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328 results for “Analysis results”
Table 3 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 3:</b> T Tes</p><table><tbody><tr><th></th><th></th><th></th><th></th><th><b>Test Value = 0</b></th><th></th></tr></tbody><tbody><tr><th></th><td><b>t</b></td><td><b>95% Confidence interval of the difference df Sig.(2 tailed) Mean Difference Lower Upper</b></td></tr><tr><th>Catch</th><td>11.129</td><td>23</td><td>.000</td><td>144.275 117.46</td><td>171.09</td></tr><tr><th>Catching tool</th><td>14.387</td><td>23</td><td>.000</td><td>1.500 1.28</td><td>1.72</td></tr></tbody></table><p><b>Table 3:</b> T Tes</p>
Table 1 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 1:</b> Production of Red Snapper (<i>Lutjanus sp</i>) in Fishing Equipment Uni</p><table><tbody><tr><th><b>No</b></th><th><b>Months</b></th><th><b>Fishing Equipmen Hand Line Long Line</b></th><th><b>Production quantity (Kg)</b></th></tr></tbody><tbody><tr><th>1</th><td>Januari</td><td>96.4</td><td>78.3</td><td>174.7</td></tr><tr><th>2</th><td>Februari</td><td>98.2</td><td>57.4</td><td>155.6</td></tr><tr><th>3</th><td>Maret</td><td>112.1</td><td>102.4</td><td>214.5</td></tr><tr><th>4</th><td>April</td><td>195.2</td><td>144.5</td><td>339.7</td></tr><tr><th>5</th><td>Mei</td><td>135.2</td><td>118.7</td><td>253.9</td></tr><tr><th>6</th><td>Juni</td><td>174.2</td><td>154.2</td><td>328.4</td></tr><tr><th>7</th><td>Juli</td><td>213.3</td><td>215.4</td><td>428.7</td></tr><tr><th>8</th><td>Agustus</td><td>325.4</td><td>222.1</td><td>547.5</td></tr><tr><th>9</th><td>September</td><td>221.1</td><td>155.9</td><td>377</td></tr><tr><th>10</th><td>Oktober</td><td>154.2</td><td>124.2</td><td>278.4</td></tr><tr><th>11</th><td>November</td><td>89.3</td><td>130</td><td>219.3</td></tr><tr><th>12</th><td>Desember</td><td>89.5</td><td>55.4</td><td>144.9</td></tr><tr><th>TOTAL</th><td>1904.1</td><td>1558.5</td><td>3462.6</td></tr></tbody></table><p>t</p>
Table 2 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 2:</b> CPUE of Red Snapper</p><table><tbody><tr><th><b>No Month</b></th><th><b>Production (kg)</b></th><th><b>Standar Effort (Trip)</b></th><th><b>Standar CPUE (Kg/Trip)</b></th></tr></tbody><tbody><tr><th>1</th><td>Januari</td><td>174.7</td><td>24</td><td>7.279</td></tr><tr><th>2</th><td>Februari</td><td>155.6</td><td>24</td><td>6.483</td></tr><tr><th>3</th><td>Maret</td><td>214.5</td><td>27</td><td>7.944</td></tr><tr><th>4</th><td>April</td><td>339.7</td><td>30</td><td>11.323</td></tr><tr><th>5</th><td>Mei</td><td>253.9</td><td>27</td><td>9.404</td></tr><tr><th>6</th><td>Juni</td><td>328.4</td><td>27</td><td>12.163</td></tr><tr><th>7</th><td>Juli</td><td>428.7</td><td>27</td><td>15.878</td></tr><tr><th>8</th><td>Agustus</td><td>547.5</td><td>21</td><td>26.071</td></tr><tr><th>9</th><td>September</td><td>377</td><td>23</td><td>16.391</td></tr><tr><th>10</th><td>Oktober</td><td>278.4</td><td>26</td><td>10.708</td></tr><tr><th>11</th><td>November</td><td>219.3</td><td>25</td><td>8.772</td></tr><tr><th>12</th><td>Desember</td><td>144.9</td><td>26</td><td>5.573</td></tr><tr><th></th><td>Amount</td><td>3462.6</td><td>307</td><td>137.990</td></tr><tr><th></th><td>Average</td><td>288.550</td><td>26</td><td>11.499</td></tr></tbody></table>
Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study
<p>Este conjunto de dados foi gerado como parte do estudo "Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study". O estudo focou na avaliação do desempenho das ferramentas Copilot, ChatGPT e Gemini, utilizando problemas do LeetCode em quatro linguagens de programação: Python, Java, JavaScript e C.</p> <p>O conjunto de dados atualizado está organizado nas seguintes pastas:</p> <ol> <li> <p><strong>c_programs</strong>: Esta pasta contém os scripts Python utilizados para calcular a complexidade ciclomática e a complexidade cognitiva do código C gerado pelas ferramentas.</p> <ul> <li><code>calculate_cyclomatic_complexity.py</code>: Script para calcular a complexidade ciclomática.</li> <li><code>calculate_cognitive_complexity.py</code>: Script para calcular a complexidade cognitiva.</li> </ul> </li> <li> <p><strong>codes_suggested_by_the_tools</strong>: Esta pasta contém as sugestões de código geradas pelo Copilot, ChatGPT e Gemini para cada problema do LeetCode.</p> <ul> <li>Subpastas: <code>ChatGPT</code>, <code>Copilot</code>, <code>Gemini</code>, cada uma contendo as sugestões de código correspondentes nos formatos das linguagens.</li> </ul> </li> <li> <p><strong>complexity_of_codes</strong>: Esta pasta contém dois arquivos CSV que fornecem os resultados da análise de complexidade para o código gerado.</p> <ul> <li><code>AI analysis results table - Cognitive.csv</code>: Resultados da complexidade cognitiva do código gerado.</li> <li><code>AI analysis results table - Cyclomatic.csv</code>: Resultados da complexidade ciclomática do código gerado.</li> </ul> </li> </ol> <p>Este conjunto de dados atualizado oferece insights valiosos sobre o desempenho das ferramentas de geração de código com IA e pode ser utilizado para análises futuras ou estudos de replicação.</p>
Results files for "End-to-end Bayesian analysis for summarizing sets of radiocarbon dates"
<p>These are the results files for the following peer-reviewed article:</p> <p>Price, M.H., J.M. Capriles, J. Hoggarth, R.K. Bocinsky, C.E. Ebert, and J.H. Jones, (2021). End-to-end Bayesian analysis for summarizing sets of radiocarbon dates. Journal of Archaeological Science.</p> <p>They were generated inside a Docker container as outlined in the README of this github repository:</p> <p>https://github.com/MichaelHoltonPrice/price_et_al_tikal_rc</p> <p>The analyses rely on an R package located in this github repository:</p> <p>https://github.com/eehh-stanford/baydem</p> <p>For the results archived here, the commits for each repository are:</p> <pre>price_et_al_tikal_rc 3ac1e35f4277ef878f8e3aac3d05159928a09a2b baydem 1220a60a860633b51f9f07cbff3eb78f458efc1a</pre>
Results of the analysis of the structure of biomass after microwave-assisted hydrotropic pretreatment.
<p>Results of analyzes of the structure of lignocellulosic biomass of various origins. The analyzes include the use of FTIR, SEM, XRD and NMR techniques. The work was supported by the National Science Centre, Poland, grant No. 2020/37/B/NZ9/00372.</p>
Data analysis results for: "MoDLE: High-performance stochastic modeling of DNA loop extrusion interactions"
<p>Due to technical issues we are unable to upload the updated version of this dataset on Zenodo.<br> <br> The latest version of this dataset can be found on the NRID research data archive at DOI <a href="https://doi.org/10.11582/2022.00056">10.11582/2022.00056</a>.</p>
Archive of the microtremor data observed at rock/stiff-soil sites and the analysis results
<p>This archive includes the microtremor data observed at 15 rock/stiff-soil sites and the analysis results, which were fully described in a paper "Spatial autocorrelation method for a simple microtremor array survey at rock/stiff-soil sites" by Ikuo Cho (2023, Geophysical Journal International, in press).</p>
ALOS-2 deformation analysis results, Teller 47, Seward Peninsula
<p>ALOS-2 deformation analysis results from the Seward Peninsula</p> <p>ALOS-2 stripmap observations from<br> 2015-07-16<br> 2016-07-14<br> 2017-07-13<br> 2019-07-11</p> <p>Estimates of the line-of-sight deformation velocity (_fitted_velocity.tif) and the estimated standard error (_fitted_se.tif) were obtained by Short-BAseline Subset (SBAS) processing. Two SBAS interferogram subsets were considered, one encompassing all years (2015-2019, filenames 1519) and one encompassing years 2015-2017 (filenames 1517). The first band of these images contains the line-of-sight velocity in (m/s), the second band the estimated displacement in the middle of the study period, referenced to the beginning of the respective study period.</p> <p>Regions with questionable results (e.g., where closure analyses did not successfully correct unwrapping errors) were delineated manually in two geopackage files.</p> <p> </p>
Processed results supporting MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics
<p>Search results supporting the manuscript "MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics". Processed PSM, ion, peptide, and protein tables for each search are provided, sorted by figure within the zip file. FragPipe workflows with parameters are also provided for all searches. </p>
Table 5. Results of calculation of the percentage of wound healing analysis of variance (ANOVA) one way with SPSS 23.00
<p>Table 5. Results of calculation of the percentage of wound healing analysis of variance (ANOVA) one way with SPSS 23.00</p>
Table 11. Results of statistical calculation of hydroxyproline levels analysis of variance (ANOVA) two way spss 23.00
<p>Table 11. Results of statistical calculation of hydroxyproline levels analysis of variance (ANOVA) two way spss 23.00</p> <p> </p>
Data and analysis result for "A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics"
<p>Data set and analysis results from our paper "A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics" (pre-print). This file contains GWAS summary statistics of 2,483 traits and 51,399 SNP variants from European individuals, originally downloaded and processed from Pan-UK Biobank (https://pan.ukbb.broadinstitute.org/). Additionally, we include results of 100 pleiotropic factors inferred by our method and tSVD as comparison. Please see README for detailed breakdown.</p>
Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps"
<p>Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps" (<a href="https://www.biorxiv.org/content/10.1101/2022.05.07.490900v1">biorxiv</a>) of <a href="https://doi.org/10.5281/zenodo.7299516">4i data</a>. Analysis of this dataset was done with <a href="https://pypi.org/project/campa/">CAMPA</a>, and all scripts to reproduce these results are available in the<a href="https://github.com/theislab/campa_ana"> CAMPA reproducibility repository</a>.</p>
Comparison between the results from JGA analysis somatic short variant discovery workflow and those from the compatible Terra workflow
<p>Files starting from <code>HCC1143.somatic</code> are the results from <a href="https://github.com/ddbj/jga-analysis/tree/main/somatic-short-variant">JGA analysis somatic short variant discovery workflow</a>. Files starting from <code>submissions_</code> are the results from the compatible Terra workflow.</p> <p>VCFs are identical between two workflows except for the header lines. MAFs are also identical except for the header lines.</p>
Comparison between the results from JGA analysis mitochondrial short variant discovery workflow and those from the compatible Terra workflow
<p>Files starting from <code>NA12878.chrM</code> are the results from <a href="https://github.com/ddbj/jga-analysis/tree/mitocondrial-variant">JGA analysis mitochondrial short variant discovery workflow</a>. Files starting from <code>submissions_</code> are the results from the compatible Terra workflow.</p> <p>VCFs are identical between two workflows except for the header lines.</p>
Data and results of "Retractions in Arts and Humanities: an analysis of the retraction notices"
<p>This repository contains the datasets and visualizations generated in our work: <strong>"Retractions in Arts and Humanities: an analysis of the retraction notices"</strong>.</p> <p><strong>Note:</strong> the data are all contained inside the <strong><em>data.zip</em> </strong>file. You need to unzip the container to get access to all the files and directories listed below.</p> <p><strong>Metadata</strong></p> <p>The directory <em><strong>metadata/ </strong></em>contains a CSV with the citation count of all the retracted papers we have considered. Metadata retrieved from Retraction Watch cannot be published in this repository due to copy rights issues. </p> <p><strong>Content analysis</strong></p> <p>We run a topic modeling analysis on the content of the retraction notices. The topic modeling analysis has been done using MITAO, a tool for mashing up automatic text analysis tools and creating a completely customizable visual workflow [1]. The topic modeling data and results are separated into the following directories/files: </p> <ul> <li> <p><em><strong>workflow/ </strong></em>contains the workflow used in MITAO.</p> </li> <li> <p><em><strong>datasets_and_views/: </strong></em>the datasets and visualizations generated using MITAO. </p> </li> <li> <p><em><strong>ldamodel_corpus_dict/: </strong></em>it contains the dictionary, the LDA topic model, and the tokenized and vectorized corpus.</p> </li> <li><em><strong>rawdata/: </strong></em>the textual collection, metadata, and stopwords used as input in the workflow of MITAO</li> </ul> <p><strong>References</strong></p> <p>[1] Ferri, P., Heibi, I., Pareschi, L., & Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. <a href="https://doi.org/10.19245/25.05.pij.5.2.3">https://doi.org/10.19245/25.05.pij.5.2.3</a></p>
Results of article : "Prospective European District Heating Scenarios based on Geographical Analysis"
<p>Results of the paper "Prospective European District Heating scenarios based on geographical analysis".</p> <p>Three scenarios are generated : Ambitious, Circular, and Conservative. For each scenario, there is a gpkg file and an excel file. The gpkg file is the whole dataset of inputs and results, each row being a European city. The excel summarizes the results for each EU27+UK country.</p> <p> </p>
Results of the Macro-Economic Analysis by the REMES model in the openENTRANCE project
<p>This dataset contains scenario results from the REMES:EU model as part of the macro-economic analysis in the openENTRANCE project.</p> <p>The data file follows the IAMC data format and the conventions established by the openENTRANCE project. See <a href="https://github.com/openENTRANCE/openentrance">https://github.com/openENTRANCE/openentrance</a> for details.</p> <p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information about the openENTRANCE project and other datasets.</p>
Results from participants in interlaboratory analysis of a candidate of reference material for hydrogen cryoadsorption
<p>Isothermal data from participants in an interlaboratory analysis of adsorption at 77 K of a candidate reference material for hydrogen cryoadsorption in the framework of the MefHySto project (Metrology for advance hydrogen storage solutions).</p>
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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.