• Energy Research
• 2025-2025close

The dataset is used to optimize the time-average sum of uplink transmission throughput rates for NOMA-based LPWA networks powered by ambient energy.

The dataset used in the paper is a collection of historical scenarios of net load and price, which are used for empirical learning and offline optimization.

Climate suitability scores were calculated for 291 useful tree species included in the Know Some Useful Trees and Shrubs for Agricultural and Pastoral Communities of Rwanda or in the list of native useful tree species obtained from the GlobalUsefulNativeTrees database (Kindt et al. 2023a). After compiling the list of species, we checked afterwards for the availability of globally observed environmental ranges for these species from the TreeGOER database (Kindt 2023). Species with fewer than 10 observations in TreeGOER were excluded. The climate scoring system is the same that is used in the GlobalUsefulNativeTrees database: Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the TreeGOER) for all variables Score = 2 corresponds to the 5% - 95% species's range for all variables Score = 1 corresponds to the 0% - 100% species's range for all variables Score = 0.5 means that the planting site occurs outside the 0% - 100% species's range for some of the variables, but for heat-related bioclimatic variables (used to produce the maps shown here: BIO01, monthCountByTemp10, growingDegDays5, BIO05 and BIO06) to be below the minimum (‘too cold but not too hot’) and for water-related bioclimatic variables (used here: BIO12, climaticMoistureIndex, BIO16, BIO17 and MCWD) to be above the maximum (‘too wet but not too dry’) Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables Climate scores were obtained for future climates (2050s: 2041-2060) from the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 22 GCMs for SSP 3-7.0. Future and baseline bioclimatic layers were processed from raster layers obtained from WorldClim 2.1 at resolutions of 2.5 arc-minutes. Similar methods were used to obtain median values for the ClimateForecasts and CitiesGOER databases. Calculations of climate scores were made with similar scripting pipelines in the R statistical environment as documented here: https://rpubs.com/Roeland-KINDT/1168650. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt 2023), and used internally in the GlobalUsefulNativeTrees online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification. Alternative future climate maps were obtained by calculating climate scores for each GCM and SSP separately, then counting the number of GCMs that projected that the species would be suitable under the future climatic conditions. A species was estimated to be suitable for a particular combination of GCM and SSP if its score was 1 or above. The maps distinguished areas where 33% or fewer of the GCMs predicted that the species would be suitable, and areas where 66% or more of the GCMs predicted that the species would be suitable. Those thresholds correspond to the Mastrandea et al. (2011) likelihood scale, which was adopted earlier in another climate change atlas (Kindt et al. 2023; https://atlas.worldagroforestry.org/). A separate mapping category shows where 66% or more GCMs had a climate score of 2 or 3. The maps include a red polygon showing the country outline of Rwanda obtained from the GADM database. The first map for the baseline climate includes presence observations in the country obtained from the RAINBIO database (Dauby et al. 2016) and from the Global Biodiversity Information Facility (filtered from the occurrences that informed the TreeGOER database; GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq). The MS Excel file contains columns that include information from World Flora Online, including hyperlinks to this online flora. Other columns indicate whether the species was included among assemblages of native useful tree species for the country in the GlobalUsefulNativeTrees database. References Nduwayezu, J.B., Ruffo, C.K., Minani, V., Munyaneza, E. and Nshutiyayesu, S. 2009. Know Some Useful Trees and Shrubs for Agricultural and Pastoral Communities of Rwanda. Institute of Scientific and Technological Research, Butare, Rwanda. Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914. Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13132613 Kindt, R., Graudal, L., Lillesø, JP.B. et al. (2023a). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. Sci Rep 13, 12640. https://doi.org/10.1038/s41598-023-39552-1 Kindt R, Abiyu A, Borchardt P, Dawson IK, Demissew S, Graudal L, Jamnadass R, Lillesø J-PB, Moestrup S, Pedercini F, Wieringa JJ, Wubalem T. 2023. The Climate change atlas for Africa of tree species prioritized for forest landscape restoration in Ethiopia: A description of methods used to develop the atlas. Working Paper No. 17. Bogor, Indonesia; and Nairobi, Kenya: Center for International Forestry Research and World Agroforestry (CIFOR-ICRAF). https://doi.org/10.17528/cifor-icraf/008977 Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10004594 Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12679832 Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086 Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. https://doi.org/10.1111/ecog.02880 Mastrandrea, M.D., Mach, K.J., Plattner, GK. et al. The IPCC AR5 guidance note on consistent treatment of uncertainties: a common approach across the working groups. Climatic Change 108, 675 (2011). https://doi.org/10.1007/s10584-011-0178-6 Dauby G, Zaiss R, Blach-Overgaard A, Catarino L, Damen T, Deblauwe V, Dessein S, Dransfield J, Droissart V, Duarte MC, Engledow H, Fadeur G, Figueira R, Gereau RE, Hardy OJ, Harris DJ, de Heij J, Janssens S, Klomberg Y, Ley AC, Mackinder BA, Meerts P, van de Poel JL, Sonké B, Sosef MSM, Stévart T, Stoffelen P, Svenning J-C, Sepulchre P, van der Burgt X, Wieringa JJ, Couvreur TLP (2016) RAINBIO: a mega-database of tropical African vascular plants distributions. PhytoKeys 74: 1-18. https://doi.org/10.3897/phytokeys.74.9723 Funding The development of this climate change atlas for Rwanda was supported by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project, by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

The dataset used in the paper is a microgrid energy management problem under uncertainty, with 20 households, 17 PV systems, 15 EVs, and one jointly used battery system.

This dataset supports a study on homeowners' preferences for financing options for energy-efficient (EE) heat pump systems in the Southeastern U.S. The data was collected through an online structured survey conducted from November 17–27, 2021. It includes information on loan attributes (e.g., interest rates, payback periods, loan types, and funding sources), socio-demographic characteristics, and consumer preferences derived from choice-based conjoint (CBC) experiments. All respondent identifiers have been removed to protect confidentiality, and data has been anonymized.

This dataset is used for training a data-driven method for energy reduction in large buildings.

The dataset used in this paper is a building dataset for multi-zone building control, including the design of those key components.

This data is from the Global Change Biology publication by Clark et al., entitled "Winners and losers from climate change: An analysis of tree growth and survival responses to temperature and precipitation for roughly 150 species across the contiguous U.S." The dataset has all the inputs and outputs for the assessment of tree species responses to temperature and precipitation. There is a Readme file on Dryad that describes the 12 tabular data files associated with this manuscript.