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13,017 results for “Positionning”
Figure 2 from: Özüdoğru B, Fırat (2016) Arabis watsonii (P.H.Davis) F.K.Mey.: An overlooked cruciferous species from eastern Anatolia and its phylogenetic position. PhytoKeys 75: 57-68. https://doi.org/10.3897/phytokeys.75.10568
Figure 2 - Distrubution of Arabis watsonii (red dots). Map was taken from Fırat (2016).
Figure 2 from: Ezzine IK, Pfarrer B, Dimassi N, Said K, Neubert E (2017) At home at least: the taxonomic position of some north African Xerocrassa species (Pulmonata, Geomitridae). ZooKeys 712: 1-27. https://doi.org/10.3897/zookeys.712.13066
Figure 2 - Distribution of Xerocrassa latastei, Xerocrassa latasteopsis and Xerocrassa lacertara.
Figure 9 [Continued] from: Ezzine IK, Pfarrer B, Dimassi N, Said K, Neubert E (2017) At home at least: the taxonomic position of some north African Xerocrassa species (Pulmonata, Geomitridae). ZooKeys 712: 1-27. https://doi.org/10.3897/zookeys.712.13066
Figure 9 [Continued] -
Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics
<p><span>High-resolution IM-MS-based four-dimensional lipidomics strategy for glycerophospholipid analysis.</span></p>
[5G-IANA] UC1 - Vehicle position, speed and angle
<p>Linear and angular movement commands that allow to know position, speed and angle of the vehicle. </p>
Data and software for "Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument"
<p>Supplementary material to the DESI publication "Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument".</p> <p>The main "turbfigures.py" script generates the figures in the paper from the included data files.</p> <p>Contents:</p> <p><strong>Software files</strong></p> <ul> <li>turbfigures.py: Turbulence plotting and analysis plotting routines.</li> <li>turbulence.py: Analysis routines called by turbfigures.py. The live version of this code in production for desi is in the desimeter package (https://github.com/desihub/desimeter/blob/main/py/desimeter/turbulence.py)</li> </ul> <p><strong>Data files</strong></p> <ul> <li>coord-summary-20240124-pm.fits <ul> <li>Measured centroids of fibers in for 400 consecutive images of the focal plane while at zenith. File contains the following columns: <ul> <li>expid - exposure ID</li> <li>location - "location" of fiber in focal plane; ranges from 0 - 10000. Fibers on petal 0 have numbers between 0 and 1000, etc. There are only 500 positioners per petal, so most locations are not populated.</li> <li>move - in real data, this indexes over the moves in a DESI positioning loop; garbage information here.</li> <li>fpa_{x,y} - measured position of the fiber in this exposure</li> <li>req_{x,y} - requested position of the fiber in this exposure; not used for this data set where positioners are fixed in location</li> <li>{x,y}turb - empty in this file; gets filled in by turbfigures.py with measured turbulence</li> <li>flags_cor - flags indicating that a positioner or fiber may be problematic</li> <li>postype - flags indicating that a location corresponds to a real positioner vs. a fiducial</li> <li>expected_{x,y} - empty in this file; gets filled in by turbfigures.py with "expected" positions of each fiber, so that fpa_{x,y} - expected_{x,y} is a noisy estimate of the turbulence seen by the fiber.</li> </ul> </li> <li>Note that this file has an awkward structure. It has 10000 rows, one for each possible location. Most fields are then 400 element arrays that gives the corresponding values, corresponding to the 400 exposures present in the file.</li> </ul> </li> <li>coordinates-stats.ecsv <ul> <li>statistics of positioning accuracy and turbulence amplitude in DESI positioning loops. Measured RMSes are the 5-sigma clipped root-mean-square positioning offset in 2D: sqrt(mean(dx^2 + dy^2)). Measured medians are median(sqrt(dx^2 + dy^2)).</li> <li>Contents: <ul> <li>coord_filename - file name of DESI coordinates file statistics were drawn from</li> <li>rms_turbulence - RMS for the turbulent contribution to the positioning error</li> <li>rms_positioning - RMS for positioning after removing turbulence</li> <li>rms_total - Total RMS</li> <li>med_turbulence - median turbulence in exposure</li> <li>med_positioning - median positioning error in exposure</li> <li>med_total - median total turbulence + positioning error in exposure</li> <li>expid - exposure id number</li> </ul> </li> </ul> </li> </ul> <p>Dependencies: The included software uses the DESI software stack and otherwise the usual python astronomy packages: numpy scipy matplotlib astropy. Alternatively, people with access to NERSC can load the default DESI environment and pull in all needed dependencies.</p>
Fig. 2 in New skeleton from the early Oligocene of Germany indicates a stem-group position of diomedeoidid birds
Fig. 2. Map of the immediate surroundings of the Rheinweiler locality.
Figure 5 from: Zhou L-W, Wang X-W, Vlasák J, Ren G-J (2018) Resolution of phylogenetic position of Nigrofomitaceae within Hymenochaetales (Basidiomycota) and Nigrofomes sinomelanoporus sp. nov. (Nigrofomitaceae) from China. MycoKeys 29: 1-13. https://doi.org/10.3897/mycokeys.29.21250
Figure 5 A basidiocarp of Nigrofomes melanoporus in situ (Vlasák 1704/39). Scale bar: 2 cm.
Figures 1-4 from: Jordal BH (2018) The smallest known species of Afrotropical Scolytoplatypus Schaufuss (Curculionidae, Scolytinae) – with unique features and an isolated phylogenetic position. ZooKeys 749: 125-130. https://doi.org/10.3897/zookeys.749.24199
Figures 1-4 Habitus, head and elytral declivity of Scolytoplatypus unipilus sp. n.
Figure 6 from: Li J, Lin R-R, Zhang Y-Y, Hu K-J, Zhao Y-Q, Li Y, Huang Z-R, Zhang X, Geng X-X, Ding J-H (2018) Characterization of the complete mitochondrial DNA of Theretra japonica and its phylogenetic position within the Sphingidae (Lepidoptera, Sphingidae). ZooKeys 754: 127-139. https://doi.org/10.3897/zookeys.754.23404
Figure 6 Barcoding analysis of T. japonica.
Figure 2 from: Li J, Lin R-R, Zhang Y-Y, Hu K-J, Zhao Y-Q, Li Y, Huang Z-R, Zhang X, Geng X-X, Ding J-H (2018) Characterization of the complete mitochondrial DNA of Theretra japonica and its phylogenetic position within the Sphingidae (Lepidoptera, Sphingidae). ZooKeys 754: 127-139. https://doi.org/10.3897/zookeys.754.23404
Figure 2 The amino acids usage in the mitogenome of T. japonica. CDpT = codons per thousand codons.
Figure 4 from: Li J, Lin R-R, Zhang Y-Y, Hu K-J, Zhao Y-Q, Li Y, Huang Z-R, Zhang X, Geng X-X, Ding J-H (2018) Characterization of the complete mitochondrial DNA of Theretra japonica and its phylogenetic position within the Sphingidae (Lepidoptera, Sphingidae). ZooKeys 754: 127-139. https://doi.org/10.3897/zookeys.754.23404
Figure 4 The cloverleaf secondary structure of transfer RNA of T. japonica.
Figure 48 from: González-Obando R, Aldrete ANG, Carrejo-Gironza N, Mendivil J (2018) A new genus of Ptiloneuridae, its position within the family, and descriptions of five species (Psocodea, 'Psocoptera'). ZooKeys 780: 11-34. https://doi.org/10.3897/zookeys.780.26753
Figure 48 Distribution of the species of Colocania.
Figure 3 from: Sukhorukov AP, Nilova MV, Krinitsina AA, Zaika MA, Erst AS, Shepherd KA (2018) Molecular phylogenetic data and seed coat anatomy resolve the generic position of some critical Chenopodioideae (Chenopodiaceae – Amaranthaceae) with reduced perianth segments. PhytoKeys 109: 103-128. https://doi.org/10.3897/phytokeys.109.28956
Figure 3 Pericarp of Blitumantarcticum. Scale bar: 200 μm.
Figure 6 from: Niu W, Yu S, Tian P, Xiao J (2018) Complete mitochondrial genome of Echinophyllia aspera (Scleractinia, Lobophylliidae): Mitogenome characterization and phylogenetic positioning. ZooKeys 793: 1-14. https://doi.org/10.3897/zookeys.793.28977
Figure 6 Putative secondary structures of two tRNA of Echinophylliaaspera.
Figure 5 from: Niu W, Yu S, Tian P, Xiao J (2018) Complete mitochondrial genome of Echinophyllia aspera (Scleractinia, Lobophylliidae): Mitogenome characterization and phylogenetic positioning. ZooKeys 793: 1-14. https://doi.org/10.3897/zookeys.793.28977
Figure 5 The PCG-codons use frequency of mitochondrial genome of Echinophylliaaspera.
Figure 4 from: Niu W, Yu S, Tian P, Xiao J (2018) Complete mitochondrial genome of Echinophyllia aspera (Scleractinia, Lobophylliidae): Mitogenome characterization and phylogenetic positioning. ZooKeys 793: 1-14. https://doi.org/10.3897/zookeys.793.28977
Figure 4 The PCGs' AT-skew and GC-skew of mitochondrial genome of Echinophylliaaspera.
Figure 3 from: Niu W, Yu S, Tian P, Xiao J (2018) Complete mitochondrial genome of Echinophyllia aspera (Scleractinia, Lobophylliidae): Mitogenome characterization and phylogenetic positioning. ZooKeys 793: 1-14. https://doi.org/10.3897/zookeys.793.28977
Figure 3 Codon usage bias in different regions of mitochondrial genome of Echinophylliaaspera.
Figure 1 from: Niu W, Yu S, Tian P, Xiao J (2018) Complete mitochondrial genome of Echinophyllia aspera (Scleractinia, Lobophylliidae): Mitogenome characterization and phylogenetic positioning. ZooKeys 793: 1-14. https://doi.org/10.3897/zookeys.793.28977
Figure 1 Example of Echinophylliaaspera used in the present study.
Coarse-grained simulations of cholesterol in symmetric lipid bilayers with restraints on cholesterol positions
<p>Membranes consisting of 520 phospholipids varying levels of chain unsaturation together with 56 (10 mol%) cholesterol molecules were simulated at 298 K. The phospholipids had either 2 (DLiPC) or 4 (DAPC) double bonds in both of their chains. The Martini force field [1] was used and the membranes were generated using insane [2]. In the simulations, a flat-bottom potential was applied to the cholesterol molecules to either prevent them from partitioning to the membrane core, or from performing complete flip–flops.</p> <p>The simulations were run for 10 microsecond using the GROMACS simulation suite [3]. Simulation parameters are found in the common mdp file (note that the temperature varies between simulations).</p> <p>The upload contains simulation inputs and outputs that allows the replication, extension, or analysis of the simulation data:</p> <ul> <li>Topology files (top) and molecular definitions (itp)</li> <li>Index files (ndx)</li> <li>Run parameter files (mdp)</li> <li>A run input file (tpr)</li> <li>Trajectory file (xtc) written every 1 ns</li> <li>Energy file (edr) written every 100 ps</li> <li>Log file (log)</li> <li>Final structure file (gro)</li> <li>Continue point file (cpt)</li> </ul> <p>The files are named LLLL_CG_CHOLXX_RR_TTT.FFF, where</p> <ul> <li>LLLL is the type of phospholipid</li> <li>CG stands for coarse-grained (All atom data in a separate upload)</li> <li>CHOLXX stands for the cholesterol concentration (CHOL10 for 10 mol%)</li> <li>TTT is the temperature</li> <li>RR is the width of the flat-bottom potential</li> <li>FFF is the tile type (see above)</li> </ul> <p>Note that topologies/index files are the same regardless of temperature, and hence their file names do not have the TTT section. Moreover, md_RR.mdp is common for both lipid types yet activates a different flat-bottom potential based on RR.</p> <p>[1] <strong>DOI: </strong>10.1021/jp071097f</p> <p>[2] <strong>DOI: </strong>10.1021/acs.jctc.5b00209</p> <p>[3] <strong>DOI: </strong>10.1016/j.softx.2015.06.001</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.