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184 results for “variability analysis”
Datasets for 'Reducing variability in along-tract analysis with diffusion profile realignment'
<p>Datasets we used in the manuscript 'Reducing variability in along-tract analysis with diffusion profile realignment'.</p> <p>Additional code available at https://github.com/samuelstjean/dpr</p>
Text-fig. 9. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83), as well as according to recent Howells's database (n = 2524, f = 1156, m = 1368) with variables M1 (GOL) and M45 (ZYB). in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 9. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83), as well as according to recent Howells's database (n = 2524, f = 1156, m = 1368) with variables M1 (GOL) and M45 (ZYB).
Text-fig. 10. Regional affinity of the Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using the Henke's Late Upper Palaeolithic and Mesolithic database (Europe, n = 76) with variables M1 (GOL), M5 (BNL), M8 (XCB), M40 (BPL), W – West Europe, CE – Central-East Europe, S – South Europe. in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 10. Regional affinity of the Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using the Henke's Late Upper Palaeolithic and Mesolithic database (Europe, n = 76) with variables M1 (GOL), M5 (BNL), M8 (XCB), M40 (BPL), W – West Europe, CE – Central-East Europe, S – South Europe.
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe. in Revision of common snipe, Gallinago gallinago in morphometric analysis and building the standard reference haematological values for further studies
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe.
Fig. 7 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 7. RW1/RW2 plot showing the neat separation of Krithe compressa from Krithe iniqua specimens. Deformation grids along RW1 (set at values of –0.2 and 0.2) are reported. A. Plot of RW1 against RW2 scores. B, C. Shell deformation at extreme values along RW1.
Fig. 4 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 4. Main morphological features of studied ostracods species. A. Krithe iniqua Abate, Barra, Aiello, and Bonaduce, 1993, right valve, transparence drawing from external view, sample 59, B.O.C. 2518, upper Pliocene, KI−29, sample 59. B. Krithe compressa (Seguenza, 1880), right valve, transparence drawing from external view, KC−29, sample 58, B.O.C. 2547, upper Pliocene.
Fig. 2 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 2. Krithe iniqua Abate, Barra, Aiello, and Bonaduce, 1993, right valves; transparence drawings from external view; sample 59; upper Pliocene. A. KI−01, B.O.C. 2490. B. KI−02, B.O.C. 2491. C. KI−03, B.O.C. 2492. D. KI−04, B.O.C. 2493. E. KI−05, B.O.C. 2494. F. KI−06, B.O.C. 2495. G. KI−07, B.O.C. 2496. H. KI−08, B.O.C. 2497. I. KI−09, B.O.C. 2498. J. KI−10, B.O.C. 2499. K. KI−11, B.O.C. 2500. I. KI−12, B.O.C. 2501. L. KI−13, B.O.C. 2502. M. KI−14, B.O.C. 2503. N. KI−15, B.O.C. 2504. O. KI−16, B.O.C. 2505. P. KI−17, B.O.C. 2506. Q. KI−18, B.O.C. 2507. R. KI−19, B.O.C. 2508. S. KI−20, B.O.C. 2509. T. KI−21, B.O.C. 2510. U. KI−22, B.O.C. 2511. V. KI−23, B.O.C. 2512. W. KI−24, B.O.C. 2513. Y. KI−25, B.O.C. 2514. Z. KI−26, B.O.C. 2515. AA. KI−27, B.O.C. 2516. BB. KI−28, B.O.C. 2517.
Fig. 9 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 9. Continuous shape variation in Krithe compressa valves drawn along RW 2. Deformation grids relate to specimen of the three different samples belonging to Krithe compressa from the highest (A) to the lowest (C) RW 2 scores (see Fig. 7). Deformation grid in B refers to undeformed shape. From the above, a valve from sample 58 (specimen KC 25), a specimen from sample 51 (KC 16), and a specimen from sample 50 (KC 1).
Fig. 5 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 5. The logarithm of number of pairs C of points with mutual distance smaller than R (̊m), as a function of log(R). Vertical dashed lines are the limits inside which the linear slope of log(C) on log(R) provides the best fitting to the data.
Fig. 1. A in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 1. A. Ideal uniform network of 225 points spaced 2 mm apart over an area of 30 × 30 mm2. B. The log of number of pairs C of the stations, with mutual distance smaller than R, as a function of log(R) (mm); the vertical dashed lines represent the lower (4 mm) and upper (16 mm) limits of R, inside which the linear slope provides the best fitting to the investigated co−ordinates.
Fig. 8 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 8. This plot is the same as in Fig. 7, except for marks have been appended according to sample of provenance instead of species.
Fig. 3 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 3. Krithe compressa (Seguenza, 1880), right valves; transparence drawings from external view; sample 50 (A–G), sample 51 (H–R), sample 58 (S–BB); upper Pliocene. A. KC−01, B.O.C. 2519. B. KC−02, B.O.C. 2520. C. KC−03, B.O.C. 2521. D. KC−04, B.O.C. 2522.E. KC−05, B.O.C. 2523. F. KC−06, B.O.C. 2524. G. KC−07, B.O.C. 2525. H. KC−08, B.O.C. 2526. I. KC−09, B.O.C. 2527. J. KC−10, B.O.C. 2528. K. KC−11, B.O.C. 2529. L. KC−12, B.O.C. 2530. M. KC−13, B.O.C. 2531. N. KC−14, B.O.C. 2532. O. KC−15, B.O.C. 2533. P. KC−16, B.O.C. 2534. Q. KC−17, B.O.C. 2535. R. KC−18, B.O.C. 2536. S. KC−19, B.O.C. 2537. T. KC−20, B.O.C. 2538. U. KC−21, B.O.C. 2539. V. KC−22, B.O.C. 2540. W. KC−23, B.O.C. 2541. X. KC−24, B.O.C. 2542. Y. KC−25, B.O.C. 2543. Z. KC−26, B.O.C. 2544. AA. KC−27, B.O.C. 2545. BB. KC−28, B.O.C. 2546.
Raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy "
<p>The folder contains raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using <sup>1</sup>H HR MAS NMR Spectroscopy ".</p> <p> </p> <p> Raw data measured on Bruker Avance III 400 MHz NMR spectrometer:</p> <p>- 1D <sup>1</sup>H HR MAS NMR spectra (path: <em>Patient_code – Sample_code/500/fid</em>)</p> <p>- 2D <sup>1</sup>H-<sup>1</sup>H J-resolved HR MAS NMR spectra (path: <em>Patient_code – Sample_code/600/ser</em>).</p> <p> </p> <p>Metadata is included in <em>Metadata.xlsx</em> file.</p> <p>Each sample is described with the following parameters:</p> <p>- patient code (after anonymization),</p> <p>- sample code (the label <em>l</em> or <em>r</em> denotes the <em>left</em> or <em>right</em> ovary in patients from whom samples were obtained bilaterally),</p> <p>- sample weight,</p> <p>- clinic-pathological parameters (such as: age, BMI, menopausal status, diagnosis, FIGO stage),</p> <p>- percentage tissue content obtained from histopathological analysis after HR MAS NMR studies (cancer cells, epithelial compartment within benign tumors, necrosis, inflammation, fibrosis, calcification, normal ovary, vessels, fatty tissue).</p> <p> </p> <p>Some samples were considered representative of particular tissue components:</p> <p>- cancer (HGSOC) compartment,</p> <p>- fibrotic stroma within malignant (HGSOC) tumors,</p> <p>- fibrotic stroma within benign tumors,</p> <p>- normal ovary tissue (the samples collected from the control group),</p> <p>- normal ovary tissue (the samples collected from the cancer patients),</p> <p>- necrosis,</p> <p>- non-tumoral fibrous tissue / fibrous tumor capsule (obtained from the patients with benign non-neoplastic lesions)</p> <p>- corpus albicans</p> <p>The assignment of the samples to these categories is indicated in the column <em>Tissue components.</em></p> <p><em> </em></p> <p>The samples classified as outliers in PCA model 1 are indicated in the column <em>Outliers</em>.</p> <p>The samples included in multivariate models are indicted in the columns: <em>PCA 2, PCA 3, PCA 4, PCA 5, PCA 5a, PCA 6, OPLS-DA 1, OPLS-DA 2, OPLS-DA 3, OPLS-DA 4, OPLS-DA 5, OPLS-DA 6 and OPLSR.</em></p> <p><em> </em></p>
Text-fig. 45. Scanning electron microscope (SEM) images of monocolpate pollen of Dinisia portugallica gen. et sp. nov. from a fragmentary stamen; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure; b) Two pollen grains showing poorly defined distal aperture (arrowhead) and distinctive vermiform reticulum forming luminae of variable shapes and sizes; note especially the irregularly and incomplete reticulum in the grain on the left; c) Reticulum showing smooth, vermiform muri attached to the smooth surface of the foot layer by long columellae; note that columellae often terminate segments of muri that are not closed; d, e) Pollen grains showing proximal surface (d), poorly defined distal aperture (e, arrowhead) and distinctive vermiform reticulum supported by long columellae; note dense covering of small, spherical orbicules on the inner surface of the anther wall. Specimen, TV44-S148216 (holotype). Scale bars 300 Μm (a), 6 Μm (b, d, e), 3 Μm (c). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 45. Scanning electron microscope (SEM) images of monocolpate pollen of Dinisia portugallica gen. et sp. nov. from a fragmentary stamen; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure; b) Two pollen grains showing poorly defined distal aperture (arrowhead) and distinctive vermiform reticulum forming luminae of variable shapes and sizes; note especially the irregularly and incomplete reticulum in the grain on the left; c) Reticulum showing smooth, vermiform muri attached to the smooth surface of the foot layer by long columellae; note that columellae often terminate segments of muri that are not closed; d, e) Pollen grains showing proximal surface (d), poorly defined distal aperture (e, arrowhead) and distinctive vermiform reticulum supported by long columellae; note dense covering of small, spherical orbicules on the inner surface of the anther wall. Specimen, TV44-S148216 (holotype). Scale bars 300 Μm (a), 6 Μm (b, d, e), 3 Μm (c).
Text-fig. 51. Scanning electron microscope (SEM) images of tricolpate pollen of Mcdougallia irregularis gen. et sp. nov. from a stamen fragment; Torres Vedras locality, Portugal. a) Holotype; stamen fragment that yielded the pollen in this Text-figure; b) Polar view of tricolpate pollen grain showing the continuous psilate tectum in the apocolpium and the poorly developed discontinuous foveolate-reticulate tectum in the mesocolpial areas, note irregular fold in tectum wall (arrowhead); c) Equatorial view of pollen grain showing the discontinuous tectum in the mesocolpial areas; d) Pollen grains showing variably developed tectum, colpi with a finely granular aperture membrane and the folds (arrowheads) associated with the irregular development of the colpi; e) Orbicule showing faintly striate surface; f) Pollen wall showing very short columellae, thin tectum and thin foot layer. Specimen, TV44-S148215 (holotype). Scale bars 300 Μm (a), 6 Μm (b–d), 3 Μm (e, f). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 51. Scanning electron microscope (SEM) images of tricolpate pollen of Mcdougallia irregularis gen. et sp. nov. from a stamen fragment; Torres Vedras locality, Portugal. a) Holotype; stamen fragment that yielded the pollen in this Text-figure; b) Polar view of tricolpate pollen grain showing the continuous psilate tectum in the apocolpium and the poorly developed discontinuous foveolate-reticulate tectum in the mesocolpial areas, note irregular fold in tectum wall (arrowhead); c) Equatorial view of pollen grain showing the discontinuous tectum in the mesocolpial areas; d) Pollen grains showing variably developed tectum, colpi with a finely granular aperture membrane and the folds (arrowheads) associated with the irregular development of the colpi; e) Orbicule showing faintly striate surface; f) Pollen wall showing very short columellae, thin tectum and thin foot layer. Specimen, TV44-S148215 (holotype). Scale bars 300 Μm (a), 6 Μm (b–d), 3 Μm (e, f).
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric).
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1).
Recording and analysing physical control variables used in clarinet playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT)
<p>Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a clarinettist while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. Applied to the clarinet, the instrument sensors record blowing pressure, reed position, tongue contact and sound pressures in the mouth, mouthpiece and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase or excerpt. An example of its application is given showing how to compare performances from different musicians.</p>
The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe
<p>This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 to 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from liquid fossil fuel combustion, mostly traffic (eBC<sub>T</sub>) was higher than that of residential and commercial sources (eBC<sub>RC</sub>) in all European sites studied. Nevertheless, eBC<sub>RC</sub> still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBC<sub>T</sub> over the last decade, while eBC<sub>RC</sub> remained relatively constant or even increased slightly in some cities.</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.