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1,034 results for “Empirical”
Dataset for Close Miking Empirical Practice Verification: A Source Separation Approach
<p>A dataset consisting of audio files which serve as support material to the following work: "Close Miking Empirical Practice Verification: A Source Separation Approach" by K. Drossos, S.I. Mimilakis, A. Floros, T. Virtanen and G. Schuller.</p> <p>The audio files contain information about two kind of signals a noise (pink noise) and a guitar (musical source) signal. For each signal multiple recordings, using a sampling frequency of 44.1kHz@16-bit, exist which are dependent upon the following variables:<br> - Mircophone type (polar pattern: Omni-directional and cardioid lobe) @Corresponding Folders: Omni & Card<br> - Microphone angle (30 and 45 degrees only for the cardioid microphone) @Corresponding Folders: C30 & C45<br> - Sound pressure levels for the source (SPLs) and the noise(SPLn) (SPLs : 94dB, 97dB, 100dB || SPLn: 88dB, 91dB, 94dB, 97dB, 100dB)<br> - 12 Distances in meters: 0.03m - 0.30m with a step size of 0.03m & 0.30m - 1.00m with a step size of 0.35m</p> <p>Each recording set contains also the original "clean" sources and their mixture for all the above configurations.<br> <br> The equipment used to record the above signals:<br> -Sound level meter (SLM): B&K 2250 Type A SLM <br> -Mic. A: & Shure SM57, dynamic, cardioid<br> -Mic. B: & Behringer ECM8000, condenser, omni-directional<br> -Laptop: Macbook Pro 15''<br> -Recording software: Digidesign ProTools M-Powered 8<br> -Musical instrument amplifier: Behringer V-Tone GMX212<br> -Digital sound card: M-Audio Fast Track Ultra<br> -Loudspeaker: Electrovoice SX300<br> <br> All recordings took place on the main stage of an empty municipal theater in Lixoyri, Kefallonia, Greece.<br> The authors would like to thank the Department of Technology of Sound and Musical Instruments,<br> Technological Educational Institute of Ionian Islands, for providing the equipment for the measurements.</p>
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"
<p>This upload contains the Datasets and code to generate the results of the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities".</p><p>The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. </p><p>The code folder contains the code to generate the results, and to generate the figures of the manuscript. </p>
Empirical data on growth and residency of juvenile Pacific salmon in North America estuaries
<p>Dataset compiling empirical data on estuarine growth and residency of Pacific salmon in North America. We conducted a systematic literature review to create this database, starting with a literature search in <i>Web of Science Core Collection</i> through Simon Fraser University's library proxy on April 24th, 2018, using the search parameters (salmon, Oncorhynchus) AND (estuary*) AND (residen* OR growth OR survival OR mortality), which returned 681 results (these results were presented in Arbeider 2018). We updated the search on March 29th, 2020, which produced 24 additional papers published after April 2018, and again on July 7th, 2022 which yielded another 31 papers published since March 2020. From these results, we extracted papers that research Pacific salmonids and whose study estuary was in North America. From this reduced list of papers, we extracted growth and residency parameters, as well as other relevant data. For complete methods please refer to:</p><p>Arbeider, M. et al. (In press). The estuarine growth and residency of juvenile Pacific salmon in North America: a compilation of empirical data. <i>Canadian Journal of Fisheries and Aquatic Sciences.</i></p>
Replication data for An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia
<p>This is the dataset to replicate all the tables and figures in the paper <a href="https://doi.org/10.1093/epolic/eiad027">"An Empirical Approximation of the Effects of Trade Sanctions with an Application to Russia"</a>, published in <i>Economic Policy</i>, 2023, by Jean Imbs and Laurent Pauwels. All data manipulations and programming are detailed on the GitHub site:<a href="https://github.com/laurentpauwels/sanctionpaper"> https://github.com/laurentpauwels/sanctionpaper</a>. The raw and processed data are in this <i>sanctionpaperdata_v1/matlab/data folder. </i>For convenience the simulation output <i>(simulationoutput.txt) </i>required to build the scatter plots in Figure 1 with STATA is available in<i> sanctionpaperdata</i>_v1<i>/matlab/output</i>.</p><p><strong>Instructions</strong> </p><p> If you clone the GitHub repository:</p><p>1. Place the downloaded <i>data</i> folder (located in <i>sanctionpaperdata_v1/matlab/)</i> in the <i>matlab</i> folder of the GitHub repository. </p><p>2. Place the downloaded <i>simulation_output.txt</i> I(located in <i>sanctionpaperdata_v1/matlab/output/) </i>in the <i>matlab/output </i>folder of the GitHub repository if you do not want to run the simulations as detailed on GitHub.</p><p><strong>Description</strong></p><p>The <i>matlab/data/raw</i> folder contains an <i>ICIO21</i> folder with the ICIO21 data, and a <i>WIOD</i> folder with the SEA16 data (in <i>data/raw/WIOD/SEA16</i>) and the WIOT16 data in CSV format (in <i>data/raw/WIOD/WIOT16</i>).</p><p>NOTE: WIOD provides the data in XLSB format. The XLSB WIOD data is in the <i>WIOT_in_EXCEL.zip</i> located in the <i>matlab/data/raw/WIOD/</i>. Python is used to convert XLSB into CSV files. See python code in GitHub repository for unzipping and conversion to CSV. The converted CSV files are provided for convenience.</p><p>The parsed and pre-processed ICIO21, SEA16, and WIOT16 data are stored in the <i>/matlab/data/processed</i> folder into three separate .mat structure files:</p><p><i>icio21_strc.mat</i> contains:</p><ul><li>the meta data (<i>icio21_text</i>), i.e., the information about the structure of the numerical data such as lists of countrycode, countries, industrycode, industries, isic_rev4 codes, years covered, name of final categories, etc.</li><li>the numerical data (<i>icio21_data</i>):<ul><li>Z (<i>icio21_data.Z</i>), the intermediate IO data for the listed industries (R), countries (N), and years (T). Its structure is 3-dimensionsal: (NxR)x(NxR)xT.</li><li>F (<i>icio21_data.F</i>), the final demand data for the same countries, industries and years. Its structure is 3-dimension: (NxR)x(NxC)xT. The columns are NxC where C are the number of final demand categories.</li></ul></li></ul><p><br><i>wiod16_strc.mat</i> has the same structure as <i>icio21_strc.ma</i>t with the meta data in <i>wiot16_text</i> and the numerical data in <i>wiot16_data</i>.</p><p><i>sea16_strc.mat</i> has the meta data in <i>sea16_text</i> and the numerical data in <i>sea16_data</i>. SEA16 contains 16 variables instead of Input-Output type data. The country, industry, and year coverage is not the same as ICIO21.</p><p>NOTE: <i>matlab/scripts/convertMatlabStruc2data.m</i> in the GitHub repository converts <i>MATLAB v7.3 </i>format ("structure data") to an updated format without structure so that it is more easily compatible with other software. All data parsing and preprocessing are done with MATLAB, see GitHub repository for details.</p><p><strong>Sources</strong></p><p>The raw data come from these sources:</p><p>1. OECD Inter-Country Input-Output (ICIO) data November 2021 release (downloaded on 2 July 2023)</p><p>- Source: OECD-ICIO 2021 release data is available at <a href="http://oe.cd/icio">http://oe.cd/icio</a></p><p>2. WIOD Socio-Economic Accounts (SEA) data 2016 release (downloaded on 30 May 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a></p><p>3. WIOD World Input-Output Tables (WIOT) data November 2016 (downloaded on 23 June 2023)</p><p>- Source: <a href="https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release">https://www.rug.nl/ggdc/valuechain/wiod/wiod-2016-release</a> </p>
Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets
<p>This archive contains the empirical data analysed in the paper 'Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets' by Touzalin et al. (https://doi.org/10.24072/pci.ecology.100416). The dataset is provided as a .Rdata file ('TLoss_GMdata.Rdata'), and full description of the content is provided in the file 'Readme_TLdata.csv'. All additional details are available in the main text (https://doi.org/10.24072/pci.ecology.100416) or in the supporting information (https://doi.org/10.5281/zenodo.10204538).</p>
SCOPING REVIEW OF EMPIRICAL LITERATURE ON EVALUATION OF EDITORIAL POLICIES SUPPORTING OPEN SCIENCE PRACTICES IN SCHOLARLY JOURNALS
<p>Excel file containing the description of methods & materials used in the scoping review, the whole dataset of included studies, and respective descriptive metadata.</p>
Artifact for "Challenges in Empirically Testing Memory Persistency Models"
<p>Presented here are the litmus tests, auxiliary scripts, and output data constituting the artifact accompanying the paper titled 'Challenges in Empirically Testing Memory Persistency Models,' published in ICSE NIER'24. For detailed information, please refer to the README.</p>
An Evaluation of Equatorial Perturbation Electric Fields Using Empirical Vertical Drift Models
<p>Dataset for the article titled "An Evaluation of Equatorial Perturbation Electric Fields Using Empirical Vertical Drift Models", submitted to <em>Space Weather</em>. The dataset includes the outputs from the four empirical vertical drift models used in the article: Fejer and Scherliess (1997), Kelley and Retterer (2008), Manoj and Maus (2012), and Scherliess and Fejer (1999).</p>
Dataset: an empirical study on architectural smells through a pipeline for continuous technical debt assessment
<h2><strong>Dataset of the study "An empirical study on architectural smells through a pipeline for continuous technical debt assessment"</strong></h2> <h3><strong>Abstract</strong></h3> <p>In recent years, researchers spent an increasing amount of effort investigating technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.<br>In response to this concern, researchers strive to expand the scope of their studies by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p>To address this issue, this paper presents an approach for tackling this problem and enabling researchers to study architectural smells, a well-known indicator of architectural technical debt, at a large scale. Specifically, we introduce a novel approach to a data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets with any static analysis tool.</p> <p>Finally, we use the data collected through the pipeline to study the correlation between architectural smells and logical coupling in order to understand how smells influence maintenance efforts.</p>
Replication package for "Why do people persist in sea-level rise threatened coastal regions? Empirical evidence on risk aversion and place attachment"
<p><strong>Steps to replicate the tables and figures in “Why do people persist in sea-level rise threatened coastal regions? Empirical evidence on risk aversion and place attachment”</strong></p> <p><em>by Ivo Steimanis, Matthias Mayer and Björn Vollan</em></p> <p><strong>General information:</strong></p> <ul> <li>Instructions for replication of the results using Stata. All do-files were created in Stata 16.</li> <li>There are 4 folders (DO-FILES, DTA-FILES, OUTPUT, XLS-FILES), in the replication package. Copy these folders to your computer in a common directory</li> </ul> <p> </p> <p><strong>Do-files:</strong></p> <ul> <li>In the DO-FILES folder run the <strong>“00_master.do”</strong> to replicate the results reported in the main manuscript and the supplementary materials. The results will be saved in the OUTPUT folder. All additional Stata packages will be automatically installed.</li> <li><strong>“01_merge_generate.do” </strong>merges the different datasets and creates additional variables using in the analysis</li> <li><strong>“02_analysis.do” </strong>provides the code to replicate all figures and tables reported in the main manuscript and supplementary materials</li> </ul> <p> </p> <p><strong>Data sets:</strong></p> <ul> <li>“bd_combine.dta”: cleaned survey data from Bangladesh</li> <li>“vn_combine.dta”: cleaned survey data from Vietnam</li> <li>“data_analysis.dta”: main data set with the survey data from Bangladesh and Vietnam merged</li> </ul>
An Empirical Study on the Survival Rate of GitHub Projects
<p>This zip provides the replication package for the paper titled <em>An Empirical Study on the Survival Rate of GitHub Projects</em> sent to the <em>Mining Software Repositories 2022</em> conference, currently under review.</p>
Dataset for "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"
<p>This package provides all published resources used and produced in the context of the research study leading to the article <em>"Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"</em>, presented in ICSA 2022's technical track.</p>
Data from: Central Mongolian lake sediments reveal new insights on climate change and equestrian empires in the Eastern Steppes
<p>The data set includes the results of ICP-OES, CNS, biomarker, and stable isotope analyses published in the research paper:</p> <p><strong>Struck, J., Bliedtner, M., Strobel, P., Taylor, W., Biskop, S., Plessen, B., Klaes, B., Bittner, L., Jamsranjav, B., Salazar, G., Szidat, S., Brenning, A., Bazarradnaa, E., Glaser, B., Zech, M., Zech, R.: Central Mongolian lake sediments reveal new insights on climate change and equestrian empires in the Eastern Steppes. Scientific Reports, 12, 2829, (2022). DOI: https://doi.org/10.1038/s41598-022-06659-w</strong></p> <p>For further information, in particular, the analyses and methods applied, we refer the reader/user to the original research paper and the supporting information published in Scientific Reports.</p> <p> </p> <p> </p>
A Social Network of the Prosopography of the Neo-Assyrian Empire
<p>The data used for creating "A Social Network of the Prosopography of the Neo-Assyrian Empire"</p> <p>The networks were created from the text and pdf file versions of the Prosopography of the Neo-Assyrian Empire (PNA). There are two different networks created from the same data. In the two-mode network, persons are connected to the texts in which they are attested. The one-mode network connects two persons if they are attested in the same document. </p> <p>In order to study and visualise the networks, it is sufficient to download the folder Networks.zip. SupplementaryData.zip contain more in-depth explanations of the different stages of creating the networks, the normalization lists used for that, and the output of different stages of the procedure.</p> <p>The dataset has also been described in Jauhiainen, H., & Alstola, T. (2022). A Social Network of the Prosopography of the Neo-Assyrian Empire. <em>Journal of Open Humanities Data</em>, <em>8</em>, 8. DOI: <a href="http://doi.org/10.5334/johd.74">http://doi.org/10.5334/johd.74</a>.</p> <p>For the reasons of copyright, we are not able to publish the data from which the networks have been extracted. But we hope that by publishing all the source codes, concordance lists, and outputs with explanations, we give the user a picture of how the networks were created.</p>
Research Artefact: An Empirical Study of React-Library Related Issues via Stack Overflow
<p>This research artifact accompanies the paper titled "An Empirical Study of React-Library Related Issues via Stack Overflow." It is a comprehensive repository that includes the collected dataset containing 447,542 React-related Stack Overflow question posts, as well as 384 representative samples obtained randomly. The primary objective of this artifact is to facilitate the replication of our dataset for researchers and allow them to utilize it for further investigations and research purposes.</p>
Empirical evidence for recent global shifts in vegetation resilience
<p>Data and codes for the publication:</p> <p>Smith, T., Traxl, D. & Boers, N. Empirical evidence for recent global shifts in vegetation resilience. <em>Nat. Clim. Chang.</em> (2022). https://doi.org/10.1038/s41558-022-01352-2</p> <p>Data is described in 'README.txt'.</p>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots
<p><strong>Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots</strong></p> <p>Fire regimes are areas having similar fire characteristics, and show the spatial pattern, frequency and intensity of fires that prevail in that area over long periods of time. Fire regimes are created and maintained by multivariate interactions between climate, vegetation/fuels, and ignitions. Like ecoregions, fire regimes indicate the extent and overlap of particular vegetative/fuel communities and climatic conditions, and are important for understanding, monitoring, predicting and managing fire.</p> <p>More than 83M MODIS “hotspot” thermal detections from 2002-2019 were grouped into 10km cells, and 21 derived variables describing fire characteristics of fire intensity, return frequency, and seasonality within each cell were developed and subjected to unsupervised Multivariate Geographic Clustering to produce world maps of Global Fire Regimes (GFRs), each having similar fire intensity and timing characteristics.</p> <p>Methodology behind these datasets are described in manuscript currently in review.</p> <p><strong>W. W. Hargrove, Jitendra Kumar, Steven P. Norman, Forrest M. Hoffman (2022), "Empirical Characterization of Global Fire Regimes Show Shared Fire Relationships" 2022 (in review)</strong></p> <p>This data collection includes:</p> <p>1. Multivariate Geographic Clustering Global Fire Regimes at 3000, 1000, 500, 100, 50, 20, 10 levels of divisions in form of geospatial raster in IMG formats, and associated color tables.</p> <p>2. Characteristics of GFRs</p> <p>3. Location groups</p> <p>4. Geospatial maps of global fire frequency modes, global seasonality strength, and 12 types of global fires.</p> <p>5. PNG maps for all data products </p> <p>6. Description and script for global date transform algorithm.</p>
Dataset for An Empirical Study of Hackathon Code Creation and Reuse
<p>This dataset is corresponds to our extended analysis done for “The Secret Life of Hackathon Code Where does it come from and where does it go?” (<a href="https://doi.org/10.1109/MSR52588.2021.00020">https://doi.org/10.1109/MSR52588.2021.00020</a>, pre-print at: <a href="https://arxiv.org/abs/2103.01145">https://arxiv.org/abs/2103.01145</a>) and “Tracking Hackathon Code Creation and Reuse” (<a href="https://doi.org/10.1109/MSR52588.2021.00085">https://doi.org/10.1109/MSR52588.2021.00085</a>, pre-print at: <a href="https://arxiv.org/pdf/2103.10167">https://arxiv.org/pdf/2103.10167</a>). The replication package including the scripts used for generating this dataset from the “World of Code” (https://worldofcode.org/) dataset is available in Github link <a href="https://github.com/woc-hack/track_hack">https://github.com/woc-hack/track_hack</a>.</p> <p>The dataset contains the blob hashes used in the scope of the analysis and the analysis outcome.</p> <p>The columns are as following</p> <ul> <li><strong>DevpostID</strong>: Devpost identification for the hackathon project and it can be used to get the URL for the devpost.com website. Example DevpostID -q9nd5 can be translated to https://devpost.com/software/-q9nd5</li> <li><strong>ProjectID</strong>: The Github project name</li> <li><strong>HackathonEndDate</strong>: Hackathon event end date</li> <li><strong>BlobHash</strong>: The blob hash used in the analysis</li> <li><strong>BeforeHackathon-DuringHackathon-AfterHackathon</strong>: This column represents if the blob was first introduced before/during/after the hackathon (1: before, 2: during, 3: after)</li> <li><strong>SameAuthor-Contributor-OtherAuthor</strong>: This column represents if the blob was first created by someone in the hackathon team, or someone who was a contributor to a project in which one of the members of the hackathon project contributed to as well (contributor), or someone else outside of the hackathon team (1: Author is a hackathon team member, 2: Author Contributed before with a hackathon team member, 3: Author is not related to the hackathon team).</li> <li><strong>UsedBySmallProject-UsedByMediumProject-UsedByLargeProject</strong>: This column represents if the hackathon blob is reused again after the hackathon event and what is the project size that reused the code (1: not reused, 3: reused in small project, 4: reused in medium project, 5: reused in large project, NA: not in scope of the reuse analysis)</li> <li><strong>IsTemplate</strong>: This column represents if the blob is identified as a template file or not ( YES: File is template, NO: File is not template). The template definition and method is explained in the research paper.</li> </ul>
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.