Find research datasets worth reusing

FIG. 6 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives

FIG. 6. — Miocene Freshwater invertebrates from La Venta, Huila, Colombia: A-D, trichodactylid freshwater crab remains, assigned to Sylviocarcinus sp., from La Victoria and Villavieja formations (Honda Group); A, fixed finger (pollex) of right cheliped (claw), specimen VPPLT-0954; B, mobile finger (dactylus) of right cheliped, specimen VPPLT-1306; C, D, articulated right cheliped, outer (C) and inner (D) views, showing details of the dactylus, propodus and pollex, specimen IGM 89-499, Colombian Geological Survey; E, F, freshwater bivalves, tentatively assigned to Anodontites Bruguière, 1792; G, H, gastropods. The bivalves and gastropods are possibly from the Barzalosa Formation, surveyed at locality Río Cabrera (La Venta region), from the Hoffstetter's collection housed by Muséum national d'Histoire naturelle, Paris. Scale bars: A, 5 mm; B-H, 10 mm.

FIG. 3 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives

FIG. 3. — Paleontological research and outreach in La Victoria, Colombia: A, B, Images of the exhibit Fossil Territory, Living Stories at the Museo de Historia Natural La Tatacoa (MHNT) (Oviedo et al. 2023); C, researchers working at the MHNT; D, the Vanegas brothers, Rubén (left) and Andrés (right), who manage the MHNT and founders of Vigías; E, celebration event in La Victoria organized by the MHNT on the occasion of the 100 years of paleontological research in the region; F, César Perdomo, fossil collector and founder of the Museo La Tormenta prospecting for fossils; G, researchers, students, journalists and members of Vigías that participated in the fieldwork in 2023. All the photos were taken during the fieldwork in May 2023 by C. Ziegler.

FIG. 4 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives

FIG. 4. — Selection of fossils from La Tatacoa Desert housed at the Museo de Historia Natural La Tatacoa, including some specimens featured in this thematic issue: A, Lepidosiren paradoxa (VPPLT 1483); B, parieto-supraoccipital frontal fragment of Phractocephalus sp. (VPPLT 1272; Carrillo-Briceño et al. 2023); C, skull of Purussaurus neivensis; D, skeleton of Caimaninae; E, skull of Caninemys tridentata Meylan, Gaffney & de Almeida Campos, 2009 (VPPLT-1720; Cadena et al. 2021); F, shell of Podocnemis tatacoensis Cadena & Vanegas, 2023 (VPPLT 1727; Cadena & Vanegas 2023); G, skull of Miocochilius anomopodus Stirton, 1953 (VPPLT 1512); H, skull of "Prodolichotis" pridiana Fields, 1957 (VPPLT 1614); I, skull of Anachlysictis gracilis Goin, 1997 (VPPLT 1612; Suarez et al. 2023); J, partial mandible of Megadolodus molariformis McKenna, 1956 (VPPLT 974; Carrillo et al. 2023); K, skull of Cebupithecia sarmientoi Stirton & Savage, 1950. Abbreviation: VPPLT, Vigías del Patrimonio Paleontológico La Tatacoa. Specimens are not shown to scale. Photos by C. Ziegler.

FIG. 5 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives

FIG. 5. — Fabaceae fossil wood from La Venta (VPPLT 008): A, diffuse porous wood, solitary vessels and in radial multiples of two vessels, aliform parenchyma (arrow); B, banded parenchyma in tangential lines; C, intervascular pits (arrow), uniseriate rays and non-septate fibers; D, vessel-ray parenchyma pits similar to intervessel pits (arrow), simple perforation plates (PP) and weakly heterocellular rays (RLS); E, fossil wood of Fabaceae (VPPLT 008) in the Cerro Gordo Beds. Scale bars: A, 500 µm; B-D, 200 µm.

FIG. 2 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives

FIG. 2. — Cumulative number of publications from the La Venta fossil site during a century of paleontological research. The results are derived from a compilation of all the publications produced in La Venta that were compiled in a database (see Appendix 2).

FIG. 1 in The Miocene La Venta Biome (Colombia): A century of research and future perspectives

FIG. 1. — Location and landscape of La Tatacoa Desert, Colombia: A, geographic location of La Venta fossil site in the Magdalena Valley, Colombia. Modified from Zapata et al. (2023); B, badlands of La Tatacoa Desert. Abbreviations: WC, Western Cordillera; CC, Central Cordillera; EC, Eastern Cordillera; LV, La Venta. Photo by C. Ziegler.

Shelf temperature and salinity in the ACCESS-OM2-01 RYF90-91 and Future perturbation simulations

The time-mean ocean temperature and salinity (averaged over the final 10 years of the simulation) in the ACCESS-OM2-01 model for isobaths shallower than 3000m in the Antarctic. T and S in the Repeat-Year Forced (RYF) 1990-1991 simulation and future perturbation run (from Li et al., 2023) are provided in this dataset. This dataset is used in the unsupervised classification work done by Sohail & Zika, 2023. The code necessary to reproduce all figures except figures 1 and 2 is also provided.

opencc-by-4.0Dec 2022View details →

Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model

========================================================================= Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model =========================================================================----------------------- INTRODUCTION -----------------------This dataset contains the data and scripts required to reproduce the figures and tables presented in the study: "Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model" in The Cryosphere.We perform an ensemble of simulations of the Antarctic ice sheet between 1950 and 3014, forced by a panel of CMIP6 climate models, starting from present-day geometry with the Kori-ULB ice-sheet model v0.9. We calibrate our ensemble in a Bayesian framework to produce observationally-calibrated Antarctic projections used to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge on the one hand, and the surface mass balance on the other. All simulations are performed at a spatial resolution of 16 km.Hindcasts of the behaviour of the AIS over the period 1950-2014 CE are reproduced using changes in oceanic and atmospheric boundary conditions derived from the CMIP5 climate model NorESM1-M. As of the year 2015 CE, climate projections derived from a subset of CMIP6 climate models (MRI-ESM2-0, IPSL-CM6A-LR, CESM2-WACCM and UKESM1-0-LL) are used as forcing until the year 2300 CE. Afterwards, no climate trend is applied. The forcing applied is derived from both the Shared Socioeconomic Pathways (SSP) 5-8.5 and 1-2.6 scenarios. ------------------------------ PROVIDED SCRIPTS:  ------------------------------ - 'KoriModelAll.m' and 'KoriInputParams.m': Kori-ULB ice flow model (more info at https://github.com/FrankPat/Kori-ULB)  - 'Compute_Bayesian_Weight.m': calculation of the ensemble likelihood weights used in the Bayesian calibration.  - 'Plot_parameter_space_distributions.m': calculation and plots of prior and posterior parameter  probability distributions.  - 'Plot_sea_level_distributions.m': calculation and plots of prior and posterior sea-level distributions.  - 'Plot_mass_balance_components_distributions.m': calculation and plots of mass balance components distributions.  - 'Plot_mean_thickness_change.m': calculation and plots of calibrated mean thickness change.  - 'Plot_ungrounded_probability.m': calculation and plots of the marginal probability of being ungrounded.  - 'Plot_SMB_sensitivity.m': Calculation and plots of surface mass balance sensitivity.  - 'run_MISMIPplus.m' and 'MISMIPplus.m': run and compare MISMIP+ experiment------------------------- PROVIDED DATA:  -------------------------<ul><li>'LHSensemble.mat': 100x9 matrices containing the values of the 100-member ensemble sampled (using maximin Latin Hypercube) within the parameter space in Table 1.<ul><li>1rst column ((:,1)) contains values of atmospheric present-day climatology (CLIMatm): MARv3.11 (1) - RACMOv2.3p2 (2)</li><li>2nd column ((:,2)) contains values of oceanic present-day climatology (CLIMocn): Jourdain2020 (1) - Schmidtko2014 (2)</li><li>3rd column ((:,3)) contains values of the atmospheric lapse rate (°C/km)</li><li>4th column ((:,4)) contains values of the thickness of the thermally-active layer influencing surface refreezing (m)</li><li>5th column ((:,5)) contains values of the contains values of the Degree day factor for the melting of ice (mm/PDD)</li><li>6th column ((:,6)) contains values of the contains values of the Degree day factor for the melting of snow (mm/PDD)</li><li>7th column ((:,7)) contains values of the applied Sub-shelf melt parameterisation: Quadratic-local Antarctic slope parameterisation (1) - PICO model (2) - Plume model (3) - ISMIP6 Nonlocal quadratic parameterisation (4) - ISMIP6 Nonlocal quadratic parameterisation including dependency on local slope (5)</li><li>8th column ((:,8)) contains values of the effective ice-ocean heat flux: [0.1 x 10^-5 - 10 x 10^-5] m/s for gammaT* in PICO - [1 x 10^-4 - 10 x 10^-4] for Cd^1/2Gamma_TS in Plume -  [1 x 10^-4 - 10 x 10^-4] for K in Quadratic-local Antarctic slope parameterisation - [1 x 10^4 - 4 x 10^4] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation - [1 x 10^6 - 4 x 10^6] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation with slope dependency</li><li>9th column ((:,9)) contains values of the CMIP6 climate model applied for climate forcing: MRI-ESM2-0 (1) - UKESM1-0-LL (2) - CESM2-WACCM (3) - IPSL-CM6A-LR (4) 'LHval' and 'LHS' contain the absolute values and the values of the parameters scaled linearly between 0 and 1 (0: minimum value, 1:maximum value) of the nine parameters, respectively.  </li></ul></li><li>'HIST_ENSEMBLE_DATA.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014).<ul><li>H_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with ice thickness field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid.</li><li>MASK_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with grounded mask field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid.  It distinguishes grounded ice (1: grounded) from ocean or floating ice (0: ocean/floating).</li><li>mbcomp_ensemble: 3D matrix of dimension [time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr).  The components mbcomp include the following ice-sheet aggregated and grounded ice sheet components:                     (1) Ice-sheet aggregated surface mass balance                     (2) Ice-sheet aggregated accumulation                     (3) Ice-sheet aggregated surface melt                     (4) Ice-sheet aggregated runoff                     (5) Ice-sheet aggregated rain                     (6) sub-shelf melt                     (7) dynamic ice loss (calving)                     (8) surface mass balance over the grounded ice sheet                     (9) accumulation over the grounded ice sheet                     (10) surface melt over the grounded ice sheet                     (11) runoff over the grounded ice sheet                     (12) rain over the grounded ice sheet                                 (13) Net mass balance (rate of HAF change)</li><li>SLC_ensemble: 2D matrix of dimension [ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m)   </li></ul></li><li>'HIST_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.  </li><li>'HIST_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).  </li><li>'CONTROL_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 when considering constant present-day conditions as of the year 2015.  </li><li>'SSP126_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.  </li><li>'SSP585_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario. It also contains the variable Runoff_ensemble, a 4D matrix of dimension [X, Y, snap_time, ensemble member] with surface runoff field (in m/yr i.e.) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid, as used in Fig. 7.  </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback.  </li><li>'SSP585_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).  </li><li>'SSP585_ENSEMBLE_DATA_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.  </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.  </li><li>'SSP585_ENSEMBLE_DATA_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.  </li><li>'HIST_ENSEMBLE_DATA_BASIN.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) integrated over 27 drainage basins (http://imbie.org/imbie-2016/drainage-basins/).<ul><li>SLC_ensemble_basin: 3D matrix of dimension [basin, ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) by basin</li><li>mbcomp_ensemble_basin: 4D matrix of dimension [basin, time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr) by basin. The components mbcomp include the same ice-sheet aggregated and grounded ice-sheet components as in 'HIST_ENSEMBLE_DATA.mat'.  </li></ul></li><li>'HIST_ENSEMBLE_DATA_BASIN_NO_ELEVATION_DATA.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.  </li><li>'HIST_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).  </li><li>'SSP126_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA°BASIN') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.  </li><li>'SSP585_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario.  </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario when neglecting the melt-elevation feedback.  </li><li>'SSP585_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).  </li><li>'SSP585_ENSEMBLE_DATA_BASIN_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.  </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.  </li><li>'SSP585_ENSEMBLE_DATA_BASIN_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.  </li><li>'GCM_SSPXXX_mean_aTs.mat': Timeseries of the regionally-averaged (between 90–60°S) annual near-surface (2-m) air temperature anomaly (°C) projected by the climate model 'GCM' from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) between 2015 and 2300 under the SSPXXX emission scenario, compared to the 1995-2014 reference period. SSPXXX may be 'SSP126' and 'SSP585' and GCM may be 'MRI-ESM2-0', 'CESM2-WACCM', 'IPSL-CM6A-LR', or 'UKESM1-0-LL'.  </li><li>'CALIBRATION DATA.mat': values ('val'), uncertainty ('sigma'), beginning ('year1') and end ('year2') of the average time period of the 12 regionally and temporally aggregated IMBIE data used in the Bayesian calibration (Table 2 in this study, coming from Table 2 from Otosaka et al., 2023)  </li><li>'INIT_MAR_aNorESM1-M_1950.mat' and 'INIT_RACMO_aNorESM1-M_1950.mat': Ice-sheet initial states at year 1950 obtained with the 1995-2014 atmospheric climatology from MARv3.11(Kittel eta l.,2021) or RACMOv2.3p2 (van Wessem et al., 2018), respectively, adjusted with a 1945-1955 anomaly from NorESM1-M. H is the ice thickness (in meters), B is the bedrock topography (in meters), and u is the surface velocity (in m/yr). These files were provided as input files to Kori-ULB to produce the projections. More info on the input files and their variables can be found here: https://github.com/FrankPat/Kori-ULB.</li></ul>---------------------------------------------------------- MATLAB FUNCTIONS USED IN SCRIPTS:  ----------------------------------------------------------- imagescn: imagesc with transparent NaNs, by Chad Greene (2023), downloaded from MATLAB Central File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/61293-imagescn),  - brewermap: provides all ColorBrewer colorschemes for MATLAB, by Stephen23. Downloaded from https://github.com/DrosteEffect/BrewerMap. - crameri: returns perceptually-uniform scientific colormaps created by Fabio Crameri (requires CrameriColourMaps8.0.mat)---------------------------------------------------------------------------------- EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY: ----------------------------------------------------------------------------------- BedMachine data used for the present-day grounding lines in Figures 2 and 7: It is BedMachine v2 (Morlighem et al., 2020) and can be found here: https://nsidc.org/data/nsidc-0756/versions/2. - The delineation of the 27 Zwally Basins used to identify and separate the West and East Antarctic ice sheets and the Antarctic Peninsula can be found at http://imbie.org/imbie-2016/drainage-basins/ - Outputs from MAR(CNRM-CM6-1) and MAR(CESM2) used in Figures 7 and S10. The data can be downloaded at 10.5281/zenodo.4529004 and 10.5281/zenodo.4529002, respectively. It was then interpolated to the 16-km grid used by Kori-ULB. - CESM2-WACCM outputs used in Figure 7 were downloaded from the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/) and interpolated to the 16-km grid used by Kori-ULB. - The CMIP6 forcing data used in this study (and plotted in Figures S6 and S7) are accessible through the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/). They have been interpolated to the interpolated to the 16-km grid used by Kori-ULB.--------------------- REFERENCES:  ---------------------Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, 2021.Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nat. Geosci. 13, 132–137 (2020). https://doi.org/10.1038/s41561-019-0510-8Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023.Pollard, D., DeConto, R. M., and Alley, R. B.: Potential Antarctic Ice Sheet retreat driven by hydrofracturing and ice cliff failure, Earth and Planetary Science Letters, 412, 112–121, https://doi.org/10.1016/j.epsl.2014.12.035, 2015.   van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018.

opencc-by-4.0Sep 2023View details →

Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"

This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".   The README file guides through the material available to support replication of the results and figures.

opencc-by-4.0Mar 2024View details →

External Stakeholders Survey Results - The Future of Aquaculture The impact of 4.0 technologies worldwide

Aquaculture 4.0 technologies have landed and are very likely to stay, aiming to play a major role within the implementation of new Circular Bioeconomy approaches. In this context, European aquaculture has been recently applying innovative and disruptive technologies to transform fishery management strategies. The so-called “4th industrial revolution” is projected to allow a 15-20% increase in the sector by the year 2030. In addition to the growth the revolution can provide, the benefits of Industry 4.0 include improved productivity, efficiency and reduced costs. Companies will be able to produce more, in less time, while allocating resources more effectively, due to a smooth adoption of interconnectivity through the Internet of Things (IoT), access to real-time data, and the introduction of cyber-physical systems. According to FAO data, the estimated production volume of fish from European aquaculture in 2028 will increase to approximately 1.4 million tons, needing more circular, digitized solutions to cover end user demand. This data was collected from a survey investigating the Future of Aquaculture The impact of 4.0 technologies worldwide. The results of this survey were used to understand the main challenges faced within the Aquaculture 4.0 market concerning usage experience and level of awareness, in order to identify barriers for implementation and key drivers to encourage adoption of innovative technologies within their businesses. The insights gathered will help us to improve our concept and ultimately the whole value chain of the Aquaculture 4.0 market.

opencc-zeroMay 2023View details →

3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives

This repository consists of the data that have been utilized to imagine and subsequently construct Fig. 1, Fig. 3 , Fig. 5 , Fig. 6 and Fig. 7 of the article " H. B. Mamo, M. Adamiak and A. Kunwar. 3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives, Journal of the Mechanical Behavior of Biomedical Materials, 143 (2023) 105930. doi: 10.1016/j.jmbbm.2023.105930 ".  Brief introduction of the files contained in this repository <ol> <li> acronyms.csv: This file consists of  the list of acronyms associated with materials and techniques used in 3D printing of biomedical devices.</li> <li>fig1a-data.csv: The csv file provides a relative ranking of different types of 3D printing techniques for biomedical applications based upon  merits and limitations (wherever applicable). The technique listed as Rank 1 is considered as the most commonly used 3D printing procedure.</li> <li> fig1b-data.csv: The csv file enlists the benfits of the 3D printing techniques in biomedical applications as compared to subtractive manufacturing methods.</li> <li> fig3-data.csv: The file contains a comparison between conventional and customized tablet printing  methods. The illustration is made through the manufacturing or printing of pharmaceutical tablets or pills.  This illustation also applies to the production of pharmaceutical capsules.  It thus illustrates that customized medicine is enabled using 3D printing technology.</li> <li> fig5-data.csv: This file enumerates the major challenges associated with 3D printing of biomedical devices.</li> <li> fig6-data.csv: The file enlists the roles of wearable smarts, cloud-based platforms and physicians in context of the hospitals implementing IoMT.</li> <li> fig7-data.csv: The aspects of IoMT Sensors,IoMT Platforms,3D Printers,Design and Prototypes within the integrated 3D printing-IoMT ecosystem are listed in the file.</li> </ol>

F I G U R E 3 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 3 Comparison of trait-based metrics expressed in relative number of individuals computed from eDNA () and traditional electro-fishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: ns, not significant (P> 0.05)

F I G U R E 4 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 4 Boxplots (, median value;, interquartile range;, full range;, outliers) showing the variability in the eDNAadapted fish index (six metrics) computed at three sites (Brangues, Rhins and Usses) where 10 eDNA water samples were collected once. At each site, the eDNA-based six-metric fish index was computed for each of the 45 possible pairs of samples

F I G U R E 2 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 2 Comparison of trait-based metrics expressed in number of species computed from eDNA () and traditional electrofishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: P <0.05; P <0.01; ns, not significant (P> 0.05)

F I G U R E 1 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 1 Sampling locations along river stretches (RS) A to E () of the main channel of the Rhône River, France, using both traditional electro-fishing (TEF) and eDNA., Sites sampled every 2 months (September 2015– August 2016);, sites where ten eDNA water samples (filtration capsules) were collected once;, sites located on the tributaries or the Rhône River itself sampled once for eDNA. The 10 metric fish index and the adapted six-metric fish index were computed at all sites with a black filled symbol within natural water bodies

F I G U R E 6 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

F I G U R E 6 Predicted future climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) in (a) its native range in North America, and (b) its invasive regions in Australia under a future climate change scenario predicted to the year 2090 in CLIMEX using the general circular model (GCM) CSIRO Mark 3.0, run with the A1B emissions scenario the known global distributions denoted by green colour dots.

F I G U R E 5 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

F I G U R E 5 Predicted global climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) under a future climate change scenario predicted to the year 2090 in CLIMEX using the general circular model (GCM) CSIRO Mark 3.0, run with the A1B emissions scenario.

F I G U R E 2 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

F I G U R E 2 Predicted global climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) under current climatic conditions in its native region in North America. The known global distributions are denoted by green colour dots.

F I G U R E 4 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

F I G U R E 4 Predicted climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) under current climatic conditions in New Zealand under current climatic conditions. The known global distributions are denoted by green colour dots.

F I G U R E 1 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

F I G U R E 1 Predicted global climatic suitability (ecoclimatic index) for tomato potato psyllid (TPP; Bactericera cockerelli under current climatic conditions using the adjusted parameters given in Table 1 under (a) natural rainfall and (b) as composite of natural rainfall and irrigation based on areas identified by Siebert et al. (2013). The known global distributions are denoted by green colour dots.