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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: Nowak, T. E.; Augousti, Andy T.; Simmons, Benno I.; Siegert, Stefan;

    Data and Python code used for AOD prediction with DustNet model - a Machine Learning/AI based forecasting. Model input data and code Processed MODIS AOD data (from Aqua and Terra) and selected ERA5 variables* ready to reproduce the DustNet model results or for similar forecasting with Machine Learning. These long-term daily timeseries (2003-2022) are provided as n-dimensional NumPy arrays. The Python code to handle the data and run the DustNet model** is included as Jupyter Notebook ‘DustNet_model_code.ipynb’. A subfolder with normalised and split data into training/validation/testing sets is also provided with Python code for two additional ML based models** used for comparison (U-NET and Conv2D). Pre-trained models are also archived here as TensorFlow files. Model output data and code This dataset was constructed by running the ‘DustNet_model_code.ipynb’ (see above). It consists of 1095 days of forecased AOD data (2020-2022) by CAMS, DustNet model, naïve prediction (persistence) and gridded climatology. The ground truth raw AOD data form MODIS is provided for comparison and statystical analysis of predictions. It is intended for a quick reproduction of figures and statystical analysis presented in DustNet introducing paper. *datasets are NumPy arrays (v1.23) created in Python v3.8.18. **all ML models were created with Keras in Python v3.10.10.

    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/ 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: Nowak, Trish E.; Augousti, Andy T.; Simmons, Benno I.; Siegert, Stefan;

    Long-term, pre-processed, atmospheric datasets for use in Machine Learning/AI based forecasting. Initially intended to predict AOD, however can be adapted for prediction of other atmospheric particles. Pre-processed data and code Machine Learning ready NumPy* dataset constructed by pre-processing selected atmospheric variables at 5 pressure levels form ERA5 reanalysis (resulting in 35 features) and AOD data from MODIS on board of Aqua and Terra satellites. This is a long-term daily dataset which spans 20 years from 1st Jan 2003 to 31st Dec 2022 and is homogeneously structured into 1ºx1º grid cells. Missing days and AOD values from MODIS were imputed using Lattice Kriging method (Python code used for imputation included as Jupyter Notebook 'Combine_impute_AOD.ipynb'), but raw (unimputed) MODIS data are also available. All datasets were created for a purpose of training Convolutional Neural Network model designed to forecast Saharan dust (DustNet). These datasets can also be used to train other ML models, or indeed to forecast other variables. This dataset was used to train the DustNet model and predict 24-hr ahead AOD. Please see doi: 10.5281/zenodo.10631954 for further details on predicting AOD and the DustNet model code. *datasets are NumPy arrays (v1.23) created in Python v3.8.18.

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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: Pereira, M;

    Data for the paper "Arachidonic acid inhibition of the NLRP3 inflammasome: a mechanism to explain the anti-inflammatory effects of fasting". 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 . 2024
    Data sources: B2FIND
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    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/ DANS-EASYarrow_drop_down
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      DANS-EASY
      Dataset . 2024
      Data sources: B2FIND
      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 Data
      Dataset . 2024
      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/
      Mendeley Data
      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: 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
      License: CC BY
      Data sources: Datacite
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    Authors: Ranjithkumar, Ananth;

    The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available

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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: Ranjithkumar, Ananth;

    The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. 

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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: Honorato-Zimmer, Daniela; Escobar-Sánchez, Gabriela; Deakin, Katie; De Veer, Diamela; +8 Authors

    Dataset containing data on abundance, distribution, composition and sources of marine litter (macrolitter and microplastics) along the East Pacific region, generated by reviewing all the peer-reviewed literature published for the region until December 2022. The results of this literature review are presented in the manuscript "Macrolitter and microplastics along the East Pacific coasts – a homemade problem needing local solutions". All the data extracted from the literature are included in the sheets "MacroData" and "MicroData", corresponding to "macrolitter" and "microplastic" studies, respectively. The sheet "Legend_MacroData&MicroData" contains the metadata for the sheets "MacroData" and "MicroData". All the remaining sheets contain the data utilized for the elaboration of the graphs and figures presented in the manuscript.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    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
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Noone, Simon; D'Arcy, Caoilfhionn; Donegan, Seán; Durkan, William; +9 Authors

    These digitized observations were taken 3 times daily.

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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
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    Authors: Borrelli, Chiara; Lowery, Christopher M; McIntyre, Andrew; Routledge, Claire; +2 Authors

    Here, we present carbon and oxygen stable isotope data measured on bulk carbonates samples from Site U1557. This site was drilled during the International Ocean Discovery Program engineering Expedition 390C and is part of the South Atlantic Transect (SAT), a transect drilled across the western side of the southern Mid-Atlantic Ridge at ~31°S. The data set spans the early Eocene-early Oligocene, according to the shipboard preliminary age model for this site. The data reported here can be used to refine the age model for this site, to correlate this site with sites from other ocean basins, and could be of help in paleoceanography-related investigations.

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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
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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: Nowak, T. E.; Augousti, Andy T.; Simmons, Benno I.; Siegert, Stefan;

    Data and Python code used for AOD prediction with DustNet model - a Machine Learning/AI based forecasting. Model input data and code Processed MODIS AOD data (from Aqua and Terra) and selected ERA5 variables* ready to reproduce the DustNet model results or for similar forecasting with Machine Learning. These long-term daily timeseries (2003-2022) are provided as n-dimensional NumPy arrays. The Python code to handle the data and run the DustNet model** is included as Jupyter Notebook ‘DustNet_model_code.ipynb’. A subfolder with normalised and split data into training/validation/testing sets is also provided with Python code for two additional ML based models** used for comparison (U-NET and Conv2D). Pre-trained models are also archived here as TensorFlow files. Model output data and code This dataset was constructed by running the ‘DustNet_model_code.ipynb’ (see above). It consists of 1095 days of forecased AOD data (2020-2022) by CAMS, DustNet model, naïve prediction (persistence) and gridded climatology. The ground truth raw AOD data form MODIS is provided for comparison and statystical analysis of predictions. It is intended for a quick reproduction of figures and statystical analysis presented in DustNet introducing paper. *datasets are NumPy arrays (v1.23) created in Python v3.8.18. **all ML models were created with Keras in Python v3.10.10.

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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: Nowak, Trish E.; Augousti, Andy T.; Simmons, Benno I.; Siegert, Stefan;

    Long-term, pre-processed, atmospheric datasets for use in Machine Learning/AI based forecasting. Initially intended to predict AOD, however can be adapted for prediction of other atmospheric particles. Pre-processed data and code Machine Learning ready NumPy* dataset constructed by pre-processing selected atmospheric variables at 5 pressure levels form ERA5 reanalysis (resulting in 35 features) and AOD data from MODIS on board of Aqua and Terra satellites. This is a long-term daily dataset which spans 20 years from 1st Jan 2003 to 31st Dec 2022 and is homogeneously structured into 1ºx1º grid cells. Missing days and AOD values from MODIS were imputed using Lattice Kriging method (Python code used for imputation included as Jupyter Notebook 'Combine_impute_AOD.ipynb'), but raw (unimputed) MODIS data are also available. All datasets were created for a purpose of training Convolutional Neural Network model designed to forecast Saharan dust (DustNet). These datasets can also be used to train other ML models, or indeed to forecast other variables. This dataset was used to train the DustNet model and predict 24-hr ahead AOD. Please see doi: 10.5281/zenodo.10631954 for further details on predicting AOD and the DustNet model code. *datasets are NumPy arrays (v1.23) created in Python v3.8.18.

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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: Pereira, M;

    Data for the paper "Arachidonic acid inhibition of the NLRP3 inflammasome: a mechanism to explain the anti-inflammatory effects of fasting". 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 . 2024
    Data sources: B2FIND
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      DANS-EASY
      Dataset . 2024
      Data sources: B2FIND
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      Mendeley Data
      Dataset . 2024
      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/
      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    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
      License: CC BY
      Data sources: Datacite
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    Authors: Ranjithkumar, Ananth;

    The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available

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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: Ranjithkumar, Ananth;

    The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. 

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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: Honorato-Zimmer, Daniela; Escobar-Sánchez, Gabriela; Deakin, Katie; De Veer, Diamela; +8 Authors

    Dataset containing data on abundance, distribution, composition and sources of marine litter (macrolitter and microplastics) along the East Pacific region, generated by reviewing all the peer-reviewed literature published for the region until December 2022. The results of this literature review are presented in the manuscript "Macrolitter and microplastics along the East Pacific coasts – a homemade problem needing local solutions". All the data extracted from the literature are included in the sheets "MacroData" and "MicroData", corresponding to "macrolitter" and "microplastic" studies, respectively. The sheet "Legend_MacroData&MicroData" contains the metadata for the sheets "MacroData" and "MicroData". All the remaining sheets contain the data utilized for the elaboration of the graphs and figures presented in the manuscript.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    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
      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 . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Noone, Simon; D'Arcy, Caoilfhionn; Donegan, Seán; Durkan, William; +9 Authors

    These digitized observations were taken 3 times daily.

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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
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    Authors: Borrelli, Chiara; Lowery, Christopher M; McIntyre, Andrew; Routledge, Claire; +2 Authors

    Here, we present carbon and oxygen stable isotope data measured on bulk carbonates samples from Site U1557. This site was drilled during the International Ocean Discovery Program engineering Expedition 390C and is part of the South Atlantic Transect (SAT), a transect drilled across the western side of the southern Mid-Atlantic Ridge at ~31°S. The data set spans the early Eocene-early Oligocene, according to the shipboard preliminary age model for this site. The data reported here can be used to refine the age model for this site, to correlate this site with sites from other ocean basins, and could be of help in paleoceanography-related investigations.

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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND