Find research datasets worth reusing

opencc-by-4.0Aug 2023View details →

F I G U R E 3 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

F I G U R E 3 Predicted climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) under current climatic conditions in Australia current climatic conditions. The known global distributions are denoted by green colour dots.

opencc-by-4.0Aug 2023View details →

F I G U R E 7 in Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

F I G U R E 7 Predicted future climatic suitability for tomato potato psyllid (TPP; Bactericera cockerelli) in New Zealand 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.

opencc-by-4.0Apr 2024View details →

Datasets related to the study "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach"

This dataset contains the data of the manuscript "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach" under publication in Atmospheric Chemistry and Physics.  It includes CNRM-ALADIN64 simulations of surface solar radiation, cloud fraction, aerosol optical depth and water vapor content.  A directory is dedicated to HINDCAST simulations. It includes all datasets involved in the evaluation of CNRM-ALADIN64 simulations, as well as all datasets used for the analysis of the spatial variability of surface solar irradiance over the recent past.  Another directory is dedicated to future climate simulations. In this case, several sub directories can be found, representing either the simulations over the historical period (2005-2014, i.e. HIST directory), or simulations at mid (2045-2054, "mid" suffix) and long term (2091-2100, "end" suffix) horizons for SSP1-1.9 and SSP3-7.0. Each set of climate simulations is composed of three members (r1f, r2f, r3f), which were used collectively to increase the statistical significance of our analysis.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for hydroelectric generation from the open literature. Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1011 datapoints from 11 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.  Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for battery energy storage systems from the open literature. Battery energy storage is the fastest growing form of power system flexibility, and will be critical to integrating large shares of variable renewable energy. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 671 datapoints from 18 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for wind power generation from the open literature. Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1506 datapoints from 28 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.   The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for coal-fired power generation from the open literature. Coal supplies 35% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 345 datapoints from 12 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for solar power generation from the open literature. Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 753 datapoints from 31 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Dec 2023View details →

Data and literature repository for "Climate Futures are Political Futures: Integrating Political Development Into the Shared Socioeconomic Pathways (SSPs)"

The datasets provided in the repository (listed in Table 1 of the manuscript): <ul> <li>Governance (Andrijevic et al., 2020)*</li> <li>Government effectiveness (Andrijevic et al., 2020)*</li> <li>Violent conflict (Hegre et al., 2016)</li> <li>Rule of law (update to the Soergel et al., 2021)</li> </ul> *Please note that these two variables can be found in the same data file. The indicators can also be retrieved through the <a href="https://ssp-extensions.apps.ece.iiasa.ac.at/">SSP Extensions Explorer.</a>  For applications of the projections of political indicators in further analyses, please consult the following references:   Brutschin, E., Pianta, S., Tavoni, M., Riahi, K., Bosetti, V., Marangoni, G., & Van Ruijven, B. J. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abf0ce/meta">A multidimensional feasibility evaluation of low-carbon scenarios.</a> Environmental Research Letters 2021, 16(6), 064069. Gidden MJ, Brutschin E, Ganti G, Unlu G, Zakeri B, Fricko O, et al. <a title="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5" href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5">Fairness and feasibility in deep mitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate</a>. Environmental Research Letters 2023, 18(7): 074006.   Hoch JM, de Bruin SP, Buhaug H, Von Uexkull N, van Beek R, Wanders N. <a title="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2" href="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2">Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach</a>. Environmental Research Letters 2021, 16(12): 124068.   Joshi DK, Hughes BB, Sisk TD. <a title="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145" href="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145">Improving governance for the Post-2015 Sustainable Development Goals: Scenario forecasting the next 50 years</a>. World Development 2015, 70: 286-302.   Moyer JD. <a title="https://www.sciencedirect.com/science/article/pii/S0305750X23000062" href="https://www.sciencedirect.com/science/article/pii/S0305750X23000062">Blessed are the peacemakers: The future burden of intrastate conflict on poverty</a>. World Development 2023, 165: 106188.   Moyer JD, Turner SD, Meisel CJ. <a title="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740" href="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740">What are the drivers of diplomacy? Introducing and testing new annual dyadic data measuring diplomatic exchange</a>. Journal of Peace Research 2021, 58(6): 1300-1310.   Petrova, K, Olafsdottir, G, Hegre, H, Gilmore, EA (2023). <a title="https://iopscience.iop.org/article/10.1088/1748-9326/acb163" href="https://iopscience.iop.org/article/10.1088/1748-9326/acb163">The ‘conflict trap’ reduces economic growth in the shared socioeconomic pathways</a>. Environmental Research Letters, 2023, 18(2), 024028.

Future freshwater fluxes from the Antarctic ice sheet

=============================================================== Future freshwater fluxes from the Antarctic ice sheet (dataset) =============================================================== ----------------------- INTRODUCTION ----------------------- This dataset contains historically-calibrated projections of Antarctic freshwater fluxes (ice-shelf melting, iceberg calving, and surface meltwater runoff) under low and very-high emission scenarios for 27 drainage basins and 5 ocean sectors until 2300. We perform, with the Kori-ULB ice-sheet model v0.91, an ensemble of historically-calibrated simulations of the Antarctic ice sheet between 1990 and 2300 forced by atmospheric and oceanic projections inferred from a subset of models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under low- and very high-emission scenarios.  We refer to the associated manuscript for more information on the applied methodology. ------------------------- PROVIDED DATA ------------------------- <ul> <li>'SSP126_FWF_1990_2300_ZwallyBasins.nc' and 'SSP585_FWF_1990_2300_ZwallyBasins.nc' each contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface mass balance, sub-shelf melt, and calving fluxes (in Gt/yr) for each of the 27 Zwally drainage basins (see http://imbie.org/imbie-2016/drainage-basins/) under a SSP1-2.6 and SSP5-8.5 scenario, respectively. </li> <li>'SSP126_FWF_1990_2300_OceanSectors.nc' and 'SSP585_FWF_1990_2300_OceanSectors.nc' each contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface mass balance, sub-shelf melt, and calving fluxes (in Gt/yr) for each of the 5 ocean sectors (Weddell Sea, Indian Ocean, western Pacific Ocean, Ross Sea, Amundsen & Bellingshausen Sea) under a SSP1-2.6 and SSP5-8.5 scenario, respectively. </li> <li>'SSP126_FWF_1990_2300_AIS.nc' and 'SSP585_FWF_1990_2300_AIS.nc' each contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface meltwater runoff, sub-shelf melt, and calving fluxes (in Gt/yr) for the Antarctic Ice Sheet under a SSP1-2.6 and SSP5-8.5 scenario, respectively.</li> </ul>

Future Projections of Temperature Extremes and Urban Heat Island in Paris using Deep Learning

Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available. The Python script to generate the boxplots of the future projections is also included.

Preliminary land use scenarios for Nature Future Framework

This deliverable is the product of tasks 5.2, we run high-resolution (1km) spatial land-use models that quantify potential land-use change and land management changes consistent with developed NFF storylines (T5.1). The European spatial land-use model (CLUMondo) was parametrized using economic modelling results and European SSP scenarios as input.

Biomass availability at NUTS3 level for modelling European energy system with 3 future scenario

The database is built over three main sources <ul> <li>S2Biom database from where most of the numbers come from <a title="S2Biom" href="https://s2biom.wenr.wur.nl/home" target="_blank" rel="noopener">(S2Biom original repo)</a></li> <li>ENSPRESO database that we use for few energy sources that are not part of s2biom (<a title="JRC" href="https://data.jrc.ec.europa.eu/collection/id-00138" target="_blank" rel="noopener">ENSPRESO</a>)</li> <li>National data for Switzerland (<a href="https://www.envidat.ch/dataset/swiss-biomass-potentials" target="_blank" rel="noopener">FoReMA Forest Resources Management Insititute</a></li> </ul> Data processing is done with Julia code that has short documentation and additional databasePipeline.pdf to understand how the dataset was built. To rebuild the dataset, refer to the github repository linked to this dataset. Data are available as a csv file and as a sqlite database. Data query methods are available from the Github repository linked to this dataset. The dataset includes biomass energy availability, expressed in PJ, at nuts 0-3 (NUTS 2013), and ENTSOE bidding zones aggregation. For each biomass source, the roadsidecost of each source is associated. While the biomass data is varied, large, and detailed following standards (ISO 17225-1:2021, ISO 17225-2:2021, ISO 17225-3:2021, ISO 17225-4:2021, ISO 17225-5:2021, ISO 17225-6:2021, ISO 17225-7:2021, ISO 18125:2017, EN 13556), biomass sources have been aggregated into three categories: Forestry, Agriculture, Organic waste. There are 3 bioenergy potential, low, medium, and high. These were based on the available data listed above.  Note: Technical availability of biomass is often much higher than the current use. Check comparison_biofuel_amounts.xlsx to compare the potentials to actual use in Eurostat and IEA data. Full potential should often not be used, because of possible issues with biodiversity and land use emissions.

opencc-by-4.0Aug 2024View details →

Supplementary dataset for "Global water gaps under future warming levels"

The folder contains water gap data relative to the paper: Rosa, L., Sangiorgio, M. Global water gaps under future warming levels. Nat Commun 16, 1192 (2025). https://doi.org/10.1038/s41467-025-56517-2 All water gaps data are in km3/yr. Gridded data(NetCDF at 0.5°)  - baseline    water_gap_baseline.nc: Baseline water gap in the period 2001-2010  - 1.5°C warming (5 models + average)    water_gap_15C_average.nc: Water gap under 1.5°C warming (multi-model average)    water_gap_15C_h08_ipsl-cm6a-lr.nc: Water gap under 1.5°C warming (h08 + ipsl-cm6a-lr)    water_gap_15C_h08_mri-esm2-0.nc: Water gap under 1.5°C warming (h08 + mri-esm2-0)    water_gap_15C_h08_ukesm1-0-ll.nc: Water gap under 1.5°C warming (h08 + ukesm1-0-ll)    water_gap_15C_h08_mpi-esm1-2-hr.nc: Water gap under 1.5°C warming (h08 + mpi-esm1-2-hr)    water_gap_15C_h08_gfdl-esm4.nc: Water gap under 1.5°C warming (h08 + gfdl-esm4)  - 3°C warming (5 models + average)    water_gap_3C_average.nc: Water gap under 3°C warming (multi-model average)    water_gap_3C_h08_ipsl-cm6a-lr.nc: Water gap under 3°C warming (h08 + ipsl-cm6a-lr)    water_gap_3C_h08_mri-esm2-0.nc: Water gap under 3°C warming (h08 + mri-esm2-0)    water_gap_3C_h08_ukesm1-0-ll.nc: Water gap under 3°C warming (h08 + ukesm1-0-ll)    water_gap_3C_h08_mpi-esm1-2-hr.nc: Water gap under 3°C warming (h08 + mpi-esm1-2-hr)    water_gap_3C_h08_gfdl-esm4.nc: Water gap under 3°C warming (h08 + gfdl-esm4) Aggregated data (.xlsx)  - source_data.xlsx: Water gap aggregated by country and basin for all the considered scenarios (including multi-model average and agreement analysis) Note: the global water gap obtained by summing all the countries/basins is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country/basin (differences in the order of 1km/yr3, <0.3%). See sheet "Figure 3" A249:N251 (countries) and "Figure 5" A235:N237 (basins) for further details.

opencc-by-4.0Oct 2024View details →

Data for "Future global mangrove losses and the key role of protected areas in their conservation"

Content (spatial resolution, data info) <ol> <li>Mangrove table input to the machine learning model (1 table)</li> <li>Results of mangrove cumulative loss proportion under SSP1 and SPP5 scenarios  (1km, 1 ZIP)</li> <li>All processing R codes can be accessed: https://github.com/2pangp/Mangrove-TidalFlat_Distribution</li> </ol>

opencc-by-4.0Sep 2021View details →

Material requirements for future low-carbon electricity projections in Africa - dataset

This dataset is provided to supplement the paper "Material requirements for future low-carbon electricity projections in Africa". The paper calculated the material requirements of proposed electricity systems based on the <a href="https://zenodo.org/record/3521841#.YTHYP44zaUk">JRC-TEMBA </a>projections of African electricity systems, including the embodied emissions resulting from such systems from 2015 to 2065. The data included in this repository covers the total mass of materials, embodied emissions, and jobs created given by country, year and/or region.

opencc-by-4.0Dec 2021View details →

Data and code from: "A transcriptional rheostat couples past activity to future sensory responses" (Tsukahara, Brann, et al. 2021 Cell)

# A transcriptional rheostat couples past activity to future sensory responses Code and data to replicate analyses in Tsukahara, Brann et al. 2021 Cell <a href="https://doi.org/10.1016/j.cell.2021.11.022">https://doi.org/10.1016/j.cell.2021.11.022</a> ## Summary Animals traversing different environments encounter both stable background stimuli and novel cues, which are thought to be detected by primary sensory neurons and then distinguished by downstream brain circuits. Here we show that each of the ~1000 olfactory sensory neuron (OSN) subtypes in the mouse harbors a distinct transcriptome whose content is precisely determined by interactions between its odorant receptor and the environment. This transcriptional variation is systematically organized to support sensory adaptation: expression levels of more than 70 genes relevant to transforming odors into spikes continuously vary across OSN subtypes, dynamically adjust to new environments over hours, and accurately predict acute OSN-specific odor responses. The sensory periphery therefore separates salient signals from predictable background via a transcriptional rheostat whose moment-to-moment state reflects the past and constrains the future; these findings suggest a general model in which structured transcriptional variation within a cell type reflects individual experience. ## Manuscript For more details, please see our Open Access manuscript: <a href="https://www.cell.com/cell/fulltext/S0092-8674(21)01337-4">https://www.cell.com/cell/fulltext/S0092-8674(21)01337-4</a> # Code 1. The code here is a copy of that on GitHub: <a href="https://github.com/dattalab/Tsukahara_Brann_OSN">https://github.com/dattalab/Tsukahara_Brann_OSN</a>. Instructions for how to download and install it can be found in the README.md file. 2. Data is available on the NCBI GEO (accession <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173947">GSE173947</a>) and raw fastq files are available from the SRA (accession SRP318630). 3. Supplementary data (imaging traces and example preprocessed AnnData object for the home-cage dataset) can be found in the data folders of the attached Tsukahara_Brann_OSN-zenodo.zip file.

opencc-zeroDec 2021View details →

FIG. 1 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys

FIG. 1. — Number of species published by semester from the end of the expedition, with first semester: July-December 2015, and last semester: January-July 2021. The expedition was accomplished in August 2015.