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328 results for “Analysis results”
Are researchers moving away from animal models as a result of poor clinical translation in the field of stroke? an analysis of opinion papers
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Code, data and results for manuscript "A parsimonious empirical approach to streamflow recession analysis and forecasting"
<p>This repository hosts the supplementary materials associated with the paper:<br> > Delforge, D., Muñoz-Carpena, R., Van Camp, M. Vanclooster, M. (2020), A parsimonious empirical approach to streamflow recession analysis and forecasting (accepted at Water Resources Research - 29-01-2020).</p> <p>This data set contains streamflow and recession data, a python code file and a Jupyter notebook illustrating how to apply the EDM-Simplex method to forecast the recession, and the outputs of the global sensitivity analysis. All files are documented in the readme.md Markdown files. </p> <p>Streamflow data were obtained from the Aqualim portal (<a href="http://aqualim.environnement.wallonie.be/">http://aqualim.environnement.wallonie.be/</a>) of the "Service Public de Wallonie" and shared with their kind permission. This work is part of a Ph.D. supported by a FRIA grant from the Fund for Scientific Research (FSR-FNRS, Belgium). The authors acknowledge University of Florida Research Computing for providing computational resources and support that have contributed to the research results stored in this repository. URL: <a href="http://researchcomputing.ufl.edu">http://researchcomputing.ufl.edu</a>.</p>
Figure 4. Optimal maximum-likelihood tree resulting from the RAxML analysis. Bootstrap support values greater than 50 in Taxonomy of Micronesian monitors (Reptilia: Squamata: Varanus): endemic status of new species argues for caution in pursuing eradication plans
Figure 4. Optimal maximum-likelihood tree resulting from the RAxML analysis. Bootstrap support values greater than 50% are shown on the nodes. Scale bar corresponds to the mean number of nucleotide substitutions per site.
STR Analysis results BTSC 349, BTSC268 and mix of both
<p>Raw Sequencing data beloging to our (pending) F1000Research publication "Measures to increase value of preclinical research - an inexpensive and easy-to-implement approach to a QMS for an academic research lab"</p> <p>Raw .fsa files from Applied Biosystems ABI Prism 3130.</p> <p>15-1-Wiss2020-01-16.fsa = BTSC 349</p> <p>15-3-Wiss2020-01-16.fsa = BTSC268,</p> <p>15-4-Wiss2020-01-16.fsa = Mixed cell line</p> <p>Data published in "Measures to increase value of preclinical research - an inexpensive and easy-to-implement approach to a QMS for an academic research lab" in F1000Research: DOI: 10.12688/f1000research.24494.1</p>
PERMANOVA results from Principal component analysis of avian hind limb and foot morphometrics and the relationship between ecology and phylogeny
<p class="MsoNoSpacing">Principal component analysis has been used to test for similarities in ecology and life habit between modern and fossil birds, however, the two main portions of the hindlimb—the foot and the long bone elements—have not been examined separately. We examine the potential links between morphology, ecology, and phylogeny through a synthesis of phylogenetic paleoecological methods and morphospace analysis. Both hindlimb morphologies and species' ecologies exhibit extreme phylogenetic clumping, although these patterns are at least partially explainable by a Brownian motion style of evolution. Some morphologies are strongly correlated with particular ecologies, while some ecologies are occupied by a variety of morphologies. Within the morphospace analyses, the length of the hallux (toe I) is the most defining characteristic of the entire hindlimb. The foot and hindlimb are represented on different axes when all measurements are considered in an analysis, suggesting that these structures undergo morphological change separately from each other. Early birds tend to cluster together, representing an unspecialized basal foot morphotype and a hindlimb reliant on hip-driven, not knee-driven, locomotion. Direct links between morphology, ecology, and phylogeny are unclear and complicated, and may be biased due to sample size (~60 species). This study should be treated as a preliminary analysis that further studies, especially those examining the vast diversity of modern birds, can build upon.</p>
Results for eQTL analysis for each brain region
<p>This dataset is part of the manuscript: "<em>Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies</em>", by Lopes KP, Snijders GJL, Humphrey J, et al.</p> <p> </p> <p>Description of files:</p> <p><em>MFG_eur_expression_peer10.cis_qtl_nominal.txt.gz - </em>Full <strong>nominal eQTL</strong> summary statistics from medial frontal gyrus (<strong>MFG</strong>)<em> </em>(gzip-compressed)</p> <p><em>STG_eur_expression_peer10.cis_qtl_nominal.txt.gz </em>- Full <strong>nominal eQTL</strong> summary statistics from superior temporal gyrus (<strong>STG</strong>) (gzip-compressed)</p> <p><em>SVZ_eur_expression_peer5.cis_qtl_nominal.txt.gz - </em>Full <strong>nominal eQTL</strong> summary statistics from subventricular zone (<strong>SVZ</strong>)<em> </em>(gzip-compressed)</p> <p><em>THA_eur_expression_peer10.cis_qtl_nominal.txt.gz - </em>Full <strong>nominal eQTL</strong> summary statistics from thalamus (<strong>THA</strong>)<em> </em>(gzip-compressed)</p> <p><em>MFG_eur_expression_peer10.cis_qtl.txt.gz </em>- Full <strong>permuted eQTL</strong> summary statistics from medial frontal gyrus (<strong>MFG</strong>) (gzip-compressed)</p> <p><em>STG_eur_expression_peer10.cis_qtl.txt.gz -</em> Full <strong>permuted eQTL</strong> summary statistics from superior temporal gyrus (<strong>STG</strong>) (gzip-compressed)</p> <p><em>SVZ_eur_expression_peer5.cis_qtl.txt.gz - </em>Full <strong>permuted eQTL</strong> summary statistics from subventricular zone (<strong>SVZ</strong>) (gzip-compressed)</p> <p><em>THA_eur_expression_peer10.cis_qtl.txt.gz - </em>Full <strong>permuted eQTL</strong> summary statistics from thalamus (<strong>THA</strong>) (gzip-compressed)</p> <p> </p> <p>Nominal QTL results include all SNP-gene pairs tested (using a 1Mb window from each side of the transcription start site (TSS) of a gene). Table columns are formatted as follows:</p> <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30)</li> <li>variant_id - SNP tested for association (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>maf - minor allele frequency in MiGA cohort</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> </ul> <p>Permuted QTL results include only the top SNP-gene association for each gene. Table columns are formatted as follows:</p> <ul> <li>phenotype_id - ensembl ID of the gene tested (GENCODE v30)</li> <li>num_var - total number of variants tested in <em>cis</em></li> <li>beta_shape1 - first parameter value of the fitted beta distribution</li> <li>beta_shape2 - second parameter value of the fitted beta distribution</li> <li>true_df - effective degrees of freedom the beta distribution approximation</li> <li>pval_true_df - empirical <em>P</em>-value for the beta distribution approximation</li> <li>variant_id - ID of the top variant (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>maf -minor allele frequency in MiGA cohort</li> <li>ref_factor - flag indicating if the alternative allele is the minor allele in the cohort (1 if AF <= 0.5, -1 if not)</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> <li>pval_perm - first permutation <em>P</em>-value directly obtained from the permutations with the direct method</li> <li>pval_beta - second permutation <em>P</em>-value obtained via beta approximation. This is the one to use for downstream analysis</li> <li>qval - Storey q-value derived from pval_beta (FDR adjusted)</li> <li>pval_nominal_threshold - nominal <em>P</em>-value threshold for calling a variant-gene pair significant for the gene</li> </ul> <p><strong>NOTE: </strong>The effect sizes of eQTLs and sQTL are defined as the effect of the alternative allele (ALT) relative to the reference (REF) allele in the human genome reference (GRCh38). A file containing that information for all alleles tested is available at 10.5281/zenodo.4301005</p>
Results for sQTL analysis for each brain region
<p>This dataset is part of the manuscript: "<em>Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies</em>", by Lopes KP, Snijders GJL, Humphrey J, et al.</p> <p> </p> <p>Description of files:</p> <p><em>MFG_eur_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz - </em>Full <strong>nominal sQTL</strong> summary statistics from medial frontal gyrus (<strong>MFG</strong>)<em> </em>(gzip-compressed)</p> <ol> </ol> <p><em>STG_eur_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz </em>- Full <strong>nominal sQTL</strong> summary statistics from superior temporal gyrus (<strong>STG</strong>) (gzip-compressed)</p> <p><em>SVZ_eur_splicing_peer0_gene.cis_qtl_nominal_tabixed.tsv.gz -</em> Full <strong>nominal sQTL</strong> summary statistics from the subventricular zone (<strong>SVZ</strong>) (gzip-compressed)</p> <p><em>THA_eur_splicing_peer5_gene.cis_qtl_nominal_tabixed.tsv.gz - </em>Full <strong>nominal sQTL</strong> summary statistics from the thalamus (<strong>THA</strong>) (gzip-compressed)</p> <p> </p> <ul> </ul> <p><em>MFG_eur_splicing_peer5_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster </strong>from medial frontal gyrus (<strong>MFG</strong>) (gzip-compressed)</p> <p><em>STG_eur_splicing_peer5_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster</strong> from superior temporal gyrus (<strong>STG</strong>)<em> </em>(gzip-compressed)</p> <p><em>SVZ_eur_splicing_peer0_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster</strong> from the subventricular zone (<strong>SVZ</strong>)<em> </em>(gzip-compressed)</p> <p><em>THA_eur_splicing_peer5_cluster.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by intron cluster</strong> from the thalamus (<strong>THA</strong>)<em> </em>(gzip-compressed)</p> <p> </p> <p><em>MFG_eur_splicing_peer5_gene.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by gene </strong>from medial frontal gyrus (<strong>MFG</strong>)<em> </em>(gzip-compressed)</p> <p><em>STG_eur_splicing_peer5_gene.cis_qtl.txt.gz </em> - Full <strong>sQTL</strong> summary statistics <strong>permuted by gene</strong> from superior temporal gyrus (<strong>STG</strong>) (gzip-compressed)</p> <p><em>SVZ_eur_splicing_peer0_gene.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by gene</strong> from the subventricular zone (<strong>SVZ</strong>)<em> </em>(gzip-compressed)</p> <p><em>THA_eur_splicing_peer5_gene.cis_qtl.txt.gz - </em>Full <strong>sQTL</strong> summary statistics <strong>permuted by gene</strong> from the thalamus (<strong>THA</strong>) (gzip-compressed)</p> <p> </p> <p>Nominal QTL results include all SNP-junction pairs tested (using a 100kb window from the center of each intron cluster). Table columns are formatted as follows:</p> <ol> <li>phenotype_id - id composed by splicing junction position, splicing cluster id, and gene Ensembl id (GENCODE v30) each separated by a colon</li> <li>variant_id - SNP tested for association (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the SNP to the gene transcription start site (TSS)</li> <li>maf - minor allele frequency in MiGA cohort</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> <li>chr - chromosome of the SNP (hg38)</li> <li>pos - position for the SNP in the chromosome (hg38)</li> </ol> <p>Permuted QTL results include only the top SNP-junction association (by cluster or gene level). Table columns are formatted as follows:</p> <ol> <li>phenotype_id - id composed by splicing junction position, splicing cluster id, and gene Ensembl id (GENCODE v30), each separated by a colon</li> <li>num_var - total number of variants tested in <em>cis </em>per group (cluster or gene, depending on the file)</li> <li>beta_shape1 - first parameter value of the fitted beta distribution</li> <li>beta_shape2 - second parameter value of the fitted beta distribution </li> <li>true_df - effective degrees of freedom the beta distribution approximation</li> <li>pval_true_df - empirical <em>P</em>-value for the beta distribution approximation</li> <li>variant_id - ID of the top variant (rsid or chr:position:ref:alt)</li> <li>tss_distance - distance of the top SNP to the gene transcription start site (TSS)</li> <li>ma_samples - number of samples carrying the minor allele</li> <li>ma_count - total number of minor alleles across individuals</li> <li>maf - minor allele frequency in MiGA cohort</li> <li>ref_factor - flag indicating if the alternative allele is the minor allele in the cohort (1 if AF <= 0.5, -1 if not)</li> <li>pval_nominal - nominal <em>P</em>-value from linear regression</li> <li>slope - slope of the linear regression</li> <li>slope_se - standard error of the slope</li> <li>pval_perm - first permutation <em>P</em>-value directly obtained from the permutations with the direct method</li> <li>pval_beta - second permutation <em>P</em>-value obtained via beta approximation. This is the one to use for downstream analysis</li> <li>qval - Storey q-value derived from pval_beta (FDR adjusted)</li> <li>pval_nominal_threshold - nominal <em>P</em>-value threshold for calling a variant-gene pair significant for the group (cluster or gene, depending on the file)</li> </ol> <p><strong>NOTE: </strong>The effect sizes of eQTLs and sQTL are defined as the effect of the alternative allele (ALT) relative to the reference (REF) allele in the human genome reference (GRCh38). A file containing that information for all alleles tested is available at 10.5281/zenodo.4301005</p>
Data from: Analysis of personal and family factors in the persistence of attention deficit hyperactivity disorder: results of a prospective follow-up study in childhood
Objectives: To study the course of ADHD during childhood and analyze possible personal and family predictor variables of the results. Method: Sixty-one children with ADHD who were between 6 and 12 years old at the baseline assessment were evaluated 30 months later (mean age at baseline: 8.70 ± 1.97; mean age at follow-up: 10.98 ± 2.19). Status of ADHD in follow-up was identified as persistent (met DSM-IV-TR criteria according to parents' and teachers' ratings), contextually persistent (met ADHD criteria according to one informant, and there was functional impairment) and remitted ADHD (with subthreshold clinical symptomatology). Associated psychological disorders of the three groups were analyzed in the follow-up with the Conners' Rating Scales. The groups were compared on ADHD characteristics (symptoms of ADHD and impairment), child psychopathology, executive functioning (EF; inhibition, working memory) and parenting characteristics (parental stress and discipline styles) at baseline. Results: At the follow-up, 55.7% of the children continued to meet the DSM-IV-TR criteria for ADHD, 29.5% showed contextual persistence, and 14.8% presented remission of the disorder. The persistent and contextually persistent ADHD groups showed more associated psychological disorders. Inattention, oppositional problems, cognitive problems and impairment at baseline distinguished the remitted ADHD children from the persistent and contextually persistent ADHD children. Moreover, the persistent groups had significantly more emotional liability and higher parental stress than the group in remission, while no differences in EF where found among the groups. Conclusions: ADHD children continue to present symptoms, as well as comorbid psychological problems, during adolescence and early adulthood. These findings confirm that persistence of ADHD is associated with child psychopathology, parental stress and impairment in childhood.
Data from: Investigation of the effect of cochlear implant electrode length on speech comprehension in quiet and noise compared with the results with users of electro-acoustic-stimulation, a retrospective analysis
Objectives: This investigation evaluated the effect of cochlear implant (CI) electrode length on speech comprehension in quiet and noise and compare the results with those of EAS users. Methods: 91 adults with some degree of residual hearing were implanted with a FLEX20, FLEX24, or FLEX28 electrode. Some subjects were postoperative electric-acoustic-stimulation (EAS) users; the other subjects were in the groups of electric stimulation-only (ES-only). Speech perception was tested in quiet and noise at 3 and 6 months of ES or EAS use. Speech comprehension results were analyzed and correlated to electrode length. Results: While the FLEX20 ES and FLEX24 ES groups were still in their learning phase between the 3 to 6 months interval, the FLEX28 ES group was already reaching a performance plateau at the three months appointment yielding remarkably high test scores. EAS subjects using FLEX20 or FLEX24 electrodes outscored ES-only subjects with the same short electrodes on all 3 tests at each interval, reaching significance with FLEX20 ES and FLEX24 ES subjects on all 3 tests at the 3-months interval and on 2 tests at the 6- months interval. Amongst ES-only subjects at the 3- months interval, FLEX28 ES subjects significantly outscored FLEX20 ES subjects on all 3 tests and the FLEX24 ES subjects on 2 tests. At the-6 months interval, FLEX28 ES subjects still exceeded the other ES-only subjects although the difference did not reach significance. Conclusions: Among ES-only users, the FLEX28 ES users had the best speech comprehension scores, at the 3- months appointment and tendentially at the 6 months appointment. EAS users showed significantly better speech comprehension results compared to ES-only users with the same short electrodes.
Results from DSC analysis over SMC paste (50009092 grade by Menzolit supplier)
<p>Results from DSC analysis over SMC paste automotive grade (50009092 grade provided by Menzolit supplier)</p>
Cost Analysis Results of the Generation Circuits of the Entanglement Classes of Graph States with up to 7 Qubits
<p>This dataset encompasses a comprehensive analysis of entanglement classes of graph states. We have explored all locally equivalent graphs, along with every isomorphism, for each of the 45 entanglement classes associated with graph states featuring 7 qubits or fewer. The results, detailing various parameters of the quantum circuits generated for all distinct isomorphisms within each graph and local equivalence class, are stored in CSV files. </p>
Data analysis and benchmark results for: "hictk: blazing fast toolkit to work with .hic and .cool files"
<p><strong>README</strong></p> <p>Data found in this dataset was generated as part of the following publication: "hictk: blazing fast toolkit to work with .hic and .cool files" (preprint available soon).<br><br>The data analysis pipeline used to generate the data is hosted on GitHub at <a href="https://github.com/paulsengroup/2023-hictk-paper">github.com/paulsengroup/2023-hictk-paper</a> and archived on Zenodo <a href="https://doi.org/10.5281/zenodo.10868277">doi.org/10.5281/zenodo.10868277</a>.</p> <p>Refer to the README found in the .zip file for more information on how the data is organized.</p> <p><strong>Contact information</strong></p> <p>Inquiries regarding this dataset should be addressed to the corresponding author for "hictk: blazing fast toolkit to work with .hic and .cool files" (Jonas Paulsen).</p>
Results from the test of the MCDA-MSS with the 56 case studies from the energy systems analysis literature
<p>Results from the test of the MCDA-MSS with the 56 case studies from the energy systems analysis literature</p>
Source and result datasets for ""Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS"
<p>These files are the LRZip [0] compressed datasets used in the research paper 'Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS' [1]. The code can be found on Github [2].</p> <p>ls 2021-12-22-10\:49\:40<br> total 21M<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:38 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 7.7M May 25 12:10 domainfile.log.new.gz<br> -rw-r--r-- 1 sneef sneef 17K May 24 20:53 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 13M May 24 20:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 125 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 27K May 24 20:52 server.log.new.gz</p> <p>ls 2021-12-22-15\:04\:21<br> total 11G<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:43 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 4.2G May 23 12:43 certstream.log.new.gz<br> -rw-r--r-- 1 sneef sneef 3.3M May 23 12:37 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 6.0G May 23 12:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 104 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 6.3M May 23 12:37 server.log.new.gz</p> <p><br> ls results_certstream<br> total 17G<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 19:26 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 22:54 certstream_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 22:39 certstream_analysis_skipped_domains.txt<br> -rw-r--r-- 1 sneef sneef 4.5G May 23 13:21 certstream_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 450 May 23 13:22 certstream_counted_unique_domains_otherlines.csv<br> -rw-r--r-- 1 sneef sneef 602M May 23 13:22 certstream_counted_unique_domains_skipped_domains.csv<br> -rw-r--r-- 1 sneef sneef 3.6G May 23 13:22 certstream_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 6.1K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 39K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt_errors.txt<br> -rw-r--r-- 1 sneef sneef 3.1M Jun 8 19:20 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 94 Jun 8 19:26 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 37K May 23 13:41 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 45M May 23 13:41 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 22M May 23 13:41 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 2.1K Jun 8 23:09 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 23:09 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 1.4K Jun 8 23:09 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 23:08 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 2.6K May 23 13:41 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 76M May 23 13:41 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 206M May 23 13:41 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 3.4G May 23 13:41 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 7.0M May 23 13:41 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 423K May 23 13:41 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 3.9G May 23 13:40 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 6.0K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 259 Jun 8 00:51 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 305K Jun 8 00:24 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 1.4M May 23 22:41 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 63K May 23 22:41 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 74 May 23 22:41 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 9.1K May 23 22:41 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 239 May 23 22:41 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.7M May 23 22:41 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 1.7K May 23 22:41 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 17K May 23 22:41 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 79 May 23 22:41 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 70M May 23 22:41 serverlog_structured_data_structued_data.json</p> <p> </p> <p>ls results_tranco1m<br> total 79M<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 17:03 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 850 Jun 8 22:38 domainfile_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 domainfile_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 19M May 25 12:28 domainfile_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 4.0K May 27 03:03 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 16K Jun 8 16:32 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 83 Jun 8 17:04 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 226 May 25 12:28 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 66K May 25 12:28 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 31K May 25 12:28 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 0 Jun 8 22:38 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 900 Jun 8 22:38 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 915 Jun 8 22:38 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 909 Jun 8 22:38 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 844 Jun 8 22:38 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 829 Jun 8 22:38 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 27 May 25 12:28 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 589K May 25 12:28 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 269K May 25 12:28 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 18M May 25 12:28 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 58K May 25 12:28 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.8K May 25 12:28 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 4.4K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 241 Jun 8 11:45 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 5.4K Jun 7 23:58 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 2.0K Jun 7 15:57 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 69 Jun 7 15:57 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.5K Jun 7 15:57 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 129K Jun 7 15:57 serverlog_structured_data_structued_data.json<code> </code></p> <p><br> <br> [0] https://github.com/ckolivas/lrzip<br> [1] TBD<br> [2] https://github.com/gehaxelt/sshfp-dns-measurement</p>
Accompanying VTune results for "An Analysis of Performance Bottlenecks in MRI Pre-Processing"
Open the record for dataset details and reuse information.
[CCS24] ASLR Analysis Results
Open the record for dataset details and reuse information.
Text-fig. 2. "Site screen" scheme of complete results of the IPR-vegetation analysis derived from the database. in The Integrated Plant Record Vegetation Analysis: Internet Platform And Online Application
Text-fig. 2. "Site screen" scheme of complete results of the IPR-vegetation analysis derived from the database.
Supporting data and results for manuscript: "Analysis of the limited M. tuberculosis accessory genome reveals potential pitfalls of pan-genome analysis approaches"
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Table ¹: Comparison of analysis of variance results for skull (occlusal view) and mandible (side view) shape in Rhipidomys mastacalis from three vegetation classes in Brazil. Object asymmetry and correspondence methods were employed to assess asymmetry for skulls and mandibles, respectively. in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
<p><b>Table ¹:</b> Comparison of analysis of variance results for skull (occlusal view) and mandible (side view) shape in <i>Rhipidomys mastacalis</i> from three vegetation classes in Brazil.Object asymmetry and correspondence methods were employed to assess asymmetry for skulls and mandibles,respectively.</p><table><tbody><tr><th><b>Shape procrustes ANOVA</b></th></tr></tbody><tbody><tr><th><b>Effect Sum of squares</b></th><td><b>Mean squares</b></td><td><b>Degrees of freedom</b></td><td><i>F statistic</i></td><td><i>p -Value</i></td><td><b>Pillai tr.</b></td><td><i>p -Value</i></td></tr><tr><th><b>Skulls</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.19908517</td><td>0.0004253957</td><td>468</td><td>22.36</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.00366522</td><td>0.0002036232</td><td>18</td><td>10.70</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.00890443</td><td>0.0000190266</td><td>468</td><td>2.24</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.00825565</td><td>0.0000084935</td><td>972</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.37829478</td><td>0.0003965354</td><td>954</td><td>18.57</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.00547536</td><td>0.0003041869</td><td>18</td><td>14.25</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.02037065</td><td>0.0000213529</td><td>954</td><td>1.89</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.02201359</td><td>0.0000113239</td><td>1944</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.0645902300</td><td>0.0001302222</td><td>496</td><td>5.18</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.0113531900</td><td>0.0007095741</td><td>16</td><td>28.23</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.0124666800</td><td>0.0000251344</td><td>496</td><td>1.88</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.0136608800</td><td>0.0000133407</td><td>1024</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Mandibles</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.70443879</td><td>0.0012579264</td><td>560</td><td>8.10</td><td><0.0001</td><td>14.16</td><td><0.0001</td></tr><tr><th>Side</th><td>0.00549957</td><td>0.0002749783</td><td>20</td><td>1.77</td><td>0.0207</td><td>0.0207</td><td>0.0069</td></tr><tr><th>Individual × side</th><td>0.08696012</td><td>0.0001552859</td><td>560</td><td>2.46</td><td><0.0001</td><td>10.75</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.07312665</td><td>0.0000387718</td><td>1160</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>1.19843989</td><td>0.0011984399</td><td>1000</td><td>8.16</td><td><0.0001</td><td>14.70</td><td><0.0001</td></tr><tr><th>Side</th><td>0.01169771</td><td>0.0005848855</td><td>20</td><td>3.98</td><td><0.0001</td><td>0.74</td><td>0.0001</td></tr><tr><th>Individual × side</th><td>0.14685738</td><td>0.0001468574</td><td>1000</td><td>3.03</td><td><0.0001</td><td>11.21</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.09880745</td><td>0.0000484350</td><td>2040</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.3269927600</td><td>0.0004808717</td><td>680</td><td>4.52</td><td><0.0001</td><td>14.14</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0143644400</td><td>0.0007182221</td><td>20</td><td>6.75</td><td><0.0001</td><td>0.86</td><td>0.0017</td></tr><tr><th>Individual × side</th><td>0.0723474900</td><td>0.0001063934</td><td>680</td><td>2.39</td><td><0.0001</td><td>10.41</td><td>0.0017</td></tr><tr><th>Error 1</th><td>0.0622041800</td><td>0.0000444316</td><td>1400</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
Table ²: Comparison of the results of analysis of variance on the shape of scapulae (occlusal view) and pelvis (side view) in Rhipidomys mastacalis from three vegetation classes in Brazil. Correspondence asymmetry was the only method used for asymmetry analysis. in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
<p><b>Table ²:</b> Comparison of the results of analysis of variance on the shape of scapulae (occlusal view) and pelvis (side view) in <i>Rhipidomys mastacalis</i> from three vegetation classes in Brazil. Correspondence asymmetry was the only method used for asymmetry analysis.</p><table><tbody><tr><th><b>Shape procrustes ANOVA</b></th></tr></tbody><tbody><tr><th><b>Effect Sum of squares</b></th><td><b>Mean squares</b></td><td><b>Degrees of freedom</b></td><td><i>F statistic</i></td><td><i>p -Value</i></td><td><b>Pillai tr.</b></td><td><i>p -Value</i></td></tr><tr><th><b>Scapulae</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.0941373400</td><td>0.0010459705</td><td>90</td><td>3</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.0100439600</td><td>0.0010043960</td><td>2.88</td><td>0.0037</td><td>0.0003</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.0314069500</td><td>0.0003489662</td><td>90</td><td>5.89</td><td><0.0001</td><td>4.91</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0118544100</td><td>0.0000592721</td><td>200</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.2064168200</td><td>0.0010320841</td><td>200</td><td>4.82</td><td><0.0001</td><td>7.15</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0262808000</td><td>0.0026280796</td><td>10</td><td>12.28</td><td><0.0001</td><td>0.86</td><td>0.0022</td></tr><tr><th>Individual × side</th><td>0.0428160400</td><td>0.0002140802</td><td>200</td><td>2.68</td><td><0.0001</td><td>4.98</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0335675700</td><td>0.0000799228</td><td>420</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.2508635400</td><td>0.0009291242</td><td>270</td><td>4.07</td><td><0.0001</td><td>7.11</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0256608100</td><td>0.0025660812</td><td>10</td><td>11.24</td><td><0.0001</td><td>0.87</td><td><0.0001</td></tr><tr><th>Individual × side</th><td>0.0616394000</td><td>0.0002282941</td><td>270</td><td>3.10</td><td><0.0001</td><td>5.72</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0412323300</td><td>0.0000736292</td><td>560</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Pelvis</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.0543411200</td><td>0.0004312787</td><td>126</td><td>4.63</td><td><0.0001</td><td></td><td></td></tr><tr><th>Side</th><td>0.0043155600</td><td>0.0003082544</td><td>14</td><td>3.31</td><td>0.0002</td><td></td><td></td></tr><tr><th>Individual × side</th><td>0.0117297800</td><td>0.0000930935</td><td>126</td><td>2.31</td><td><0.0001</td><td>6.07</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0112943700</td><td>0.000040337</td><td>280</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.1059661700</td><td>0.0003440460</td><td>308</td><td>4.42</td><td><0.0001</td><td>9.69</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0049395300</td><td>0.0003528236</td><td>14</td><td>4.53</td><td><0.0001</td><td>0.85</td><td>0.0311</td></tr><tr><th>Individual × side</th><td>0.0239852500</td><td>0.0000778742</td><td>308</td><td>2.00</td><td><0.0001</td><td>6.64</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0251368400</td><td>0.0000390324</td><td>644</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.1292837500</td><td>0.0003420205</td><td>378</td><td>5.68</td><td><0.0001</td><td>10.51</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0043550500</td><td>0.0003110747</td><td>14</td><td>5.17</td><td><0.0001</td><td>0.84</td><td>0.0016</td></tr><tr><th>Individual × side</th><td>0.0227608400</td><td>0.0000602139</td><td>378</td><td>2.24</td><td><0.0001</td><td>6.17</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0210413800</td><td>0.0000268385</td><td>714</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
ScienceDex guides
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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.