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3,878 results for “Molecular Data”
Unraveling the molecular mechanism of hyperlipidemia using integrated lncRNA and mRNA microarray data
GEO Series GSE169398. Mus musculus. 10 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Transcriptomic analysis of autistic brain reveals convergent molecular pathology [high-throughput sequence data]
GEO Series GSE30573. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Molecular Characterization of Heat Intolerance in Mice (mRNA data)
GEO Series GSE48269. Mus musculus. 12 samples. Type: Expression profiling by array.
Figure 9 from: Urbaniak J, Kwiatkowski P (2019) Taxonomic studies on the Chara section Hartmania in Poland based on morphological and molecular data. PhytoKeys 135: 71-90. https://doi.org/10.3897/phytokeys.135.36714
Figure 9 Principal Components Analysis ordination of the species from section Hartmania.
Figure 4 from: Urbaniak J, Kwiatkowski P (2019) Taxonomic studies on the Chara section Hartmania in Poland based on morphological and molecular data. PhytoKeys 135: 71-90. https://doi.org/10.3897/phytokeys.135.36714
Figure 4 C. polyacantha with long spine cells (SP), as long as axis diameter.
Figure 10 from: Urbaniak J, Kwiatkowski P (2019) Taxonomic studies on the Chara section Hartmania in Poland based on morphological and molecular data. PhytoKeys 135: 71-90. https://doi.org/10.3897/phytokeys.135.36714
Figure 10 Discriminant analysis of the species from section Hartmania.
Figure 3 from: Urbaniak J, Kwiatkowski P (2019) Taxonomic studies on the Chara section Hartmania in Poland based on morphological and molecular data. PhytoKeys 135: 71-90. https://doi.org/10.3897/phytokeys.135.36714
Figure 3 C. hispida with spine cells (SP) in bunches.
Figure 7 from: Urbaniak J, Kwiatkowski P (2019) Taxonomic studies on the Chara section Hartmania in Poland based on morphological and molecular data. PhytoKeys 135: 71-90. https://doi.org/10.3897/phytokeys.135.36714
Figure 7 C. rudis with opposite spine cells (SP) on the axis.
Figure 6 from: Urbaniak J, Kwiatkowski P (2019) Taxonomic studies on the Chara section Hartmania in Poland based on morphological and molecular data. PhytoKeys 135: 71-90. https://doi.org/10.3897/phytokeys.135.36714
Figure 6 C. rudis with short, regular stipulodes (ST).
Figure 8 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175
Figure 8 Distribution of suitable niches of P. omphalodes (A) and P. pinnatifida (B) in America.
A1904 molecular dynamics trajectories data
<p>Molecular dynamics (MD) trajectories of water solutions of eight zwitterionic amino-acids (L- form) glycine (GLY), alanine (ALA), proline (PRO), threonine (THR), leucine (LEU), glutamine (GLN), histidine (HIS) and tyrosine (TYR) using various force field (OPLS-AA, Amber99ff-SB, GROMOS96 54a7, CHARMM19) and water model (SPC/E, TIP3P) combinations.</p> <p>OPLS-AA molecular dynamics (MD) trajectories for alanine (ALA), leucine (LEU), glutamine (GLN), and tyrosine (TYR) varying the values of major force field parameters: charge on all amino acid atoms, bond length (all amino acid bonds), Lennard-Jones potential epsilon parameter and stiffness of bond angles. </p>
Figure 3 from: Chao C-T, Huang B-H, Chang J-T, Liao P-C (2020) Taxonomic notes on Scutellaria taipeiensis (Lamiaceae) from morphological and molecular data. PhytoKeys 140: 33-45. https://doi.org/10.3897/phytokeys.140.48578
Figure 3 Inflorescence morphology of S. barbata (A) and S. taipeiensis (B).
Primary data for "Identification of molecular quantum states using phase-sensitive forces"
<p>Figure4_v3.m is a Matlab file that creates Figure4 of the manuscript. It uses auxiliary functions to calculate the polarizability of N2+.</p> <p>Table_I_v3.m is a Matlab file that crates Table I of the manuscript. It uses same auxiliary functions as the last script.</p> <p>RawData.pdf gives the Rabi signals from which the data points in Figure 4 are calculated.</p> <p>CalibrationProcedure.pdf gives additional information on the calibration made using Ca+.</p>
Figures 4 from: Wei Z, Zhang L, Shi A (2020) A note on the larva of Chalcophora japonica chinensis (Coleoptera, Buprestidae) based on morphological characters and molecular data. ZooKeys 944: 147-155. https://doi.org/10.3897/zookeys.944.37765
Figures 4 Maximum Likelihood tree of Chalcophora from China based on COI.
3x 1 µs all-atom MD trajectories; AMBER ff15ipq & SPC/Eb; T4 Lysozyme; 'Fitting side-chain NMR relaxation data using molecular simulations'
<p>Simulation data for "Fitting side-chain NMR relaxation data using molecular simulations" (https://doi.org/10.1101/2020.08.18.256024).</p> <ul> <li>3 x 1 µs all-atom MD simulations of T4 Lysozyme</li> <li>Force field: AMBER ff15ipq with modified methyl rotation barriers<sup>1</sup></li> <li>Water model: SPC/Eb</li> <li>Compressed protein coordinates saved every 1 ps to enable calculation of side-chain NMR relaxation parameters</li> </ul> <p>Contains:</p> <ul> <li>3 x GROMACS .xtc trajectory files for 3 independent simulations</li> <li>3 x corresponding GROMACS .tpr topology files</li> </ul> <p><sup>1</sup> Hoffmann, F., Mulder, F. A. A., & Schäfer, L. V. (2020). Predicting NMR relaxation of proteins from molecular dynamics simulations with accurate methyl rotation barriers. <em>Journal of Chemical Physics</em>, <em>152</em>(8). https://doi.org/10.1063/1.5135379</p>
5x 1 µs all-atom MD trajectories; AMBER ff99SB*-ILDN & TIP4P/2005; T4 Lysozyme; 'Fitting side-chain NMR relaxation data using molecular simulations'
<p>Simulation data for "Fitting side-chain NMR relaxation data using molecular simulations" (https://doi.org/10.1101/2020.08.18.256024).</p> <ul> <li>5 x 1 µs all-atom MD simulations of T4 Lysozyme</li> <li>Force field: AMBER ff99SB*-ILDN with modified methyl rotation barriers<sup>1</sup></li> <li>Water model: TIP4P/2005</li> <li>Compressed protein coordinates saved every 1 ps to enable calculation of side-chain NMR relaxation parameters</li> </ul> <p>Contains:</p> <ul> <li>5 x GROMACS .xtc trajectory files for 5 independent simulations</li> <li>5 x corresponding GROMACS .tpr topology files</li> </ul> <p><sup>1</sup> Hoffmann, F., Mulder, F. A. A., & Schäfer, L. V. (2018). Accurate Methyl Group Dynamics in Protein Simulations with AMBER Force Fields. <em>The Journal of Physical Chemistry B</em>, <em>122</em>(19), 5038–5048. https://doi.org/10.1021/acs.jpcb.8b02769</p>
3x 5 µs all-atom MD trajectories; AMBER ff99SB*-ILDN & TIP4P/2005; T4 Lysozyme; 'Fitting side-chain NMR relaxation data using molecular simulations'
<p>Simulation data for "Fitting side-chain NMR relaxation data using molecular simulations" (https://doi.org/10.1101/2020.08.18.256024).</p> <ul> <li>3 x 5 µs all-atom MD simulations of T4 Lysozyme</li> <li>Force field: AMBER ff99SB*-ILDN with modified methyl rotation barriers<sup>1</sup></li> <li>Water model: TIP4P/2005</li> <li>Compressed protein coordinates saved every 1 ps to enable calculation of side-chain NMR relaxation parameters</li> </ul> <p>Contains:</p> <ul> <li>3 x GROMACS .xtc trajectory files for 3 independent simulations</li> <li>3 x corresponding GROMACS .tpr topology files</li> </ul> <p><sup>1</sup> Hoffmann, F., Mulder, F. A. A., & Schäfer, L. V. (2018). Accurate Methyl Group Dynamics in Protein Simulations with AMBER Force Fields. <em>The Journal of Physical Chemistry B</em>, <em>122</em>(19), 5038–5048. https://doi.org/10.1021/acs.jpcb.8b02769</p>
Figure 1 in The contribution of molecular data to our understanding of cephalopod evolution and systematics: a review
Figure 1. Relationships between molluscan classes as recovered by Smith et al. (2011).
Figure 3 in The contribution of molecular data to our understanding of cephalopod evolution and systematics: a review
Figure 3. Summary tree of phylogenetic relationships between higher taxa.
Data related to: MACHINE LEARNING AND QUANTUM MECHANICS APPROACH TO MORE CHEMICALLY-AWARE MOLECULAR DESCRIPTORS FOR MEDICINAL CHEMISTRY APPLICATIONS
<p>DEMIN VS QM ELECTRONIC POTENTIAL CORRELATIONS FOR THE N:= ATOM TYPE AND THE N1 ATOM TYPE</p> <p>dEmin versus H-bond basicity scale and H-bond acidity scale </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.