using EDTA (Ou et al., 2019), and curated by excluding consensus sequences with InterPro domains of known cellular genes. Repeats were then masked using RepeatMasker (parameters -s -no_is -norna -nolow -div 40) (Smit et al., 2015). Unclassified repeats are grey-coloured and repeats classified into transposable elements categories are colour-coded: LINEs are blue, DNA transposons are pink and LTRs are green.<br> File: Rhiir3_PRJNA885267_repeats.gff3</p> <p><strong>Highly methylated CG sites, called via direct Nanopore genomic DNA sequencing of <em>R. irregularis </em>spores.<em> </em></strong><br> 161Gb of raw FAST5 files obtained from three R9.4.1 Nanopore flow cells were basecalled with Guppy5, producing 985,449 reads which were successfully processed by tombo (Stoiber et al., 2017) and used by DeepSignal2 (Ni et al., 2019) to extract CG motifs and to call 5mC modifications using a human model (model.dp2.CG.R9.4_1D.human_hx1.bn17_sn16.both_bilstm.b17_s16_epoch4.ckpt. Only CG sites with >80% 5mC are shown, and the track indicates methylation ratios measured as a fraction of 1 (0.80 to 1.00).<br> File: Rhiir3_PRJNA885267_high_meth_CG.bed</p> <p><strong>Index for CG methylation sites.</strong><br> File: Rhiir3_PRJNA885267_high_meth_CG.bed.idx</p> <p><strong>Nanopore RNA-Sequencing reads, poly(A)+ cDNA-PCR, from <em>R. irregularis</em> spores. </strong><br> Reads were trimmed of adapters and cleaned with seqclean to remove a percentage of undetermined bases, polyA tails, overall low complexity sequences and short terminal matches. Cleaned sequences were then mapped using minimap2 (options: -G max intron length=3000, -ax, map-ont).<br> File: Rhiir3_PRJNA885267_nano_cDNA.bam</p> <p><strong>Index for Nanopore RNA-Sequencing reads.</strong><br> File: Rhiir3_PRJNA885267_nano_cDNA.bam.bai</p> <p><strong>Small RNA loci.</strong><br> 70,956,710 small RNA-Seq reads from two replicates of oxidised and two replicates of column-purified spore RNA (Dallaire et al., 2021) were used to run ShortStack (Axtell, 2013) (parameters --dicermin 20 --dicermax 27 --foldsize 300 --pad 200 --mincov 10.0rpm --strand_cutoff 0.8 --mmap r).<br> File: Rhiir3_PRJNA885267_small_RNA_loci.gff3</p> <p><strong>Small RNA sequencing reads.</strong><br> Shortstack small RNA-Seq alignments, with multi-mappers randomly distributed.<br> File: Rhiir3_PRJNA885267_small_RNA.bam<br> <br> <strong>Index for small RNA sequencing reads.</strong><br> File: Rhiir3_PRJNA885267_small_RNA.bam.bai</p>
Tensile Properties of Flax Fibre Bundles with Graphene Oxide Coating
<p>In the current datasheet, authors report the effect of graphene oxide treatment on tensile behaviour of single flax fibre bundles. As graphene oxide is hydrophilic with many hydroxyl functional groups, it is expected to bond with technical fibres and increase the stress transfer in a flax yarn.</p> <p> Graphene oxide (GO) aqueous dispersion with 1.2 wt % is prepared based on the modified Hummer’s method. GO is physically adsorbed on fibres by immersion of flax yarns into the aqueous dispersion for 24 hr. Fibres are dried at 80 C for 2 hr followed by 48 hr at 60 C. To differentiate between the effect of GO treatment and the potential loss in the tensile strength and tensile stiffness of fibres, authors report the data in 4 subclasses:</p> <ul> <li>As received flax yarns (dried at 60 C for 48 hr): labelled ‘as received’</li> <li>Kept in deionised water for 30 min: tagged ’30 min’</li> <li>Placed in deionised water for 24 hr: marked ’24 hr’</li> <li>Flax fibres immersed in 1.2 wt % GO aqueous dispersion for 24 hr: labelled ‘GO’</li> </ul> <p>Tensile test of single natural fibres is a challenging measurement. This is mainly due to the hierarchical and nonhomogenous structure of single fibres and difficulty in their extraction. The test methods are not standard, and the final data is very scattered. As an alternative method, we report the tensile properties of flax fibre bundles based on the impregnated fibre bundle test (IFBT) [1].</p> <p>Materials and brief description of the methodology can be found in the datasheet under ‘method’ tab. Flax fibre bundles were extracted from AmpliTex 5009 flax fabrics kindly provided by Bcomp. The matrix was Epikote 828 LVEL epoxy resin with Dytek DCH-99 hardener.</p> <p>Impregnated fibre bundle tests were performed with Instron 5567 and 30 kN loadcell, with 120 mm gauge length and 4% min <sup>-1</sup> strain rate. The strain was measured by a 50 mm clip-on extensometer. The abrasive paper was placed without glue in between the testing clamps and the samples. All samples were stored one week before test in a controlled environment of RH 50 % and 25 C.</p> <p>In the current datasheet, authors report the effect of graphene oxide treatment on tensile behaviour of single flax fibre bundles. As graphene oxide is hydrophilic with many hydroxyl functional groups, it is expected to bond with technical fibres and increase the stress transfer in a flax yarn.</p> <p> Graphene oxide (GO) aqueous dispersion with 1.2 wt % is prepared based on the modified Hummer’s method. GO is physically adsorbed on fibres by immersion of flax yarns into the aqueous dispersion for 24 hr. Fibres are dried at 80 C for 2 hr followed by 48 hr at 60 C. To differentiate between the effect of GO treatment and the potential loss in the tensile strength and tensile stiffness of fibres, authors report the data in 4 subclasses:</p> <ul> <li>As received flax yarns (dried at 60 C for 48 hr): labelled ‘as received’</li> <li>Kept in deionised water for 30 min: tagged ’30 min’</li> <li>Placed in deionised water for 24 hr: marked ’24 hr’</li> <li>Flax fibres immersed in 1.2 wt % GO aqueous dispersion for 24 hr: labelled ‘GO’</li> </ul> <p>Tensile test of single natural fibres is a challenging measurement. This is mainly due to the hierarchical and nonhomogenous structure of single fibres and difficulty in their extraction. The test methods are not standard, and the final data is very scattered. As an alternative method, we report the tensile properties of flax fibre bundles based on the impregnated fibre bundle test (IFBT) [1].</p> <p>Materials and brief description of the methodology can be found in the datasheet under ‘method’ tab. Flax fibre bundles were extracted from AmpliTex 5009 flax fabrics kindly provided by Bcomp. The matrix was Epikote 828 LVEL epoxy resin with Dytek DCH-99 hardener.</p> <p>Impregnated fibre bundle tests were performed with Instron 5567 and 30 kN loadcell, with 120 mm gauge length and 4% min <sup>-1</sup> strain rate. The strain was measured by a 50 mm clip-on extensometer. The abrasive paper was placed without glue in between the testing clamps and the samples. All samples were stored one week before test in a controlled environment of RH 50 % and 25 C.</p>
Map of SESs/STs Bundles for Estonian agricultural land
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p><span>This map is the outcome of applying a bundles cookbook developed in SERENA based on soil threats (ST) SOC loss and erosion potential and soil ecosystem service (SES) biomass production. The resulting bundles are clusters of SESs/STs where the intra-cluster variability is lower than the inter-cluster variability in the mean values of the selected SESs/STs to identify the bundles. </span><span>The generated map of SESs/STs Bundles for Estonian agricultural land is at the resolution of 100m. The input data for the cookbook was the Map of soil organic carbon loss of mineral soils in Estonia ; Soil water erosion potential in agricultural soils modelled by USLE, and primary biomass production. </span></p>
SERENA EJP Soil: Bundles' cookbook exemplary datasets
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p>
Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"
<p>This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh.</p> <p>We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution).</p> <p>Matlab code Description:</p> <p>Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis;</p> <p>Untitled 2- the code for comparison between the experimental data and the proposed model;</p> <p>Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity;</p> <p>Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.</p>
Bundle Spectra from the SIMP Survey
<p>This is a .ZIP bundled version of the Zenodo dataset https://zenodo.org/deposit/135220/.</p> <p>This dataset contains all spectra published in the SIMP survey paper (http://adsabs.harvard.edu/abs/2016arXiv160706117R). Please reference it if you use any of the data.</p> <p>All spectra are provided in three formats : FITS files, ascii TXT files, and PNG previews.</p> <p>These data are part of the Montreal Spectral Library, located at https://jgagneastro.wordpress.com/the-montreal-spectral-library/</p>
Matrix multiplication software and results bundle for paper "Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library" for P^3MA submission
<p>This is the archive containing the matrix multiplication software and the results of the publication "<em>Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library</em>" submitted to the P^3MA workshop 2017.</p> <p><strong>The archive has the following content:</strong></p> <ul> <li>Source code for the (tiled) matrix multiplication in "src": <ul> <li>regular version in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-compatible-alpaka-0-1-0</li> <li>Commit: a63ba4810d6bfcca62c68dd57408af15028e78a3</li> </ul> </li> <li>forked version for XL in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-xl-workaround</li> <li>Commit: 1fee028eccb8cf7b677e8071233e08aa9f81846a</li> </ul> </li> </ul> </li> <li>The compiled binaries and the results of the tuning and scaling runs are in "runs" in sub folders for each type of run and architectures.</li> </ul>
FIGURE 7 in Bundles of Sperm: Structural Diversity in Scorpion Sperm Packages Illuminates Evolution of Insemination in an Ancient Lineage
FIGURE 7. Boxplot of Tukey HSD test illustrating three major length types (a–c) of single folded sperm packages in Scorpiones. Representatives of the three groups in boldface (see text).
Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle Dataset
<p>This data repository presents representative data of the data we use in the paper "Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle" on the study of the USAF-1951 resolution target measurements. It includes a raw dataset of 3,000 measurements used for the reconstruction given in Fig. 2e-h.</p> <p>Directory Structure<br>LED Repository: This repository contains images using an LED to identify the fiber core centers for the sampling of the measurements. </p> <p>Core reflection Directory: Features back-reflected core measurements captured when illuminating the laser core by core. This dataset is collected without the USAF target at the distal facet, providing baseline data for the core reflection so that it can be removed from the measurements digitally.</p> <p>Measurements Directory: Consists of the raw data for the measurements taken with the USAF target placed at a 700-micron distance. This directory is the core of our dataset, offering raw, unprocessed measurements crucial for the analysis presented in our paper.</p> <p>Contact_LED_transmission: Contains a contact transmission image of the resolution target, captured with the target placed at contact distance and illuminated by an LED from behind. This serves as a reference image for transmission (Fig.2h).<br>Data Usage</p> <p>For more information, refer to Weinberg, Gil, et al. "<a href="https://arxiv.org/abs/2402.00148">Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle</a>." arXiv preprint arXiv:2402.00148 (2024).</p>
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore. in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore.
TYNDP 2024 data bundle for PyPSA-Eur
<p>Bundled input dataset for the TYNDP 2024 scenarios for use in PyPSA-Eur. Originally published by ENTSO-E and ENTSOG under Creative Commons Attribution 4.0 International License. The original data files can be found here: <a href="https://2024.entsos-tyndp-scenarios.eu/download/">https://2024.entsos-tyndp-scenarios.eu/download/</a>.</p>
Sodium binding stabilizes the outward-open state of SERT by limiting bundle domain motions
<p>Measured distances, angles, RMSD, RMSF, vestibule diameters and principal components along with the structural representations in pymol pse files and the manuscript images. The measurements have a 1ns time resolution.</p> <p> </p> <p>DATA_sodium_stabilize_SERT.zip<br> ├── fig1<br> │ ├── fig1_v3.png<br> │ ├── occ_3ions_rmsf_TMH_fitted_250_500.xvg<br> │ ├── occ_Cl_rmsf_TMH_fitted_250_500.xvg<br> │ ├── occ_ionless_rmsf_TMH_fitted_250_500.xvg<br> │ ├── out_3ions_rmsf_TMH_fitted_250_500.xvg<br> │ ├── out_Cl_rmsf_TMH_fitted_250_500.xvg<br> │ └── out_ionles_rmsf_TMH_fitted_250_500.xvg<br> ├── fig2<br> │ ├── distances_n_angles_fig2.pse<br> │ ├── fig2_v2.png<br> │ ├── occ_apo_nosalt_3ions.rep1.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_3ions.rep1.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_3ions.rep4.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_3ions.rep4.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_3ions.rep5.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_3ions.rep5.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_Cl.rep4.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_Cl.rep4.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_Cl.rep5.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_Cl.rep5.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_ionless.rep4.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_ionless.rep4.TM1b-TM9up.dat<br> │ ├── occ_apo_nosalt_ionless.rep5.TM1b-TM8down.dat<br> │ ├── occ_apo_nosalt_ionless.rep5.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_3ions.rep4.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_3ions.rep4.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_3ions.rep5.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_3ions.rep5.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_Cl.rep4.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_Cl.rep4.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_Cl.rep5.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_Cl.rep5.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_ionless.rep4.TM1b-TM8down.dat<br> │ ├── out_apo_nosalt_ionless.rep4.TM1b-TM9up.dat<br> │ ├── out_apo_nosalt_ionless.rep5.TM1b-TM8down.dat<br> │ └── out_apo_nosalt_ionless.rep5.TM1b-TM9up.dat<br> ├── fig3<br> │ ├── fig3_v2.png<br> │ ├── occ_apo_nosalt_3ions.rep1.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep1.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_3ions.rep1.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep1.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_3ions.rep4.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep4.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_3ions.rep5.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep5.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep4.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep4.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_Cl.rep5.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep5.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM9-TM3-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM9-TM3-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep4.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep4.TM9-TM8-TM6a.dat<br> │ ├── occ_apo_nosalt_ionless.rep5.TM9-TM8-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep5.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep4.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep4.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_3ions.rep5.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep5.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep4.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep4.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_Cl.rep5.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep5.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM9-TM3-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM9-TM3-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep4.TM9-TM8-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep4.TM9-TM8-TM6a.dat<br> │ ├── out_apo_nosalt_ionless.rep5.TM9-TM8-TM1b.dat<br> │ └── out_apo_nosalt_ionless.rep5.TM9-TM8-TM6a.dat<br> ├── fig4<br> │ ├── cluster_centr_250_500_concat_bundle-fit_bundle-measure_in_fig4.pse<br> │ ├── fig4_v2.png<br> │ ├── occ_apo_nosalt_3ions.rep1.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep1.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_3ions.rep4.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep4.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_3ions.rep5.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_3ions.rep5.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_Cl.rep4.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep4.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_Cl.rep5.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_Cl.rep5.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_ionless.rep4.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep4.TM6a-TM6b.dat<br> │ ├── occ_apo_nosalt_ionless.rep5.TM1a-TM1b.dat<br> │ ├── occ_apo_nosalt_ionless.rep5.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep1.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep2.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep3.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_3ions.rep4.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep4.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_3ions.rep5.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_3ions.rep5.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep1.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep2.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep3.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_Cl.rep4.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep4.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_Cl.rep5.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_Cl.rep5.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep1.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep2.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep3.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_ionless.rep4.TM1a-TM1b.dat<br> │ ├── out_apo_nosalt_ionless.rep4.TM6a-TM6b.dat<br> │ ├── out_apo_nosalt_ionless.rep5.TM1a-TM1b.dat<br> │ └── out_apo_nosalt_ionless.rep5.TM6a-TM6b.dat<br> ├── fig5<br> │ ├── concat_0_500_dt_RMSF_bundle_fit_bundle_measure_colored.pse<br> │ ├── fig5_v3.png<br> │ ├── occ_apo_nosalt_3ions.concatenated_trajectories.rmsf_bundle_fitted_0_500_concat_protein.xvg<br> │ ├── occ_apo_nosalt_3ions.rep1.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_3ions.rep2.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_3ions.rep3.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_3ions.rep4.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_3ions.rep5.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_Cl.concatenated_trajectories.rmsf_bundle_fitted_0_500_concat_protein.xvg<br> │ ├── occ_apo_nosalt_Cl.rep1.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_Cl.rep2.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_Cl.rep3.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_Cl.rep4.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_Cl.rep5.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_ionless.concatenated_trajectories.rmsf_bundle_fitted_0_500_concat_protein.xvg<br> │ ├── occ_apo_nosalt_ionless.rep1.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_ionless.rep2.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_ionless.rep3.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_ionless.rep4.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── occ_apo_nosalt_ionless.rep5.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_3ions.concatenated_trajectories.rmsf_bundle_fitted_0_500_concat_protein.xvg<br> │ ├── out_apo_nosalt_3ions.rep1.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_3ions.rep2.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_3ions.rep3.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_3ions.rep4.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_3ions.rep5.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_Cl.concatenated_trajectories.rmsf_bundle_fitted_0_500_concat_protein.xvg<br> │ ├── out_apo_nosalt_Cl.rep1.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_Cl.rep2.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_Cl.rep3.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_Cl.rep4.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_Cl.rep5.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_ionless.concatenated_trajectories.rmsf_bundle_fitted_0_500_concat_protein.xvg<br> │ ├── out_apo_nosalt_ionless.rep1.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_ionless.rep2.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_ionless.rep3.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ ├── out_apo_nosalt_ionless.rep4.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> │ └── out_apo_nosalt_ionless.rep5.RMSD_fit-boundle_CA_measure-boundle_CA_preEQreference.xvg<br> ├── fig6<br> │ ├── fig6_v5.png<br> │ ├── occ_3ions_projected_scaffoldFIT_bundleMEASURE.xvg<br> │ ├── occ_Cl_projected_scaffoldFIT_bundleMEASURE.xvg<br> │ ├── occ_ionless_projected_scaffoldFIT_bundleMEASURE.xvg<br> │ ├── out_3ions_projected_scaffoldFIT_bundleMEASURE.xvg<br> │ ├── out_Cl_extreme_scaffoldFIT_bundleMEASURE1.pdb<br> │ ├── out_Cl_extreme_scaffoldFIT_bundleMEASURE2.pdb<br> │ ├── out_Cl_projected_scaffoldFIT_bundleMEASURE.xvg<br> │ ├── out_ionless_projected_scaffoldFIT_bundleMEASURE.xvg<br> │ └── scaffoldFIT_bundleMEASURE_global_covar_extrame1.pse<br> ├── fig7<br> │ ├── fig7_v2.png<br> │ ├── occ_apo_nosalt_3ions.rep1.radii_refitted.dat<br> │ ├── occ_apo_nosalt_3ions.rep2.radii_refitted.dat<br> │ ├── occ_apo_nosalt_3ions.rep3.radii_refitted.dat<br> │ ├── occ_apo_nosalt_3ions.rep4.radii_refitted.dat<br> │ ├── occ_apo_nosalt_3ions.rep5.radii_refitted.dat<br> │ ├── occ_apo_nosalt_Cl.rep1.radii_refitted.dat<br> │ ├── occ_apo_nosalt_Cl.rep2.radii_refitted.dat<br> │ ├── occ_apo_nosalt_Cl.rep3.radii_refitted.dat<br> │ ├── occ_apo_nosalt_Cl.rep4.radii_refitted.dat<br> │ ├── occ_apo_nosalt_Cl.rep5.radii_refitted.dat<br> │ ├── occ_apo_nosalt_ionless.rep1.radii_refitted.dat<br> │ ├── occ_apo_nosalt_ionless.rep2.radii_refitted.dat<br> │ ├── occ_apo_nosalt_ionless.rep3.radii_refitted.dat<br> │ ├── occ_apo_nosalt_ionless.rep4.radii_refitted.dat<br> │ ├── occ_apo_nosalt_ionless.rep5.radii_refitted.dat<br> │ ├── out_apo_nosalt_3ions.rep1.radii_refitted.dat<br> │ ├── out_apo_nosalt_3ions.rep2.radii_refitted.dat<br> │ ├── out_apo_nosalt_3ions.rep3.radii_refitted.dat<br> │ ├── out_apo_nosalt_3ions.rep4.radii_refitted.dat<br> │ ├── out_apo_nosalt_3ions.rep5.radii_refitted.dat<br> │ ├── out_apo_nosalt_Cl.rep1.radii_refitted.dat<br> │ ├── out_apo_nosalt_Cl.rep2.radii_refitted.dat<br> │ ├── out_apo_nosalt_Cl.rep3.radii_refitted.dat<br> │ ├── out_apo_nosalt_Cl.rep4.radii_refitted.dat<br> │ ├── out_apo_nosalt_Cl.rep5.radii_refitted.dat<br> │ ├── out_apo_nosalt_ionless.rep1.radii_refitted.dat<br> │ ├── out_apo_nosalt_ionless.rep2.radii_refitted.dat<br> │ ├── out_apo_nosalt_ionless.rep3.radii_refitted.dat<br> │ ├── out_apo_nosalt_ionless.rep4.radii_refitted.dat<br> │ └── out_apo_nosalt_ionless.rep5.radii_refitted.dat<br> └── Sfig1<br> ├── distances_n_angles_fig2.pse<br> ├── occ_apo_nosalt_3ions.rep1.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_3ions.rep1.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_3ions.rep2.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_3ions.rep2.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_3ions.rep3.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_3ions.rep3.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_3ions.rep4.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_3ions.rep4.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_3ions.rep5.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_3ions.rep5.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_Cl.rep1.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_Cl.rep1.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_Cl.rep2.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_Cl.rep2.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_Cl.rep3.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_Cl.rep3.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_Cl.rep4.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_Cl.rep4.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_Cl.rep5.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_Cl.rep5.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_ionless.rep1.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_ionless.rep1.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_ionless.rep2.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_ionless.rep2.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_ionless.rep3.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_ionless.rep3.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_ionless.rep4.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_ionless.rep4.TM6a-TM9up.dat<br> ├── occ_apo_nosalt_ionless.rep5.TM6a-TM8down.dat<br> ├── occ_apo_nosalt_ionless.rep5.TM6a-TM9up.dat<br> ├── out_apo_nosalt_3ions.rep1.TM6a-TM8down.dat<br> ├── out_apo_nosalt_3ions.rep1.TM6a-TM9up.dat<br> ├── out_apo_nosalt_3ions.rep2.TM6a-TM8down.dat<br> ├── out_apo_nosalt_3ions.rep2.TM6a-TM9up.dat<br> ├── out_apo_nosalt_3ions.rep3.TM6a-TM8down.dat<br> ├── out_apo_nosalt_3ions.rep3.TM6a-TM9up.dat<br> ├── out_apo_nosalt_3ions.rep4.TM6a-TM8down.dat<br> ├── out_apo_nosalt_3ions.rep4.TM6a-TM9up.dat<br> ├── out_apo_nosalt_3ions.rep5.TM6a-TM8down.dat<br> ├── out_apo_nosalt_3ions.rep5.TM6a-TM9up.dat<br> ├── out_apo_nosalt_Cl.rep1.TM6a-TM8down.dat<br> ├── out_apo_nosalt_Cl.rep1.TM6a-TM9up.dat<br> ├── out_apo_nosalt_Cl.rep2.TM6a-TM8down.dat<br> ├── out_apo_nosalt_Cl.rep2.TM6a-TM9up.dat<br> ├── out_apo_nosalt_Cl.rep3.TM6a-TM8down.dat<br> ├── out_apo_nosalt_Cl.rep3.TM6a-TM9up.dat<br> ├── out_apo_nosalt_Cl.rep4.TM6a-TM8down.dat<br> ├── out_apo_nosalt_Cl.rep4.TM6a-TM9up.dat<br> ├── out_apo_nosalt_Cl.rep5.TM6a-TM8down.dat<br> ├── out_apo_nosalt_Cl.rep5.TM6a-TM9up.dat<br> ├── out_apo_nosalt_ionless.rep1.TM6a-TM8down.dat<br> ├── out_apo_nosalt_ionless.rep1.TM6a-TM9up.dat<br> ├── out_apo_nosalt_ionless.rep2.TM6a-TM8down.dat<br> ├── out_apo_nosalt_ionless.rep2.TM6a-TM9up.dat<br> ├── out_apo_nosalt_ionless.rep3.TM6a-TM8down.dat<br> ├── out_apo_nosalt_ionless.rep3.TM6a-TM9up.dat<br> ├── out_apo_nosalt_ionless.rep4.TM6a-TM8down.dat<br> ├── out_apo_nosalt_ionless.rep4.TM6a-TM9up.dat<br> ├── out_apo_nosalt_ionless.rep5.TM6a-TM8down.dat<br> ├── out_apo_nosalt_ionless.rep5.TM6a-TM9up.dat<br> └── Sfig1_v2.png</p> <p> </p>
Lunar ASP DEM Test PDS4 Bundle
<p>A pair of Ames Stereo Pipeline (ASP) generated LROC NAC Digital Elevation Models in NASA PDS4 structure.</p> <p><strong>Purpose:</strong> Created as part of a testing effort funded by the LPI. The purpose of this dataset is to provide example LROC NAC Digital Elevation Models (DEMs) generated using rapid Ames Stereo Pipeline (ASP) processing. The example DEMs were assessed for suitability for scientific analysis.</p> <p><strong>Data Set Overview: </strong>The archive contains 2 DEMs, in GeoTiff format, as a right image and a left image. The DEMs were generated using the ASP online tutorial and version of the software downloaded in March 2022 from github using the latest build (https://github.com/NeoGeographyToolkit/StereoPipeline). The DEMs cover a portion of Glushko crater's extensive ejecta ray system, Earth's Moon.</p> <p>The DEMs were generated using map-projected Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) input images that were collected as a stereo pair but have not yet been processed into a DEM using photogrammetric techniques. The base shape model for the projection is the LROC Wide Angle Camera (WAC) GLD100 topographic product. The map-projected images were run using parallel_stereo and point2dem processes in the ASP toolkit. The output is a DEM in geotiff format.</p> <p>The included test DEMs, archive structure, related documents, and xml files are formatted to best effort in pds4 format following online documentation by NASA PDS (as of Sept 2022 at https://pds.nasa.gov). Files were validated using the PDS Validate tool (downloaded Sept 2022, version v2.3.0, from https://github.com/NASA-PDS/validate). Xml templates were modified from existing related examples (Watkins 2018, Herrick and Ward 2020, Hare and Trent 2018). The provided files have been self-validated but are not validated by the Planetary Data System (PDS) and are provided for educational and training purposes only, and could contain errors or inconsistencies.</p>
Text-fig. 5. SRXTM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Transverse sections of flower (a, orthoslice xy0665 close to the apex of placenta; b, orthoslice xy0800 in middle part of placenta) showing remains of calyx with distinct bundles (arrows), ovary wall (ow) and numerous ovules (ov) on the central mushroom-shaped globose placenta (pl) with central column (cc). c: Transverse section of flower (orthoslice xy0620) through perianth and ovary (ow) at a level above the placenta showing ovules (ov) and cellular preservation of the sepal bundles (arrows shown for one sepal); note abaxial surface of sepals with thick-walled epidermal cells, thick cuticle, and fine pointed verrucae. d: Longitudinal section of flower (orthoslice xz0500) showing perigynous position of calyx and semi-inferior ovary (ow, ovary wall) with a central placenta (pl), central column (cc) and numerous ovules (ov); note spiny verrucae on abaxial surface of calyx lobes. Specimens, Mira 100-S153145 (a, b), Mira 100-S170155 (c, d, holotype). Scale bars = 600 µm (a–d). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 5. SRXTM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Transverse sections of flower (a, orthoslice xy0665 close to the apex of placenta; b, orthoslice xy0800 in middle part of placenta) showing remains of calyx with distinct bundles (arrows), ovary wall (ow) and numerous ovules (ov) on the central mushroom-shaped globose placenta (pl) with central column (cc). c: Transverse section of flower (orthoslice xy0620) through perianth and ovary (ow) at a level above the placenta showing ovules (ov) and cellular preservation of the sepal bundles (arrows shown for one sepal); note abaxial surface of sepals with thick-walled epidermal cells, thick cuticle, and fine pointed verrucae. d: Longitudinal section of flower (orthoslice xz0500) showing perigynous position of calyx and semi-inferior ovary (ow, ovary wall) with a central placenta (pl), central column (cc) and numerous ovules (ov); note spiny verrucae on abaxial surface of calyx lobes. Specimens, Mira 100-S153145 (a, b), Mira 100-S170155 (c, d, holotype). Scale bars = 600 µm (a–d).
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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.