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1,243 results for “Statistics”
Statistical data collected in the case studies of the SPOT report - dataset
<p>Statistical data was collected for fifteen case studies in the context of the SPOT project, funded by the European Commission within the framework programme Horizon 2020.</p>
GWAS summary statistics and code for "Sequence variants affecting voice pitch in humans"
<p>Contents: GWAS summary statistics for voice pitch (median F0 in reading) and code for acoustic analysis</p> <p>Please refer to the corresponding publication:</p> <p>Gisladottir et al. Sequence variants affecting voice pitch in humans. <em>Science Advances</em></p> <p>The GWAS summary statistics is also available at: https://www.decode.com/summarydata/</p> <p>The code for acoustic analysis is also available at: https://github.com/cadia-lvl/deCODE</p> <p> </p> <p> </p> <p> </p>
The Relationship between LRP5 (rs556442 and rs638051) Polymorphisms and Mutation with Bone Metabolism in Xinjiang women with Type 2 Diabetes after Menopause(Table 1 and Table 2 Statistical Values of Analysis Process)
<p>The Relationship between LRP5 (rs556442 and rs638051) Polymorphisms and Mutation with Bone Metabolism in Xinjiang women with Type 2 Diabetes after Menopause(Table 1 and Table 2 Statistical Values of Analysis Process)</p>
Statistics of the Network of Linked Vocabularies
<p>Reusing terms in the <a href="https://lod-cloud.net/">Linked Open Data cloud</a> results in a <a href="https://sites.google.com/view/nelo-evolution">Network of Linked vOcabularies (NeLO)</a>, where the nodes are the vocabularies that use at least one term from some other vocabulary and thus depend on each other. These dependencies become a problem when vocabularies in the network change, e.g., when terms are deprecated or deleted. In these cases, all dependent vocabularies in the network need to be updated. To address this shortcoming, we compute the state of NeLO from the available versions of the vocabularies in a period of time of over 17 years.</p> <p>Specifically, we provide the following statistics for each vocabulary in each year from 2001 and 2018 in three different formats (RDF/XML, JSON-LD, and CSV):</p> <ul> <li>in-degree;</li> <li>out-degree;</li> <li>degree;</li> <li>eccentricity;</li> <li>closeness centrality;</li> <li>harmonic closeness centrality;</li> <li>betweenness centrality;</li> <li>Authority;</li> <li>Hub;</li> <li>PageRank.</li> </ul> <p>The publication in the reference contains also further information on the analyzed vocabularies and on the methodology.</p> <p>This dataset is provided for non-commercial use only. If you find this dataset useful in your work, please cite the publication in the reference.</p>
Table 3: Descriptive statistics for the test anxiety questionnaire
<p>The first research question was concerned with the degree of the general test-taking anxiety<br> of ESP students learning general English. Table 3 presents the students' mean anxiety (x=2.16, sd. =<br> .34).</p>
QR GWAS summary statistics for 39 quantitative traits in the UK Biobank
<p>Quantile regression (QR) GWAS summary statistics from the study "Genome-wide discovery for biomarkers using quantile regression at biobank scale". The preprint is available at <a href="https://doi.org/10.1101/2023.06.05.543699" target="_blank" rel="noopener">https://doi.org/10.1101/2023.06.05.543699</a>. </p> <p><strong>List of traits</strong></p> <p>A comma-delimited text file, QRGWAS.Traits_n39.csv, includes the list of 39 quantitative traits from the UK Biobank reported in the QR GWAS analyses above.</p> <p><strong>Summary statistics</strong></p> <p>The tab-delimited text files are QR GWAS summary statistics, which are bgzip compressed (.tsv.gz files) and tabix indexed (.tbi files).</p> <ul> <li>Column "CHR": chromosome</li> <li>Column "POS": based pair position</li> <li>Column "ID": variant ID</li> <li>Column "REF": non-effect allele</li> <li>Column "ALT": effect allele tested in GWAS</li> <li>Column "EAF": frequency of the effect allele</li> <li>Column "N": sample size</li> <li>Column "P_QR": integrated p-value of the quantile regression (QR) model across multiple quantile levels.</li> <li>Column "P_LR": p-value of the linear regression (LR) association statistic</li> <li>Columns from "P_Q10" to "P_Q90": quantile-specific QR p-value for the quantile levels 0.1, 0.2, ..., 0.9 (10th, 20th, ..., 90th quantiles).</li> </ul>
Optimized summary-statistic-based single-cell meta-analysis. Input files
<p>This dataset contains information about the input files used in the Optimized summary-statistic-based single-cell meta-analysis research project. </p> <p> </p>
Influence of statistical size effects on the plastic deformation of coronary stents: Supporting data
<p>Data including UMATs and Abaqus input files related to the paper 'Influence of statistical size effects on the plastic deformation of coronary stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmbbm.2012.12.008" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.jmbbm.2012.12.008</span></a></p>
The New Acropolis Museum: Short Statistical Analysis for a Sustainable Operation with Active Visitors Based on a Sample of Students of the University of Athens
<p>The purpose of this study is to examine the new Acropolis Museum and its potential visits, with a focus on the number of students at the University of Athens visiting it. The dimensions studied are the new museum’s functionality, accessibility, and the intention to and motives for visiting it. The new museum has been open for 14 years and is viewed as a symbol of an exceptional cultural experience by both Greek and foreign visitors. As the focus of our field study, the students replied to mainly quantitative questions via computer, and we then performed a statistical analysis of their replies.</p>
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals in Verification of the bottle-based method for estimating abundance of small mammals using biomarkers
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals
Fig. 1 in Golden jackal (Canis aureus Linnaeus, 1758) and Red fox (Vulpes vulpes Linnaeus, 1758) population dynamics in Sarnena Sredna Gora Mts., Bulgaria based on hunting statistics
Fig. 1. Golden jackal (Canis aureus L.) and Red fox (Vulpes vulpes L.) population dynamics trends in Sarnena Sredna Gora Mts., Bulgaria, for a period of 11 years based on the analysis of the hunting data base (number of shot individuals)
FIGURE 4. P and S in BoneProfileR: The next step to quantify, model, and statistically compare bone section compactness profiles
FIGURE 4. P and S values ffor radial compactness analysis of (A, B) Erinaceus europaeus and (C, D) Eryops megacephalus femur using the flexit model.
FIGURE 3 in BoneProfileR: The next step to quantify, model, and statistically compare bone section compactness profiles
FIGURE 3. Posterior distribution of K1 and K2 parameters of the flexit model applied on Erinaceus europaeus femur (A, B) and on Eryops megacephalus femur (C, D). A -1000 to +1000 uniform prior distribution was used. K1 = K2 = 1 shown in interrupted line is the logistic equation.
FIGURE 2 in BoneProfileR: The next step to quantify, model, and statistically compare bone section compactness profiles
FIGURE 2. Comparison of logistic and flexit fits (A, B) of the Erinaceus europaeus femur shown in Figure 1A and (C, D) of the Eryops megacephalus femur shown in Figure 1B. Table 1 shows the AIC and Akaike weight values for these models.
FIGURE 1 in BoneProfileR: The next step to quantify, model, and statistically compare bone section compactness profiles
FIGURE 1. Example of background and foreground automatic detection in (A) Erinaceus europaeus femur, and (B) Eryops megacephalus femur. Centers were automatically detected in (A) and manually positioned in (B). Here, sections are segmented in 100 concentric circles and 60 slices for measures of global and radial compactness. Details on bone section preparation can be found in Laurin et al. (2004) and Quémeneur et al. (2013).
Fig. 2 Parasite abundance and distribution statistics. A in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
Fig. 2 Parasite abundance and distribution statistics. A total of 58 Aphrodita longipalpa were dissected and examined for parasite presence. The upper horizontal bars graphically depict the proportional parasitism rates and the corresponding distribution among male, female, and juvenile parasites, along with various cohabitation configurations. The box plots show the relationship between host size and the occurrence of parasites, presented collectively and then individually for female, male, and juvenile parasites
Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations (3rd version)
<p><strong>Summary</strong></p> <p>This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2018, ApJ, DOI: <a href="https://doi.org/10.3847/1538-4357/aac88f">10.3847/1538-4357/aac88f</a>, <a href="https://arxiv.org/abs/1604.04631">arXiv:1604.04631</a>).</p> <p>The compressed archive file "ddmc-nosample-v3.1.tar.xz" contains only the compressed SNIa data, the BAO measurements, and 3rd-party data files used in this work. The random samples can be re-created by the tools included in the package. This is the file suitable for low-speed download.</p> <p>The file "ddmc-v3.1.tar.xz" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample-v3.1.tar.xz" file. This is the archive containing all the data and figure files used directly in the paper.</p> <p>To uncompress the files, the XZ Utils software package is required.</p> <p>The file "CHECKSUM.asc" is a GPG-clearsigned text file containing the SHA-512 checksum values for file integrity verification. The text file itself is signed with the GPG key 0xE977A6E990102402 available from keyservers.</p> <p>Please read the README files in each package for more details and instructions.</p> <p><strong>Release notes for version 3.1</strong></p> <p>Version 3.1 is a minor revision with the addition of some alternative input parameter distributions.</p> <p><strong>Release notes for version 3</strong></p> <p>This is the 3rd version representing a re-written analysis of the distance-duality test. This new version updated and renamed the complementary parameter (CP) sets to match the ones used in the paper. New results concerning the interpretation of results as a diagnostics of distance measurement systematics are presented. Also included are updated utility scripts, new tests for Gaussian approximation to the results, and new data-visualization scripts.</p> <p><strong>Earlier versions</strong></p> <p>Earlier versions are available from Zenodo. Links: <a href="https://doi.org/10.5281/zenodo.49825">v1</a>, <a href="https://doi.org/10.5281/zenodo.57982">v2</a>.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 4. Statistical Metrics distribution of model on RIO as response of Terasort application
<p>Figure 4 shows the statistical metrics distribution of regression model on read rate as the response of Terasort application. The filled triangle point-up indicates the minimum stable sampling time for statistical metrics. The top-half of figure 4 shows the residual standard error (RSE) distribution as training data size increase. The remaining half is for the distribution of R2.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 8. Minimum sample time of statistical metrics of MapReduce applications
<p>Figure 8 presents the minimum sampling time distribution of statistic metrics which ensures the stable modeling. Overall, the minimum sampling time of statistic metrics is smaller than sampling time of estimated coefficients. For different applications, a time-consuming application like Terasort needs the largest sampling time to tend to be stable. The Pi application shows the smallest minimum sampling time to reach stability.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 5. Smart features as the main beneficiaries of AI in terms of the respondent's age (statistically significant differences only for 7.1 and 7.3)
<p>The majority of the respondents who found the smart features to be the main beneficiaries of AI facilities were ranging between 31-40 years old and +41 age old, followed by the 18-25 age group (M=3.80, SD =0.75), 26-30 (MD=4.0, SD =.75) (Figure 5).</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.