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256 results for “submissions”
data and code from: Submissive behaviour is affected by group size in a social fish
<p>This repository contains data on individual aggression and submissive behaviour, collected during laboratory observations of agonistic interactions within <em>Neolamprologus pulcher </em>daffodil cichlids social groups, and the R code used to analyse the data.</p>
Replication package for the paper "What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor.".
<p>Replication package for the paper "What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor." Accepted to ESEM '24.</p>
Data and analysis scripts for the submission "Seamlessly Scaling Applications with DAPHNE"
<p>To regenerate the plots:</p> <p>You will need to install `R` (4.3.2) and the `tidyverse` package (2.0.0)</p> <p>We give a Nix flake that captures this environment (Install Nix: https://nixos.org/download/ and activate the flake feature: https://nixos.wiki/wiki/Flakes#Other_Distros.2C_without_Home-Manager)</p> <p>With Nix: `nix develop --command Rscript analysis_compas.R`</p> <p>Without Nix: `Rscript analysis_compas.R`</p> <p> </p>
Dataset for Interspeech 2018 submission: Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
<p>This dataset contains the materials for training, testing the joint and HSMM models mentioned in the paper "<em>Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions"</em>.</p> <p>The filename list of this dataset can be found in the function <em>get_train_test_recordings_joint()</em> of <em>./general/trainTestSeparation.py</em> file. The dataset contains the Praat TextGrids and .wavs of the variables: <em>train_primary_school, val_primary_school</em> and <em>test_primary_school</em>. For accessing other datasets such as <em>train_nacta_2017, train_nacta</em> and <em>train_sepa</em>, please download them from the links:</p> <p>jingju dataset part1: <a href="https://zenodo.org/record/1185154">https://zenodo.org/record/1185154</a></p> <p>jingju dataset part2: <a href="https://doi.org/10.5281/zenodo.842229">https://doi.org/10.5281/zenodo.842229</a></p> <p>Once you have downloaded these three datasets, you need to set the paths in <em>./general/filePathShared.py</em>.</p> <p>Set <em>path_jingju_dataset</em> to the parent path of these three datasets.</p> <p>Set <em>primarySchool_dataset_root_path</em> to the path of the interspeech2018 dataset (the current dataset).</p> <p>Set <em>nacta_dataset_root_path</em> to the path of the jingju dataset part1.</p> <p>Set <em>nacta2017_dataset_root_path</em> to the path the jingju dataset part2.</p> <p>For more information on this paper, please refer to the Github page: <a href="https://github.com/ronggong/interspeech2018_submission01">https://github.com/ronggong/interspeech2018_submission01</a></p> <p> </p>
Experiment dataset supplementary materials for DLfM 2018 submission
<p>This is the experiment dataset supplementary materials for the DLfM 2018 paper submission:</p> <blockquote> <p>An extended jingju solo singing voice dataset and its application on automatic assessment of singing pronunciation and overall quality at phoneme-level</p> </blockquote> <p><strong>Files: </strong></p> <ol> <li> dlfm_experiment_dataset_file_list.ods: recording file names of train, validation and test sets split.</li> <li> dlfm_experiment_dataset_phoneme_numbers.ods: phoneme numbers of each phone class in train, validation and test sets.</li> <li> freesound_extractor_feature_list.ods: freesoundExtractor feature name list used in ANOVA feature analysis.</li> <li> log-mel-scaler-keys-label-encoder.zip: files required for training the embedding model, includes logarithmic features, feature scaler, phoneme dictionary keys and label encoder.</li> <li>anova_analysis_essentia_feature.zip: Essentia freesoundExtractor features of each phoneme for ANOVA analysis.</li> <li>pretrained_embedding_models.zip: classification embedding models pretrained on the below datasets.</li> </ol> <p>The recordings listed in dlfm_experiment_dataset_file_list.ods are taken from a collection of jingju solo singing voice audio datasets, which contains three parts:</p> <ul> <li>Part 1: <a href="https://doi.org/10.5281/zenodo.780559">https://doi.org/10.5281/zenodo.780559</a></li> <li>Part 2: <a href="https://doi.org/10.5281/zenodo.842229">https://doi.org/10.5281/zenodo.842229</a></li> <li>Part 3: <a href="https://doi.org/10.5281/zenodo.1244732">https://doi.org/10.5281/zenodo.1244732</a></li> </ul> <p><strong>Contact information</strong>:</p> <p><em>If you have any question, please contact the authors:</em></p> <p>Rong Gong: Email - rong<dot>gong<at>upf<dot>edu</p>
fwilhelmi/potential_pitfalls_mabs_spatial_reuse: Submission to JNCA
<p>This repository contains the Code and the LaTeX files used for the journal article "Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs", which has been sent to "Journal of Network and Computer Applications".</p>
Festival of Frequency Measurement Submission
<p>Festival of Frequency Measurement 1 October 2019</p> <p>Event: WWV Centennial</p> <p>UTC Date: 30 September 2019 2232Z</p> <p>Beacon frequency: 5 MHz</p> <p>Station Name: K7JKM Jerry Martin</p> <p>Maidenhead Grid Square: CN85LA</p> <p>City: Keizer, Oregon, United States</p> <p>Antenna: AV680B Vertical</p> <p>Receiver Type: Yaesu FT950 Amateur Radio Transceiver</p> <p>Frequency Reference Type: Internal BFO</p> <p>Frequency Measurement Technique: Signal Link USB interface to Fldigi as described in your materials</p> <p>No extraordinary difficulties, the usual varying noise conditions peaking around local noon, 1900 UTC</p> <p> </p>
Festival of Frequency measurement Submission
<p>Festival of Frequency Measurement Submission</p> <p>—-</p> <p>event: WWV Centennial</p> <p>UTC date: 2019-10-01</p> <p>beacon frequency: 5MHz</p> <p> </p> <p>station name: N7XZ</p> <p>latitude and longitude and/or maidenhead grid square: DN41BR </p> <p>city: Logan</p> <p>state or region: Utah</p> <p> </p> <p>Station: Kenwood TS-480 SAT with high stability crystal</p>
Festival of Frequency Measurement Submission 1 October 2019
<p>Festival of Frequency Measurement 1 October 2019</p> <ul> <li>Denis Hinz W3FAY</li> <li>38.9975N, 76.755278W - FM18ox</li> <li>Bowie, Maryland</li> <li>Beacon Frequency - 5 MHz</li> <li>Start time 0000 UTC, 1 October 2019</li> <li>A description of station hardware configuration, including <ul> <li>Antenna type - vertical</li> <li>Receiver type - Elecraft K3</li> <li>Frequency reference type - internal .5 ppm TXCO</li> <li>Frequency measurement technique - FLDIGI Frequency Analysis procedure</li> </ul> </li> <li>Any other information you believe would be necessary for proper scientific interpretation of your measurements - Vertical is sub-optimal (random length of wire helically wound on 28' fiberglass telescoping pole with autotuner tuned to 40 Meters and currently no radials); K3 zero-beat to 15 MHz WWV 48 hours prior to measurement event.</li> </ul>
Festival of Frequency Measurement Submission
<p>Festival of Frequency Measurement, 1 October 2019, WWV/WWVH at 5 MHz</p> <p>Data measured and submitted by Jan A. Tarsala, WB6VRN</p> <p>Measurements made at K6TY, 34.1175, -118.0198, DM04xc</p> <p>One-wavelength 40 m vertical loop antenna with maximum response at 70 degrees to true North</p> <p>Kenwood TS-870S receiver with SO-2 TCXO option (all local oscillators and DSP are derived from this one frequency reference) operated in a temperature-stable residential environment</p> <p>TS-870S mode was CW (i.e. upper sideband), 50 Hz IF bandwidth, 1000 Hz beat note frequency</p> <p>Baseband audio beat note frequency measured using Fldigi 4.0 running under Mac OS 10.11 with time set using NTP and audio sampling rate corrected in accordance with Fldigi instructions</p> <p>Two files are uploaded due to a late start: the file K6TY DM04xc 5 MHz.csv covers 1500 to 2400 UTC 1 October 2019 (nine hours), while the file K6TY 34.1175 -118.0198 5 MHz.csv covers 1500 UTC 1 October 2019 to 1500 UTC 2 October 2019 (twenty-four hours). The initial frequency error and the aging of the SO-2 TCXO are obvious when the data are plotted.</p>
Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
<p>Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.</p>
Anonymous Submission
<p>The dataset provided in this repository encompasses extensive code quality metrics and description completeness measures for each pull request (PR) analyzed in the study.</p> <p><strong>Code Quality Metrics:</strong> The dataset includes 15 code quality metrics for each PR, obtained using SonarQube scans of the changed source code files within those PRs. These metrics provide insights into various aspects of code quality including but not limited to cognitive complexity, maintainability, and technical debt.</p> <p><strong>Description Completeness:</strong> Each PR's description completeness was assessed using the Llama model to identify and evaluate the presence of key components within each description. This process involved automated analysis to determine how well each PR description was crafted, focusing on the inclusion of essential information that aids in understanding and evaluating the PR's purpose and impact.</p>
Dataset for article submission 153972 in Journal F1000Research
<p>Dataset for article submission 153972 in Journal F1000Research.</p>
data for submission to GRL by Wu et al.
<p>Excess <sup>210</sup>Pb profile styles, sediment accumulation rates derived from <sup>210</sup>Pb and <sup>137</sup>Cs methods, and the related sampling information used in Figure 1, 2 and 5 were presented in 'Radionuclide data and sampling information.docx'. Grain size composition data used in Figure 3 were presented in 'Grain size composition.xlsx'.</p>
beadling_gfdl_esm4_submission
<p>Analysis code, scripts, and data associated with beadling et al., (2024): <em>From the surface to the stratosphere: large-scale atmospheric response to Antarctic meltwater.</em></p> <p>The data is compressed and organized by ensemble member: esm4_antXXX with the branch year from the piControl appended at the end (101, 151, 201, 251, 301). Year 101 indicates that year 0101 in the experiment corresponds to year 0001 in the CMIP6 piControl data published at the Earth System Grid Federation (https://esgf.llnl.gov/) (Krasting et al., 2018a). For ant101, the data is labeled as 0101 in file due to failure to reset calendar during this first ensemble member, while all other members begin their calendar count at 0001. Ensemble member 151 corresponds to year 0051 in the CMIP6 piControl output, member 201 corresponds to year 0101 in the CMIP6 piControl, member 0251 corresponds to year 0151 in the CMIP6 piControl, etc. </p> <p>The 1pctCO2 simulation results are archived and available for download at ESGF (Krasting et al., 2018b).</p> <p>Krasting, J. P., and Coauthors, 2018a: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP piControl. Earth System Grid Federation. [Dataset] <a href="https://doi.org/10.22033/ESGF/CMIP6.8669">https://doi.org/10.22033/ESGF/CMIP6.8669</a>.</p> <p>Krasting, J. P., and Coauthors, 2018b: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP 1pctCO2. Earth System Grid Federation. [Dataset] <a href="https://doi.org/10.22033/ESGF/CMIP6.8473">https://doi.org/10.22033/ESGF/CMIP6.8473</a>.</p>
beadling_gfdl_cm4_submission
<p>Analysis code, scripts, and data associated with Beadling et al., (2024): <em>From the surface to the stratosphere: large-scale atmospheric response to Antarctic meltwater.</em></p> <p>The data is compressed and organized by ensemble member: cm4_antXXX with the branch year from the piControl appended at the end (251, 290, 332, 269, 424). Year 251 indicates that year 0001 in the experiment corresponds to year 0251 in the CMIP6 piControl data published at the Earth System Grid Federation (https://esgf.llnl.gov/) (Guo et al., 2018a). Year 290 corresponds to year 0290 in the CMIP6 piControl output, etc.</p> <p>The 1pctCO2 simulation results are archived and available for download at ESGF (Guo et al., 2018b).</p> <p>Guo, H., and Coauthors, 2018a: NOAA-GFDL GFDL-CM4 model output piControl. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.8666.</p> <p>Guo, H., and Coauthors, 2018b: NOAA-GFDL GFDL-CM4 model output 1pctCO2. Earth System Grid Federation. [Dataset] https://doi.org/10.22033/ESGF/CMIP6.8470.</p>
Dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview"
<p>This is the dataset for ESE submission "Pull Request Latency Explained: An Empirical Overview".</p> <p>For research purpose, if you need `pull request id`, please request <a href="https://zenodo.org/record/7299639#.Y2mQ-HpBwUE">the column</a>.</p>
Raw Dataset for UNFCCC SBSTA Ocean Dialogue Submission Analysis
<p>This raw dataset accompanies the manuscript titled "A New Way Forward for Ocean Climate Policy as Reflected in the UNFCCC Ocean and Climate Change Dialogue Submissions", submitted by co-authors to the journal Climate Policy. </p> <p>In order to evaluate the Ocean Dialogue submissions, we identified key themes reflected in the text and defined subtopics within each theme. Each of the 47 submissions was independently reviewed by two of the co-authors (typically one natural scientist and one with expertise in law or policy). Reviewers recorded the number of times each theme was mentioned within a submission. Totals for each submission were averaged across the two reviewers, to decrease biases from individual-level differences in annotation. The averaged counts are presented in two tables. Table OD 1 for Party submissions and Table OD2 for non Party submissions. The Non-Party acronyms are explained in Figure 1 of the manuscript text. In Table OD1, Party submissions are indicated as coming from Annex 1 Parties (i.e. More highly developed economies), Non-Annex 1 Parties, or representing Group Submissions (see UNFCCC website for further description of differentiation between Annex 1 and non-Annex 1 Parties: https://unfccc.int/parties-observers). The last table (OD Submission Length) shows the total page count for each of the 47 Ocean Dialogue submissions analyzed, ordered from shortest to longest. </p>
A supplementary file for manuscript submission - Videos of flume test Events
<p>The ZIP file contains 7 videos clips of a flume test for landslide dam breach. The videos are the Supplementary Materials for a manuscript to be submitted to a journal in August, 2021 for possible publication. The ZIP file is uploaded on August 19, 2021 by Prof. Zheng-yi Feng.</p>
Artifacts of the TOSEM Submission Mario
<p>This is the online repository of <em><strong>Mario</strong></em>, a journal-first paper under review by ACM TOSEM.</p> <p> </p> <p><strong>Dataset: </strong>The dataset used in our study are all open-sourced. We provide the links to them below.</p> <ul> <li>Empirical dataset -- collected by Liu et al. (<a href="https://github.com/TruX-DTF/debug-method-name">here</a>)</li> <li>Evaluation dataset -- collected by Alon et al. (<a href="https://github.com/tech-srl/code2seq">here</a>)</li> </ul> <p> </p> <p><strong>Source code: </strong>We release three files here.</p> <ol> <li><em>Transformer.py:</em> This is the Transformer model used in <em><strong>Mario</strong></em> which is implemented in Pytorch. </li> <li><em>evaluation.py:</em> This file shows the workflow of <em><strong>Mario</strong></em> and calculates the overall performance of it.</li> <li><em>prior_knowledge.json:</em> This file stores the prior knowledge extracted from our empirical dataset for predicting field-relevant method names to unique fields.</li> </ol> <p>We will build a homepage for <em><strong>Mario</strong> </em>on Github and release the whole project upon acceptance.</p> <p> </p> <p><strong>Explanation of Figure3b:</strong></p> <p>In our experiment, we find that <span class="math-tex">\(\overline{\mathbb{S}_{M}}\)</span> is slightly higher than <span class="math-tex">\(\overline{\mathbb{S}_{T}}\)</span>. Through our manual observation, we find that it happens because tokens composing the investigated method names are repetitive. In the following, we give a concrete example.</p> <p>For the ErrorsTag.java class of <a href="https://github.com/apache/struts">Apache struts1</a> project, it contains 12 field-relevant method names obtained by combining the Verbs = {<em>get</em>, <em>set</em>} and the Fields = {<em>bundle</em>, <em>footer</em>, <em>locale</em>, <em>name</em>, <em>property</em>, <em>header</em>} in pairs. When <span class="math-tex">\(\alpha\)</span> = 0.5, its proximate classes totally have 14 field-relevant method names composed by the above 12 ones plus with <em>prepareName</em> and <em>createLocale</em>. Under such a condition, the Jaccard similarity of the method name level is 0.857 (12/14), higher than that of the token level which is 0.8 (8/10).</p> <p> </p> <p><strong>User Study:</strong></p> <p>We release the queries from the developers and the predictions of Mario in our user study. Note that due to the confidential policy, we only show the last two words of the full qualified class name for each query.</p> <p> </p> <p>***.impl.MarketService.java: {Init, set config file name, parse config, get config, get config file name}</p> <p>***.user.UserController.java: {Login, register, logout, delete role, find roles, create admin user, is login, get user by username, find permissions expired}</p> <p>***.db.Bot.java: {Respond, create kernel handler, init}</p> <p>***.controller.LoginController.java: {On click, show login form, login, logout}</p> <p>***.controller.UserController.java: {Delete user, save user, get user by id, get customer users, serve user, send activation email, get activation link, set passward}</p> <p>***.login.LoginService.java: {Login, logout, login with scm, get authentication}</p> <p>***.dto.ClientLoginDto.java: {To string, get topology}</p> <p>***.impl.IdaasServiceImpl.java: {Get mapper id, execute, get job id}</p> <p>***.router.DBRouterJoinPoint.java: {Materialize string, to string infix, get id}</p> <p>***.aspect.LogAspect.java: {Log to db, do before, do around, do after returning, do after in service layer, web service}</p> <p>***.service.DocService.java: {Get project docs, add enum doc strings, search docs, service added, add exception doc strings, doc values string, find supported services, copy of docs}</p> <p>***.dataclean.convertMessageStructureService.java: {Start up, delete, add module, list types, get type, shut down, delete all, list modules}</p> <p>***.dataclean.messageRouteAndSendService.java: {On bind, on create, on destroy, send message, on start command, handle message, configure, get error message}</p> <p>***.addresssimilarity.addressSimilarityService.java: {Delete, get hosted connection, start limited on connection, notify, opened change, on disconnected list changed, create, on initialize, on map changed}</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.