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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: Lemasson, Anaëlle; Somerfield, Paul; Schratzberger, Michaela; Thompson, Murray S.A.; +7 Authors

    Marine artificial structures (MAS), including oil and gas installations (O&G) and offshore wind farms (OWFs), have a finite operational period and require decommissioning when reaching end-of-life. Selecting the most suitable decommissioning options remains a challenge, in part because their effects are still largely undetermined. Whether decommissioned structures could act (sensu “function”) as artificial reefs (ARs) and provide desired ecological and societal benefits is of particular interest. Here, we use a meta-analysis approach of 531 effect sizes from 109 articles to assess the ecological effects of MAS, comparing O&G and OWFs to shipwrecks and ARs, with a view to inform their decommissioning. This synthesis demonstrates that whilst MAS can bring ecological benefits, important idiosyncrasies exist. In particular, we find limited conclusive evidence that O&G and OWFs would provide significant ecological benefits if decommissioned as ARs. We conclude that decommissioning options aimed at repurposing MAS into ARs may not provide the intended benefits. Hre, we provide the supplementary datasets and R code used to undertake this meta-analysis.

    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 . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    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 . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      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: Haigh, Michael; Holland, Paul R.;

    Massachusetts Institute of Technology general circulation model (MITgcm, https://github.com/MITgcm/MITgcm) model output used in study 'Decadal variability of ice-shelf melt in the Amundsen Sea driven by freshwater fluxes' by Michael Haigh and Paul R. Holland (2024), Geophysical Research Letters. Also provided are Python scripts for processing the data. The model uses a 1/10 degree resolution, 50 vertical levels configuration of the Amundsen Sea. The domain spans 140°W to 80°W and 76°S to 62°S. The model includes ocean, ice-shelf and sea-ice thermodynamics. The model is forced by ERA5 atmospheric data during 1979-2019. 6 NetCDF files are included in the dataset: state2D.nc stateExf.nc stateRho.nc stateTheta.nc stateUvel.nc stateVvel.nc Also provided is a zip file containing: (1) *.data and *.meta files of model grid data, (2) Python scripts for processing model output and (3) *.npy files which are outputs of PROCESS_DATA.py and can be plotted using PLOT_DATA.py. The provided Python scripts produce figures for 'Haigh & Holland (2024), Decadal variability of ice-shelf melt in the Amundsen Sea driven by freshwater fluxes.' Sea README.md for details. This research was funded by the NERC project ``Drivers of Oceanic Change in the Amundsen Sea'', NE/T012803/1.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    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
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      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: Ballantyne, John;

    The ability to represent perturbative expansions of interacting quantum field theories in terms of simple diagrammatic rules has revolutionized calculations in particle physics (and elsewhere). Moreover, these rules are readily automated, a process that has catalyzed the rise of symbolic algebra packages. However, in the case of extended theories of gravity, such as scalar-tensor theories, it is necessary to precondition the Lagrangian to apply this automation or, at the very least, to take advantage of existing software pipelines. We present a Mathematica code FeynMG, which works in conjunction with the well-known package FeynRules, to do just that: FeynMG takes as inputs the FeynRules model file for a non-gravitational theory and a user-supplied gravitational Lagrangian. FeynMG provides functionality that inserts the minimal gravitational couplings of the degrees of freedom specified in the model file, determines the couplings of the additional tensor and scalar degrees of freedom (the metric and the scalar field from the gravitational sector), and preconditions the resulting Lagrangian so that it can be passed to FeynRules, either directly or by outputting an updated FeynRules model file. The Feynman rules can then be determined and output through FeynRules, using existing universal output formats and interfaces to other analysis packages.

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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/ Mendeley Dataarrow_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/
    Authors: Fidai, Yanna Alexia;

    This dataset includes the date, time, address, latitude, longitude, speed, and altitude data collected by GPS trackers deployed in sargassum mats. For more information on the methods and data see Fidai et al. (2023) 'Tracking and detecting sargassum pathways across the Tropical Atlantic'. 

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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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      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: Dixit, Tanmay; Town, Christopher P.; Spottiswoode, Claire N.; Chen, Kuan-Chi; +2 Authors

    What makes a perfect signature? Optimal signatures should be consistent within individuals and distinctive between individuals. In defence against avian brood parasitism, some host species have evolved ‘signatures’ of identity on their eggs, comprising inter-individual variation in colour and pattern. Tawny-flanked prinia (Prinia subflava) egg signatures facilitate recognition and rejection of parasitic cuckoo finch (Anomalospiza imberbis) eggs. Here, we show that consistency and distinctiveness of pattern are negatively correlated in prinia eggs, perhaps because non-random, repeatable pattern generation mechanisms increase consistency but limit distinctiveness. We hypothesise that pattern properties which are repeatable within individuals but random between individuals (‘invariant properties’) allow hosts to circumvent this trade-off. To find invariant properties, we develop a method to quantify entire egg phenotypes from images taken from different perspectives. We find that marking scale (a fine-grained measure of size), but not marking orientation or position, is an invariant property in prinias. Hosts should therefore use differences in marking scale in egg recognition, but instead field experiments show that these differences do not predict rejection of conspecific eggs by prinias. Overall, we show that invariant properties allow consistency and distinctiveness to coexist, yet receiver behaviour is not optimally tuned to make use of this information. Dataset consists of pattern metrics extracted from egg images, and egg rejection data, from field experiments conducted from 2018-2020.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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  • Authors: Ballantyne, J;

    ULYSSES is a Python package that calculates the baryon asymmetry produced from leptogenesis in the context of a type-I seesaw mechanism. In this release, the new features include code which solves the Boltzmann equations for low-scale leptogenesis; the complete Boltzmann equations for thermal leptogenesis applying proper quantum statistics without assuming kinetic equilibrium of the right-handed neutrinos; and, primordial black hole-induced leptogenesis. ULYSSES version 2 has the added functionality of a pre-provided script for a two-dimensional grid scan of the parameter space. As before, the emphasis of the code is on user flexibility, rapid evaluation and is publicly available at https://github.com/earlyuniverse/ulysses. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Warburton, Katarzyna; Hewitt, Duncan; Neufeld, Jerome;

    Code for generating simulations of glacial ribs as described in the associated manuscript and in the README file.

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    Apollo
    Dataset
    License: CC BY
    Data sources: Apollo
    Apollo
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      Apollo
      Dataset
      License: CC BY
      Data sources: Apollo
      Apollo
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Ballantyne, J;

    We introduce NSC++, a header-only C++ library that simulates the evolution of the plasma and a decaying fluid in the early Universe. NSC++ can be used in C++ programs or called directly from python scripts without significant overhead. There is no special installation process or external dependencies. Furthermore, there are example programs that can be modified to handle several cases. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    DANS-EASY
    Dataset . 2023
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Ballantyne, J;

    We present the pyerrors python package for statistical error analysis of Monte Carlo data. Linear error propagation using automatic differentiation in an object oriented framework is combined with the Γ-method for a reliable estimation of autocorrelation times. Data from different sources can easily be combined, keeping the information on the origin of error components intact throughout the analysis. pyerrors can be smoothly integrated into the existing scientific python ecosystem which allows for efficient and compact analyses. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    DANS-EASY
    Dataset . 2023
    Data sources: B2FIND
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    Authors: Angelopoulos, Konstantinos; Mancy, Rebecca; Stewart, Gillian;

    Overview of the record This record provides the main analysis dataset for the following paper: Angelopoulos K, Stewart G, Mancy R. 2022 Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proc. R. Soc. B 20221986. https://doi.org/10.1098/rspb.2022.1986 It also serves as a portal for the remaining code and datasets used in the paper. Specifically, the upload consists of: The main analysis dataset, hosted directly in DataDryad as an Excel spreadsheet. This is the final dataset used to conduct the statistical analysis reported in the paper. 'Related Works: Software': a link to the R code used for the data transformations and analysis, and hosted on Zenodo. Note that this also includes a copy of the main dataset and the shapefiles in (3) below. 'Related Works: Dataset': a link to the shapefiles used to generate maps and other spatial manipulations required for the manuscript, and hosted on Zenodo Each Related Work contains its own README files and/or appropriate documentation. The following is a description of the methods for constructing the main dataset (1). Main dataset construction and processing COV_Main_Dataset.xlsx contains data relating to smallpox and conscientious object to smallpox vaccination (COV), alongside socioeconomic variables, at municipal ward and registration district level for Glasgow 1900-1913. It contains the following worksheets: Data dictionary: provides descriptions of variables and explains abbreviations used. Ward_All: provides variables at municipal ward level, for all 25 wards that existed in Glasgow in 1900. W_Drop_Blyt_Exch: provides variables at municipal ward level, excluding two atypical central wards, Blythswood and Exchange. RD_All: provides variables at registration district level, for all 20 registration districts, or parts of these registration districts that cover the area of the 25 municipal wards that existed in 1900. RD_Drop_Blyt: provides variables at registration district level, excluding Blythswood where the maternity hospital was situated and where data are atypical. To construct this dataset, data were first manually transcribed into Excel, primarily from Medical Officer of Health reports and census reports. They were then processed in R as described in the manuscript (code and further documentation are available in 'Related Works: Software', a repository that also includes the raw dataset as transcribed). The file provided here is the final dataset used for the statistical analysis reported in the paper. Usage notes The main analysis dataset: provided as an Excel spreadsheet that can be opened with Excel or open-source alternative such as OpenOffice. 'Related Works: Software': a link to the R code. This can be opened using R, R Studio or similar code editors, and run in R. This repository also contains Excel files and CSV files, as well as shapefiles. These can all be opened/imported into R using the code provided. Excel and CSV files can also be opened in Excel or open-source alternative such as OpenOffice. Shapefiles can be opened in R, or other GIS software such as the open-source software package QGIS. 'Related Works: Dataset': a link to a collection of shapefiles. Shapefiles can be opened in R, or other GIS software such as the open-source software package QGIS. How to cite Reuse of the main analysis dataset and/or other information included in the R code repository: please cite the paper and this DataDryad record. Reuse of the shapefile(s) only: please cite the paper and the shapefiles collection. Citation for paper Angelopoulos K, Stewart G, Mancy R. 2022 Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proc. R. Soc. B 20221986. https://doi.org/10.1098/rspb.2022.1986 Citation for this DataDryad Record Angelopoulos, Konstantinos; Mancy, Rebecca; Stewart, Gillian (2023), Data and code for Angelopoulos, Stewart and Mancy (2023) "Local infectious disease experience influences vaccine refusal rates: a natural experiment", Proceedings of the Royal Society B: Biological Sciences., Dryad, Dataset, https://doi.org/10.5061/dryad.rbnzs7hfx Citation for shapefiles collection Mancy, Rebecca. (2023). Shapefiles of administrative boundaries, Subway and main rivers in Glasgow, UK, around 1910 (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7550897 Vaccination has been critical to the decline in infectious disease prevalence in recent centuries. Nonetheless, vaccine refusal has increased in recent years, with complacency associated with reductions in disease prevalence highlighted as an important contributor. We exploit a natural experiment in Glasgow at the beginning of the 20th century to investigate whether prior local experience of an infectious disease matters for vaccination decisions. Our study is based on smallpox surveillance data and administrative records of parental refusal to vaccinate their infants. We analyse variation between administrative units of Glasgow in cases and deaths from smallpox during two epidemics over the period 1900–1904, and vaccine refusal following its legalisation in Scotland in 1907 after a long period of compulsory vaccination. We find that lower local disease incidence and mortality during the epidemics were associated with higher rates of subsequent vaccine refusal. This finding indicates that complacency influenced vaccination decisions in periods of higher infectious disease risk, responding to local prior experience of the relevant disease, and has not emerged solely in the context of the generally low levels of infectious disease risk of recent decades. These results suggest that vaccine delivery strategies may benefit from information on local variation in incidence.

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    ZENODO
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    DRYAD
    Dataset . 2023
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    Data sources: Datacite
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      ZENODO
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      License: CC 0
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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: Lemasson, Anaëlle; Somerfield, Paul; Schratzberger, Michaela; Thompson, Murray S.A.; +7 Authors

    Marine artificial structures (MAS), including oil and gas installations (O&G) and offshore wind farms (OWFs), have a finite operational period and require decommissioning when reaching end-of-life. Selecting the most suitable decommissioning options remains a challenge, in part because their effects are still largely undetermined. Whether decommissioned structures could act (sensu “function”) as artificial reefs (ARs) and provide desired ecological and societal benefits is of particular interest. Here, we use a meta-analysis approach of 531 effect sizes from 109 articles to assess the ecological effects of MAS, comparing O&G and OWFs to shipwrecks and ARs, with a view to inform their decommissioning. This synthesis demonstrates that whilst MAS can bring ecological benefits, important idiosyncrasies exist. In particular, we find limited conclusive evidence that O&G and OWFs would provide significant ecological benefits if decommissioned as ARs. We conclude that decommissioning options aimed at repurposing MAS into ARs may not provide the intended benefits. Hre, we provide the supplementary datasets and R code used to undertake this meta-analysis.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Haigh, Michael; Holland, Paul R.;

    Massachusetts Institute of Technology general circulation model (MITgcm, https://github.com/MITgcm/MITgcm) model output used in study 'Decadal variability of ice-shelf melt in the Amundsen Sea driven by freshwater fluxes' by Michael Haigh and Paul R. Holland (2024), Geophysical Research Letters. Also provided are Python scripts for processing the data. The model uses a 1/10 degree resolution, 50 vertical levels configuration of the Amundsen Sea. The domain spans 140°W to 80°W and 76°S to 62°S. The model includes ocean, ice-shelf and sea-ice thermodynamics. The model is forced by ERA5 atmospheric data during 1979-2019. 6 NetCDF files are included in the dataset: state2D.nc stateExf.nc stateRho.nc stateTheta.nc stateUvel.nc stateVvel.nc Also provided is a zip file containing: (1) *.data and *.meta files of model grid data, (2) Python scripts for processing model output and (3) *.npy files which are outputs of PROCESS_DATA.py and can be plotted using PLOT_DATA.py. The provided Python scripts produce figures for 'Haigh & Holland (2024), Decadal variability of ice-shelf melt in the Amundsen Sea driven by freshwater fluxes.' Sea README.md for details. This research was funded by the NERC project ``Drivers of Oceanic Change in the Amundsen Sea'', NE/T012803/1.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
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    Authors: Ballantyne, John;

    The ability to represent perturbative expansions of interacting quantum field theories in terms of simple diagrammatic rules has revolutionized calculations in particle physics (and elsewhere). Moreover, these rules are readily automated, a process that has catalyzed the rise of symbolic algebra packages. However, in the case of extended theories of gravity, such as scalar-tensor theories, it is necessary to precondition the Lagrangian to apply this automation or, at the very least, to take advantage of existing software pipelines. We present a Mathematica code FeynMG, which works in conjunction with the well-known package FeynRules, to do just that: FeynMG takes as inputs the FeynRules model file for a non-gravitational theory and a user-supplied gravitational Lagrangian. FeynMG provides functionality that inserts the minimal gravitational couplings of the degrees of freedom specified in the model file, determines the couplings of the additional tensor and scalar degrees of freedom (the metric and the scalar field from the gravitational sector), and preconditions the resulting Lagrangian so that it can be passed to FeynRules, either directly or by outputting an updated FeynRules model file. The Feynman rules can then be determined and output through FeynRules, using existing universal output formats and interfaces to other analysis packages.

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    Authors: Fidai, Yanna Alexia;

    This dataset includes the date, time, address, latitude, longitude, speed, and altitude data collected by GPS trackers deployed in sargassum mats. For more information on the methods and data see Fidai et al. (2023) 'Tracking and detecting sargassum pathways across the Tropical Atlantic'. 

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    ZENODO
    Dataset . 2023
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    ZENODO
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