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.
10,554
datasets available to search
ShareScore release 0.9.0
Dataset results
10,554 results for “measurements”
Supplementary Dataset for "Grain Size Measurements of the Eolian Stimson Formation, Gale Crater, Mars and Implications for Sand Provenance and Paleoatmospheric Conditions"
<p>Grain size measurements and results in support of "Grain Size Measurements of the Eolian Stimson Formation, Gale Crater, Mars and Implications for Sand Provenance and Paleoatmospheric Conditions".</p> <p>The subfolder "MAHLI images" consists of images taken by the Mars Science Laboratory <em>Curiosity</em> Mars Hand Lens Imager; these images are accesible via the MSL Analyst's Notebook at an.rsl.wustl.edu.</p> <p>The subfolder "ImageJ ROIs" contains .zip files that can be opened with the ImageJ software, available at imagej.nih.gov/ij.</p> <p>The subfolder "GRADISTAT results" contains PDFs with grain size statistics that were created from the data in "Grain size measurements" using the GRADISTAT software, available at https://doi.org/10.1002/esp.261. </p>
Raw temperature measurements from SmartSantander sensors reported between January 1st 2021 and July 31st 2022
<p>This is a smart city domain dataset, and more specifically a environmental one generated within the framework of the SmartSantander research testbed.</p> <p>It contains raw temperature measurements reported by SmartSantander sensors deployed in the spanish city of Santander, covering a period of 17 months between January 1st 2021 and July 31st 2022, and comprising more than 24 million data points. The dataset includes not only the temperature dimension but also spatial and temporal information, as well as the specific device identifier and some labels to differentiate between static/mobile and indoor/outdoor devices.</p> <p>As is common with large-scale sensor deployments, there are occasional sensor malfunctions, which have deliberately not been filtered out of this raw dataset.</p>
Fig. 4 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 4. The relationship between diversity (D) and rarity (RAR-index) of the samples
Fig. 7 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 7. Bivariate plot of MW and B with the discrimination equation and line.
Fig. 2 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 2. Map of Hungary showing the collection regions. 1–5: geographic regions (see Table 1)
Fig. 4 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 4. Bivariate plot of individual scores on PC1 and PC2.
FIGURE 4 in An electrogoniometer to measure spinal curvature
FIGURE 4: Litarachna communis, female lectotype (a-e), male paralectotype (f and g) and nymph paralectotype (h): a – Right Leg-I, lateral view; b – Right Leg-II, lateral view; c – Right Leg-III, lateral view; d – Right Leg-IV, lateral view; e – Right palpus, lateral view; f – Right palpus, lateral view; g – Left palpus, lateral view; h – Right palpus, lateral view. Scales = 100 µm (a-d); 50 µm (e-h).
FIGURE 2 in An electrogoniometer to measure spinal curvature
FIGURE 2: Litarachna communis female, Gnathosoma, lateral view (No. 2700). The outline of chelicera is given with the broken line. Scale = 50 µm.
FIGURE 1 in An electrogoniometer to measure spinal curvature
FIGURE 1: Specimens on original five glass slides of Litarachna communis: a – No. 2698, 2 ♀; b – No. 2699, 3 ♂, 5 ♀, 1 nymph; c – No. 2700, 1 ♀; d – No. 2701, 1 ♂; e – No. 2702, 1 nymph.
FIGURE 3 in An electrogoniometer to measure spinal curvature
FIGURE 3: Litarachna communis, female lectotype (a and b), male paralectotype (c) and nymph paralectotype (d): a – Idiosoma, ventral view; b – Same, dorsal view; c – Idiosoma, ventral view; d – Idiosoma, ventral view. Scales = 100 µm (3-6).
Tower-based C-band radar measurements of an alpine snowpack
<p>This repository contains the data presented in the following paper: Brangers, I., Marshall, H.-P., De Lannoy, G., Dunmire, D., Matzler, C., and Lievens, H.: Tower-based C-band radar measurements of an alpine snowpack, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2927, 2023. </p> <p>The data consist of C-band tower based radar data measured in the Idaho Rocky mountains during the snow seasons of 2021-2022 and 2022-2023. The site lies within a local ski area called 'Bogus Basin'. Information about the data collection and processing is presented in the aforementioned paper.</p> <p>The data contains arrays with the timestamps of the measurements, the sampling frequency, and matrices of the time-domain traces of radar bakcscatter at 4 channels (rows=samples/timedomain bins, increases with distance from radar; columns=traces, 1 for each ~hourly timestep ). An example script on how to read and process the data using Matlab is included.</p> <p>For any further questions please contact Isis Brangers (isis.brangers@kuleuven.be) or Hans Lievens (hans.lievens@ugent.be) </p>
Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis
<div>This dataset contains key analysis and plotting scripts, data, and sample images.</div> <div> </div> <div>Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis</div> <div> </div> <div>Magnetic Resonance in Medicine Journal | DOI: 10.1002/mrm.30206</div> <div> </div> <div>§ Swetha Pala(1), § Antti Paajanen(1), Aapo Ristaniemi(1), Ervin Nippolainen(1), Isaac O. Afara(1), Olli Nykänen (1), Mikko J. Nissi (1*)</div> <div> </div> <div>1Department of Technical Physics, University of Eastern Finland</div> <div>§Shared authorship </div> <div> </div> <div> </div> <div> </div> <div>*Corresponding author</div> <div>Mikko J. Nissi</div> <div>Department of Technical Physics</div> <div>University of Eastern Finland, Kuopio Finland</div> <div>POB 1627</div> <div>70211 Kuopio</div> <div>mikko.nissi@uef.fi</div> <div>+358-50-5955517</div> <div> </div> <div> </div> <div>Keywords: Quantitative MRI, T1ρ relaxation, T1ρ dispersion, Compressed-sensing, radial acquisition.</div> <div> </div> <div>Included folders and files are:</div> <div>- Article_figures: all figures published in the manuscript (.eps format)</div> <div>- Data: MRI data files from 27 human cadaver samples with subfolders and files:</div> <div>- Human samples data: raw data files, along with generic analysis ROIs, zone divison inside specific samples folder, and within the parameter related data folder there are smaple specific analysis ROIs, computed profiles per spin lock amplitude. </div> <div>- CS reconstructed data files: </div> <div>- DataTables_used_for_analysis: Contains data tables per AF and reference data used for data analysis </div> <div>- Scripts: Matlab functions used for data processing and T1ρ computation, aedes plugins, and data analysis with subfolders and files:</div> <div> - Aedes_plugins: Plugins for aedes (http://aedes.uef.fi) for calculation of profiles from ROI. </div> <div> - Data analysis: Key scripts used for analysis and plotting.</div> <div> - Common functions: Some common functions that are required by the scripts/plugins. </div> <div> </div> <div>- README.txt: this file describing the contents of the dataset.</div> <div> </div> <div> </div> <div>See more info in separate readme files included in sub-folders.</div> <div> </div> <div> </div> <div>(Swetha Pala, 02 July 2024)</div> <p> </p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Fig. 2 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures
Fig. 2. Image illustrating tourists at the beginning of the snorkeling excursion (Lima, 2008).
Measuring the benefit of a risk-induced trait response: vigilance and survival probability
<p>Defensive traits are hypothesized to benefit prey by reducing predation risk from a focal predator but come at a cost to the fitness of the prey. Variation in the expression of defensive traits is seen among individuals within the same population, and in the same individual in response to changes in the environment (i.e., phenotypically plastic responses). It is the relative magnitude of the cost and benefit of the defensive trait which underlies the defensive trait expression and its consequences to the community. However, whereas the cost has received much attention in ecological research, the benefit is seldom examined. Even in a defensive trait as extensively studied as vigilance, there are few studies of the purported benefit of the behavior, namely that vigilance enhances survival. We examined if prey vigilance increased survival and quantified that benefit in a natural system, with white-tailed deer (Odocoileus virginianus) experiencing unmanipulated levels of predation risk from Florida panther (Puma concolor coryi). Deer that spent more time vigilant (as measured by head position using camera trap data) had a higher probability of survival. Indeed, an individual deer that were vigilant 75% of the time were more than three times as likely to be killed by panthers over the course of a year compared to a deer that were vigilant 95% of the time. Our results therefore show that within-population variation in the expression of a defensive trait has profound consequences to the benefit it confers. Our results provide empirical evidence supporting a long-held but seldom tested hypothesis, that vigilance is a behavior that reduces the probability of predation and quantified the benefit of this defensive trait. Our work furthers an understanding of the net effects of a trait on prey fitness and predator-prey interactions, within-population variation in traits, and predation risk effects.</p>
Raw Picarro L2140i data - Support to "A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements"
<p>Support data to reproduce Figure 3 - Figure 10 from the article:</p> <p><em>A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements</em></p> <p>by Hans Christian Steen-Larsen and Daniele Zannoni</p> <p>Accepted in Atmospheric Measurement Techniques on 25/05/2024 (Preprint available at <a href="https://doi.org/10.5194/amt-2023-160" rel="nofollow">https://doi.org/10.5194/amt-2023-160</a>)</p> <p>Figure numbers refer to the final (peer-reviewed and accepted) version of the manuscript.</p> <p>To reproduce the figures, the data can be used in conjunction with the code available at <a href="https://github.com/danielez83/AMT-2023-160" target="_blank" rel="noopener">https://github.com/danielez83/AMT-2023-160 </a>(https://zenodo.org/doi/10.5281/zenodo.12741980)</p> <p><strong>File description</strong></p> <p>Figure numbers are referring to the final (peer-reviewd and accepted) version of the manuscript.</p> <ul> <li>ADEV_BER17k_withmemory_CALIBRATED_R1.csv <ul> <li>Allan Deviation data without removing memory effect, used in FIgure 3</li> </ul> </li> <li>Cal_Pulses_MultiOven_new20230609.csv <ul> <li>Data obtained with the multioven configuration, used in Figure 9</li> </ul> </li> <li>Cal_Pulses_Selector.csv<br> <ul> <li>Data obtained with the VICI selector configuration (only one oven working), used in Figure 9</li> </ul> </li> <li>HKDS2092.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2092) raw data.</li> </ul> </li> <li>HKDS2156.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2156) raw data.</li> </ul> </li> <li>HKDS2156_IsoWater_20221116_165037.csv<br> <ul> <li>Results of liquid injections with Picarro vaporizer, used in Figure 4 and Figure 10</li> </ul> </li> <li>SP_BER_inj_time.csv <ul> <li>Date and times of injections, used in Figure 4 and Figure 10</li> </ul> </li> <li>Timings_Picarro.xlsx <ul> <li>Excel spreadsheet with time and dates of experiment. It is used as a lookup table to retrieve the raw data correctly</li> </ul> </li> </ul>
Exemplary dataset measured by Bresser WIFI Weather Station from 21.August 2023
<p><span>The Bresser WIFI Colour Weather Station is a convenient weather station for citizen science projects. It is equipped with sensors to measure wind speed, wind direction, humidity, temperature, rainfall, UV levels and light intensity. The WIFI feature allows users to share their data on weather platforms such as AWEKAS, Weather Underground or Weather Cloud.</span></p>
Supplementary Material to "Evaluation of Active Occlusion Effect Cancellation in Earphones by Subjective, Real-Ear and Coupler Measurements"
<p>Supplementary figures for the Publication</p> <p> </p> <p>F. Denk, L. Jürgensen, H. Husstedt (2024) <a href="https://doi.org/10.17743/jaes.2022.0124." target="_blank" rel="noopener"><strong>Evaluation of Active Occlusion Effect Cancellation in Earphones by Subjective, Real-Ear and Coupler Measurements</strong></a>, <em>Journal of the Audio Engineering Society</em> 72(3), pp. 145-160</p> <p> </p> <p>This supplementary material contains 3 types of figures:</p> <ul> <li>Overview_REMall_Levels_T: Overview of 1/6 octave long-term average levels of Real-Ear Measured (REM) sound pressure levels at eardrum for each subject (panel columns), Device (panel rows, rows 1-4 correspond to devices A-D, respectively) and condition (see line color). For better readability, two separate figures 1 and 2 are given for subjects 1-6 and 7-12.</li> <li>Overview_REMall_R: As Overview_REMall_Levels_T, but showing the Occlusion Effect OE, i.e., the difference to the Open-Ear condition.</li> <li>REM_Retest: Test and Retest results for three subjects (individual panels) for which the experiment was repeated. The results for the individual devices are given in separate figures.</li> </ul>
Genotyping measures and population genetic indices for assesing reproductive modes of polyploid Ludwigia grandiflora subsp. hexapetala in western Europe
<p>Raw data used to assess reproductive modes in 53 sampled populations in western Europe (France and northern Spain).</p> <p><em>Ludwigia grandiflora </em>subsp.<em> hexapetala</em> (<em>Lgh</em>) is a hermaphrodite, polyploid, partially clonal and heteromorphic plant that recently colonized multiple countries worldwide. Individuals in this species are either self-incompatible caused by a late-acting self-incompatible (LSI) system developing long-styled flowers, or self-compatible (SC) developing short-styled flowers. We used a SNP approach allowing confident allele dosage to genotype 53 LSI and SC populations of <em>Lgh</em> in France and northern Spain. We measured their genetic diversity and assessed their reproductive modes using methods adapted to autopolyploid species. </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.