Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
17,474
datasets available to search
ShareScore release 0.7.1
Dataset results
17,474 results for “Complexes”
Accompanying data for paper "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots"
<div> </div> <div>This repository contains the numerical data of the conductivity of complex-shaped contact spots on isotropic and linear conducting half-space obtained by Boundary and Finite Element methods. These data were used to construct some figures from the manuscript "Electrical and Thermal Conductivity of Complex-Shaped Contact Spots". The data is organized in folders corresponding to different types of contact spots: annular, flower-, star- and gear-shaped, Koch's snowflake, and self-affine spots. Each folder contains the results of numerical simulations in the form of `.npz` files, which can be loaded using `numpy` library in Python. The data is used to construct figures in the manuscript and can be used to reproduce the results or to perform additional analysis.</div> <div> </div>
Alphafold and ColabFold models of E. coli and consensus Bcs complexes
<p>ColabFold and AlphaFold 3 models used for structure modeling and interpretation in Anso et al. 'Structural basis for synthase activation and cellulose modification in the <em>E. coli</em> Type II Bcs secretion system'. </p>
Artifact for "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment"
<p>This artifact includes the setup, data, and analysis for the experiment described in the article "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment" published in the journal "The Art, Science, and Engineering of Programming<em>" </em>Volume 9.</p> <p>The artifact has the following structure:</p> <ul> <li> <p>experiment-setup</p> <ul> <li>experiment-environment.zip: Archive including the fully configured Squeak/Smalltalk environment</li> <li>experiment-protocol.pdf: Full protocol for conducting the experiment</li> <li>system: Includes the base system used for the experiment without any seeded faults</li> <li>tasks <ul> <li>task-descriptions.txt: The task descriptions of steps to reproduce and symptoms for the tasks used in the experiment</li> <li>patches: Patch files for all generated tasks</li> </ul> </li> <li>infrastructure: Contains source code of the tools used for controlling tasks in the development environment</li> <li>questionnaire <ul> <li>survey.dfglive.2023-01-06.xml: Questionnaire configuration for a SoSci survey server, includes demographics, experience, and skill questionnaire</li> </ul> </li> </ul> </li> <li> <p>results</p> <ul> <li>data <ul> <li>export-NN.zip: Each archive contains the raw data from one run <ul> <li>starTrackData-NN: Contains the detailed event log</li> <li>experimentState.json: Contains the measurements and the final state for each task (completed, notStarted, etc.)</li> <li>skillTestResult-NN.json: Contains the score reached in the skill test</li> <li>task-X-NN.cs: The submitted patch for task X</li> </ul> </li> </ul> </li> <li>questionnaire <ul> <li>data_dfglive-analysis.ods: Demographics and experience questionnaire results (gender column is redacted due to potential deanonymization)</li> <li>results of skill test are only available in graded form in the participant export files</li> </ul> </li> </ul> </li> <li> <p>analysis</p> <ul> <li>analysis-r: Main analysis scripts <ul> <li>main.R, main-contrast-based.R: Two versions of the main and moderation effect analysis using two different processes</li> <li>live-tools-usage-time.R: Main and moderation effect analysis on tool usage</li> <li>demographics.R: Analysis of demographics and rendering of charts</li> </ul> </li> <li>analysis-tool-usage-correlation: Includes scripts to extract tool usage frequencies from the event logs. Assumes that export-NN.zip files are in the parent folder.</li> </ul> </li> </ul>
PWAS Hub: exploring gene-based associations of complex diseases with sex dependency - backing data
<p>The contents of the PWAS database is presented on <a title="The PWAS hub" href="https://pwas.huji.ac.il/?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il</a>. The frontend and backend were build on top of a dynamical databse system. Please consult the direct API for PWAS if you wish to query the database directly: <a title="The PWAS API" href="https://pwas.huji.ac.il/API?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il/API</a></p> <p>This is a PostgreSQL dump file that was created using <code>pg_dump</code>, the backup/restore procedure for PostgreSQL. To restore this into PostgreSQL do</p> <p>[a] create a database</p> <p><code>createdb DATABASE</code></p> <p>[b] on the terminal run</p> <p><code>pg_restore -vcC -h HOST -p PORT -d DATABASE < pwas_dump.20220628.psql</code></p> <p>The HOST and PORT are determined by your installation and DATABASE is given by you in step [a] abobe.</p> <p> </p> <p>To access the PWAS tables, look for table names that begin with <code>pwasAPI_</code></p> <p>A possible query to the database may look like this:</p> <p><code>SELECT * FROM "pwasAPI_genediseasestatpwas" WHERE uniprot_id = 'P09914' AND disease = 'C44';</code></p> <p>This query lists the data that associate uniprot id <strong>P09914</strong> (gene symbol IFIT1) and disease ICD-10 <strong>C44</strong> (Other malignant neoplasms of skin)</p>
Simulation systems for: "Pore formation in complex biological membranes: torn between evolutionary needs"
<p>Simulation systems for the publication:</p> <div> <div> <div> <p>Leonhard J. Starke, Christoph Allolio, and Jochen S. Hub, <em>Pore formation in complex biological membranes: torn between evolutionary needs</em>, BioRxiv (2024), doi: <a href="https://doi.org/10.1101/2024.05.06.592649">10.1101/2024.05.06.592649</a></p> <p>Required software:<br>GROMACS Chain Coordinate, a modified GROMACS variant for pore formation across membranes or stalk formation between membranes: <a href="https://gitlab.com/cbjh/gromacs-chain-coordinate">https://gitlab.com/cbjh/gromacs-chain-coordinate</a></p> <p>See README_small.sh and README_large.sh files for instructions on how to run pulling simulations for inducing pores in the provided complex membrane models.</p> </div> </div> </div>
Graphic Illustration of Kelly Speer's Keynote Talk: Hosts, parasites, and microbiomes: A system for studying natural complexity in a changing world
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded and synthesized the Keynote Talk by Kelly Speer at the Digital Data 2024 Conference in Lawrence, Kansas in May of 2024. We include this resource, with permission, because of its relevance to our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Topography drives microgeographic adaptations of closely-related species in two tropical tree species complexes
<p>Combining LiDAR-derived topography, tree inventories, and single nucleotide polymorphisms (SNPs) from gene capture experiments, we explored genome-wide population genetic structure, covariation of environmental variables, and genotype-environment association to assess microgeographic adaptations to topography within the species complexes <em>Symphonia</em> (Clusiaceae), and <em>Eschweilera</em> (Lecythidaceae) with three species per complex and 385 and 257 individuals genotyped, respectively.</p>
Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones
<p>This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint.</p> <p>Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software.</p> <p>Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.</p>
Dataset for "Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools"
<p>This dataset contains the data to replicate the findings in the publication "Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools". The dataset includes the bibliographies and intermediate analysis tables for the analyzed literature. Further, it contains the experiment materials providing context for the task discussed in Section V of the publication.</p> <p>The artifact contains the following files:</p> <ul> <li>icpc-from-survey.bib: The bibliographic entries of all publications selected from the corpus of the paper "Confounding parameters on program comprehension: a literature survey" by Janet Siegmund and Jana Schumann (https://doi.org/10.1007/s10664-014-9318-8)</li> <li>icpc-manually-added.bib: The bibliographic entries of manually added publications.</li> <li>icpc-phase-2-and-3.[csv|xlsx]: The intermediate analysis tables for phases 2 and 3 as described in the paper. The table also contains columns transferred from the original dataset of the Siegmund and Schumann study (marked as "transferred")</li> <li>experiment-materials.zip: Includes the materials for the experiment used in Section V <ul> <li>introduction-english.[docx|rtf]: The experiment protocol we read to participants, including the introduction to the system architecture.</li> <li>JumpODrom.package.zip: The code of the used system.</li> <li>task-16-description.txt: The textual description of the task discussed in Section V.</li> <li>task-16.patch: The change applied to the system code prior to the task, which causes the faulty behavior outlined in the task description.</li> </ul> </li> </ul> <p> </p>
Dataset and supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks"
<p>Dataset and associated supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks".</p>
Data for Figure 8 of Publication "Additional global climate cooling by clouds due to ice crystal complexity"
<p>This repository contains the data to produce Figure 8 in the paper:</p> <p>"Järvinen, E., Jourdan, O., Neubauer, D., Yao, B., Liu, C., Andreae, M. O., Lohmann, U., Wendisch, M., McFarquhar, G. M., Leisner, T., and Schnaiter, M.: Additional global climate cooling by clouds due to ice crystal complexity, Atmos. Chem. Phys., 18, 15767–15781, https://doi.org/10.5194/acp-18-15767-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.8095469)</p>
EPR and SQUID interrogations of Cr(III) trimer complexes in the MIL-101(Cr) and bimetallic MIL-100(Al/Cr) MOFs
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_08082023_01_MIL_CW </strong>folder includes X-, Q- and W-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_08082023_02_MIL_SQUID </strong>folder include SQUID magnetometry data; original data are in DAT file</li> <li>File <strong>PARACAT_WP4_08082023_02_MIL_Origin </strong>include origin plotted data</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul> <p> </p>
Integrative structure determination of PTBP1-viral IRES complex in solution
<p>Ensemble structure model of the RNA-binding protein PTBP1 in complex with the internal ribosome entry site (IRES) of encephalomyocarditis virus (EMCV) RNA and data underlying these models.</p> <ul> <li>Main ensemble based on all restraints (corresponding to Figure 2 in the associated paper)</li> <li>Ensemble obtained with only DEER distance distribution restraints corresponding to Figure S7(A) in the Supplementary Material of the associated paper</li> <li>Validation ensemble obtained with all restraints after removing the conformers of the main ensemble from the raw ensemble corresponding to Figure S7(B) inthe Supplementary Material of the associated paper</li> <li>Ensemble obtianed with all restraints by fitting populations with a non-negative linear least squares (NNLLSQ) approach corresponding to Figure S8(A) in ths Supplementary Material of the associated paper</li> <li>Primary DEER-EPR data underlying site-to-site distance distributions for 35 spin-label pairs and corresponding distanace distributions</li> <li>Small-angle neutron scattering (SANS) curves a two detector distances with corresponding resolution files and a small-angle x-ray scattering (SAXS) curve</li> <li>Restraint file for the ensemble fit with MMMx software, specifying the mean distances and standrad deviations of distance distributions that were also used for specifying lower and upper distance bounds in CYANA generation of the raw ensemble</li> <li>Source data for the figures in the associated paper</li> <li>Source data for the tables in the associated paper</li> </ul> <p>All ensembles are ZIP files containing single PDB files for all conformers and an ensemble specification that reports populations for all conformers.</p>
Molecular and Taxonomic Reevaluation of the Digitaria filiformis Complex (Poaceae) including a Globally Extinct, Single Site Endemic from New Hampshire, USA, and a New Species from Mexico
<p>We examine the <em>Digitaria filiformis </em>complex, to determine the proper taxonomic rank and rarity of each taxon. The taxonomy of the <em>D. filiformis </em>complex is highly debated and includes two widespread species, <em>D. filiformis </em>and <em>D. villosa</em>; a possibly extinct species endemic to a single-site in New Hampshire, <em>D. laeviglumis</em>; and a rare species of southern Florida and the West Indies, <em>D.</em><em> dolichophylla. </em>We conducted morphologic comparisons and molecular analysis of the four members of the <em>D. filiformis</em> complex, together with specimens from Mexico and Venezuela purportedly identified as <em>D. laeviglumis</em> (morphology only). Based on results of phylogenetic analyses of plastid and nuclear ITS sequences and morphologic comparisons, we recognize five species in the <em>D. filiformis </em>complex, including a newly described Mexican endemic <em>D. glabrifloris. </em>After field investigation we have moved the global rank of <em>D. laeviglumis </em>from globally historical (GH) to extinct (GX), as there is virtually no likelihood of rediscovery. <em>Digitaria</em><em> dolichophylla </em>is much rarer than previously recognized, moving from secure (T5) to imperiled with extinction (G2).</p>
Data to accompany "Exploring the Complexity of Ocean Acidification: An Ecosystem Comparison of Coastal pH Variability"
The goal of this project was to create a science lesson at the middle school level with data illustrating the variablilty of pH and temperature in nature. The lesson allows students to interpret pH data and gain knowledge of abiotic and biotic processes that contribute to pH differences between tropical, temperate and polar marine ecosystems. Students use what they have learned to interpret data from a 'mystery' site and develop a hypothesis as to which ecosystem the unknown data was collected from. The full cirriculum is described in Kapsenberg, L, AL Kelley, LA Francis, and SB Raskin (2015) Exploring the complexity of ocean acidification: an ecosystem comparison of coastal pH variability. Science Scope 39(3): 51-60. doi: 10.2505/4/ss15_039_03_51 This dataset contains an Excel workbook with five worksheets: A "Readme" tab with citations for additional reading and data attribution.Three time-series of pH and temperature from coastal locations: a temperate kelp forest in the Santa Barbara Channel (Spring season, 2 months, 20 min interval). a coral reef near the island of Moorea, Tahiti (Summer season, 3 weeks, 30 min interval), and the polar ocean of Cape Evans, McMurdo Bay, Antarctica (Spring/Summer, 6 months, bi-hourly interval). A fourth worksheet contains data for a "mystery site" for student examination. Data for the study were contributed by the Santa Barbara Coastal LTER, Moorea Coral Reef LTER and the G. Hofmann lab (University of California, Santa Barbara). Additional pH data are available from both LTER sites. Antarctic data in this dataset are also available from NSF's Biological and Chemical Oceanographic Data Management Office (see Cape Evans Mooring, 2012).
SIRAH-CoV2 initiative: S1 Receptor Binding Domain in complex with human antibody CR3022 (PDBid: 6W41)
<p>This dataset contains the trajectory of a 12 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV-2 receptor binding domain in complex with a human antibody CR3022 (PDB id: 6W41). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycans have been removed from the structures.</p> <p>The file 6W41_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W41_SIRAHcg_12us_prot.tar contains only the protein coordinates, while 6W41_SIRAHcg_12us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W41_SIRAHcg_12us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w41_SIRAHcg_prot.prmtop 6w41_SIRAHcg_prot.ncrst 6w41_SIRAHcg_prot_12us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: NSP16 - NSP10 Complex (PDB id:6W4H)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 NSP16 - NSP10 Complex with Zn ions bound (PDB id: 6W4H). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The file 6W4H_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W4H_SIRAHcg_10us_prot_Zn.tar contains only the protein coordinates, while 6W4H_SIRAHcg_10us_prot_Zn_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W4H_SIRAHcg_10us_prot_Zn_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w4h_SIRAHcg_prot.prmtop 6w4h_SIRAHcg_prot.ncrst 6w4h_SIRAHcg_prot_Zn_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p> <p> </p>
`hectordata` inputs: Reduced Complexity Model Intercomparison (RCMIP) Phase 1 Emissions and Concentrations
<p>The attached data was downloaded on April 30, 2020 from <a href="https://www.rcmip.org/">https://www.rcmip.org/</a>. These data have no formal citation or DOI. Since we use this specific version as inputs to the `hectordata` R package (<a href="https://github.com/JGCRI/hectordata">https://github.com/JGCRI/hectordata</a>), we offer these as an archived resource. The `hectordata` package is used to prepare the inputs used in the simple climate model Hector (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>).</p> <p>The datasets contained were listed as "Version 4.0.0, 31st December 2019" for both Emissions and Concentrations. Units are described in the files.</p>
SIRAH-CoV2 initiative: co-factor complex of NSP7 and the C-terminal domain of NSP8 from SARS CoV-2 (PDBid:6WIQ)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of the co-factor complex of NSP7 and the C-terminal domain of NSP8 from SARS CoV-2 (PDBid:6WIQ). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6WIQ_SIRAHcg_rawdata_0-5us.tar, and 6WIQ_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6WIQ_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6WIQ_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6WIQ_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6WIQ_SIRAHcg_prot.prmtop 6WIQ_SIRAHcg_prot.ncrst 6WIQ_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 (PDB id:7BTF)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 and Zinc (PDB id: 7BTF). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 7BTF_SIRAHcg_rawdata_0-2us.tar, 7BTF_SIRAHcg_rawdata_2-6us.tar, and 7BTF_SIRAHcg_rawdata_6-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 7BTF_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 7BTF_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 7BTF_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 7BTF_SIRAHcg_prot.prmtop 7BTF_SIRAHcg_prot.ncrst 7BTF_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martin Soñora (msonora@pasteur.edu.uy) Sergio Pantano (spantano@pasteur.edu.uy).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.