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849 results for “linear”
KaKiOS-16: a probabilistic, non-linear, absolute location catalog of the 1981-2011 Southern California seismicity
<p>This is the KaKiOS-16 earthquake catalog for southern California. We locate the southern California seismicity using the state-of-the-art probabilistic and nonlinear method NonLinLoc. We use only the P wavepicks to avoid introducing the velocity-model and picking-time errors of the S phase, which is harder to detect and thus less constrained. Using a subset of the best locatable earthquakes, we conduct a joint inversion using the VELEST software to obtain a minimum 1D velocity model and station corrections. We use the NonLinLoc method with this 1D velocity model and the inferred model uncertainties to obtain realistic location distributions for each event.<br> </p>
FIGURE 2. Landmarks used for obtain the linear measurements, 1 in Two new species of the genus Xenotoca Hubbs and Turner, 1939 (Teleostei, Goodeidae) from central-western Mexico
FIGURE 2. Landmarks used for obtain the linear measurements, 1 to 9 standard length (SL); 1 to 5 head length (HL); 4 to 17 head high (HH); 1 to 2 preorbital length (PrOL); 3 to 5 postorbital length (POL); 2 to 3 eye diameter (ED); 8 to 11 body least depth (BLD); 13 to 14 pelvic-anal fin distance (PAD); 14 to 6 pelvic-dorsal fin distance (PDD); 14 to 15 pelvic-pectoral fin distance (PPD); 6 to 13 dorsal-anal fin distance (DAD); 6 to 12 dorsal fin origin to anal fin posterior extent distance (DOAE); 7 to 13 dorsal fin posterior extent to anal fin origin distance (DEAO); 7 to 9 end of dorsal fin-hypural plate distance (EDHP); 9 to 12 end of the anal fin-hypural plate distance (EAHP); 6 to 7 dorsal fin base length (DFL); 12 to 13 anal fin base length (AFL); 15 to 16 pectoral fin base length (PFL); 10 to 12 caudal peduncle length (CPL).
Linear Time Varying System Examples for Model Order Reduction
<p>Three linear time varying system benchmarks implemented in MATLAB. </p> <p>1.) a time varying version of the Oberwolfach Steel Cooling Benchmark</p> <p>2.) a one dimensional heat equation with a moving point heat source</p> <p>3.) a linearized Burgers equation</p> <p>Model 1 comes in the same 5 resolutions as the original time-invariant version. the other two are freely scalable.</p> <p> </p>
aps_linear_patches
<div> <div> <div> <div> <div> <div> <div> <p dir="auto">just use the command: aps_linear_patches('y',0, 'hgt', 'lonlat', 'phuw', [101.5 102.3], [ 29.34 39.92], isosata_parameter)</p> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div>
Some results on self-complementary linear codes
<p>We present classification results for self-complementary codes with maximum possible minimum distance and length up to 20. Each directory contains information files for codes with the given length e.g. the file "9_3_4.2_m_m" gives information for binary linear self-complementary [9,3,4] codes. Each file gives generator matrices and weight distribution of each code. </p>
Fig. 1. Seta shape terminologies. 1. Fine. 2–3. Stout and truncated. 4–5. Plank-like. 6–7. Acicular. 8. Narrowly elliptic. 9. Elliptic. 10. Linear. 11 in Further additions to the knowledge of Strumigenys (Formicidae: Myrmicinae) within South East Asia, with the descriptions of 20 new species
Fig. 1. Seta shape terminologies. 1. Fine. 2–3. Stout and truncated. 4–5. Plank-like. 6–7. Acicular. 8. Narrowly elliptic. 9. Elliptic. 10. Linear. 11. Short linear or short subspatulate. 12–13. Subspatulate. 14–15. Spatulate. 16–17. Oblanceolate. 18–19. Small obovate. 20. Large obovate. 21. Suborbicular / orbicular. 22. Orbicular. 23. Remiform. 24. Remiform / narrowly claviform (red arrow). 25–26. Claviform. 27. Shoehorn-shaped. 28. Spoon-shaped.
Fig. 1. Merodon aureus Fabricius, 1805 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 1. Merodon aureus Fabricius, 1805, ♂, right wing with the character used in linear morphometric: a = intersection of R4+5 with r-m vein; b = intersection of R4+5 vein with a line drawn in the middle between a and c; c = the intersection of R4+5 with M1 vein; D = the angle formed by the lines that connect a, b and c.
Fig. 4 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 4. Results of the geometric morphometric wing shape analysis of species of the Merodon aureus complex. A. Scatter plot of individual scores showing R4+5 vein shape variability. B. Scatter plot of individual scores showing wing shape variability from Vujić et al. (2020c). C. Scatter plot of individual scores showing semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing R4+5 vein shape differences among investigated species.
Fig. 5 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 5. Results of the geometric morphometric wing shape analysis of males of the Merodon natans group. A. Scatter plot of individual scores showing the R4+5 vein shape variability. B. Scatter plot of individual scores showing the wing shape variability from Vujić et al. (2021c). C. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing R4+5 vein shape differences among males of the investigated species.
Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle
Fig. 2. Merodon aureus Fabricius, 1805 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 2. Merodon aureus Fabricius, 1805, ♂, right wing with the location of 20 semilandmarks selected for geometric morphometric analysis.
Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle line for all species used in the analysis.
Fig. 7 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 7. Results of the geometric morphometric wing shape analysis of males of the Merodon clavipes and pruni groups. A–B. Scatter plot of individual scores showing the R4+5 vein shape variability. C–D. Scatter plot of individual scores showing the wing shape variability from Vujić et al. (in prep.). E–F. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability. G–H. Superimposed outline drawings showing the R4+5 vein shape differences between the males of the investigated species.
Fig. 6 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 6. Results of the geometric morphometric wing shape analysis of females of the Merodon natans group. A. Scatter plot of individual scores showing the R4+5 vein shape variability. B. Scatter plot of individual scores showing wing the shape variability from Vujić et al. (2021c). C. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing the R4+5 vein shape differences among females of the investigated species.
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm.
Demonstrations for imitation learning for the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives"
<p>Demonstrations for the coathanger experiment in the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives". https://elib.dlr.de/205110/</p>
X-ray linear dichroic tomography of crystallographic and topological defects
<p>Open Data for "X-ray linear dichroic tomography of crystallographic and topological defects" published in <a href="https://www.nature.com/articles/s41586-024-08233-y">Nature <strong>636</strong>, 354 (2024) </a></p> <div> </div> <div>Full citation:</div> <div>A. Apseros, V. Scagnoli, M. Holler, M. Guizar-Sicairos, Z. Gao, C. Appel, L. J. Heyderman, C. Donnelly & J. Ihli </div> <div>X-ray linear dichroic tomography of crystallographic and topological defects.</div> <div><em>Nature <strong>636</strong>, 354</em> (2024).</div> <div>https://www.nature.com/articles/s41586-024-08233-y</div>
Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations
<p>This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations. </p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">the MMGP paper</a> Sections 4.1 and A.2.</p> <p>The file format is PLAID, see <a href="https://plaid-lib.readthedocs.io/ ">the plaid documentation</a>.</p> <p>The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.</p> <p>Seven nested training sets of sizes 8 to 500 are provided, with complete input-output data. A testing set of size 200, as well as two out-of-distribution sample, are provided, for which outputs are not provided. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p>from plaid.containers.dataset import Dataset<br>from plaid.problem_definition import ProblemDefinition</p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())<br>print("nodal_tags =", sample.get_nodal_tags())</p> <p> </p>
Probabilistic linear inversion of satellite gravity gradient data applied to the northeast Atlantic
<p>% MATLAB scripts to calculate and plot figures as in manuscript by<br> %<br> % Minakov, A., & Gaina, C. (2021).<br> % Probabilistic linear inversion of satellite gravity gradient data applied<br> % to the northeast Atlantic. Journal of Geophysical Research: Solid Earth,<br> % 126, e2021JB021854. https://doi.org/10.1029/2021JB021854<br> % <br> % Last modified by alexamin@uio.no, 26/11/2021<br> %<br> % version v1.1<br> % </p> <p>% Contents of arhcive<br> % /data contains requiried and generated datasets <br> % /fig folder for output figures <br> % /plot scripts to produce figures <br> % /tools additional matlab tools and routines</p> <p>% Dataset in ..data/GOCE_NEATLANTIC is structure containing the full model<br> % <br> % Cm: [6670×6670 double] posterior model covariance matrix<br> % m: [29×23×10 double] mean denstity perturbation model<br> % Cd: [667×667 double] data covariance matrix<br> % d: [29×23 double] data vector (Trr)<br> % r: [1×10 double] distance<br> % lat: [29×1 double] latitute<br> % lon: [23×1 double] longitude<br> %<br> % Run /plot/fig_results.m to produce all figures <br> %<br> % Some scripts require GMT (Wessel et al. 2019) and SHBUNDLE (Sneeuw et al. 2018) software to be installed</p> <p>% and corresponding folders must be added to the matlab search path.</p> <p>% Also ScientificColorMaps7 by F. Crameri (2021) maybe required and have been included in the archive.</p>
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
ScienceDex guides
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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.