Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

List of capacity building resources for climate change adaptation created by EU-funded projects

This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.

opencc-by-4.0Feb 2022View details →

Small-scale fisheries adaptations understudied in climate change hotspots - database

Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. The adaptations were extracted from academic publications and grey literature (reports and Ph.D. theses) published from 2008 to 2020. The database provides coordinates and/or location, climate change hazard identified as motivating the response, small-scale fisher adaptation response, and any other stressor related to the response.

opencc-by-4.0Oct 2024View details →

Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice

Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. Global Environmental Change, 89, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database. <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in Nature Sustainability <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program.  Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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-4.0Sep 2020View details →

AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations

This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios. The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019). References Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481 Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542

opencc-by-4.0Dec 2019View details →

Questionnaire data to research small-scale farmers' information sharing for adapting to climate change in Mozambique (2019-2020)

Data collected from individual questionnaires with local communities of 4 districts of Mozambique in November 2019 and July 2020. It contains as well data from nine individual questionnaires to institutions (government and NGOs) working with local communities for their development. Data are replies from interviews containing open and closed questions about a) climate change adaptation options necessary for Mozambican small scale farmers, about b) the most used and preferred information sources of farmers, about c) the main barriers for a better exchange of information, and about d) proposals for improving it. The questionnaire can be consulted in Appendix A (in English and Portuguese). The open questions had the purpose to understand the causes and explanations about the themes presented. The closed questions followed a 0-5 likert scale approach, where 5 meant a very important factor and 0 non important one. This format was pursued for developing statistical analysis and comparison between the different types of participants. We used the same questions and format for interviewing farmers and stakeholders, although the questionnaire for farmers included also personal aspects like gender, age, and education.

opencc-by-4.0Nov 2024View details →

Dataset for the paper "Combining near-term benefits of climate adaptation with long-term benefits of emissions abatement"

This repo archives all data used in Duan et al. (2024), including model codes, raw model outputs, and post-processing scripts.  A Readme file describes the data and structure included here. If you have any questions, please contact the lead author (Lei Duan: leiduan@carnegiescience.edu).  We have updated the post-process codes to reflect changes in the revised manuscript == Paper associated with this dataset can be found at: https://www.nature.com/articles/s43247-024-01976-6#:~:text=Adaptation%20deployed%20in%20conjunction%20with,adaptation%20reducing%20near%2Dterm%20damage.

opencc-by-4.0Dec 2024View details →

Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes

Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896.

opencc-by-4.0Dec 2024View details →

Survey answers to identify barriers and enablers to climate change adaptation solutions (as part of the Adaptation AGORA project)

This dataset s the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This survey aimed to capture the key factors supporting or hindering adaptation practitioners experienced with engaging citizens and stakeholders in climate change adaptation initiatives.  The survey targeted European adaptation practitioners, i.e., all professionals in charge of implementing climate change adaptation initiatives, and more particularly, those involved in collaborative processes engaging stakeholders and citizens at the local and/or regional scale. The survev protocol can be found here: Euro-Mediterranean Center for Climate Change, University of Geneva, Stockholm Environment Institute, Barcelona Supercomputing Center, & Agenzia per la Promozione della Ricerca Europea. (2024). Protocol to carry out surveys to identify barriers and enablers to climate change adaptation solutions. Zenodo. <a href="https://doi.org/10.5281/zenodo.13385305" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13385305</a>

opencc-by-4.0Jul 2023View details →

Archetypes of climate change adaptation among large-scale arable farmers in southern Romania

Supplementary material belonging to the publication. Two files: 1. Excel file with database containing raw data and information resulted from surveying a sample of 30 farmers/farm managers in southern lowlands of Romania between April and June 2020. 2. PDF with interview guideline

opencc-by-4.0Aug 2024View details →

Dataset for: Infectious disease responses to human climate change adaptations

Original and derived data products referenced in the original manuscript are provided in the data package. <h3>Description of the data and file structure</h3> Original data: <code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript. <ol> <li>ID: The paper identification number</li> <li>Topic: The broad topic (i.e., each row of Table 1)</li> <li>Authors: The names of the authors of the paper</li> <li>Article Title: The title of the paper</li> <li>Source Title: The name of the journal in which the paper was published</li> <li>Abstract: The paper's abstract, retrieved from the Web of Science search</li> <li>study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li>pathogen_broad: Broad classification of the type of pathogen discussed in the paper.</li> <li>transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li>pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li>country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> Derived data: <code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript. <ol> <li>County Name: The name of the county in Kenya</li> <li>Sheep and goats 1980: The estimated number of sheep and goats in 1980</li> <li>Sheep and goats 2016: The estimated number of sheep and goats in 2016</li> <li>pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016</li> <li>Cattle 1980: The estimated number of cattle in 1980</li> <li>Cattle 2016: The estimated number of cattle in 2016</li> <li>pct_change_cattle: The percent change in cattle numbers from 1980 to 2016</li> <li>Camel 1980: The estimated number of camels in 1980</li> <li>Camel 2016: The estimated number of camels in 2016</li> <li>pct_change_camel: The percent change in camel numbers from 1980 to 2016</li> <li>human_pop 1980: The estimated human population in the county in 1980</li> <li>human_pop 2016: The estimated human population in the county in 1980</li> <li>pct_change_human: The percent change in the human population from 1980 to 2016</li> <li>area_sq_km: The land area of the county</li> <li>change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016</li> <li>change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016</li> <li>change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016</li> </ol> <code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript. <ul> <li>Country: The country in which the schistosome prevalence studies were performed.</li> <li>Latitude: The latitute in decimal degrees</li> <li>Longitude: The longitute in decimal degrees</li> <li>Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript. <ul> <li>Precipitation (mm): Binned annual precipitation values</li> <li>1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period</li> <li>1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period</li> <li>1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> Data were derived from the following sources: <ul> <li> Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249 </li> <li> London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0 </li> <li> World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections </li> </ul>

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The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).

The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr

openOpenDec 2008View details →

opencc-by-4.0Nov 2020View details →

Participatory Conceptual Diagrams to research small-scale farmers´ information sharing for adapting to climate change in Mozambique

Data collected from focus groups discussions with local communities of 4 distrcits of Mozambique in November 2019. The data are a series of conceptual maps describing a) the farming practices improvements most needed to adapt to climate change, and b) the most useful information for enabling the selected improvements, the most effective information sharing sources - e.g. institutional actors, members of the community, technical support, etc. - and means of communication - e.g. radio, mobile phone, word-of-mouth, etc. For the second purpose, connections were drawn by the members of the community between information sources and the actions needed for climate change adaptation. Participants also assigned a weight to the connections, selecting between: strong, medium or a weak connection. Notes about the discussions and opinions expressed by participants, written down by the research team, are also included. Together with the data, PDF files describing metadata and detailed methodology followed are included.

dryad40/100

Data from: Local adaptation (mostly) remains local: reassessing environmental associations of climate-related candidate SNPs in Arabidopsis halleri

Numerous landscape genomic studies have identified single-nucleotide polymorphisms (SNPs) and genes potentially involved in local adaptation. Rarely, it has been explicitly evaluated whether these environmental associations also hold true beyond the populations studied. We tested whether putatively adaptive SNPs in Arabidopsis halleri (Brassicaceae), characterized in a previous study investigating local adaptation to a highly heterogeneous environment, show the same environmental associations in an independent, geographically enlarged set of 18 populations. We analysed new SNP data of 444 plants with the same methodology (partial Mantel tests, PMTs) as in the original study and additionally with a latent factor mixed model (LFMM) approach. Of the 74 candidate SNPs, 41% (PMTs) and 51% (LFMM) were associated with environmental factors in the independent data set. However, only 5% (PMTs) and 15% (LFMM) of the associations showed the same environment–allele relationships as in the original study. In total, we found 11 genes (31%) containing the same association in the original and independent data set. These can be considered prime candidate genes for environmental adaptation at a broader geographical scale. Our results suggest that selection pressures in highly heterogeneous alpine environments vary locally and signatures of selection are likely to be population-specific. Thus, genotype-by-environment interactions underlying adaptation are more heterogeneous and complex than is often assumed, which might represent a problem when testing for adaptation at specific loci.

opencc-zeroDec 2015View details →

opencc-by-4.0Jun 2021View details →

Datasets from: Adaptation of Mediterranean forest species to climate: lessons from common garden experiments

We include information (raw Datasets) corresponding to the paper; Adaptation of Mediterranean forest species to climate: lessons from common garden experiments. Table Journal of Ecology Review.xls. Material used in the metaanalysis JoE Row data Common garden.xls. Raw data for survival and height used in the study. Multi-environment commong garden data for Pinus canariensis, P. halepensis, P. nigra, P. pinaster, Quercus ilex, and Q. suber. JoE Dataset4.xls. Data used for the analysis of local adaptation.

opencc-by-4.0Nov 2023View details →

SSH CENTRE - Mini-reports : Focus groups on "Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030"

SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU's transition to carbon neutrality.  SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages –  including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU's journey to a sustainable future.The aim of the focus groups was to gather citizen's perspectives, their hopes, concerns and ideas related to the Horizon Mission of Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030. The focus group discussion topics while remaining close to the Mission, avoid specific technical references to allow citizens to contribute based on their differing levels of understanding. As part of the SSH CENTRE project, in total, four focus group series will be conducted relating to Adaptation to Climate Change; Restore our Ocean and Waters by 2030; 100 Climate-Neutral and Smart Cities by 2030; A Soil Deal for Europe.  Notes were taken during each focus groups and turned into mini-reports. These mini-reports sum up the essence of the discussion: the participants' main ideas and some interesting quotes.