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Supplementary material 3 from: Frem M, Chapman D, Fucilli V, Choueiri E, Moujabber ME, Notte PL, Nigro F (2020) Xylella fastidiosa invasion of new countries in Europe, the Middle East and North Africa: Ranking the potential exposure scenarios. NeoBiota 59: 77-97. https://doi.org/10.3897/neobiota.59.53208
Table S2
Supplementary material 5 from: Frem M, Chapman D, Fucilli V, Choueiri E, Moujabber ME, Notte PL, Nigro F (2020) Xylella fastidiosa invasion of new countries in Europe, the Middle East and North Africa: Ranking the potential exposure scenarios. NeoBiota 59: 77-97. https://doi.org/10.3897/neobiota.59.53208
Table S4
SIM4NEXUS target scenario data from IMAGE 3.0 model
<p>Target scenario dataset aiming for improvement in different nexus sectors developed for the H2020 project SIM4NEXUS using the IMAGE 3.0 integrated assessment model framework.</p>
Reference Climate (1971-2005) and RCP 8.5 scenario (2010-2100) of Destra sele for SWAP simulation
<p>The database refers to the weather files for the simulation run of SWAP model (Kroes et al., 2017) in the Destra Sele area (Regione Campania, southern Italy) under Reference Climate (RC, 1971-2005) and future climate scenario (RCP 8.5, 2010-2100).</p> <p>The future climate scenarios were obtained by using the high resolution regional climate model (RCM) COSMO-CLM (Rockel et al., 2008), with a configuration employing a spatial resolution of 0.0715°(about 8 km), which was optimised over the Italian area. The validations performed showed that these model data agree closely with different regional high-resolution observational datasets, in terms of both average temperature and precipitation in Bucchignani et al. (2015) and in terms of extreme events in Zollo et al. (2015). In particular, the Representative Concentration Pathway (RCP) 8.5 scenario was applied, based on the IPCC (Intergovernmental Panel on Climate Change) modelling approach to generate greenhouse gas (GHG) concentrations (Meinshausen et al., 2011). Initial and boundary conditions for running RCM simulations with COSMO-CLM were provided by the general circulation model CMCC-CM (Scoccimarro et al., 2011), whose atmospheric component (ECHAM5) has a horizontal resolution of about 85 km. The simulations covered the period from 1971 to 2100; more specifically, the CMIP5 historical experiment (based on historical greenhouse gas concentrations) was used for the period 1976–2005 (Reference Climate scenario - RC), while for the period 2006–2100, a simulation was performed using the IPCC scenario mentioned. The analysis of results was made on RC (1971–2005) and RCP 8.5 divided into three different time periods (2010–2040, 2040–2070 and 2070–2100). Daily reference evapotranspiration (ET<sub>0</sub>) was evaluated according to Hargreaves and Samani, (1985) equation (HS). The reliability of this equation in the study area was perrformed by Fagnano et al., (2001) comparing the HS equation with the Penman–Monteith (PM) equation (Allen et al., 1998).</p> <p>Under the RCP 8.5 scenario the temperature in Destra Sele is expected to increase approximately two degrees celsius respectively every 30 years to 2100 starting from the RC. The differences in temperature between RC and the period 2070–2100 showed an average increase of minimum and maximum temperatures of about 6.2°C (for both min and max). The projected increase of temperatures produces an increase of the expected ET<sub>0</sub>. In particular, during the maize growing season, an average increase of ET<sub>0</sub> of about 18% is expected until 2100.</p> <p>The climate data were provided by the “Regional Models and Geo-Hydrogeological Impacts Division” of the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Capua (CE) – Italy, through the support of Dr. Paola Mercogliano and Dr. Edoardo Bucchignani.</p> <p><strong>References</strong></p> <p>Allen, R. G., Pereira, L. S., Raes, D., Smith, M. and W, a B.: Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56, Irrig. Drain., 1–15, doi:10.1016/j.eja.2010.12.001, 1998.</p> <p>Bucchignani, E., Montesarchio, M., Zollo, A. L. and Mercogliano, P.: High-resolution climate simulations with COSMO-CLM over Italy: performance evaluation and climate projections for the 21st century, Int. J. Climatol., 36(2), 735–756, 2015.</p> <p>Fagnano, M., Acutis, M. and Postiglione, L.: Valutazione di un metodo semplificato per il calcolo dell'ET<sub>0</sub> in Campania, Model. di Agric. sostenibile per la pianura meridionale Gest. delle risorse idriche nelle pianure irrigue. Gutenberg, Salerno, ISBN, 88–900475, 2001.</p> <p>Hargreaves, G. H. and Samani, Z. A.: Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1(2), 96–99, 1985.</p> <p>Kroes, J. G., Van Dam, J. C., Bartholomeus, R. P., Groenendijk, P., Heinen, M., Hendriks, R. F. A., Mulder, H. M., Supit, I. and Van Walsum, P. E. V: Theory description and user manual SWAP version 4, http://www.swap.alterra.nl, Wageningen [online] Available from: www.wur.eu/environmental-research (Accessed 24 July 2019), 2017.</p> <p>Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K. and others: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Clim. Change, 109(1–2), 213, 2011.</p> <p>Rockel, B., Will, A. and Hense, A.: The regional climate model COSMO-CLM (CCLM), Meteorol. Zeitschrift, 17(4), 347–348, 2008.</p> <p>Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Fogli, P. G., Manzini, E., Vichi, M., Oddo, P. and Navarra, A.: Effects of Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled General Circulation Model, J. Clim., 24(16), 4368–4384, doi:Doi 10.1175/2011jcli4104.1, 2011.</p> <p>Zollo, A. L., Turco, M. and Mercogliano, P.: Assessment of hybrid downscaling techniques for precipitation over the Po river basin, in Engineering Geology for Society and Territory-Volume 1, pp. 193–197, Springer., 2015.</p> <p> </p> <p> </p>
Data for Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market
<p>This page contains the datasets and codes used to generate the figures for the article Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market.</p> <p>Below you will find two datasets (.dta) and four codes (.do) files. Please note that the .do files contain the original paths to where the datasets were saved, you should change them to where they are saved in your computer. </p> <ol> <li>The file <em>clustering_prer_ok.do</em> uses dataset <em>yearlyvars_raw.dta</em> to generate three additional datasets <em>clusters.dta</em>, <em>yearly_scenarios.dta</em> and <em>xwalk.dta</em> <ul> <li><em>clusters.dta</em> is used in <em>Figs_2-5-6_ok.do</em></li> <li><em>yearly_scenarios.dta</em> is used in <em>Figs_3_ok.do</em></li> </ul> </li> <li>Dataset <em>hprice_raw.dta</em> is used together with the generated <em>xwalk.dta</em> in <em>Fig_4_ok.do</em>.</li> </ol>
Model results for Economic Shock In a Climate Scenario
<p>Files include simulated surface temperature, aerosol optical depth and sea level pressure in the baseline experiment and the three sensitivity simulations, atmospheric carbon dioxide concentration under different RCP scenarios from 2000 to 2100 (from the prescribed CO2 concentration of different scenarios in CESM1.2), altered aerosols and aerosol-precursors emission inventory and altered carbon dioxide concentration (<a href="https://zenodo.org/api/files/69107766-aecf-45a9-a7dc-079b418dbe5a/ghg_rcp85_1765-2500_c100203_phase1.nc">ghg_rcp85_1765-2500_c100203_phase1.nc</a> and <a href="https://zenodo.org/api/files/69107766-aecf-45a9-a7dc-079b418dbe5a/ghg_rcp85_1765-2500_c100203_phase1.nc">ghg_rcp85_1765-2500_c100203_phase2.nc</a>, phase1 and phase2 mean that the data is for 2020-2021 and 2022-2050, respectively). </p>
Hull fouling marine invasive species pose a very low, but plausible, risk of introduction to East Antarctica in climate change scenarios
<p><strong>Aims: </strong>To identify potential hull fouling marine invasive species that could survive in East Antarctica presently and in the future.</p> <p><strong>Location: </strong>Australia's Antarctic continental stations: Davis, Mawson and Casey, East Antarctica; and subantarctic islands: Macquarie Island and Heard and McDonald Islands.</p> <p><strong>Methods: </strong>Our study uses a novel machine-learning algorithm to predict which currently known hull fouling MIS could survive in shallow benthic ecosystems adjacent to Australian Antarctic research stations and subantarctic islands, where ship traffic is present. We used gradient boosted machine learning (XGBoost) with four important environmental variables (sea surface temperature, salinity, nitrate and pH) to develop models of suitable environments for each potentially invasive species. We then used these models to determine if any Australia's three Antarctic research stations and two subantarctic islands could be environmentally suitable for MIS now and under two future climate scenarios.</p> <p><strong>Results: </strong>Most of the species were predicted to be unable to survive at any locations between now and the end of the century, however, four species were identified as potential current threats, and five as threats under future climate change. <em>Asterias amurensis</em> was identified as a potential threat to all locations.</p> <p><strong>Main conclusions: </strong>This study suggests that the risk are very low, but plausible, that known hull fouling species could survive in the shallow benthic habitats near Australia's East Antarctica locations and suggest a precautionary approach is needed by way of surveillance and monitoring in this region, particularly if propagule pressure increases. Whilst some species could survive as adults in the region, their ability to reach these locations and undergo successful reproduction is considered unlikely based on current knowledge.</p>
Data from: Impacts of silicon-based grass defences across trophic levels under both current and future atmospheric CO2 scenarios
Silicon (Si) has important functional roles in plants, including resistance against herbivores. Environmental change, such as increasing atmospheric concentrations of CO2, may alter allocation to Si defences in grasses, potentially changing the feeding behaviour and performance of herbivores, which may in turn impact on higher trophic groups. Using Si-treated and untreated grasses (Phalaris aquatica) maintained under ambient (400 ppm) and elevated (640 and 800 ppm) CO2 concentrations, we show that Si reduced feeding by crickets (Acheta domesticus), resulting in smaller body mass. This, in turn, reduced predatory behaviour by praying mantids (Tenodera sinensis), which consequently performed worse. Despite elevated CO2 decreasing Si concentrations in P. aquatica, this reduction was not large enough to affect the feeding behaviour of crickets or their predator. Our results suggest that Si-based defences in plants have adverse impacts on both primary and secondary trophic taxa, and these are not likely to decline under future climate change scenarios.
Potential power scenario for solar, wind and hydropower in Europe
<p>Data for power scenario used to assess climate impact on solar, wind and hydropower over a 35-year historical period.</p><p> </p><p><strong>Structure and content of data repository</strong></p><p>Here, we provide information on the data used in the investigation of the scientific article "Continental complementarity of renewable energy mixes" by Wörman et al., Nature Communications Engineering.</p><p>Hydro-climatic data was obtained from the Copernicus ECMWF database for an area of 13 106 km2 covering most parts of Europe and the Middle East. The hydropower potential was calculated at the locations of hydropower stations included in the GranD data base (Beams et al., 2019). Runoff was calculated based on the E-HEPE model (Hundecha et al., 2016) and this was used to estimate the hydropower potential at station locations (Wörman et al., 2017) and to generalize these values to 362 of totally 1,055 uniformly distributed sub-areas covering Europe (see figure below). The primary data used to derive the hydropower data contained in this repository is available at this link:</p><p>Virtual Energy Storage – Hydropower, DOI: 10.5281/zenodo.3706758</p><p>Daily data of the Surface Solar Radiation Downwards (SSRD) from 01-01-1979 to 31-12-2020 was obtained from Copernicus ECMWF database and converted to radiation incident on a fixed, south-facing panel with an inclination equal to the latitude and, further, covered to PV power potential according to Huld et al (2011, 2015). The power potential was averaged over 24 hours (both night and day) under consideration of changes in the solar elevation and azimuth angles as well as aggregated for 995 of the 1,055 sub-areas. A data report is available in catalogue 4. </p><p>Meteorological data with the relevance to wind power potential was obtained from ERA5, a reanalysis product of the ECMWF's General Circulation Model available in the Copernicus Climate Data Store. For comparison, data was also taken from Merra 2 and JRA 55 and used to derive wind speed time-series from 01/01/1979 till 31/12/2019 at the location of 20,010 onshore wind farms from the "World Wind Farm Database". The primary data used to derive the solar PV power data contained in this repository is available in catalogue 4 of this repository. The primary data used to derive the wind power data contained in this repository is available at this link:</p><p>Virtual Energy Storage - Wind power, DOI: 10.5281/zenodo.7749150</p><p>The data representing power scenarios for solar, wind and hydropower are structured in five folders sharing information on different variables and their physiographic characteristics. A ReadMe file is provided in each folder to describe the format of every file:</p><p><strong>1. Temporal mean power for solar-wind-hydro at 1,055 areas</strong></p><p>This folder provides the mean power for the three renewable sources with the following geographical division (Mean_Hydro, Mean_Solar, Mean_Wind). This catalogue also contains information on area id referring to the geographical data files as well as area values and coordinates (ReadMe_mean power CSV).</p><p><strong>2. Geographical data</strong></p><p>This folder contains the following sub-folders and information:</p><ol><li>Shape files for the 1,055 areas depicted above (shapefile_solar_domain)</li><li>Shape file of Europe and parts of the Middle East including different nations (Europe_Shapefile)</li><li>An Excel file with geodata för the 1,055 areas (areas_points_land)</li></ol><p><strong>3. GranD_Hydropower time-series</strong></p><p>This folder contains the following sub-folders and information:</p><ol><li>A ReadMe file</li><li>Temporal mean values of potential hydropower production estimated at GranD hydropower stations (Temporal mean values)</li><li>Linear scaling of the above time-series to match the reported national annual mean hydropower production</li></ol><p><strong>4. Solar power_Time-series_Excel</strong></p><p>This folder contains the following files:</p><ol><li>A data report describing how Copernicus ERA5 data has been used to estimate solar radiation density and conversion to panel power for different panel types (Readme_Accessing_Solar_Data)</li><li>Excel sheets with power density time series for the incident solar radiation (cSolarTimeSeries_ssrd24.xlsx) and two panel types (cSolarTimeSeries_ssrd24, cSolarTimeSeries_CdTe24). The values represents 24h averages.</li></ol><p><strong>5. Time-series of 1055 regions </strong></p><p>This folder contains the daily time-series used in a full assessment of solar, wind and hydropower system based on the above solar power, wind power and hydropower.</p><p>A readme file is also provided.</p><ol><li>Various information, including electric consumption data</li><li>Data on electric consumption extracted on 25/10/2022 13:35:55 from [ESTAT]</li><li>Matlab file used to derive average monthly consumption pattern based on 6a)</li><li>Energy storage capacity in Euopean Hydropower according to data collected by Prof. em. Killingtveit.</li><li>National hydropower production used to scale hydropower estimated at GranD stations to the national production level</li><li>Simulation results used for Figure 3</li></ol><p><strong>6. Various information including electric consumption</strong></p><p> </p><p><strong>References</strong></p><p>Beames at al., 2019. Global Reservoir and dam (GRanD) Database: technical documentation – version 1.3. February 2019. <a href="http://globaldamwatch.org/">http://globaldamwatch.org</a></p><p>Huld, T. and Ana M.G. Amillo. Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum. In: Energies 8 (2015), pp. 5159{5181. doi: <a href="http://dx.doi.org/10.3390/en8065159">http://dx.doi.org/10.3390/en8065159</a>.</p><p>Huld, T.A.; Friesen, G.; Skoczek, A.; Kenny, R.A.; Sample, T.; Field, M.; Dunlop, E.D. A, power-rating model for crystalline silicon PV modules. Solar Energy Mater. Solar Cells 2011, 95, 3359–3369.</p><p>Hundecha, Y., Arheimer, B., Donnelly, C. and Pechlivanidis, I.: A regional parameter estimation scheme for a pan-European multi-basin model, Journal of Hydrology: Regional Studies, 6(Supplement C), 90–111, doi:<a href="https://doi.org/10.1016/j.ejrh.2016.04.002">https://doi.org/10.1016/j.ejrh.2016.04.002</a>, 2016.</p><p>Wörman, A., Lindström, G., Riml, J., 2017. "The Power of Runoff", J. Hydrology, 548(2017): 784-793, dx.doi.org/10.1016/j.jhydrol.2017.03.041</p>
CAIRT FL2S Results of Case Study Scenario 5 (CSS5) for Stratospheric Sudden Warming
<p>Results of the fast level-2 simulator (FL2S) of CAIRT developed within the Earth Explorer 11 Phase 0 Science and Requirements Consolidation Study (SciReC) – CAIRT. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak'), additional application of noise ('_aknoi'), application of systematic uncertainties ('_sys'), and application of all effects ('_aknoisys'). Further information is available from the authors.</p>
Long time-series (2020-2100) high-resolution (1km) multi-scenario and multi-depth soil organic carbon dataset in China
<p>unit: kg C m-2 (soil oganic carbon density)</p><p>0100: denote 0-100 cm</p><p>020: denote 0-20 cm</p><p>Example 2020: 2020-2024 (five years mean soc)</p>
PPARγ-bla dataset curated and enriched using the Enalos tools and Enalos KNIME nodes for machine learning analysis (SCENARIOS project)
<p>A curated and enriched dataset for PPARγ-bla, intended for in silico model development, was obtained from PubChem BioAssay under the numeric identifier AID 743194 using Enalos tools and Enalos KNIME nodes. This dataset specifically utilizes compounds from the Tox21 10K chemical library that underwent screening against the PPARγ-bla HEK293H cell line. The cell line contains a beta-lactamase reporter gene, and all the information was extracted from PubChem Bioassay ID 743194 using Enalos tools and Enalos KNIME nodes. The original bioassay, consisting of 6587 compounds, assessed the antagonist activity of small molecules and classified them as 'active', 'inactive' or 'inconsistent' based on their AC50 (potency) score. The curated PPARγ dataset comprises 1230 compounds selected from the original bioassay and enriched with 777 molecular descriptors extracted from their 2D structure using EnalosMold2 KNIME nodes.</p>
Supplementary material 2 from: Lessi BF, Geneletti D, Cortinovis C, Dias MM, Reis MG (2024) Bird richness and Ecosystems Services across an urban to natural gradient in south-eastern Brazil: implications for landscape planning and future scenarios. One Ecosystem 9: e114955. https://doi.org/10.3897/oneeco.9.e114955
Supplementary material 2
Supplementary material 3 from: Lessi BF, Geneletti D, Cortinovis C, Dias MM, Reis MG (2024) Bird richness and Ecosystems Services across an urban to natural gradient in south-eastern Brazil: implications for landscape planning and future scenarios. One Ecosystem 9: e114955. https://doi.org/10.3897/oneeco.9.e114955
Supplementary material 3
Supplementary material 1 from: Lessi BF, Geneletti D, Cortinovis C, Dias MM, Reis MG (2024) Bird richness and Ecosystems Services across an urban to natural gradient in south-eastern Brazil: implications for landscape planning and future scenarios. One Ecosystem 9: e114955. https://doi.org/10.3897/oneeco.9.e114955
Supplementary material 1
Dataset for "Projected thermally driven elderly mortality for Beijing under greenhouse gas and stratospheric aerosol geoengineering scenarios"
Open the record for dataset details and reuse information.
CCG Starter Kits - Base SAND file for Africa Natural Gas Scenario
<p>This file is the Base SAND file for Africa with natural gas.</p> <p>This is published as part of the MethodsX paper titled <strong>How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. </strong>The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.</p>
CCG Starter Kits - Base SAND file for Asia - Coal and Natural Gas Scenario
<p>This file is the Base SAND file for Asia with coal and natural gas.</p> <p>This is published as part of the MethodsX paper titled <strong>How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. </strong>The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.</p>
CCG Starter Kits - Base SAND file for Africa Coal and Natural Gas Scenario
<p>This file is the Base SAND file for Africa including coal and natural gas.</p> <p>This is published as part of the MethodsX paper titled <strong>How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. </strong>The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.</p>
Electricity transmission system (2040) according to the Active Economy scenario from Portugal – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about the full transmission test network of Portugal as in 2040, considering an Active Economy scenario which is more trending towards renewables and electrification of the economy. The grid topology has been anonymized and contains 312 nodes, 592 branches and 8 interconnections with Spain. It is operated at 400 kV, 220 kV and 150 kV. There are 299 generators, and a label classifies each of them according to the generation technology type (e.g., Fossil Gas, Wind Onshore, etc.). A network model (MatPower format) including the grid information and considering realistic assumptions for unknown data was built from scratch taking into account the available public data released by the Portuguese TSO as in 2020 and then updated to 2040 conditions.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.