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256 results for “submissions”
Data Repository submission for "The influence of density driven mixing mechanisms on ureolysis induced carbonate precipitation"
<p>This folder includes the data and files that support the manuscript titled "The Influence of Density-Driven Mixing Mechanisms on Ureolysis-Induced Carbonate Precipitation". Please see included Readme for more information.</p>
Dataset and code for submission of "Aesthetic values predict bird trade, but the association varies across product types and trade regions"
<p>Data and code used for analyses in the manuscript titled "Aesthetic values predict bird trade, but the association varies across product types and trade regions". The raw data files are given as .xlsx files for the trade data ("birdtrade_data_clean.xlsx" & "EU_birdtrade_data_clean.xlsx"), as a .csv file ("iratebirds_data_151122.csv") for the aeshtetic value data. and all final merged datasets used in the analysis and figure codes are given as .RData -files. All code is given as .R files.<br><br>The data descriptor is currently given in the submitted manuscript and it's supplements, and will be added here too in more detail upon acceptance of the manuscript.</p>
Examples for submission to CONCUR'22
<p>These are the examples we discussed in our submission "Regular Model Checking Upside-Down: An Invariant-Based Approach" to CONCUR'22.</p>
Data and code repository for Science Advances submission: Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy
<p>Data and codes related to the findings reported in the manuscript, "Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy", are deposited. Please refer to the notes located within each folder for further descriptions.</p>
Supplemental data for submission "Bridging between LegalRuleML and TPTP for Automated Normative Reasoning"
<p>These files are supplementary material to the submission<br> Bridging between LegalRuleML and TPTP for Automated Normative Reasoning<br> by<br> Alexander Steen and David Fuenmayor<br> submitted to the 6th International Joint Conference on Rules and Reasoning (RuleML+RR 2022), 2022.</p> <p>Files ex1.lrml.xml and ex2.lrml.xml are two example LegalRuleML files.<br> Files ex1.dsl.p and ex2.dsl.p are two examples from above translated to the NMF DSL.<br> The files ex1.output.X.p and ex2.output.X.p are the translations of the NMF files into the concrete logic X (X = SDL or X = cJ (Carmo Jones) or X = aqvist (system E)).</p> <p>Alexander Steen, <alexander.steen@uni-greifswald.de></p>
Datasets supporting the original submission of Harris et al., "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model"
<p>Datafiles used in the analyses described by Harris et al, "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model", to be submitted to the Journal of Geophysical Research.</p> <p>Model output was created by X-SHiELD 2021 <a href="http://doi.org/10.5281/zenodo.6941034">https://doi.org/10.5281/zenodo.6941034</a> described in the paper:</p> <p>Harris, L., Zhou, L., Lin, S.-J., Chen, J.-H., Chen, X., Gao, K., et al. (2020). GFDL SHiELD: A unified system for weather-to-seasonal prediction. <em>Journal of Advances in Modeling Earth Systems</em>, 12, e2020MS002223.<a href="https://doi.org/10.1029/2020MS002223"> https://doi.org/10.1029/2020MS002223</a></p> <p>GPM data used for Figure 6b is derived from</p> <p>Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 4 August 2021,<a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/06"> 10.5067/GPM/IMERG/3B-HH/06</a></p> <p> </p>
DCASE2016 Challenge Submissions Package
<p>This package contains all submitted systems to DCASE2016 Challenge: system outputs and technical reports describing the systems. This package is meant to archive the DCASE0216 Challenge outcome and enable later evaluation with additional evaluation metrics.</p> <p>When referring to the <strong>DCASE2016 Challenge Submissions Package</strong> use the following:</p> <p>A. Mesaros, T. Heittola, E. Benetos, P. Foster, M. Lagrange, T. Virtanen, and M. D. Plumbley. <em>Detection and classification of acoustic scenes and events: outcome of the DCASE 2016 challenge.</em> IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(2):379–393, Feb 2018.</p>
travc/paper-Predicted-MF-Quarantine-Length-Data-and-Code: Initial submission
<p>Data and code for the paper "Evaluation of predicted Medfly (Ceratitis capitata) quarantine length in the United States utilizing degree-day and agent-based models"</p>
DCASE2017 Challenge Submissions Package
<p>This package contains all submitted systems to DCASE2017 Challenge: system outputs and technical reports describing the systems. This package is meant to archive the DCASE0217 Challenge outcome and enable later evaluation with additional evaluation metrics.</p> <p>When referring to the <strong>DCASE2017 Challenge Submissions Package</strong> use the following:</p> <p>A. Mesaros, A. Diment, T. Heittola, B. Elizalde, E. Vincent, B. Raj, and T. Virtanen. <em>Sound Event Detection in the DCASE 2017 Challenge</em>. IEEE/ACM Transactions on Audio, Speech, and Language Processing, accepted</p>
Festival of Frequency Measurement Submission
<p>The location of this measurement was La Canada, California<br> A very stable GPS locked receiver was used. (Icom 7610) Test measurements of GPS locked broadcast stations on 980, 1070 and 1260 kHZ showed a system frequency error of .03 Hz high. This .03 Hz high error existed before and after the 24 hour 5 mHz measurement. Thus for absolute frequencies a .03 Hz must be subtracted.</p> <p>The antenna used was a ground mounted electrostatic shielded loop antenna.</p> <p>Software used was fldigi.</p>
Data underlying submission "Tracing stream flow in confluent rivers – a journey from chaos to order"
<p>These data underly the analysis presented in the manuscript "Tracing stream flow in confluent rivers – a journey from chaos to order" by Erwin Zehe, Samuel Schroers and Hubert Savenije.</p> <p>The data used for this analysis are available were taken from the globally available the HydroSHEDS data base <em>at https://www.hydrosheds.org created by Lehner et al. (2008)</em>. The extracted data for analyzing the 18 of the largest Rivers are stored as csv files sorted by river names. The metadata are provided in "HydroSHEDS_TechDoc_v1_4.pdf" and "BasinATLAS_Catalog_v10.pdf".</p> <p>The matal codes for data analysis and simulations were tested and should be self-explaining.</p> <p> Lehner, B., Verdin, K. & Jarvis, A. New Global Hydrography Derived From Spaceborne Elevation Data. <em>Eos, Transactions American Geophysical Union</em> <strong>89</strong>, 93-94, doi:<a href="https://doi.org/10.1029/2008EO100001">https://doi.org/10.1029/2008EO100001</a> (2008).</p>
Supplementary Information Materials for the G-Cubed submission by Zakharov et al.
<p>This is an upload for the purposes of review at the G-Cubed journal by AGU. The supporting information is provided for the MGL opal-CT, as well as the results of the SIMS and EMPA measurements. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.</p>
Research Scholars Workshop: Submission and Review Gauntlet
<p>In this workshop, we will explore the research manuscript submission process, discuss why articles are rejected, options if they are rejected, and how to respond to reviewer and editor feedback. </p>
Reproduction Package for STTT Submission `Cooperative Verification: A Literature Review'
<p>This artifact contains the aggregated data used for the article “Cooperative Verification: A Literature Review”.</p> <p>The artifact consists of the following data files:</p> <p>|– stage-1_search-space.csv<br> |– stage-2-3-4_keyword-search_process-title-abstract.csv<br> |– review_sheet.csv</p> <p><strong>Search space for literature review</strong></p> <p>The file <code>stage-1_search-space.csv</code> contains metadata for the articles comprising the search space for our literature review. These data correspond to the output of <code>Stage 1</code> in the methodology described in the paper.</p> <p><strong>Filtering process</strong></p> <p>The file <code>stage-2-3-4_keyword-search_process-title-abstract.csv</code> contains the data corresponding to the stages 2, 3, and 4 of the methodology described in the paper. It contains metadata for the articles that passed the filter of <code>keyword-search</code>, and the decisions based on reviewing titles and abstracts.</p> <p><strong>Review sheet</strong></p> <p>The file <code>review_sheet.csv</code> contains metadata for the articles we reviewed. It contains the information related to the application of our definition to the techniques presented in these articles, as well as the class assigned to the cooperative techniques.</p> <p><strong>Generating numbers</strong></p> <p>Following commands can be executed to reproduce the numbers used in our literature review.</p> <pre><code># Change directory to the directory containing the CSV files of this artifact. # Number of papers in our search space. cat stage-1_search-space.csv | tail -n +2 | wc -l # Number of papers after keyword search. cat stage-2-3-4_keyword-search_process-title-abstract.csv | tail -n +2 | wc -l # Number of papers that are excluded based on titles. cut -f11 stage-2-3-4_keyword-search_process-title-abstract.csv | tail -n +2 | sort | uniq -c # Number of papers that are excluded based on abstracts. cut -f12 stage-2-3-4_keyword-search_process-title-abstract.csv | tail -n +2 | sort | uniq -c # Number of papers in different combination classes. cut -f8 review_sheet.csv | tail -n +2 | sort | uniq -c</code></pre>
Additional information for QJRMS (QJ-23-0159) submission
<p>This file contains the following information:</p> <p>1. energy-spectrum: Energy spectrum for three components of velocity and turbulent kinetic energy.</p> <p>2. time-series: The time series of total kinetic energy.</p> <p>3. input-files: The settings and input topography as required by PALM simulation.</p>
NPJ Climate Action AR6 scenarios database submission histograms by model family and project
<p>Based on submissions to the IPCC AR6 Scenarios Database, this datasets uses the metadata to construct histograms of the submitted scenarios by model family and project, noting the total submissions, vetted scenarios, and climate assessed scenarios. A total of 2304 scenarios were submitted to the global emissions database, of these, 618 did not passing vetting for sufficiently consistency with historical energy and emissions data, and a further 484 did not have sufficient data to perform a climate assessment, leaving a total of 1202 used in the primary assessment of scenarios. </p> <p>The database based on the scenario metadata. The ‘model family’ was determined by removing version numbers from the full model name. The ‘project family’ was obtained using the ‘Scenario family’ variable in metadata, supplemented by manually checking against cited literature. The classification of vetted scenarios was based on the variable ‘Historical vetting’ and the climate assessment on the ‘Climate Category’.</p> <p>This version is based on version 1.0 of the AR6 scenarios database.</p>
Datasets for Suzuki et al. (GRL submission)
<p>Dataset for the manuscript entitled "Control of vent geometry on the fluid dynamics of volcanic plumes: insights from numerical simulations" by Suzuki Y. J., Costa A., and Koyaguchi T. for submission in Geophysical Research Letter. This contains datasets for each run.</p>
Astronomy paper submissions to ArXiv until 2017
<p>Figure showing the increase of submissions to the pre-print server ArXiv for the astro-ph category until 2017</p>
Open anonymous repo hosting code and data for our submission in ASE 2020
<p>This repository presents sample publicly available anonymous source code and data for our submission in ASE 2020 conference.</p> <p>ProgressDroid source code is provided.</p> <p>Data for 10 top apps with the highest number of installs from our dataset are presented.</p> <p>For each app, we provide the following information:</p> <p>- The original APK file for the examined app.</p> <p>- The instrumented APK file using the extended Instrumenter module</p> <p>- Complete trace from running the extended AndroidSlicer tool on each app</p> <p>- Complete list of all UI update points in each specific app</p> <p>- List of slicing criteria for dynamic slicing </p> <p>- List of slices from the automated dynamic slicing analysis </p> <p>- List of all progress indicator occurrences for each trace </p> <p>- Complete runtime trace info including all events and states (including screenshots) </p> <p>Upon acceptance, we’ll complete the data sharing for all our dataset.</p>
FloodHydrology/DMV_Carbon_Storage: submission_v1.0
<p>This repository contains calculations supporting the initial submission of Kottkamp et al., a study of carbon stabilization mechanisms in seasonally inundated Delmarva Bay wetlands in the Mid-Atlantic US. The study specifically focuses on carbon stabilization mechanisms along a gradient from wetland to upland, and authors use hydrologic metrics (e.g., mean depth to water over the course of a year) as explanatory variables.</p> <p>Kottkamp A, Tully K, Jones CN, Palmer M. Both organo-mineral associations and physical protection in aggregates contribute to carbon stabilization at the transition zone of seasonally flooded wetlands. Planned submission: Summer 2020.</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.