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36 results for “Longitudinal measurements”
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
<p>We present a one-year-long <strong>M</strong>ulti-<strong>S</strong>ensor dataset with <strong>P</strong>henotypic trait measurements from honey <strong>B</strong>ees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), <em>Varroa</em> destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, <em>Varroa </em>infection detection, hive population estimation, biological analysis of bees, etc.</p> <h3>Related Info</h3> <p>The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a></p> <p>Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information.</p> <h3>Citation</h3> <p>Kindly cite the following paper:</p> <p>@misc{zhu2023mspb,</p> <p> title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees}, </p> <p> author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},</p> <p> year={2023},</p> <p> eprint={2311.10876},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={eess.AS}</p> <p>}</p> <h3>Contact</h3> <p>You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.</p>
Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset
<p>The dataset used in the paper entitled "Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset" published in IEEE Transactions for Network and Service Management. </p>
Longitudinal stability of brain and spinal cord quantitative MRI measures
<p><strong>About: </strong>Tabular (CSV) files are generated by the Courtois Neuromod<a href="https://github.com/courtois-neuromod/anat-processing"> structural data processing workflow</a>. These files contain quantitative MRI metrics such as T1, MTsat, and MTR, in addition to fundamental diffusion tensor imaging indices like RD and FA derived from brain data. The results also encompass the same metrics for spinal cord imaging data, supplemented with additional metrics for spinal cord morphometry. For details regarding the raw data please visit <a href="https://www.cneuromod.ca">https://www.cneuromod.ca</a>. </p><p>Dataset provided for NeuroLibre preprint. Author repo: https://github.com/courtois-neuromod/anat-processing-paper NeuroLibre fork:https://github.com/roboneurolibre/anat-processing-paper</p><p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/18">NeuroLibre technical screening.</a></p><p><a href="https://neurolibre.org"><strong>https://neurolibre.org</strong></a></p>
Time series of the longitudinal gradient of Venus's brightness temperature measured by Akatsuki LIR
<p>The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper. The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.</p>
Evaluation of Myocardial Function by Global Longitudinal Strain (GLS) Measurement in 2D Echocardiography of Patients With Fibromyalgia
ClinicalTrials.gov study NCT07150455. IPD Sharing: NO. Countries: 1. Publications: 17.
Data for: Diagnostic potential of blood plasma longitudinal viscosity measured using Brillouin light scattering
<p>Data for "Diagnostic potential of blood plasma longitudinal viscosity measured using Brillouin light scattering"</p>
Target ALS Biomarker Study; Longitudinal Biofluids, Clinical Measures, and At Home Measures
ClinicalTrials.gov study NCT05137665. IPD Sharing: YES. Countries: 3. Publications: 9.
Longitudinal Recovery of Laboratory, Clinical, and Community-Based Measures of Head and Trunk Control in People With Acquired Vestibulopathy
ClinicalTrials.gov study NCT04594057. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of Multi-point Pacing and Conventional CRT Through Non-invasive Hemodynamics Measurement and Global Longitudinal Strain Assessment
ClinicalTrials.gov study NCT04299360. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Evaluation of Medial Longitudinal Arch Morphology in Children With Spastic Diplegic Cerebral Palsy by Arch Height Index Measurement Method
ClinicalTrials.gov study NCT05025007. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Longitudinal diffusion MRI as surrogate outcome measure for myelopathy in adrenoleukodystrophy
Objective: To prospectively determine the potential of diffusion MRI (dMRI) of the cervical spinal cord and the corticospinal tracts in brain as surrogate outcome measure for progression of myelopathy in men with adrenoleukodystrophy, as better outcome measures to quantify progression of myelopathy would enable clinical trials with less patients and shorter follow-up. Methods: Clinical assessment of myelopathy included Expanded Disability Status Scale (EDSS), Severity Scoring system for Progressive Myelopathy (SSPROM), timed up-and-go and 6-minute walk test. Applied dMRI metrics included fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity. Results: Data was available for 33 controls and 52 patients. First, cross-sectionally, differences between groups (controls vs. patients; controls vs. asymptomatic patients vs. symptomatic patients) were statistically significant for fractional anisotropy, mean diffusivity and radial diffusivity in spinal cord and brain corticospinal tracts (effect size 0.31-0.68). Correlations between dMRI metrics and clinical measures were moderate to strong (correlation coefficient 0.35-0.60). Second, longitudinally (n=36), change on clinical measures was significant after 2-year follow-up for EDSS, SSPROM and timed up-and-go (p≤0.021, effect size ≤0.14). Change on brain fractional anisotropy and radial diffusivity was slightly larger (p≤0.002, effect sizes 0.16-0.28). In addition, a statistically significant change was detectable in asymptomatic patients using brain dMRI and not using the clinical measures. Change on clinical measures did not correlate to change on dMRI metrics. Conclusions: Although effect sizes were small, our prospective data illustrate the potential of dMRI as surrogate outcome measures for progression of myelopathy in men with adrenoleukodystrophy.
Data and code for 'Telomere length measurement for longitudinal analysis: the role of assay precision'
<p>Data and code for<strong> </strong>'Telomere length measurement for longitudinal analysis: implications of assay precision' by Nettle, Gadalla, Susser, Bateson and Aviv'</p> <p>The R script will run both the numerical simulations and the analysis of the empirical data. The exact results of the simulations will vary slightly from run to run.</p>
Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods: We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. Results: In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. Conclusions: The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.
Data from: Longitudinal diffusion MRI as surrogate outcome measure for myelopathy in adrenoleukodystrophy
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Changes in measures of cognitive function in patients with end-stage kidney disease on dialysis and the effect of dialysis vintage: A longitudinal cohort study
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Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
Open the record for dataset details and reuse information.
Longitudinal measures of RNA expression and disease activity in FSHD muscle biopsies
GEO Series GSE140261. Homo sapiens. 35 samples. Type: Expression profiling by high throughput sequencing.
Comparing health of migrant and native populations over time. Testing intercultural and longitudinal measurement equivalence of the SF-12 Health Survey.
<p>Dataset, script and variable codebook for publication: </p> <p>"<strong>Comparing health of migrant and native populations over time. Testing intercultural and longitudinal measurement equivalence of the SF-12 Health Survey."</strong></p> <p> </p> <p><strong>Content:</strong></p> <p>- data_long.dta: GSOEP data set in long format </p> <p>- script_1: R script for measurement equivalence tests</p> <p>- script_2: R script for latent mean calculation </p> <p>- script_3: R script output gathering and visualization </p> <p>- variable_codebook: txt-file with variable description </p>
Longitudinal Study of Outcomes Measures in ALS Trials
ClinicalTrials.gov study NCT01911130. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.