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In this letter, we introduce The Polar Vocabularies and Semantics Working Group, originally established as a joint effort between the joint SAON/IASC Arctic Data Committee and the Data Management Collaboration Team of the Interagency Arctic Research Policy Committee. We Invite, communities of practice to actively engage with us in our activities (described below), to advance the state of semantics-based applications in polar activities (and to increase interoperability between stakeholders and rights holders within existing and emerging digital ecosystems).

The file ModelingFLC_BASPRO_solution.zip is a BAS-PRO model solution output archived as a zip file. After extracting the zip file, the solution will be spread across multiple plaintext files. The solution is a grid of proton phase space density multiplied by proton rest mass cubed, f, with units km-6 s3. f is specified in terms of the first, second and third adiabatic invariants μ, K and L as well as time. The solution files can be loaded using the BAS-PRO plotting library, available at https://github.com/atmosalex/BAS-PRO_plotting. A copy of the BAS-PRO plotting library has also been bundled with this dataset (BAS-PRO_plotting-main.zip) to prevent potential compatibility issues arising from future updates to the online repository. It is recommend to following the steps in the "Getting start" section of the plotting library README.md file, as this will result in plots of the solution, and will also convert the plaintext solution files into a single file in binary .cdf format which allows for faster loading. The plaintext solution included in this dataset is made up of two sets of files which correspond to different grid resolutions: Files ending in 'dyn.txt' are 'dynamic output' files, containing the sampled time evolution of f throughout the simulation period. The dynamic output grid is lower resolution than the original BAS-PRO simulation grid in order to save disk space. These files are useful for producing plots. Files not ending in 'dyn.txt' are 'simulation grid' files, containing f at the final simulation epoch only, at the original simulation grid resolution. These files are useful for loading into BAS-PRO as an initial condition, or for plotting the final epoch at higher resolution. The coordinate range of the 'simulation grid' ('dynamic output') files is as follows: log10(μ/ (1MeV/G)) ranges from: 0.029384425 to 4.2519649 (0.17108176 to 4.1952860) K ranges from: 0 to 5.729029 (0 to 5.729029) in units G0.5 RE L ranges from: 1.13 to 4.0 (1.13 to 4.0) time ranges from 1388534400 to 1517443200, given in terms of seconds passed since January 1, 1970 UTC, and this time range is from January 1, 2014 to February 1, 2018. The following table gives a description of each file included: axis_mu.txt first dimension axis: a list of log10(μ/ (1MeV/G)) for the μ of each simulation grid point axis_K.txt second dimension axis: a list of K for each simulation grid point, with units G0.5 RE axis_L.txt third dimension axis: a list of L at each simulation grid point axis_t.txt time axis: a list of each simulation epoch, showing the history of timestepping map_iK-aeq.txt a 2D grid of equatorial pitch angle (degrees) corresponding to each L (rows) and K (columns) listed in the corresponding simulation axis files. A fill value of -1 is used to signify coordinates outside the trapping region. iK-0001_2D_en.txt a 2D grid of energy, with units of megaelectron volt, at each μ (rows) and L (columns) coordinate defined in the simulation axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the axis_K.txt file. iK-0001_2D_f.txt a 2D grid of f, with units km-6 s3, at each μ (rows) and L (columns) coordinate defined in the simulation axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the axis_K.txt file. iK-0001_axis_aeq.txt a list of equatorial pitch angle (degrees) at each L in the axis_L.txt file, at the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the axis_K.txt file. A fill value of -1 is used to signify coordinates outside the trapping region. ... ... axis_mu_dyn.txt first dimension axis: a list of log10(μ/ (1MeV/G)) for the μ of each grid point in the dynamic output of the model axis_K_dyn.txt second dimension axis: a list of K for each grid point in the dynamic output of the model, with units G0.5 RE axis_L_dyn.txt third dimension axis: a list of L at each grid point in the dynamic output of the model axis_t_dyn.txt time axis: a list of each dynamic output epoch map_iK-aeq_dyn.txt a 2D grid of equatorial pitch angle (degrees) corresponding to each L (rows) and K (columns) listed in the corresponding axis files for the dynamic output. A fill value of -1 is used to signify coordinates outside the trapping region. iK-0001_2D_en_dyn.txt a 2D grid of energy, with units of megaelectron volt, at each μ (rows) and L (columns) coordinate defined in the dynamic output axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the axis_K_dyn.txt file. iK-0001_2D_f_dyn.txt a 2D grid of f, with units km-6 s3, at each μ (rows) and L (columns) coordinate defined in the dynamic output axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the axis_K_dyn.txt file. The 2D grid is output for every timestep and appended to the file, so subsequent 2D grids correspond to subsequent timesteps at the same K. iK-0001_axis_aeq_dyn.txt a list of equatorial pitch angle (degrees) at each L in the axis_L_dyn.txt file, at the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the axis_K_dyn.txt file. A fill value of -1 is used to signify coordinates outside the trapping region. ... ... progress.txt a file used by the BAS-PRO model to continue from partially complete simulations. It contains three values (one per line): epoch of the simulation start time; total simulation time elapsed (seconds); and a mode select value (1 for dynamic, 0 for steady state) resume.config a backup of the original configuration options used to execute the BAS-PRO simulation, used only by the model

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.

The dataset contains abundance data (ind. m-3) of total mesozooplankton, main groups and copepod taxa from the Punta Faro station (44° 17.750’ N, 9° 13.050’ E) in the Long-Term Ecological Research site eLTER-IT15-001-M, Promontory of Portofino – Ligurian Sea, over the years 2003-2015. Zooplankton samples were collected in the upper 50 m of the water column by vertical tows of a WP2 net (200-µm mesh, 57-cm mouth diameter) and fixed with buffered 4% formaldehyde. In the laboratory, the samples were concentrated and resuspended in filtered seawater to a volume of 500 ml to remove the formaldehyde. Aliquots ranging from 1/4 to 1/16, depending on sample density, were taken using a Folsom plankton sample splitter, transferred to a gridded Petri dish and examined under a stereomicroscope, and all the non-copepod zooplankton taxa were identified and counted. For copepods, an aliquot was taken with a large glass pipette from the 500 ml resuspended sample, transferred to a mini-Petri dish and individuals were counted; subsequent aliquots were added until at least 100 copepods were counted. The level of taxonomic consistency has generally been consistent over the years. Identification to genus or species level has been consistent only for copepods and cladocerans, while all other zooplankton taxa were identified as broader taxonomic groups. Taxa that were inconsistently recorded were homogenized to a higher taxonomic level to ensure data consistency over the entire time-series. The taxon abundances reported here correspond to the whole population (adult females and males, and juveniles). Groups in the dataset are defined as follows: Appendicularia (Fritillaria spp. + Oikopleura spp.)Chaetognatha (Sagittidae)Thaliacea (Salpidae + Doliolidae)Meroplankton (larvae of Mollusca, Bryozoa, Cirripedia, Echinodermata, Malacostraca, Nemertina, Polychaeta, Pisces larvae and eggs, Undetermined eggs)Offshore copepods (Aetideidae, Calanidae, Candaciidae, Euchaetidae, Eucalanidae, Heterorhabdidae, Lucicutiidae, Pleuromamma spp., Scolecitrichidae)Neritic copepods (rare Acartiidae, rare Centropagidae, Isias clavipes, Ctenocalanus vanus, Paracalanus denudatus, Paracalanus nanus, Corycaeus spp., Euterpina acutifrons)

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.10722953 for further details on predicting AOD and the DustNet model code. *datasets are NumPy arrays (v1.23) created in Python v3.8.18.

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.

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.

This study developed a unified taxonomic classification system for recent epifaunal benthic foraminifera species collected from surface sediment samples along the Southeast Pacific margin, including Cibicidoides wuellerstorfi, C. lobatulus, C. cf. ungerianus, Planulina ariminensis, P. ornata, and P. limbata. Morphological characteristics of these epifaunal benthic foraminifera specimens, such as test shape, periphery, chamber inflation, elongation, suture curvature, and wall porosity, were measured between December 2022 and June 2023. Furthermore, variations in morphological features like planoconvex tests, proloculus size, suture curvature, and pore patterns were observed among these foraminifera. The surface sediment samples were collected off the coasts of Chile and Peru between 1962 and 2014, covering latitudes from 12.70°S to 44.09°S and water depths ranging from 24 to 3,267 m. The temporal coverage of the samples spans the Holocene, with a focus on the late Holocene and modern samples. These samples were collected using various gear devices, including a multicorer (Sonne 156, Sonne 211, Meteor 92), a box corer (BIAC072014), a Petersen grab (USNS Eltanin), and a gravity core (Sonne 161). Detailed morphological examinations of the specimens were conducted using TM4000Plus HITACHI SEM imaging and a Leica S8 APO stereomicroscope, complemented by manual illustrations. The unified taxonomic criteria will enhance the accuracy of foraminifera-based proxies, such as stable isotopes and morphological studies, which are vital for paleoceanographic reconstructions. 1: Presence0: Absence

Important data from the African Centre of Meteorological Applications for Development (ACMAD) collection have been recently rescued from unstable fiche media and scanned to digital images by the EU funded Copernicus Climate Change Service and the Royal Meteorological Institute (RMI) of Belgium. The team at the C3S2-311 Lot 1 Collection and Processing of In Situ Observations service led by the Irish Climate Analysis and Research UnitS (ICARUS) at Maynooth University, Ireland enrolled the help of 2nd year university undergraduate students to transcribe quickly and effectively some of these important ACMAD meteorological surface observations. New and unique datasets for Macenta, Guinea (1947-1953) and Andapa, Madagascar (1949-1957) were digitised with each station consisting of sub-daily observations for: cloud, temperature, humidity, evaporation, pressure and wind as well as daily observations for: evaporation, precipitation and temperature. The newly digitised Sub-Saharan African data will increase the temporal and spatial coverage of data in this important data-sparse region where climate change impact studies are crucial., Students gained new skills and a deep appreciation of historical climatology while helping the global scientific community unearth new insights into past sub-Saharan African climate. The Climate Data Rescue Africa project (CliDaR-Africa project) model has the potential for a broader roll-out to other educational contexts and there is certainly no shortage of data to be rescued with millions of images remaining untouched. Therefore, this paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organisations to instigate similar programs in future.