Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “linear measurements”
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<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 measurements of Aterian tanged stone artifacts from North African collections
<p>Linear measurements collected from Aterian tanged tools from various museum collections in Morocco and France. Edge lengths were computed using photographs of the artifacts and ImageJ. </p>
Fig. 7 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 7. Simulated and observed values of Borges et al.'s (Borges et al. 2019) δ values for simulated trait distributions on the tree in Fig. 1 for sets of traits (A, m1: m2 length; B, M1: M2 length; C, M2: M3 length) that show phylogenetic signal (red triangles) and phylogenetic retention (gray circles).
Fig. 2 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 2. Illustration of measurements taken on lower (m1–m3, A–C) and upper (M1–M3, D–F) teeth in occlusal views to calculate potentially diagnostic traits. Measurements are illustrated on specimen UMZC H5101A, Procavia capensis. Abbreviations: LE, length; MW, width of the crown at metaloph; PW width of the crown at paraloph.
Fig. 6 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 6. Overlap in potentially diagnostic trait values for upper molar loci of three example hyracoid taxa (A1, B1, Procavia capensis; A2, B2, Saghatherium bowni; A3, B3, Thyrohyrax meyeri). A. Length vs. proportional frequency, showing how a single trait, length, would be modeled in a univariate discriminant analysis using observed means and variances. Colored sections of the distributions show length values that are within 95% confidence intervals of the means of more than one tooth locus, indicating regions of ambiguous lengths. B. Length vs. relative width, showing scatter plots overlaid on 2D density diagrams showing the distribution of values for individual teeth.
Fig. 8 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 8. Overlap in potentially diagnostic trait values for a case study of isolated molars of Meroehyrax kyongoi. In contrast to Fig. 4, molar locus identifications are based on occupation of space in this scatterplot. Question marks are overlaid over two specimens whose inferred tooth position conflicts with published diagnoses. In parentheses original identification in publication.
Fig. 1 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 1. Phylogenetic tree and tooth size distribution in hyracoids (topology from Cooper et al. 2014). Taxa are time-scaled along the x-axis of the tree to reflect fossil occurrences based on the literature, with branches rescaled between these tip dates and a root age estimated at 70.1 million years. Taxa in bold text were included in analyses. Minimum monophyletic clade including taxa in bold indicates the range of the phylogenetic bracket applied for both length and width measures (base of clade indicated by black star). Minimum monophyletic clade for length measures from the literature is indicated by a white star at the base of the clade. Shapes to the right of tips indicate whether there is a significant fit with a model of ascending (increasing) tooth size down the molar row. Abbreviations: M, upper molars; m, lower molars.
Fig. 3 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 3. Distribution of values for a set of univariate, potentially locus-diagnostic traits (A, length vs. m1 length; B, trigonid width vs. talonic width) described in Fig. 2 in lower molars of a range of hyracoid species.
Fig. 5 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 5. Distribution of values for a set of univariate, potentially locus-diagnostic traits (A, length vs. M1 length; B, paraloph vs. metaloph; C, metaloph vs. length; D, paraloph vs. length) described in Fig. 2 in upper molars of a range of hyracoid species.
Fig. 4 in Evaluating the utility of linear measurements to identify isolated tooth loci of extinct Hyracoidea
Fig. 4. Overlap in potentially diagnostic trait values for lower molar loci of three example hyracoid taxa (A1, B1, Procavia capensis; A2, B2, Saghatherium bowni; A3, B3, Thyrohyrax domorictus). A. Length vs. proportional frequency, showing how a single trait, length, would be modeled in a univariate discriminant analysis using observed means and variances. Colored sections of the distributions show length values that are within 95% confidence intervals of the means of more than one tooth locus, indicating regions of ambiguous lengths. B. Length vs. relative width, showing scatter plots overlaid on 2D density diagrams showing the distribution of values for individual teeth.
METADATA for results of irradiation-induced complex DNA damage measurements using plasmid pBR322 along a typical Proton Treatment Plan at the MedAustron proton and carbon beam therapy facility (energy 137–198 MeV and Linear Energy Transfer (LET) range 1–9 keV/μm), by means of Agarose Gel Electrophoresis and DNA fragmentation using Atomic Force Microscopy (AFM)
Open the record for dataset details and reuse information.
FIGURE 1. Linear measurements used for morphological comparison. B-L in Fossil moles from the Gray Fossil Site (Tennessee): Implications for diversification and evolution of North American Talpidae
FIGURE 1. Linear measurements used for morphological comparison. B-L - buccolingual width; upper teeth - posterior-most tip of the metastyle to the lingual margin of the protocone; lower teeth - lingual-most tip of the metaconid to the buccal margin of the talonid. A-P - anteroposterior length; upper teeth - posterior-most tip of the metastyle to the anterior-most tip of the parastyle; lower teeth - anterior tip of the trigonid (paraconid) to the posterior end of the talonid (entostylid). TRI - width of the trigonid. TAL - width of the taloned basin. HL - total humerus length; proximal-most tip of greater tuberosity to distal-most point on capitulum. PW - width of the proximal end of the humerus: lateral most extent of the lesser tuberosity to the medial most aspect of the greater tuberosity. GT - length of the greater tuberosity. LT - length of the lesser tuberosity. DW - width of the distal end of the humerus: lateral most extent (entepicondylar process) to the medial most extent (ectepicondylar process). RL - total radius length: proximal-most tip of capitular process to distal-most tip of lunar articular facet. DL - length of the diaphysis. UF - length of the lunar articular facet. TUL - total ulna length; proximal-most tip of olecranon process to distal-most tip of terminal process. UL - ulna length without the olecranon fossa. Elements not to scale.
MATLAB scripts and raw experimental data for the paper "Optimizing measurements of linear changes of NMR signal parameters" by Javier Agustin Romero, Krzysztof Kazimierczuk and Paweł Kasprzak
<p>Classical_fit.m - Comparison of simulation results and theoretical predictions for the linear fit of the resonance frequencies. </p> <p>Radon_transform - the same, but using Radon transform to determine linear coefficients.</p> <p>Amplitude.m - Comparison of errors of the linear coefficients for varying amplitude fit in simulations and theory.</p> <p>process_measurements.m - script to process experimental data (caffeine peak at 7.90 ppm). The data are stored in real.mat and imag.mat</p> <p>For the details of theoretical formulas, see the paper "Optimizing measurements of linear changes of NMR signal parameters" by Javier Agustin Romero, Krzysztof Kazimierczuk, and Paweł Kasprzak. The scripts were used to generate Figures in the paper.</p>
Data from: A method of separating linear internal wave and vortical mode energies using shipboard ADCP velocity measurements
Open the record for dataset details and reuse information.
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).
TEXT-FIGURE 3. Scatterplots of morphometric measurements of Thecidellina leipnitzae sp. nov. Abbreviations: L, length; W, width; LDV, length of dorsal valve; T (max), maximal thickness; Lint, length of the interarea, Wint, width of hinge line or interarea. Relationships between ratios L/W and width, LDV/W and width, T(max)/W and width, Lint/W and width and Wint/W and width. Linear regression and regression coefficient (R²) indicated. The regression coefficient (R²) is indicated. N is the number of specimens measured. in Recent thecideide brachiopods from a submarine cave in the Department of Mayotte (France), northern Mozambique Channel
TEXT-FIGURE 3. Scatterplots of morphometric measurements of Thecidellina leipnitzae sp. nov. Abbreviations: L, length; W, width; LDV, length of dorsal valve; T (max), maximal thickness; Lint, length of the interarea, Wint, width of hinge line or interarea. Relationships between ratios L/W and width, LDV/W and width, T(max)/W and width, Lint/W and width and Wint/W and width. Linear regression and regression coefficient (R²) indicated. The regression coefficient (R²) is indicated. N is the number of specimens measured.
Dataset for paper "Field sensor for linearity measurement of high voltage capacitor"
<p>Dataset for publication <i>Field sensor for linearity measurement of high voltage capacitor</i> by Esa-Pekka Suomalainen, Jari Hällström, and Kari Lahti, <a href="https://doi.org/10.5281/zenodo.8251648">https://doi.org/10.5281/zenodo.8251648</a> </p><p> </p>
Cardiac Linear and Torsional Contractility Measurements With a New Technology: Multi Dimensional Kineticardiography
ClinicalTrials.gov study NCT03107351. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.