Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050

******************* Please view the README.txt or README.md file for detailed documentation of data. ******************** Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050 Date of release: 25/11/2020 Identifier: 10.5281/zenodo.4275759 Permalink: http://dx.doi.org/10.5281/zenodo.4275759 Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, Under Review, 2020. Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files. Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi   Dates of data: 2030, 2050 Type of data: Outdoor Temperature Geographic location: Jyväskylä Time resolution: hourly, full year Format: All data is stored in .csv files Number of files: 1 .zip --> 50 files + README.txt + README.md This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files   Notifications: Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data). Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate). Contains modified Ruosteenoja et al. [2016] information. Other data and information sources are described in README.txt, README.md, references and on the associated publication.

opencc-by-4.0Oct 2024View details →

Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.

We reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here.

opencc-by-4.0Oct 2024View details →

Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios

This repository contains the data and results from the paper Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities published in Nature Medicine (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>). It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals.  The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>. <h3>Content</h3> This repository contains three zip files, each with an internal codebook: <ul> <li>data.zip: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li>results_csv.zip: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li>results_parquet.zip: contains the same information as the results_csv.zip but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> It is recommended to only download results_csv.zip for a quick exploration of the results, or only results_parquet.zip when the results are to be loaded into a software for deeper analysis.

opencc-by-4.0Dec 2022View details →

Current and future global distribution of potential biomes under climate change scenarios

Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023)   conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080. Files are named according to the following naming convention, e.g.: <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> with the following fields: <ul> <li>generic theme: biomes,</li> <li>variable name: graminoid.and.forb.tundra.rcp85,</li> <li>variable type, e.g. probability ("p"), hard class ("c"), model deviation ("md")</li> <li>spatial resolution: 1km,</li> <li>depth reference, e.g. below ("b"), above ("a") ground or at surface ("s"),</li> <li>begin time (YYYYMMDD): 20610101,</li> <li>end time: 20801231,</li> <li>bounding box, e.g. global land without Antarctica ("go"),</li> <li>EPSG code: epsg.4326,</li> <li>version code, e.g. creation date: v20230410.</li> </ul> We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system. For probability layers, the uncertainty (model deviation: md) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel. For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the md layers to properly interpret the results of the map. Styling files are provided in both .SLD and .QML format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics. The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication: Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a>

zenodo44/100

Data provided for the Preenacting Climate Change Scenarios project 2021

CMIP6 model output data processed using the scripts provided here: https://github.com/lukasbrunner/preenact/

opencc-by-4.0Dec 2021View details →

opencc-by-4.0Aug 2019View details →

Investigation on the Use of Passive Microclimate Frames in View of the Climate Change Scenario

Passive microclimate frames are exhibition enclosures able to modify their internal climate in order to comply with paintings’ conservation needs. Due to a growing concern about the effects of climate change, future policies in conservation must move towards affordable and sustainable preservation strategies. This study investigated the hygrothermal conditions monitored within a microclimate frame hosting a portrait on cardboard with the aim of discussing its use in view of the climate expected indoors in the period 2041–2070. Its effectiveness in terms of the ASHRAE classification and of the Lifetime Multiplier for chemical deterioration of paper was assessed comparing temperature and relative humidity values simultaneously measured inside the microclimate frame and in its surrounding environment, first in the Pio V Museum and later in a residential building, both located in the area of Valencia (Spain). Moreover, heat and moisture transfer functions were used to derive projections over the future indoor hygrothermal conditions in response to the ENSEMBLES-A1B outdoor scenario. The adoption of microclimate frames proved to be an effective preventive conservation action in current and future conditions but it may not be sufficient to fully avoid the chemical degradation risk without an additional control over temperature.

SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018. Paper ". Impacts of climate change on water quality, benthic mussels and suspended mussel culture in a shallow, eutrophic estuary by Maar et al. Heliyon,

SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018

Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"

Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5) We use bash, CDO, and python.SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp

opencc-zeroJan 2024View details →

dryad40/100

Projected trophic changes in species-carrying capacities under climate change scenarios

Climate controls the amount of energy available for plants, which in turn determines the quantity of resources available for animals. It follows that when climate changes, so should trophic communities. Using a novel modeling approach, we investigate how bird and mammal trophic communities might disassemble and reassemble under 21st century climate changes. We show that trophic structures are expected to undergo profound changes globally, chiefly in the tropics and across high latitudes in the northern hemisphere. This trophic reorganization of communities is characterized by shifts in species richness within trophic guilds. While some guilds might face population collapses, others are projected to find new opportunities to maintain stable populations in previously inhospitable areas. The proposed models offer a tool for projecting and understanding the trophic ramifications of climate change, highlighting their potential in guiding future research and conservation efforts.

opencc-by-4.0Sep 2021View details →

Summer Rainfall Scenarios and Climate Change Factor Projections over Wanzhou County, China

This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China. Precipitation Reference Period (1979-2018) The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season. <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.1 degrees x 0.1 degrees</li> <li>Time period: 1979-2018</li> <li>Data Format: .csv files (.xyz file extensions)</li> <li>Variable: Rainfall (pr)</li> <li>Units: mm/day </li> </ul> Ensemble Projections and Climate Change Factors The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios. <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.22 degrees x 0.22 degrees</li> <li>Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)</li> <li>Data Format: .csv files</li> <li>Variable: Climate Change Factor (ccf)</li> <li>Unit: Dimensionless</li> <li>Included ensemble projection statistics: <ul> <li>Standard deviation (sd)</li> <li>Coefficient of Variation (cv)</li> </ul> </li> </ul>

opencc-by-4.0Oct 2022View details →

Dataset: Global range dynamics of the Bearded Vulture (Gypaetus barbatus) from the Last Glacial Maxima to climate change scenarios

This dataset consists of Bearded Vulture Gypaetus barbatus occurrence points which were used to develop a distribution model to study its suitable habitat of this species. Using these data, we modelled the current distribution of Bearded Vulture throughout its entire range and projected the Last Glacial Maxima (LGM), Mid-Holocene (MH) and future distribution under 2070s climate change scenarios. We compiled these data from the entire distribution range in Asia, Europe and Africa using different sources: freely accessible online resources including, eBird and GBIF repositories, published reports and grey literature and occurrence data collected by the authors in the field, mostly in Nepal.

Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.

Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 5 in The range dynamics of a cactophilic Drosophila species under climate change scenarios

Figure 5. Last Interglacial, Last Glacial Maximum, Present (1960–1990), and the Future (2050 and 2070) predictions of the potential distribution of two cacti species (C. hildmannianus and P. machrisii) based on 10% thresholding approaches. The abbreviations are defined as follows: LGM-Last Glacial Maximum, LIG-Last Interglacial.

Figure 2 in The range dynamics of a cactophilic Drosophila species under climate change scenarios

Figure 2. Occurrence points used for ecological niche modeling are shown in red. Squares equal approximately 2 decimal degrees and the background image on thmap shows the elevational structure of Brazil.

Figure 1 in The range dynamics of a cactophilic Drosophila species under climate change scenarios

Figure 1. Approximate distribution of D. gouveai (green area) showed Caatinga and Cerrado domains and the localities sampled for the species (based on Moraes et al., 2009), descriptive statistics (n, number of individuals; H, the number of haplotype; H d, haplotype diversity; pi, nucleotide diversity) and median joining network of 48 individuals of D. gouveai. All statistics based on nucleotide sequences were adopted from Moraes et al. (2009). MIR: Pirapotanga; FOR: Morro do Forno; FUR: Furnas; CEU: Vale do Céu; CRI: Cristalina; FER: Fercal; PIR: Pirenópolis; SER: Serrinha; IBO: Ibotirama; BAX: Baxio.

Figure 4 in The range dynamics of a cactophilic Drosophila species under climate change scenarios

Figure 4. Last Interglacial, Last Glacial Maximum, Present (1960–1990), and the Future (2050 and 2070) predictions of the potential distribution of D. gouveai based on two thresholding approaches. Arrows shows very limited potential distribution of D. gouveai in 2050 and 2070. The abbreviations are defined as follows: LGM-Last Glacial Maximum, LIG-Last Interglacial. Additionally, specific climate models include LGM-cc (Community Climate System Model), LGM-me (MPI-ESM-P, General Circulation Models), and LGM-mr (Model for Interdisciplinary Research on Climate, Earth System version 2 for Long-term simulations).

Figure 3 in The range dynamics of a cactophilic Drosophila species under climate change scenarios

Figure 3. Isolation-by-distance of populations of D. gouveai based on mtDNA. Linear regression lines were drawn for all comparisons among populations (full line), and for populations not included MIR (dotted line).