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525 results for “displacement”
Assessing Plant Phenological Character Displacement Across the Eastern United States Since 1895
Reproductive character displacement has long been hypothesized to be a key determinant of speciation and co-existence in flowering plants. A central tenet of this hypothesis is that reproductive traits of close relatives growing in sympatry diverge more than they do where close relatives do not grow together. However, this idea remains untested across taxa and at large spatial scales. Here, we use data collected from tens of thousands of herbarium specimens to examine evidence for character displacement in flowering time for 91 closely-related pairs of animal-pollinated angiosperm species in the eastern USA. We see no evidence for overall phenological divergence in sympatry across regions, clades, or life histories. Rather our results indicate widespread convergence of flowering times in sympatry for species pairs that generally tend to flower close in time. We also find that climate change could alter the nature of these convergent flowering events by shifting them further apart in a majority species pair comparisons. Specifically, congeneric species in New England and the Atlantic Coastal Plain are projected to flower 2–4 days further apart, on average, by the mid-21st century as warming temperatures drive species-specific phenological shifts within genera. This may have significant consequences for species interactions and gene flow, especially if current sympatric convergence in flowering times has resulted from facilitative interactions between species.
Optimal Displacement Increment for Numerical Frequencies (Dataset)
<p>1<em>H</em>-pyrrolo[3,2-<em>h</em>]quinoline [Gorski, 2012] was optimized in ORCA v3.0.3 [Neese, 2012; http://orcaforum.cec.mpg.de] using RPBE [Perdew, 1992 and 1996] with the def2-TZVP basis sets [Weigend, 1998], and the def2-TZVP/J auxiliary bases [Weigend, 2006] for the RI approximation [Vahtras, 1992]. The nuclear Hessian, normal modes, and harmonic vibrational frequencies were then computed using analytical (ANFREQ) and numerical (NUMFREQ) methodologies. The numerical Hessians were computed with nuclear (Cartesian) displacement increments ranging from 0.0001 Bohr to 0.1 Bohr. The geometry optimization was conducted using the parameters of the TIGHTOPT simple input keyword; KS-SCF and CP-SCF calculations used VERYTIGHTSCF thresholds.</p> <p>An analysis of the deviation of normal modes and harmonic frequencies for each numerical Hessian computation from the analytical Hessian results was presented as a single-figure presentation (SFP) at the 2016 Virtual Winterschool on Computational Chemistry (http://winterschool.cc). This SFP can be found at doi:10.5281/zenodo.44807.</p> <p>For the initial OPT and ANFREQ, the following files are provided:<br /> PQ_OPT_AFQ.engrad -- Gradient data<br /> PQ_OPT_AFQ.gbw -- Wavefunction <br /> PQ_OPT_AFQ.hess -- Hessian data<br /> PQ_OPT_AFQ.out -- Computation output<br /> PQ_OPT_AFQ.trj -- Optimization trajectory (multi-frame OpenBabel XYZ)<br /> PQ_OPT_AFQ.txt -- ORCA input file<br /> PQ_OPT_AFQ.xyz -- Optimized geometry</p> <p>For each following NUMFREQ, the following files are provided, where the number at the end of the filename indicates the nuclear displacement increment in Bohrs:<br /> PQ_NFQ_0.####.hess -- Hessian data<br /> PQ_NFQ_0.####.out -- Computation output<br /> PQ_NFQ_0.####.txt -- ORCA input file</p> <p>Since ORCA does not report non-mass-weighted normal modes, these are provided separately for each calculation as modes_0.####.csv (modes_A.csv for the analytical Hessian.)</p> <p>The dot products of each normal mode from the numerical Hessian computations with the corresponding mode in the analytical Hessian calculation (modes ordered as presented in the ORCA output) are provided in modes_dot_products.csv. The MAD of these data are plotted in the LH figure of the above-referenced SFP.</p> <p>For those numerical Hessian computations with normal modes out of sequence relative to the analytical calculation, permutation matrices to bring them back in accord with the analytical Hessian modes are included as swaps_0.####.csv.</p> <p>A table of the calculated vibrational frequencies for each computation, re-ordered as necessary to bring the normal modes in accord with the analytical Hessian run, is included as freqs_swapped.csv. The MAD and maximum absolute deviation of these data are plotted in the RH figure of the above-referenced SFP.</p>
Strong sequence dependence in RNA/DNA hybrid strand displacement kinetics supplementary data and code
<p>Supplementary data and code needed to replicate figures and results for the paper: Strong sequence-dependence in RNA/DNA hybrid strand displacement kinetics - Francesca G. Smith, John P. Goertz, Molly M. Stevens and Thomas E. Ouldridge. README is included to explain each folder and file in the repository.</p>
Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach
<p>Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.</p>
Displacement measurements of the open-hardware sandbox using the AS5311 high-resolution magnetic sensor
<p>This dataset includes the experimental data from the AS5311 sensor for measuring the displacement of the Open-Hardware Geological Sandbox.</p> <p>These experiments are explained in the journal article: <a href="https://doi.org/10.1109/ACCESS.2023.3262617">Designing low-cost open-hardware electromechanical scientific equipment: A geological analogue modeling sandbox</a></p> <p>To understand this dataset, go to the Tectonic Open Hardware (TectOH) Sandbox project: <a href="https://github.com/URJCMakerGroup/TectOH">https://github.com/URJCMakerGroup/TectOH</a>. Then go to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional">optional</a> folder and to the <a href="https://github.com/URJCMakerGroup/TectOH/tree/main/optional/as5311_magn_sens">magnetic sensor</a> folder.</p> <p>This data set contains two kind of files:</p> <ul> <li><strong>bin</strong>: raw binary files received from the AS5311 high resolution sensor. Although this sensor sends 12 bit data, we have truncated the most significant bits and receive only 8 bits (one byte). Therefore, each byte of these binary files is a measurement of the distance. Each distance increment corresponds to ~0.488nm (2mm/2048)</li> <li><strong>csv</strong>: csv files that can be opened with any spreadsheet app, such as Libreoffice Calc or Microsoft Excel, or even with a text editor. This file contains the processed data from the binary files. These files have been generated with the proc_magn_sensor.py Python script located in the <a href="https://github.com/URJCMakerGroup/TectOH">project repository</a>. There are some columns, which are: <ul> <li>index: measurement number</li> <li>time in milliseconds: each measurement is taken every 250 us</li> <li>median2: in micrometers, since the sensor may jitter, we have applied the median filter twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>median1: in micrometers, median filter only applied once. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean: in micrometers, mean filter. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>mean int: in micrometers, mean filter rounded to an integer value. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>orig_base: this is not in micrometers, but in the units of the sensor (~0.488nm). The only processing done is that when there is an overflow of 255 to 0, or from 0 to 255, it adds the overflow to continue the trend. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> <li>original: this is the data received from the sensor with no processing, each value is ~0.488nm</li> <li>mean2: in micrometers, mean filter applied twice. All the data has been subtracted to the lowest value, making the lowest value equal to zero.</li> </ul> </li> </ul> <p>There are two set of experiments:</p> <ul> <li><strong>Experiments with no load</strong>. These files start with <em>noload_</em><br> In these experiments the gantry is moved 1 mm alternatively to the front and then reversing direction. Moving in this alternate way a few times. There are five experiments each of them with a different speed: v= 10 mm/h; 25 mm/h; 50 mm/h; 82 mm/h and 100 mm/h. The name of the file indicates the speed: <ol> <li>noload_100mmh_1mm: FBFBF: 1mm forth, 1mm back, 1mm forth, 1mm back, 1 mm forth</li> <li>noload_25mmh_1mm: FBFFBBFB</li> <li>noload_50mmh_1mm: FBFBFB</li> <li>noload_82mmh_1mm: FBFBFB</li> <li>noload_100mmh_1mm: FBFBFB</li> </ol> </li> <li><strong>Experiments pushing a 5kg sand load</strong>. These files start with <em>load5kg_</em> <ol> <li>load5kg_25mmh_5mm: moving 5kg at 25mm/h a distance of 5mm</li> <li>load5kg_25mmh_10mm: moving 5kg at 25mm/h a distance of 10mm</li> <li>load5kg_25mmh_20mm: moving 5kg at 25mm/h a distance of 20mm</li> <li>load5kg_75mmh_20mm: moving 5kg at 75mm/h a distance of 20mm</li> <li>load5kg_75mmh_50mm: moving 5kg at 75mm/h a distance of 50mm</li> <li>load5kg_100mmh_25mm: moving 5kg at 100mm/h a distance of 20mm</li> <li>load5kg_100mmh_50mm: moving 5kg at 100mm/h a distance of 50mm</li> </ol> </li> </ul> <p> </p> <p> </p> <p> </p>
Experimental load-displacement response of a stainless steel specimen under cyclic loading
<p>Experimental load-displacement data was obtained by testing a autenitic stainless steel RHS member under cyclic bending around its major axis following a cantilever loading scheme. The database is comprised of the horizontal displacement measured by a string potentiometer attached at the free end of the specimen and the horizontal force introduced by the actuator. </p> <p>The data corresponds to the specimen named S1-L1, which was an austenitic stainless steel RHS specimen, with a cross-section S1 (120 × 80 × 6 mm) and an effective length of L1 = 1650 mm. </p> <p>The full details of the experimental test can be found in: González-de-León I., Nastri E., Arrayago I., Montuori R., Piluso V., Real E. Experimental study on stainless steel tubular members under cyclic loading. Thin-Walled Structures 181, 109969, 2022. DOI: https://doi.org/10.1016/j.tws.2022.109969.</p>
InSAR Displacements in the Delaware Basin, TX
<p>These data are the vertical and east-west horizontal cumulative displacements in the Delaware Basin, between 2015-03-05 through 2020-03-31. They are presented in "Shallow Aseismic Slip in the Delaware Basin Determined by Sentinel-1 InSAR", submitted to <em>JGR: Solid Earth</em> on September 1st, 2021. </p> <p>The format of both files is [longitude, latitude, X, Y, displacement (cm)].</p> <p>For vertical displacements, negative values indicate subsidence and positive values indicate uplift. For horizontal displacement, negative values indicate westward displacement, and positive values indicate eastward displacement.</p> <p>Version 2 (_v2) were updated Dec. 28th, 2021.</p>
Data for "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography"
<p>Raw data used to create figures for the paper "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography" <a href="https://doi.org/10.1016/j.precisioneng.2021.06.002">https://doi.org/10.1016/j.precisioneng.2021.06.002</a></p> <p>Data is available in tab delimited format (.txt) and in Excel (.xls).</p> <p> </p>
3-D displacement field produced by the 1959 Hebgen Lake earthquake
<p>The dataset contains EW, NS and vertical displacement component associated with the 1959 Hebgen Lake earthquake. The displacement maps were calculated from the aerial images collected in 1947 (pre-earthquake) and 1977 (post-earthquake). </p>
2-D ultrasound videos of the distal biceps brachii myotendinous junction displacement over varying elbow angles and tendon loads
<p>This publication contains data which was recorded during an experimental measurement campaign and was used to train a model of the <em>biceps brachii</em> distal tendon to predict myotendinous junction displacement over varying joint angles and tendon forces.</p> <p>Further Information about the data set is contained within the README.pdf file.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, <a href="https://www.dfg.de/en/">https://www.dfg.de/en/</a>) - ref. no. SCHN 1339/3-1, by the Federal Ministry of Education and Research (BMBF) within the project ITS.ML - ID 01IS18041 A (AS) and by the research training Group "DataNinja" funded by the German federal state of North Rhine-Westphalia.</p>
3D displacement field and fault-offset measurements for the northern Kaikōura ruptures
<p>Contents:</p> <p>1. East, north and vertical components of the co-seismic displacement field for three faults (the Kekerengu, Jordan and Upper Kowhai faults) that ruptured in the 2016 Kaikoura earthquake, New Zealand (east.tif, north.tif, vertical.tif).</p> <p>2. Shapefiles containing offsets across the faults of interest, measured from the displacement field (shapefiles.zip).</p> <p>3. CSV files containing offsets across the faults of interest, measured from the displacement field (csvs.zip). </p> <p>Our methodology is described in the following manuscript:</p> <p>Howell et al., 2019. 3D surface displacements during the 2016 MW 7.8 Kaikōura earthquake (New Zealand) from photogrammetry-derived point clouds, Journal of Geophysical Research Solid Earth, submitted.</p>
Displacements on selected rock glaciers in the United States
<p>Lateral displacements between two years on selected rock glaciers in the United States (Rocky Mountains and westwards). Displacements were measured using image correlation on repeat orthoimages from the United States Geological Survey archive. Correlation software used: CIAS (http://mn.uio.no/icemass).</p> <p><em><strong>The data are the raw data behind the following publication. This publication contains more details about the measurements. </strong></em></p> <p><a href="https://www.nature.com/articles/s41467-024-52093-z">Kääb, A., Røste, J. Rock glaciers across the United States predominantly accelerate coincident with rise in air temperatures. Nat Commun 15, 7581 (2024). https://doi.org/10.1038/s41467-024-52093-z</a></p> <h3>Detailed rock glacier positions [lat°, lon°]:</h3> <p>Star Peak [44.253,-120.417]<br>Galena creek [44.645,-109.791]<br>Sulphur creek [44.617,-109.756]<br>Crater Mtn [44.023,-109.627]<br>Old Hyndman [43.743, -114.106]<br>Ferguson ranch [39.270, -107.194]<br>Thomas lake [39.270,-107.155]<br>Arapaho [40.020,-105.641]<br>Mt Mears [38.018,-107.871]<br>Mt Sneffels [38.010,-107.781]<br>Teakettle Mtn [38.011,-107.768]<br>Twin Sisters [37.762,-107.803]<br>Pine creek [37.078,-118.450]<br>Birch creek [37.065,-118.431]<br>Cardinal Mtn N [37.011,-118.414]<br>Cardinal Mtn S [37.008,-118.409].</p> <h3>Names of individual displacement files:</h3> <p>Short-name-of-rock-glacier_year1_year2_correlation-window-size_search-window-size_xxx.txt/.csv<br>xxx is either 'helm' indicating that the two orthoimages have been coregistered using Helmert transformation, or 'filt' indicating Helmert transformation and filtering of grid-based measurements for outliers. </p> <h3>Data columns of each file:</h3> <p>X: UTM coordinate, Easting of displacement measurement point <br>Y: UTM coordinate, Northing of displacement measurement point<br>dx: displacement component in east<br>dy: displacement component in north<br>length: pythagoras of dx and dy (vector length)<br>direction: azimuth of vector (from North, clock-wise)<br>max_corrcoeff: correlation coefficient of displacement match<br>avg_corrcoeff: background correlation coefficent at a matching poistion. <br><em><strong> </strong></em></p> <p> </p>
ICP Displacement Fields from the 2016 Mw 7.8 Kaikōura Earthquake over the Papatea Fault, New Zealand
<p>Three-dimensional displacement fields produced over the Papatea Fault, South Island, New Zealand following the 2016 Mw 7.8 Kaikōura earthquake. The dataset was generated from pre- and post-event aerial image point clouds using a windowed implementation of the iterative closest point algorithm.</p> <p>Creation Date: 8/28/2021</p> <p>Authors: Colin Bloom (University of Canterbury, Christchurch, New Zealand), Tim Stahl (University of Canterbury), and Andy Howell (University of Canterbury/GNS Science, Lower Hutt, New Zealand)</p> <p>Projection: New Zealand Transverse Mercator</p> <p>Scale: 25 m/pixel</p> <p>Notes: There are three displacement directions, east, north, and vertical. Positive values represent east, north, and up in the vertical direction respectively in relation to the pre-event surface. Displacement values are in meters. Extremely high or low data values likely represent noise in the dataset.</p>
Water displacement device, engines characterization
<p>This dataset contains 44 formulations (rows), 23 features and 1 supervisor factors (24 columns).</p> <p>This data have been extracted from a water displacement device that measures the volume of submerged samples. The displaced water passes through a flow sensor that produces pulses. The features in the dataset are the times between the first 20 pulses (t0 - t19), the total pulse counting (pc), the total operation time (tt) and the DC (dc) component on each experiment (formulation). The supervising value is the volume of marbles with different diameters.</p> <p>The work consist on tuning the water displacement device so it yields accurate volumes from pulse patterns.</p>
Arctic shoreline displacement and validation data for two pilot study areas
<p>Arctic shoreline displacement data for two pilot study areas are supporting information for the paper <em>Nylén, Calle-Navarro and Gonzales-Inca: Arctic shoreline displacement with open satellite imagery and data fusion – Pilot study 1984–2022</em><em>. </em>The two study areas are:</p> <ul> <li>Tanafjorden: a low-arctic meso-tidal fjord coast in mainland Norway</li> <li>Ny-Ålesund: a high-arctic micro-tidal glaciated coast in north-western Svalbard</li> </ul> <p>The study areas are 2500 km² each.</p> <p>The dataset includes following files:</p> <ul> <li><em>Calculating_coastal_landcover_timeseries_summaries.R</em>: code for summarizing the coastal land cover time-series in the R software.</li> <li><em>Calculating_shoreline_timeseries.R</em>: code for calculating the shoreline by fitting and smoothing a polyline to the land cover raster in the R software.</li> <li><em>X_timeseries.tif</em>: a multiband GeoTIFF raster file, with each band describing coastal land cover during one of the eight time-steps (1984–1988, 1989–1993, 1994–1998, 1999–2003, 2004–2008, 2009–2013, 2014–2018 and 2019–2022).</li> <li><em>X_summary.tif</em>: a multiband GeoTIFF raster file, with bands that summarize the time-series from different viewpoints. These summary variables are: probability of belonging to the land class, long-term trend (between 1984-2003 and 2004-2022), change intensity, first time-step in water class, last time-step in water class, first time-step in land class and last time-step in land class.</li> <li><em>X_shoreline.geojson</em>: a GeoJSON vector file, consisting of polylines for the shoreline during each time-step. The attributes of the polyline layer describe the time-step and the total length of the shoreline.</li> <li><em>NyAlesund_timeseries_REDUCED.tif</em>: a reduced time-series for the Ny-Ålesund study area, including only the time-steps with adequate number of observations (i.e., excluding 1984–1988, 1994–1998 and 2004–2008).</li> <li><em>X_reference_shoreline.geojson</em>: a GeoJSON vector file including the manually digitized (scale 1/5000) reference shoreline corresponding to the time-step 2019–2022.</li> <li><em>X_validationpoints.geojson</em>: a GeoJSON vector file including 2000 random points (within 2 km from the reference shoreline) that have been manually classified into water and land. The classification corresponds to the time-step 2019–2022.</li> </ul>
New fault slip distribution for the 2010 Mw 7.2 El Mayor Cucapah earthquake based on realistic 3D finite element inversions of coseismic displacements using space geodetic data
<p>The .csv files included in this repository contain the data used in the numerical model as input, while the .txt file is the output (slip on a regular grid of points on the fault planes from the joint inversion of the geodetic datasets.</p>
FIGURES 10 – 15 in Mature larva of Stenichnus godarti (Latreille) (Coleoptera: Staphylinidae, Scydmaeninae): redescription, hypothesis of displaced epicranial suture and alternative interpretation of homology between chaetotaxic structures
FIGURES 10 – 15. Larva of Stenichnus godarti. Head in dorsal (10) and ventral (11) views; left (12) and right (13) antenna in dorsal view; right (14) and left (15) maxilla in ventral view. Abbreviations: Ag, antennal gland; An 1 – 3, antennomere I – III; Cd, cardo; Da, dorsoanterior seta; De, dorsoepicranial seta; Df, dorsofrontal seta; Dl, dorsolateral seta; Dp, dorsoposterior seta; Es, frontal arm of epicranial suture; Est, epicranial stem; L, lateral seta; l, lentiform structure; La, labral anterior seta; Ma, mala; Md. mandible; MdS, mandibular seta; Mn, mentum; Mxp 1 – 3, maxillary palpomere II – III; Ptp, posterior tentorial pit; SA, sensory appendage; Smn, submentum; sol, solenidion; St, stemma; Stp, stipes; V, ventral seta; Va, ventroanterior seta; Vl, ventrolateral seta.
X, Y displacement in miniLM
<p>X, Y landmark positions from miniLM mounted in Gatan 3View SBF-SEM. The difference between each point gives the slice-to-slice jitter in the positioning of the microscope. Scale 0.376 µm per pixel.</p>
Supplementary information for "Anharmonic origin of large thermal displacements in the metal-organic framework UiO-67"
<p>Supplementary information for DOI: 10.1021/acs.jpcc.7b04757</p> <p>POSCAR-XXX: DFT optimised structures</p> <p>Phonons-XXX.zip: Folders containing the force constants (FORCE_SETS), the resulting phonon frequencies (mesh.yaml), phonon partial density of states (partial_dos.dat), animations of all phonon modes (anime.ascii) e.g. to be visualized in VMD and gifs of selected phonon modes. </p> <p>XDATCAR-XXX: MD trajectories</p>
A dataset of seabird collision and displacement vulnerability factors relatively to marine wind farms in Portugal
<p>The implementation of marine wind farms has grown considerably along northern European's northern Atlantic coasts (e.g. Baltic and North Sea) and a boom in these infrastructures is expected to take place along Europe's entire Atlantic and Mediterranean coasts. Accordingly, the Portuguese government has recently proposed priority sites for the construction of wind farms along the mainland coast. We used sensitivity mapping (Garthe & Hüppop, 2004) to assess which areas along the Portuguese coast are most sensitive for seabirds and to what extent the proposed sites for wind farm construction overlap with these areas.</p><p>This dataset contains the base data to estimate a seabird Species Sensitivity Index (SSI) (following Bradbury et al., 2014, Certain et al., 2015), including scores for 11 species-specific ecological and behavioural factors related with seabird species' (i) vulnerability to collision with wind farms (4 factors), (ii) vulnerability to displacement due to disturbance by wind farms and associated maintenance (3 factors), and (iii) conservation status (4 factors). </p><p>We reviewed the literature to mine and compile data on these factors for 34 seabird species that regularly occur along the Portuguese mainland coast. We updated factor scores, particularly for those factors that have been studied in greater detail in recent years using tracking technologies (Clairbaux & Jessopp, 2021). However, in many cases empirical data were unavailable and we used the scores presented in previous sensitivity mapping studies (Garthe & Hüppop, 2004; Bradbury et al., 2014; Certain et al., 2015; Wade et al., 2016; Serratosa & Allinson, 2022).</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.