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76 Research products

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Agersnap, Anne; Borcak, Lea Wierød; Kirkegaard, Thomas Husted; Baunvig, Katrine Frøkjær;

    This comprehensive dataset is derived from a large-scale survey focused on communal singing (fællessang) in Denmark. Recently, this cultural practice has garnered significant attention, especially during the COVID-19 lockdowns. Despite its growing prominence, there has been a lack of empirical data concerning the extent, diversity, contexts, and perceptions surrounding communal singing in Denmark—until now. The dataset, compiled from responses of 2,031 Danish adults in October 2022 through a collaboration with YouGov Denmark, offers valuable insights into the current state of singing practices and attitudes among the Danish population. This dataset forms the basis for ongoing research projects and forthcoming publications by the Unit for Song Studies at Aarhus University. The dataset contains a detailed spreadsheet of the survey's comprehensive findings.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Andrea Galli; Jane W. Behrens; Manuel Gesto; Nicholas P. Moran;

    Data/code for the paper: Galli, A., Behrens, J. W., Gesto, M., & Moran, N. P. (2023). Boldness and physiological variation in round goby populations along their Baltic Sea invasion front. Physiology & Behavior, 114261. https://doi.org/10.1016/j.physbeh.2023.114261

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO; Sygma
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO; Sygma
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: David N. Matzig; Renata Pedroso de Araujo; Mercedes Okumura; Astolfo G. M. Araujo; +6 Authors

    Maintainer of this repository: David N. Matzig (<david.matzig@cas.au.dk>) Published in: Renata Pedroso de Araujo, Felix Riede, Mercedes Okumura, Astolfo G. M. Araujo, Alice Leplongeon, Colin Wren, José Ramón Rabuñal, Marcelo Cardillo, María Barrera Cruz, David N. Matzig (2023) Benchmarking methods and data for the whole-outline geometric morphometric analysis of lithic tools. Evolutionary Anthropology. Overview of contents and how to reproduce: This repository contains the data collaboratively collected and prepared in the workshop "Cultural evolutionary tools for stone tool shape analysis: Geometric morphometrics and Bayesian phylogenetics" hosted at the Aarhus Institute for Advanced Studies, in Aarhus, Denmark in September 2022. The dataset present in the folder `database` contains outlines together with their associated metadata of 646 artefacts collected from eight different countries, spanning a timeframe of ~18.000 to 500 years BP. For further information, please refer to the Supplementary Informations. After downloading, the results can be reproduced using `CES_2022_workshop.Rproj` and the existing folder structure. The required packages and their versions which have been used in this study are listed below and in the `DESCRIPTION`-file. All analyses and visualisations presented in this paper were prepared in R 4.2.1 under Ubuntu 18.04.5 LTS (64-bit). Required R-packages and their versions: The R code provides an example on how to load, inspect, and plot different aspects of the data. It requires the following R-packages: dplyr (>= 1.0.8), ggplot2 (>= 3.4.0), ggthemes (>= 4.2.4), magrittr (>= 2.0.2), Momocs (>= 1.3.0), outlineR (>= 0.1.0), raster (>= 3.5-15), readr (>= 2.1.2), remotes (>= 2.4.2), rgeos (>= 0.5-9), rworldmap (>= 1.3-6), sp (>= 1.4-6). Licenses: Code: MIT <http://opensource.org/licenses/MIT> year: 2023, copyright holder: David Nicolas Matzig

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Other ORP type . 2023
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Other ORP type . 2023
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Yaworsky, Peter M.; Hussain, Shumon T.; Riede, Felix;

    The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal Scientific Reports. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x. Below is the legend for the Supplementary Information, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in Scientific Reports. Supplementary 1 – UpperPaleoDietV4.html – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions. Supplementary 2 – Support Information 2.docx – Word document containing supplementary tables 2 and 3. Supplementary 3 – ArchaeoloigcalDataset_v8.csv – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 4 – EuroUpperPaleoFaunas_v6.csv – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 5 – Lupo2016.csv – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1. Supplementary 6 – PushkinaRaia_FaunaWeights.csv – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1. Supplementary 7 – environmental_BG.csv – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1. For more information on the data, methods, and results, please see the main paper.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Klindt, William Grynderup; Schmidt, Luchas Nickolaj; Nielsen, Jacob Peter Diesel;

    This study explores the use of Natural Language Processing as a tool for online reviews. By drawing on theories about machine learning, this article investigates how to develop a Natural Language Processing model in Python. The authors present two machine learning models which were developed through an iterative design process and by implementing various approaches from the TRIN-model. This study concludes that machine learning models like ours might not have a significant impact on the future of reviews, but we are optimistic about the potential of Natural Language Processing as a tool for categorizing specific aspects of a company’s services. This conclusion is based on the analysis and discussion of the results we gathered from our Natural Language Processing models.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Roskilde Universitet...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Roskilde Universitet...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bjørk, Rasmus; Imbaquingo Munoz, Carlos Enrique; Holm, Philip;

    This data set is for the article "Two-Dimensional Elliptically Shaped Electromagnetic Vibration Energy Harvester". The data contains the measured voltage as function of time for the different harvester configurations presented in the article. The data is structured as follows: The 1D vibrations and the 2D vibration directories contain the voltage as function of time for different frequencies for the respective configuration.  In the 1D vibration folder, the subdirectories are named after the angle that the harvester was placed at with respect to the direction of vibration. In the 2D vibration folder the subdirectories are named after the frequency of vibration along the semi minor axis. In each subdirectory the files are named according to the frequency at the semi major axis that the experiment was run at, eg. the file B825.txt contains the voltage measured as function of time for driven with a frequency of 8.25 Hz along the semi major axis.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DTU Dataarrow_drop_down
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    DTU Data
    Dataset . 2023
    License: CC BY
    Data sources: DTU Data
    https://doi.org/10.11583/dtu.2...
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    https://doi.org/10.11583/dtu.2...
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DTU Dataarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DTU Data
      Dataset . 2023
      License: CC BY
      Data sources: DTU Data
      https://doi.org/10.11583/dtu.2...
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      https://doi.org/10.11583/dtu.2...
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Liew, Jaime Yikon; Riva, Riccardo; Göçmen, Tuhfe;

    Data-set creationThis data set was created in association with a conference paper submitted to DeepWind 2023 titled Efficient Mann Turbulence Generation for Offshore Wind Farm Fatigue Load Surrogates.1000 HAWC2Farm simulations were performed of the Lillgrund wind farm consisting of 48 turbines, each with a nameplate capacity of 2.3MW. Each simulation runs for 1 hour @100Hz with a random inflow wind direction, ambient wind speed (between 4 and 20 m/s), power law shear exponents (between 0.1 and 0.3), and turbulence intensity (between 2.5% and 10%).The windfields immediately infront of each turbine are recorded every second in a 30x30 grid with dimensions 1.1Dx1.1D (where the rotor diameter is D=92.6m). The loads of the blade roots, tower base, tower top, and main shaft are recorded.Each 1 hours HAWC2Farm simulation is split into 6x10minute observation. The wind fields are temporally averaged over the 10 minute window, providing averaged wind field profiles (Fig. 1). Additionally, the residual background turbulence is measured over the 10 minute period, providing wind field deviation profiles (Fig. 2).Damage equivalent load (DEL) quantities are calculated for the corresponding 10 minute periods. DELs for blade components use a wholer exponent of 10, whereas tower and main shaft components use a wholer exponent of 4.With 6 time intervals and 48 turbines, each HAWC2Farm simulation provides 6x48=288 observation pairs, with the wind field as the input and DELs as output. The total data-set consists of 288000 observations. 230400 (80%) of these observations make up the training data set provided in this data set. The other 57600 (20%) observations have been embargoed as a blind testing set for future investigations.Data-set descriptionThe data has been split into two groups:Training set input (dataset_input.parquet)Training set output (dataset_output.parquet)Data files each contain dataframes in Apache Parquet format, which can easily loaded using Data Frame packages. For example, in Python, parquet files can be loaded with Pandas (using the pandas.read_parquet() function), Polars (using the polars.read_parquet() function), or PyArrow (using the pyarrow.parquet.read_table() function).Training set inputsThe mean and standard deviation profiles for each observation are 30x30 arrays of floats. These are stored in row-major order as two vectors of 900 elements each. The elements of the mean wind field are stored in the column labeled mean_XXX where XXX is the index (from 0 to 899) of the element. Similarly, the standard deviation field is labelled std_XXX. The 1800 columns of the input data set are therefore:mean_000mean_001...mean_899std_000std_001...std_899Training set outputsAll output quantities are averaged over a 10 minute period. Blade damage equivalent loads (DELs) are computed using a wholer exponent of 10. All other loads use a wholer exponent of 4.pelec - Electrical power [kW]MxBr - Blade root edgewise DELs [kNm]MyBr - Blade root flapwise DELs [kNm]MzBr - Blade root torsion DELs [kNm]MxTB - Tower bottom side-side DELs [kNm]MyTB - Tower bottom fore-aft DELs [kNm]MzTB - Tower bottom torsion DELs [kNm]MxTT - Tower top fore-aft DELs [kNm]MyTT - Tower top side-side DELs [kNm]MzTT - Tower top torsion DELs[kNm]MxMB - Main bearing bending DELs [kNm]MyMB - Main bearing bending DELs [kNm]MzMB - Main bearing torsion DELS [kNm]TaskBuild a predictive model mapping the wind field parameterization at the turbine rotor to the turbine damage equivalent loads and power output.PerformancePerformance of the predictions shall be compared against baseline predictions, where the mean values of the outputs from the training data-set are used as predictions.

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    DTU Data
    Dataset . 2023
    License: CC BY
    https://doi.org/10.11583/dtu.2...
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      DTU Data
      Dataset . 2023
      License: CC BY
      https://doi.org/10.11583/dtu.2...
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Garcia-Santiago, Oscar M.;

    This dataset contains model outputs and necessary files to replicate the simulations from the article "Simulating wake losses of the Danish Energy Island wind farm cluster" (https://doi.org/10.1088/1742-6596/2505/1/012015) The modelling uses the Weather research and forecasting (WRF; https://github.com/wrf-model/WRF ) model using a wind farm parametrisation (EWP, https://doi.org/10.5194/gmd-8-3715-2015 ). Two modelling approaches are used: an ideal isolated case (one specific wind direction and wind speed) and real cases (two grid spacing) that cover the simulation of a full year (2016). The simulation aimed to compare methodologies for modelling very large offshore clusters and calculate their Anual Energy Production.  Input files To perform the simulations with WRF, input files are attached as zip files for real (modelfiles_real_*km) and ideal (modelfiles_ideal_1km) simulations. This zip file contains the namelist.input*, namelist.wps, and input_souding necessary to run the models on both modes and for the specified grid spacing (1 or 2 kilometres). For the real simulations, boundary conditions (ERA5 reanalysis) are needed and can be downloaded from ftp://ftp.ecmwf.int/pub/wrf. For ideal, the input_soundig file is the only requirement. The files EnergyO.ideal, EnergyO.real, and GEN-15-236.turbine are provided to simulate the wind farms. The cluster comprises ten wind farms with 1 GW capacity. Each wind farm is simulated with 67 15 MW wind turbines. EnergyO.ideal, EnergyO.real are the locations for the ideal and real cases, respectively, and the thrust and power curves are given in GEN-15-236.turbine. Model outputs files The model's output is in NetCDF format (*.nc) and contains the wind speed ("WS") field and the power production (''POWER") generated by the wind farms. Quantities are expressed as temporal averages over 1 hour and 1 year (2016) for the ideal and real cases, respectively.

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    DTU Data
    Dataset . 2023
    License: CC BY
    https://doi.org/10.11583/dtu.2...
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      DTU Data
      Dataset . 2023
      License: CC BY
      https://doi.org/10.11583/dtu.2...
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Lovotti, Ivo Joaquin; Musco, Andrea; Kadir, Seda Elena;

    This report illustrates the factors that influence the decision-making processes of individuals whentaking part in the contemporary art market, notwithstanding the risk often attributed to such asset and the economic uncertainty of periods such as the aftermath of Covid-19. By examining the motivations from different perspectives, this report aims to shed light on the dynamics of the contemporary art market and its resilience in the face of economic instability. To answer the research question what factors drive individuals in Denmark to take part in the contemporary art market amidst the current economic uncertainty? We carry out semi-structured interviews with two amateur art buyers, one curator, and two owners of renowned contemporary art galleries. The results show that investing in contemporary art 1) yields socio-economic advantages 2) is influenced by trust towards the artist, and 3) is a matter of familial background. The research emphasizes how these factors influences the perception of risk in the individuals when investing in a piece of contemporary art. The purpose of this study is to identify the key factors that affect participants' engagement with the art market by investigating their personal motivations, risk perceptions, and investment strategies. The research is also particularly useful and relevant given the current economic uncertainty and its potential effects on the investment landscape. The results of this study will add to the body of knowledge that exists aboutthe contemporary art investment market by giving a more in-depth and precise understanding of the factors that influence people to invest in art despite the current state of the economy.

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    Authors: Thorsen, Sofie;

    “Expanding Data Imaginaries in Urban Planning” synthesizes more than three years of industrial research conducted within Gehl and the Techno–Anthropology Lab at Aalborg University. Through practical experiments with social media images, digital photovoice, and participatory mapmaking, the project explores how visual materials created by citizens can be used within a digital and participatory methodology to reconfigure the empirical ground of data-driven urbanism. Drawing on a data feminist framework, the project uses visual research to elevate community voices and situate urban issues in lived experiences. As a Science and Technology Studies project, the PhD also utilizes its industrial position as an opportunity to study Gehl’s practices up close, unpacking collectively held narratives and visions that form a particular “data imaginary” and contribute to the production and perpetuation of the role of data in urban planning. The dissertation identifies seven epistemological commitments that shape the data imaginary at Gehl and act as discursive closures within their practice. To illustrate how planners might expand on these, the dissertation uses its own data experiments as speculative demonstrations of how to make alternative modes of knowing cities possible through participatory and digital visual methods.

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76 Research products
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Agersnap, Anne; Borcak, Lea Wierød; Kirkegaard, Thomas Husted; Baunvig, Katrine Frøkjær;

    This comprehensive dataset is derived from a large-scale survey focused on communal singing (fællessang) in Denmark. Recently, this cultural practice has garnered significant attention, especially during the COVID-19 lockdowns. Despite its growing prominence, there has been a lack of empirical data concerning the extent, diversity, contexts, and perceptions surrounding communal singing in Denmark—until now. The dataset, compiled from responses of 2,031 Danish adults in October 2022 through a collaboration with YouGov Denmark, offers valuable insights into the current state of singing practices and attitudes among the Danish population. This dataset forms the basis for ongoing research projects and forthcoming publications by the Unit for Song Studies at Aarhus University. The dataset contains a detailed spreadsheet of the survey's comprehensive findings.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Andrea Galli; Jane W. Behrens; Manuel Gesto; Nicholas P. Moran;

    Data/code for the paper: Galli, A., Behrens, J. W., Gesto, M., & Moran, N. P. (2023). Boldness and physiological variation in round goby populations along their Baltic Sea invasion front. Physiology & Behavior, 114261. https://doi.org/10.1016/j.physbeh.2023.114261

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO; Sygma
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO; Sygma
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: David N. Matzig; Renata Pedroso de Araujo; Mercedes Okumura; Astolfo G. M. Araujo; +6 Authors

    Maintainer of this repository: David N. Matzig (<david.matzig@cas.au.dk>) Published in: Renata Pedroso de Araujo, Felix Riede, Mercedes Okumura, Astolfo G. M. Araujo, Alice Leplongeon, Colin Wren, José Ramón Rabuñal, Marcelo Cardillo, María Barrera Cruz, David N. Matzig (2023) Benchmarking methods and data for the whole-outline geometric morphometric analysis of lithic tools. Evolutionary Anthropology. Overview of contents and how to reproduce: This repository contains the data collaboratively collected and prepared in the workshop "Cultural evolutionary tools for stone tool shape analysis: Geometric morphometrics and Bayesian phylogenetics" hosted at the Aarhus Institute for Advanced Studies, in Aarhus, Denmark in September 2022. The dataset present in the folder `database` contains outlines together with their associated metadata of 646 artefacts collected from eight different countries, spanning a timeframe of ~18.000 to 500 years BP. For further information, please refer to the Supplementary Informations. After downloading, the results can be reproduced using `CES_2022_workshop.Rproj` and the existing folder structure. The required packages and their versions which have been used in this study are listed below and in the `DESCRIPTION`-file. All analyses and visualisations presented in this paper were prepared in R 4.2.1 under Ubuntu 18.04.5 LTS (64-bit). Required R-packages and their versions: The R code provides an example on how to load, inspect, and plot different aspects of the data. It requires the following R-packages: dplyr (>= 1.0.8), ggplot2 (>= 3.4.0), ggthemes (>= 4.2.4), magrittr (>= 2.0.2), Momocs (>= 1.3.0), outlineR (>= 0.1.0), raster (>= 3.5-15), readr (>= 2.1.2), remotes (>= 2.4.2), rgeos (>= 0.5-9), rworldmap (>= 1.3-6), sp (>= 1.4-6). Licenses: Code: MIT <http://opensource.org/licenses/MIT> year: 2023, copyright holder: David Nicolas Matzig

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    ZENODO
    Other ORP type . 2023
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      ZENODO
      Other ORP type . 2023
      Data sources: ZENODO
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    Authors: Yaworsky, Peter M.; Hussain, Shumon T.; Riede, Felix;

    The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal Scientific Reports. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x. Below is the legend for the Supplementary Information, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in Scientific Reports. Supplementary 1 – UpperPaleoDietV4.html – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions. Supplementary 2 – Support Information 2.docx – Word document containing supplementary tables 2 and 3. Supplementary 3 – ArchaeoloigcalDataset_v8.csv – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 4 – EuroUpperPaleoFaunas_v6.csv – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 5 – Lupo2016.csv – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1. Supplementary 6 – PushkinaRaia_FaunaWeights.csv – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1. Supplementary 7 – environmental_BG.csv – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1. For more information on the data, methods, and results, please see the main paper.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Klindt, William Grynderup; Schmidt, Luchas Nickolaj; Nielsen, Jacob Peter Diesel;

    This study explores the use of Natural Language Processing as a tool for online reviews. By drawing on theories about machine learning, this article investigates how to develop a Natural Language Processing model in Python. The authors present two machine learning models which were developed through an iterative design process and by implementing various approaches from the TRIN-model. This study concludes that machine learning models like ours might not have a significant impact on the future of reviews, but we are optimistic about the potential of Natural Language Processing as a tool for categorizing specific aspects of a company’s services. This conclusion is based on the analysis and discussion of the results we gathered from our Natural Language Processing models.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Roskilde Universitet...arrow_drop_down
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    Authors: Bjørk, Rasmus; Imbaquingo Munoz, Carlos Enrique; Holm, Philip;