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184 results for “Replication Study”
Perceptual correlates of homosynaptic long term potentiation in human nociceptive pathways: a replication study
<p>Animal studies have shown that high-frequency stimulation (HFS) of peripheral C-fibers induces long-term potentiation (LTP) within spinal nociceptive pathways. The aim of this replication study was to assess if a perceptual correlate of LTP can be observed in humans. In 20 healthy volunteers we applied HFS to the left or right volar forearm. Before and after applying HFS we delivered single electrical test stimuli through the HFS electrode while a second electrode at the contralateral arm served as control condition. Moreover, to test the efficacy of the HFS protocol we quantified changes in mechanical pinprick sensitivity before and after HFS of the skin surrounding both electrodes. The perceived intensity was collected for both electrical and mechanical stimuli. After HFS, the perceived pain intensity elicited by the mechanical pinprick stimuli applied on the skin surrounding the HFS-treated site was significantly higher compared to control site (heterotopic effect). Furthermore, we found a higher perceived pain intensity for single electrical stimuli delivered to the HFS-treated site compared to the control site (homotopic effect). Whether the homotopic effect reflects a perceptual correlate of homosynaptic LTP remains to be elucidated.</p>
Replication Package for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>Replication package for our analysis of Semantic Versioning in Ansible Galaxy role repositories.</p> <p>This replication package consists of three parts:</p> <ul> <li> <p>Classification Model: Contains Jupyter notebooks used to train and evaluate a Random Forest classification model based on structural features. Training and evaluation data is included.</p> </li> <li> <p>Quantitative Notebooks: Contains Jupyter notebooks used to perform quantitative analyses of versions and changes.</p> </li> <li> <p>data: CSV files of the data used in the Quantitative Notebooks, and the source data for the classification model. Should be downloaded separately fromthe classification model. Should be downloaded separately from <a href="https://doi.org/10.5281/zenodo.4991955">https://doi.org/10.5281/zenodo.4991955</a>.</p> </li> </ul> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
Data from: Correction for bias in meta-analysis of little-replicated studies
1. Meta-analyses conventionally weight study estimates on the inverse of their error variance, in order to maximize precision. Unbiased variability in the estimates of these study-level error variances increases with the inverse of study-level replication. Here we demonstrate how this variability accumulates asymmetrically across studies in precision-weighted meta-analysis, to cause undervaluation of the meta-level effect size or its error variance (the meta-effect and meta-variance). 2. Small samples, typical of the ecological literature, induce big sampling errors in variance estimation, which substantially bias precision-weighted meta-analysis. Simulations revealed that biases differed little between random- and fixed-effects tests. Meta-estimation of a one-sample mean from 20 studies, with sample sizes of 3 to 20 observations, undervalued the meta-variance by ~20%. Meta-analysis of two-sample designs from 20 studies, with sample sizes of 3 to 10 observations, undervalued the meta-variance by 15-20% for the log response ratio (lnR); it undervalued the meta-effect by ~10% for the standardised mean difference (SMD). 3. For all estimators, biases were eliminated or reduced by a simple adjustment to the weighting on study precision. The study-specific component of error variance prone to sampling error and not parametrically attributable to study-specific replication was replaced by its cross-study mean, on the assumption of random sampling from the same population variance for all studies, and sufficient studies for averaging. Weighting each study by the inverse of this mean-adjusted error variance universally improved accuracy in estimation of both the meta-effect and its significance, regardless of number of studies. For comparison, weighting only on sample size gave the same improvement in accuracy, but could not sensibly estimate significance. 4. For the one-sample mean and two-sample lnR, adjusted weighting also improved estimation of between-study variance by DerSimonian-Laird and REML methods. For random-effects meta-analysis of SMD from little-replicated studies, the most accurate meta-estimates obtained from adjusted weights following conventionally-weighted estimation of between-study variance. 5. We recommend adoption of weighting by inverse adjusted-variance for meta-analyses of well- and little-replicated studies, because it improves accuracy and significance of meta-estimates, and it can extend the scope of the meta-analysis to include some studies without variance estimates.
A systematic mapping study addressing the reliability of mobile applications: The need to move beyond testing reliability (Replication package)
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Replication Package: An Empirical Study on the Characteristics of Reusable Code Clones
<p>This is the updated code and datasets with comments to the code.</p>
Replication kit for: Problems with SZZ and Features: An empirical study of the state of practice of defect prediction data collection
<p>This is the replication package for our article "Problems with SZZ and Features: An empirical study of the state of practice of defect prediction data collection". </p> <p>All relevant information is available in the replication-notebook, which is available as executable Jupyter Notebook and as HTML page.</p> <p>The archive release-level-data.tar.gz contains the defect prediction data labeled with IND-JLMIV+R for the 398 releases and is shared for re-use by other researcher for defect prediction experiments. </p> <p>The archive DO_NOT_USE_release-leve-data.tar.gz contains the contains the defect prediction data labeled with 6M-SZZ and should not be re-used. </p> <p>The raw data with the MongoDB required to run the Jupyter Notebook is contained in the SmartSHARK Database release 1.0 (https://smartshark.github.io/dbreleases/ / https://doi.org/10.5281/zenodo.4071448).</p> <p>All materials are licensed under the Apache 2.0 License (see LICENSE file). </p>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
NanoString dataset for study: Real-time ex vivo perfusion of human lymph nodes invaded by cancer (REPLICANT): a feasibility study
<p>Raw NanoString data for study DOI:10.1002/path.5367</p>
Replication Package for "Improving Programming Education Through SonarQube: A Multiple Empirical Study of How Students Understand Rule Violations, Perceive the Criticality of the Rule, and the Fixing Difficulty"
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Lansoprazole 30 mg DR Capsule Replicate Food Study
ClinicalTrials.gov study NCT01046084. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study of LY2606368 (Prexasertib) in Patients With Solid Tumors With Replicative Stress or Homologous Repair Deficiency
ClinicalTrials.gov study NCT02873975. IPD Sharing: NO. Countries: 1. Publications: 0.
Use of Bromodeoxyuridine to Study White Blood Cell Replication and Survival in HIV-Infected Patients
ClinicalTrials.gov study NCT00001650. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Lansoprazole 30 mg DR Capsule Fasting Replicate Sprinkle Study
ClinicalTrials.gov study NCT01046253. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study 1 Replication and Extension of Prior Work
ClinicalTrials.gov study NCT04166591. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Correction for bias in meta-analysis of little-replicated studies
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Perceptual correlates of homosynaptic long term potentiation in human nociceptive pathways: a replication study
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Study of RepID/PHIP in DNA Replication inhitiation in Mammalian Cells
GEO Series GSE80298. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Purification of replicating pancreatic β-cells for gene expression studies
GEO Series GSE104387. Rattus norvegicus. 6 samples. Type: Expression profiling by array.
Systematic meta-analysis and replication of genome-wide expression studies of Parkinson's disease: 1
GEO Series GSE20159. Homo sapiens. 33 samples. Type: Expression profiling by array.
RNA-seq differential expression studies: more sequence, or more replication?
GEO Series GSE51403. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
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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.