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ShareScore release 0.9.0
Dataset results
8 results for “Usage Statistics”
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>
Drug Repurposing Central Usage Statistics Matomo
<p>Usage for the Open Science publishing portal Drug Repurposing Central created by ScienceOpen as part of the REPO4EU project. This dataset contains visits over time via Matomo Analytics. Current time frame is 01 September, 2023 to 31 August, 2024.</p>
Drug Repurposing Central Portal Usage and Geolocation Statistics
<p>Record-level usage and geolocation statistics by country and organization for the Drug Repurposing Central portal.</p> <p>Covered time frame is from October 2021 to August 2024.</p>
CAGE and txrevise promoter usage QTL summary statistics
<p>This dataset contains promoter usage QTL summary statistics obtained using <a href="https://github.com/eQTL-Catalogue/qtlmap">QTLmap</a>. QTLmap_summary_CAGE.txt contains summary statistics for CAGE, QTLmap_summary_txrevise-25.txt for regular <a href="https://github.com/kauralasoo/txrevise">txrevise</a> and QTLmap_summary_txrevise-supplemented-20.txt for txrevise with the new generated annotations.</p> <p>This dataset contains the promoter annotations obtained with CAGE as part of the FANTOM5 project, cleaned and reformatted using <a href="https://github.com/andreasvija/cage/blob/master/qtlmap_prep/clean_promoters.R">this code</a> for the purpose of QTL mapping, that were used to obtain the aforementioned summary statistics. They can be found in Cleaned_FANTOM5_promoter_annotations.tsv.</p> <p> </p> <p>QTL summary statistics column descriptions:</p> <p>molecular_trait_object_id - gene</p> <p>molecular_trait_id - top promoter for the gene</p> <p>n_traits - how many promoters were tested for the gene</p> <p>n_variants - how many genetic variants were tested for the promoter</p> <p>variant - top genetic variant for the promoter</p> <p>chromosome - chromosome</p> <p>position - promoter's top genetic variant start position</p> <p>pvalue - nominal p-value of the association between the promoter and genetic variant</p> <p>beta - the fitted linear model's regression coefficent</p> <p>p_perm - empirical p-value calculated using permutations</p> <p>p_beta - estimated empirical p-value based on the beta distribution, this is the recommended p-value column</p>
Summary statistics of transcript usage QTLs in naive and stimulated macrophages (part 2)
<p>Column names:</p> <ol> <li>phenotype_id</li> <li>pheno_chr</li> <li>pheno_start</li> <li>pheno_end</li> <li>strand - strand of the phenotype</li> <li>n_snps - number of SNPs tested per phenotype</li> <li>distance - distance from the variant to the phenotype</li> <li>snp_id</li> <li>snp_chr</li> <li>snp_start - position of the SNP</li> <li>snp_end - same as snp_start</li> <li>p_nominal - nominal p-value from QTLTools</li> <li>beta - effect size from QTLTools</li> <li>is_lead - is the variant the lead QTL for the phenotype?</li> </ol>
Summary statistics of transcript usage QTLs in naive and stimulated macrophages (part 1)
<p>Column names:</p> <ol> <li>phenotype_id</li> <li>pheno_chr</li> <li>pheno_start</li> <li>pheno_end</li> <li>strand - strand of the phenotype</li> <li>n_snps - number of SNPs tested per phenotype</li> <li>distance - distance from the variant to the phenotype</li> <li>snp_id</li> <li>snp_chr</li> <li>snp_start - position of the SNP</li> <li>snp_end - same as snp_start</li> <li>p_nominal - nominal p-value from QTLTools</li> <li>beta - effect size from QTLTools</li> <li>is_lead - is the variant the lead QTL for the phenotype?</li> </ol>
CARL-COAR Joint Webinar on IR Usage Statistics
<p>Institutional repositories (IRs), by virtue of their ability to give increased visibility to the institution’s scholarly outputs, are valued for their vast amount of open scholarly content. Libraries wishing to demonstrate use (and value) frequently report the number of file downloads sustained by their IR. However, commonly used analytics tools are unsuited for this purpose and produce results that dramatically under-count or over-count file downloads. As well, although statistics can sometimes be accessed through the various repository interfaces, without an agreed standard it is impossible to reliably assess and compare usage data across different IRs in any meaningful way.</p> <p>The first part of this webinar will explain the reasons for the inaccuracies in most IR download counts and will introduce a new web service called Repository Analytics and Metrics Portal (RAMP), which provides much more accurate counts of file downloads to IR managers, with almost no installation or training requirements. Aggregated data collected with RAMP also creates the potential for interesting new streams of research about IR. RAMP was developed with funding from the Institute of Museum and Library Services.</p> <p>The second half of this webinar will focus on another approach at standardizing institutional research data download statistics: IRUS-UK, a national aggregation service, which contains details of all content downloaded from participating IRs in the UK. By collecting raw usage data and processing them into item-level usage statistics, following rules specified by COUNTER, IRUS-UK provides comparable and authoritative standards-based data and also acts as an intermediary between UK repositories and other agencies.</p>
ScienceDex guides
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