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10,554 results for “measurements”
Polarization measurements of DOC-dependent IpaB-IpaD interactions
<p>The binding affinity between each of the engineered IpaD alanine mutants and the stable IpaB<sup>28-226</sup> construct was measured using fluorescence polarization. Here, the DOC effect on binding affinity between IpaB and the engineered IpaD π-helix mutants was quantified by holding IpaB<sup>28-226</sup>-Alexa568 concentration constant while the concentration of IpaD or engineered IpaD mutant was titrated from 0-10 μM with identical conditions then tested in the presence of 1 mM DOC.</p>
Data from "Effects of a brief mindfulness-meditation intervention on neural measures of response inhibition in cigarette smokers", Plos One 2017
<p>32-channel(+8) raw data (Biosemi Active two, 10-20, 512Hz) unfiltered from the 2018 Plos ONE paper "Effects of a brief mindfulness-meditation intervention on neural measures of response inhibition in cigarette smokers"</p>
Relative Distance Measurement with NSense
<p>We have carried out a set of experiments with our middleware NSense installed on three devices and carried by three different individuals. The devices were Samsung S5 Galaxy, Android version 6.0.1. Each device was named User1, User2, and User3. Each experimental scene involved 2 devices (e.g., User1 to User2 or User3 to User1 readings) positioned in different locations, indoor or outdoor, on the same floor or in different floors, within and without LoS. For each scenario, the real distance has been measured horizontally or, if on different floors, vertically. LoS is 1 if the path was free of obstacles and 0 if there were obstacles on the way; SameFloor is 1 if the users are on the same floor and 0 otherwise; Outdoor is 1 if users are outdoors and 0 otherwise; the real distance between the devices, in meters. The experiments have been developed over two weeks, between 10 a.m. and 17 p.m. of different working days of the week. There are two different datasets: BTDataset.csv WiFiDataset.csv Each dataset has the following variables: - timestamp - own_device (identifier of the device) - rssi, RSSI in dB - Scale, RSSI levels (1 as Very Weak, 2 as Weak , 3 as Mild, 4 as Strong and 5 as Very Strong). RSSI deterministic levels are based upon the Android getLevel() method. - freq, corresponding to the sub-frequency of the channel - computed distance, in meters - scenario, label of the different scenarios - refer to ScenarioDescription.xls - realdistance in meters - line_of_sight. 1 for LoS; 0 for nLoS - same floor. 1 for users standing on the same floor; 0 for different floors - outdoor, 1 for both users being outdoors; 0 otherwise - connected_device, device from which the RSSI is being obtained.</p>
Cooperative circumnavigation with event-triggered bearing measurements
<p>This demonstration shows a team of Aerial Robotic Workers (ARW)s performing a circumnavigation mission. The ARWs circumnavigate a virtual target whose position is progressively estimated by means of event-triggered bearing measurements.</p> <p>The control objective is that the ARWs circumnavigate the target at a desired speed, while forming a regular polygon around the target.</p> <p>Each ARW maintains a running estimation of the position of the target, which is updated every time a new bearing measurement is taken. New bearing measurements are triggered with a recursive law that guarantees that the estimated position converges to the real position of the virtual target.</p> <p>Each ARW intermittently monitors the relative position of the ARW that precedes it in the circumnavigation, and adjusts its circumnavigation speed according to such relative position. This control logic allows the ARW to converge to a regular polygon around the target.</p> <p>The control algorithm is implemented on a ROS architecture where the controller of each ARW corresponds to a different ROS node.</p>
2017 Eclipse HF Frequency Measurement Experiment
<p>Michael A. Naruta, AA8K</p> <p>Lat 42.9960408 N<br> Lon 82.4643299 W</p> <p>Start: 2017/Aug/21 14:54:08 UTC</p> <p>Antenna vertical 10 meters tall with 31 - 10 meter long radials on soil surface</p> <p>OpenHPSDR Mercury receiver, PowerSDR software, Spectrum Lab V2.92 b02 with HamSCI settings (carrier at 1 KHz), filters set to admit carrier frequency</p> <p>Trimble Thunderbolt GPSDO</p>
Replicate analysis from: Measuring complexity for hierarchical models using effective degrees of freedom
<p>Hierarchical models can express ecological dynamics using a combination of fixed and random effects, and measurement of their complexity (effective degrees of freedom, EDF) requires estimating how much random effects are shrunk towards a shared mean. Estimating EDF is helpful to (1) penalize complexity during model selection and (2) to improve understanding of model behavior. I apply the conditional Akaike Information Criterion (cAIC) to estimate EDF from the finite-difference approximation to the gradient of model predictions with respect to each datum. I confirm that this has similar behavior to widely used Bayesian criteria, and I illustrate ecological applications using three case studies. The first compares model parsimony with or without time-varying parameters when predicting density-dependent survival, where cAIC favors time-varying demographic parameters more than conventional AIC. The second estimates EDF in a phylogenetic structural equation model, and identifies a larger EDF when predicting longevity than mortality rates in fishes. The third compares EDF for a species distribution model (SDM) fitted for twenty bird species and identifies those species requiring more model complexity. These highlight the ecological and statistical insight from comparing EDF among experimental units, models, and data partitions, using an approach that can broadly adopted for nonlinear ecological models.</p>
XPS measurements of thin film on SiO2/Si(111) and powder samples (In-foil) of di-cyano-substituted tetrazolinyl radical
<p>XPS raw data underlying Figure 10 from the publication "Thermally Ultrarobust S = 1/2 Tetrazolinyl Radicals: Synthesis, Electronic Structure, Magnetism, and Nanoneedle Assemblies on Silicon Surface" (DOI: 10.1021/jacs.3c03402)</p>
An Automated Method for Measuring Tree Rings Based on Super Resolution and Image Segmentation
Open the record for dataset details and reuse information.
MicroCT Trabecular Bone Samples for Trabecular Thickness and Separation Measures
<p>Trabecular bone samples from micro CT (Xradia scanner, isotropic voxel size: 17.59 um, image size: 100x100x100) that have been segmented. These images were used to measure mean trabecular bone thickness and separation using the ORMIR_XCT Python package. Results were compared to trabecular thickness and separation values obtained from the standard workflows using Image Processing Language (IPL, Scanco Medical). File naming is as follows:</p> <ul> <li>BMLPL_XXX_XXX_SEG_SUB.nii <ul> <li>Trabecular bone segmentation image.</li> </ul> </li> <li>BMLPL_XXX_XXX_SEG_SUB_DT_THICK_CONVERT.nii <ul> <li>Distance transform for trabecular thickness obtained from IPL.</li> </ul> </li> <li>BMLPL_XXX_XXX_SEG_SUB_dt_py.nii <ul> <li>Distance transform for trabecular thickness obtained from Python.</li> </ul> </li> <li>BMLPL_XXX_XXX_SEG_SUB_DT_SP_CONVERT.nii <ul> <li>Distance transform for trabecular separation obtained from IPL.</li> </ul> </li> <li>BMLPL_XXX_XXX_SEG_SUB_inv_dt_py.nii <ul> <li>Distance transform for trabecular separation obtained from Python.</li> </ul> </li> </ul>
ScintiPi3 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives"
<p>ScintPi 3.0 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives" by Sousasantos et al. (2024).</p>
Force/Torque Sensor Measurements for Estimating the Mass Center of an Unknown Robot End Effector
<h1>Introduction</h1> <p>This dataset was created as part of a study on a novel geometric method to estimate the mass center of an unknown robot end effector. A conference paper from this study was accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR 2024) [1]. </p> <p>A force/torque sensor (FTS) was attached to the flange of a serial robot, and an unknown end effector was attached to the FTS. Vougioukas [2] described a method to calculate the FTS bias, as well as the mass and mass center using Least Squares Estimation (LSE). His method requires FTS samples from 24 specific orientations of the sensor. See his paper for a description of this calibration method. This dataset was used to evaluate and compare the estimates from the proposed geometric technique to the estimates from Vougioukas' method. </p> <p>The hardware used to generate this dataset were:</p> <ul> <li>KUKA Agilus KR6 R900 sixx (KUKA AG, Germany)</li> <li>ATI Gamma FTS (ATI Industrial Automation, Inc., USA)</li> <li>ATI Netbox (ATI Industrial Automation, Inc., USA)</li> </ul> <h1>Dataset</h1> <p>The robot was used to move the FTS with high precision and accuracy as required by the calibration method from Vougioukas. Each line in the dataset is the measured force and torque, the direction of gravity in the FTS frame, and the orientation of the FTS expressed in the world frame. The lines are ordered and correspond to the orientations described by Vougioukas in his paper. </p> <p><strong>fx,</strong> <strong>fy, fz</strong> - The force components as measured by the FTS.<br><strong>tx, ty, tz </strong>- The torque components as measured by the FTS.<br><strong>gx,gy,gz </strong>- The direction of the gravitational vector in the FTS frame.<br><strong>r11, r12, r13, r21, r22, r23, r31, r32, r33 </strong>- The components of the rotation matrix that represents the FTS orientation in the world frame.</p> <h1>References</h1> <p>[1] A. Skrede, "A Geometric Perspective on Moment Arm Estimation Using Force/Torque Sensors", Accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), Ålesund, Norway, June 2024 </p> <p>[2] S. Vougioukas, “Bias Estimation and Gravity Compen- sation For Force-Torque Sensors,” in Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 2001, pp. 82–85. </p>
Data release for "Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries"
<p>Posterior samples and code to reproduce all figures associated with <em>Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries</em>.</p> <p>The <code>parameter_estimation</code> folder contains the following:</p> <ul> <li><code>campaigns</code>: Analyses of eccentric quasi-monochromatic binaries, gridding over gravitational-wave frequency, eccentricty, and SNR. See the <code>README</code> inside for more information. The resulting posteriors are used in Figure 3, and the fitting formula Eq. 21. </li> <li><code>fiducial_source_checks</code>: Analyses that vary parameters other than SNR and frequency to investigate the effect on the minimum eccentricity that can be recovered. Used in Figure A1. Posteriors used for Figure 4 are also found in the <code>golden_binary</code> folder. </li> <li><code>nhat_runs</code>: Various analyses used for Figures 5, 6, B1, and C1. See the <code>README</code> inside for more information. Also see the <code>README</code> in <code>eccentric_gb_scripts</code> and links therein.</li> </ul> <p>Within each parameter estimation output folder there are <code>.dat</code> files for quantities such as the source SNR, log evidence, and posterior. There are also configuration <code>.yaml</code> files which are used by the BALROG code. These contain:</p> <ul> <li><code>lisa_config</code>: Parameters describing the LISA mission, including the duration in seconds. </li> <li><code>nessai_opts</code>: Settings used by nessai (the sampler used in this work). </li> <li><code>priors</code>: Lower and upper limits used for each source parameter. </li> <li><code>sources</code>: Injected values for each source parameter.</li> </ul> <p>The <code>notebooks</code> folder contains code to produce Figures 2, 3, 4, 6, and A1. Also included are notebooks to produce the fitting formula Eq. 21 (<code>emin_grid.ipynb</code>), and to inspect analyses in the <code>campaigns</code> and <code>fiducial_source_checks</code> folders.</p> <p>The <code>eccentric_gb_scripts</code> folder contains code to produce Figures 1, 5, B1 and C1. See the <code>README</code> inside for more information.</p>
Datasets for "A superconducting dual-rail cavity qubit with erasure-detected logical measurements"
<p>Title of Dataset: Demonstrating a superconducting dual-rail cavity qubit with erasure-detected logical measurements<br>---</p> <p>Included are data shown in Figures 1-4 of the main text and Extended Data Figures 2 and 3 in the Methods.<br>Data was collected by running quantum programs on hardware deployed at Quantum Circuits, Inc. The results of these experiments are decoded and assigned an appropriate label (discussed below and in the manuscript). We provide various levels of processing: number of counts, fraction of counts, and the logical assignment. Full labeled shot-by-shot outcomes can be provided upon request.</p> <p>## Description of the data and file structure</p> <p>Description of column labels<br>- xs: sweep variable (if applicable)<br>- Counts:<br> - 00_counts, 01_counts, 10_counts, 11_counts: Number of runs with outcome labeled "00", "01", "10", "11", respectively<br> - A_counts: Number of runs with outcome labeled as ambiguous outcome<br> - FSP_counts: Number of runs with outcome labeled as a failed state preparation<br> - all_shots: Total number of runs<br> - total_counts: Number of runs with a successful state preparation (e.g. all_shots - B_counts)<br>- Outcome fraction<br> - 00, 01, 10, 11, A, FSP: Fraction of counts normalized by total_counts<br> - 00_err, 01_err, 10_err, 11_err, A_err, FSP_err: Standard error for all above<br>- Logical outcomes<br> - 0L, 1L, erasures, Z_L: Computed logical dual-rail outcome for "0_L", "1_L", erasures, and expectation value of logical sigma_z <Z>, respectively<br> - 0L_err, 1L_err, erasures_err, Z_L_err: Standard error for all above</p> <p>State assignment datasets:<br>- Fig2_state_assignment_1msmts.csv: Data for Figure 2<br>- ExtendedDataFig2_state_assignment_2msmts.csv: Data for Extended Data Figure 2</p> <p>Bit-flip datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in Figure 3A<br>- Logical outcomes are plotted in Figure 3B<br>- Detail on datasets:<br> - Fig3_bit_flip_0L_1ms.csv: Data for Figure 3, left panel<br> - Fig3_bit_flip_0L_20us.csv: Data for Figure 3, left panel inset<br> - Fig3_bit_flip_1L_1ms.csv: Data for Figure 3, right panel<br> - Fig3_bit_flip_1L_20us.csv: Data for Figure 3, right panel inset</p> <p>Phase error datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in the top panels for Figure 4A and 4B<br>- Logical outcomes are plotted in the bottom panel for Figure 4A and 4B<br>- Short time data are shown in the inset of bottom panel for Figure 4A and 4B. <br>- We provide additional detail for the short time data in Extended Data Figure 3 for short-time Ramsey<br>- Detail on ramsey_short_time.csv: Data includes an additional dimension where the Ramsey phase angle is swept<br> - values in radians are enumerated in ExtendedDataFig3_ramsey_short_time_phases.csv<br>- Detail on datasets:<br> - Fig4_ramsey_long_time.csv: Data for Figure 4A<br> - ExtendedDataFig3_ramsey_short_time.csv, ExtendedDataFig3_ramsey_short_time_phases.csv: Data for Figure 4A, inset (bottom panel); Data for Extended Data Figure 3<br> - Fig4_echo_long_time.csv: Data for Figure 4B<br> - Fig4_echo_short_time.csv: Data for Figure 4B, inset (bottom panel)</p>
Datasets for the paper: Lost in Translation: Using Global Fact-Checks to Measure Multilingual Misinformation Prevalence, Spread, and Evolution
<p>FullData.csv.gz: Contains links to all claims in the data-set.</p> <ul> <li>publishing_date: Date on which the fact-check was published.</li> <li>claim_date: Date that claim was made.</li> <li>verdict: Rating given by the fact-checking organisation.</li> <li>language: Language of the claim.</li> <li>cluster_{threshold}: ID of the cluster that claim belongs to at all given clusters. Entry "0" means that claim is singleton and not clustered with any other claims.</li> </ul> <p>Embeddings.npy: Contains a dictionary linking each claim to it's embedding calculated with LaBSE.</p>
Viscosity Measurements at High Pressures: A Critical Appraisal of Corrections to Stokes' Law
<p>Included are central data and codes for the manuscript "Viscosity Measurements at High Pressures: A Critical Appraisal of Corrections to Stokes' Law", published May 2, 2024.</p> <p>Reference for full article:</p> <p>Ashley, A.W., Mookherjee, M., Xu, M., Yu, T., Manthilake, G., & Wang, Y. (2024). Viscosity measurements at high pressures: A critical appraisal of corrections to Stokes' Law. Journal of Geophysical Research: Solid Earth, 129, e2023JB028489. https://doi.org/10.1029/2023JB028489</p>
AGF212 2024: GPR ice thickness measurements of the glaciers Tellbreen and Blekumbreen (Svalbard)
Open the record for dataset details and reuse information.
Pulse sequences for measurement of magnetization exchange and effective 1H relaxation rates in condensed phase
<p>- ddHSQC based 4D pulse sequence for measurement of magnetization exchange in condensed phase samples, using gradients for coherence selection. Processing scripts for processing the data. </p> <p>- ddHMQC based 3D pulse sequence for measurement of effective proton transverse relaxation rates in condensed phase samples, using gradients for coherence selection</p> <p> </p>
Measuring social dimensions of sustainability at the community level: An illustrative but cautionary tale
<p>Many communities are working to enhance the sustainability of their physical, economic, and social systems. While economic and physical systems are routinely measured (e.g., money, energy, greenhouse gas emissions), important psychological and behavioral elements of social systems (norms, attitudes, individual behavior) are seldom tracked. This research evaluated a potentially scalable approach to measuring the impact of local sustainability initiatives on these variables in a community engaged in a holistic effort to promote sustainability. Survey data were collected at two timepoints measuring pro-environmental thought and behavior in two small towns in Ohio: Oberlin, a community engaged in holistic efforts to enhance environmentally sustainable behavior; and Berea, a similar community used as a control. Despite verifiable changes in Oberlin due to sustainability programs and awareness of these programs, our survey results did not provide strong evidence that program efforts resulted in the desired changes in attitudes, norms and behaviors. Pro-environmental attitudes about recycling and installing LED bulbs were two exceptions. Conclusions: Assessing the psychological and behavioral dimensions of sustainability poses particular challenges. In our study, we encountered ceiling effects and inadequate statistical power. Possibly norms and attitudes are not easily influenced even by a holistic community-wide effort.</p>
Data from : Measuring nearshore waves at break point in 4D with Stereo-GoPro photogrammetry
<p>This dataset was collected with a stereophotogrammetric method, using a cost-effective stereo system composed of two GoProTM (Hero 7) video cameras.</p> <p>The result is a geotiff DEM time series, with a resolution of 0.2 m, focusing on close-range measurements of nearshore waves at break point.</p> <p>Data collected in the framework of the WEST project (Natural Breaking WavEs and Sediment Transport during beach recovery - ANR-20-CE01-009) granted by the Agence Nationale pour la Recherche (ANR).</p>
Replication package for our paper entitled "Faster and Better Quantum Software Testing through Specification Reduction and Projective Measurements"
<p><br> -Experiment1<br> Contains the source files for experiment 1 and the study subjects.</p> <p> -Experiment2<br> Contains the source files for experiment 2 and a mutation generator.</p> <p> -results<br> Contains the results from experiment 1 and 2 and postprocessing files</p> <p> -RQ1,RQ2,RQ3<br> Contains source files for figures, tables and data for each research question<br> of the paper</p> <p><br>Please inspect the README.txt file for more details.</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.