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256 results for “submissions”
Fig. 1 in Identification of Twelve Species of Coccinellidae (Coleoptera) Predatory on Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae) in Mexico, and Submission of Reference Coi Sequences
Fig. 1. Coccinellid predators of Melanaphis sacchari in cultivated sorghum in Mexico. A) Brachiacantha decora, B) Coccinella septempunctata, C) Coleomegilla maculata lengi, D) Cycloneda sanguinea sanguinea, E) Diomus roseicollis, F) Diomus terminatus, G) Exochomus childreni guexi, H) Harmonia axyridis, I) Hippodamia convergens, J) Olla v-nigrum, K) Scymnus (Pullus) dozieri, and L) Scymnus (Pullus) loewii.
Artifacts for ICSE 2022 Paper Submission #1221
<p><strong>This data set is for ICSE 2022 Paper Submission #1221</strong></p>
Data used in real world-inspired experiments for submission titled "Causal Contextual Bandits with Targeted Interventions"
<p>Data used in real world-inspired experiments for submission titled "Causal Contextual Bandits with Targeted Interventions".</p>
Anonymous Submission
<p>Anonymous Submission</p>
Supplementary Materials for submission on eyebrow movement
<p>Supplementary Materials for submission on eyebrow movement.</p> <p>The measurements for the sample are contained in the file df_sample.tsv.</p> <p>The pitch measurements around the eyebrow peak are contained in the file df_sample_pitch.tsv. The corresponding eyebrow measurements are contained in the file df_sample_eb.tsv.</p> <p>The parameters for the best-fitting curves are contained in the files sin_models_pre.tsv for the pre-peak region,<br> and sin_models_post.tsv for the post-peak region.</p> <p> </p>
Anonymous dataset for submission.
<p>Anonymous dataset for submission.</p>
Global asymmetries in the influence of ENSO on flood risk based on 1600 years of hybrid simulations - Submission - Data Supplement
<p>This contribution contains data and analysis scripts for the manuscript "Global asymmetries in the influence of ENSO on flood risk based on 1600 years of hybrid simulations" by Lenin Del Rio Amador, Mathieu Boudreault and David A. Carozza.</p>
Replication package for ESEC/FSE'23 Submission of "Data-Driven Evidence-Based Syntactic Sugar Design"
<p>All scripts and data utilized to perform the actions described in the submission of "Data-Driven Evidence-Based Syntactic Sugar Design".</p>
data for the submission in Marine Geology: Turbulence and Fine Sediment Dynamics in a Coastal Benthic Boundary Layer
<p>Data used for the plots and tables can be found in the submission in Marine Geology:Turbulence and Fine Sediment Dynamics in a Coastal Benthic Boundary Layer. The data are saved as matlab data file.</p>
Example Submission for SBOX-COST
<p>This repository contains the data for 4 variants of CMA-ES run on the SBOX-COST and BBOB suites.</p> <p>In addition to the raw data in IOHprofiler format, we also include the code used to generate it. This code uses the modcma package, version 0.0.2.8.4</p>
Supplemental Figure, ICML Submission
<p>Illustration of the conditional distribution from theorem 3.3 of our submission.</p>
data for the submission in JGR: Earth Surface
<p><strong>Data explanation</strong></p> <p>The dataset includes the major data for the manuscript: ‘Modeling Multi-Decadal Morphological Evolution of the Radial-Shaped Sand Ridges in the Southern Yellow Sea, China’.</p> <p> </p> <p>There are 7 data structures in the mat file:</p> <ol> <li>Elevation_SLRscenarios: this includes the elevation data for the sea level rise scenarios at year 2059, including the base scenario (noSLR), SLR1 (3 mm/yr), and SLR2 (11 mm/yr). Besides, the spatial info for the data and shown as X and Y, and the variable area is the area of the grid.</li> <li>Elevation_tide_ww_2017: this includes the elevation data at 2017 for two cases: tide-driven model (elevation_tide), and the tide, wind and waves driven model (elevation_tww). The spatial info is also included.</li> <li>Grainsize: this includes sand and fine sediment percentages for initial condition (RSR_fine_ini, RSR_sand_ini), at 2017 from the tide-driven model (RSR_fine_tide_2017, RSR_sand_2017), and at 2017 from the tide, wind and waves driven model (RSR_fine_tww_2017, RSR_sand_tww_2017). The spatial info is also included.</li> <li>Residual1979: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 1979. The spatial info is also included.</li> <li>Residual2017: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 2017. The spatial info is also included.</li> <li>Residual2059: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 2059. The spatial info is also included.</li> <li>Sedimentflux_profiles: this includes the net sediment flux across the profiles between the radial sand ridge system (RSR) and the abandoned yellow river delta (AYRD) (p1), and the RSR and the Yangtze River Delta (YRD) (p2). The variable distance_p1 and distance_p2 show the distance from coastline for P1 and P2 profiles, respectively. And the variables with ‘tide’ show the sediment flux from the tide-driven model at 2017, while the variables with ‘tww’ show the sediment flux from the tide, wind and waves driven model at 2017.</li> </ol>
The Artifacts of OOPSLA-2023 Submission #197
<p>This is the online repository of the OOPSLA-2023 Submission #197. We have released the source code and dataset.</p> <ul> <li><strong>Dataset</strong>: Our evaluation is based on the large-scale CodeSearchNet dataset. Use the following command to download and preprocess the data:</li> </ul> <pre><code class="language-bash">cd dataset bash run.sh cd ..</code></pre> <ul> <li> <p><strong>Dependencies</strong></p> </li> </ul> <pre><code class="language-bash">pip install -r requirements.txt</code></pre> <p> [Optional] We have built the tree-sitter parser stored at `evaluator/CodeBLEU/parser/languages.so`. If it doesn't work for you, it can be rebuilt with the following command:</p> <pre><code class="language-bash">cd evaluator/CodeBLEU/parser bash build.sh</code></pre> <ul> <li> <p><strong>Training</strong></p> </li> </ul> <pre><code class="language-bash">bash sh/train.sh [python/java] [CodeT5/Natgen] [CodeBERT/GraphCodeBERT]</code></pre> <ul> <li> <p><strong>Evaluation</strong></p> </li> </ul> <p> Evaluate generator:</p> <pre><code class="language-bash">bash sh/evaluate.sh [python/java] [CodeT5/Natgen]</code></pre> <p> Evaluate discriminator:</p> <p> We evaluate the discriminator by reusing the code from <a href="https://github.com/microsoft/CodeBERT/tree/master/CodeBERT/codesearch">CodeBERT</a> and <a href="https://github.com/microsoft/CodeBERT/tree/master/GraphCodeBERT/codesearch">GraphCodeBERT</a>. According to the Evaluate section in the corresponding model's Readme, replace `model_name_or_path` with the discriminator that you want to evaluate.</p> <p> </p>
Data for Research Submission 318 for ISWC 2023
<p>Data accompanying the paper "Embedding Geospatial and Linked Data for Geo-Entity Semantic Typing" (submission 318) submitted for review to ISWC 2023.</p>
Dataset accompanying the submission titled "Adaptive covariance hybridization for the assimilation of SST observations within a coupled Earth system reanalysis"
<p>The dataset contains the data accompanying our submission entitled "Adaptive covariance hybridization for the assimilation of SST observations within a coupled Earth system reanalysis". It contains:</p> <ol> <li>The yearly outputs of the free run</li> <li>The observations</li> <li>The yearly outputs of the runs of the standard hybrid</li> <li>The yearly outputs of the runs of the adaptive hybrid</li> <li>The grid of the model</li> <li>The maps of the hybridization coefficients</li> <li>The python and matlab scripts used to plot the figures of the article</li> </ol>
Mongodb Database dump for TOSEM submission "Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement"
<p>The Mongodb Database dump for TOSEM submission "Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement"</p>
Data for the submission in Marine GeologyMARGO-D-23-00231
<p> The dataset contains raw data from tables and charts. The data are saved as .xlsx file.</p> <p>1)1 Surface sediment grain size.xlsx is used for producing figures 3, 4 and table 1.</p> <p>2)2 Moisture content and bulk weight.xlsx is used for producing table 1.</p> <p>3)3 U-GEMS Microcosm Erosion Experiments.xlsx is used for producing table 1 and figures 4, 5.</p> <p>4)4 Filtration, Erosion weight and Erosion rate.xlsx is used for producing table 1, 2 and figures4, 5, 7.</p> <p>5)5 Erosion sediment grain size.xlsx is used for producing table 3 and figures 6.</p>
Supplemental file for the submission to International Journal of Molecular Sciences
<p>This is the supplemental file for one manuscript that was submitted to International Journal of Molecular Sciences for considering potential publication.</p>
Toxicological Analysis in Chemical Submission
ClinicalTrials.gov study NCT05442255. IPD Sharing: NO. Countries: 1. Publications: 1.
Perceived and actual fighting ability: determinants of success via decision, knockout or submission in human combat sports
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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.