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382 results for “Climate Impacts”
Local Indicators of Climate Change Impacts reported by the Tuareg of Illizi (Algeria)
<p>The dataset reports the Local Indicators of Climate Change Impacts mentioned during 19 interviews and 3 focus groups with members of the Tuareg community of Illizi (Algeria). The dataset includes reports events important to the local timeline and observations of atmospheric changes including seasonal, precipitation, temperature, and biodiversity changes.</p>
Fig. 4 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 4. Occurrence records of the Trachypithecus francoisi group based on previous research studies (Nadler et al., 2003; Workman, 2010a; Ebenau et al., 2011; Hendershott et al., 2016; Blair et al., 2021).
Fig. 3 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 3. Predicted distribution of climatically suitable habitat for the Delacour's langur under a range of different future climate change scenarios: A, MIROC6 models; B, CNRM-ESM2-1 models; C, IPSL-CM6A-LR models.
Fig. 2 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 2. Potential current distribution of the Delacour's langur generated from Maxent based on eight uncorrelated WorldClim bioclimatic variables.
Fig. 1 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 1. Occurrence records of the Delacour's langur derived from previous research studies (Nadler & Long, 2001; Nadler et al., 2003; Workman, 2010b; Ebenau et al., 2011; Wojciechowski, 2013; Nadler, 2015; Hoang & Dung, 2016; Linh et al., 2019; Nguyen et al., in press) and our field surveys.
Data from: Reconstructing 120 years of climate change impacts on Joshua tree flowering
<p>Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (<em>Yucca brevifolia</em> and <em>Y. jaegeriana</em>), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records, and reveals slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years — but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.</p>
Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations
<p>The amount of water vapor injected into the stratosphere after the eruption of Hunga Tonga-Hunga Ha'apai (HTHH) was unprecedented, and it is therefore unclear what it might mean for surface climate. We use chemistry climate model simulations to assess the long-term surface impacts of stratospheric water vapor (SWV) anomalies similar to those caused by HTHH, but neglect the relatively minor aerosol loading from the eruption. The simulations show that the SWV anomalies lead to strong and persistent warming of Northern Hemisphere landmasses in boreal winter, and austral winter cooling over Australia, years after eruption, demonstrating that large SWV forcing can have surface impacts on a decadal timescale. We also emphasize that the surface response to SWV anomalies is more complex than simple warming due to greenhouse forcing and is influenced by factors such as regional circulation patterns and cloud feedbacks. Further research is needed to fully understand the multi-year effects of SWV anomalies and their relationship with climate phenomena like El Nino Southern Oscillation.</p>
Figure 4 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 4. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) with different surrounding habitats (sugarcane + sesame, monoculture, sesame) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 3 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 3. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) at different crop developmental stages (crop phenology) of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 2 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 2. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 1 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 1. Percent numbers of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during 2018 at Layyah, Punjab, Pakistan.
Q-MARE database on pre-industrial climate and human impacts on marine ecosystems
<p>A systematic literature review was carried out using two bibliographic databases the Web of Science (WoS; www.webofknowledge.com; Clarivate) and Scopus (www.scopus.com; Elsevier). In the former searches were completed by searching the “core collection” using the “topic” field (which searches the paper titles, abstracts, author keywords and keywords plus; the latter determined by a Clarivate algorithm using synonymy), and in Scopus the abstract, title and keyword fields were searched. Searches were completed between July and November 2023.</p> <p> </p>
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Fig. 3 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 3. In marginal grazing systems in Europe sheep often occupy separate summer and winter grazing areas, analogous to the summer and winter ranges of migratory ruminants. In the uplands of Wales, UK, (shown here) sheep are often grazed on extensive areas of land at low stocking densities over the summer period, and sent to lowland dairy farms for winter grazing at higher stocking densities. (Photo: Rose, H.).
Fig. 2 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 2. The relative seasonal incidence of ovine parasitic gastroenteritis (PGE) in the Southwest of England, UK, based on monthly diagnoses of (a) Nematodosis (NOS = species not otherwise specified), (b) Haemonchosis and (c) Nematodirosis (van Dijk et al., 2008).
Fig. 1 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 1. Comparison of the instantaneous daily development rate of Ostertagia ostertagi (grey) and O. gruehneri (black) at a range of constant temperatures. Instantaneous daily development rates were estimated from the time to 50% development of L3, derived from data published in the literature (O. ostertagi: Rose, 1961; Pandey, 1972; Young et al., 1980) and original data (O. gruehneri: Hoar, 2012) as described by Azam et al. (2012).
Supplements to "Digital traces of climate risks: assessing the communication impact of Paris resilience strategy".
<p>These supplements correspond the data used in the PhD thesis : “Digital traces of climate risks: assessing the impact of Paris resilience strategy”, published in 2019 on <a href="http://theses.fr/">theses.fr</a> by Rosa Vicari (HM&Co - École des Ponts ParisTech), under the supervision of Daniel Schertzer (HM&Co - École des Ponts ParisTech).</p> <p>More precisely the data set corresponds to the corpora and term lists that are used in the analysis (based on advanced text mining and graph representation) of press articles and tweets concerning flood events in France and strategic documents released by public authorities.</p> <p>The dataset includes the following files:</p> <p>- Suppl2-2016Parisflood-termlist.csv : the list of terms extracted from the corpus of press articles covering the 2016 Seine River flood.</p> <p>- Suppl6-Cotedazflood-termlist.csv: the list of terms extracted from the corpus of press articles covering the 2015 Côte-d'Azur flood.</p> <p>- Suppl7-Corpus-PA-Strategies.csv : the corpus of strategic documents released by public authorities to cope with flood risk in Paris.</p> <p>- Suppl8-Term-list-PA-Strategies.csv : the list of terms extracted from the corpus of strategic documents released by public authorities to cope with flood risk in Paris (2003 - 2017).</p> <p>The following files are not available and cannot be shared for copyright reasons:</p> <p>- Suppl1-2016Parisflood-corpus.csv : the corpus of press articles covering the 2016 Seine River flood.</p> <p>- Suppl3-2018Parisflood-corpus.csv : the corpus of press articles covering the 2018 Seine River flood.</p> <p>- Suppl5-Cotedazflood-corpus.csv : the corpus of press articles covering the 2015 Côte-d'Azur flood.</p> <p>The following file is not available and cannot be shared for privacy reasons:</p> <p>- Suppl4 - Tweet corpus - 2016 Paris flood.csv : the corpus of tweets covering the 2016 Seine River flood.</p>
Dataset: Climate experts' views on geoengineering depend on their beliefs about climate change impacts
<p>This is the dataset and corresponding do-files to reproduce the main results from the Paper "Climate experts’ views on geoengineering depend on their beliefs about climate change impacts" and from the Supplemenatary Information.</p> <p>This fith versions incorporates additional work done after journal review.</p>
Data for recreating figures of scientific paper by Hermanson et al on volcanic impacts on climate
<p>This data is the data necessary to recreate the figures that appear in a draft manuscript submitted to the AGU journal Journal of Geophysical Research - Atmospheres for peer review. When / if the manuscript is accepted then the article will be linked from here. The data is in netcdf files. The data was created by processing (mainly averaging) data from five different institutions. It was given to the authors for the purpose of scientific research only.</p>
Figure 4 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 4. Overlay of Iranian Conservation Network with the habitat suitability map of the Mesopotamian spiny-tailed lizard.
ScienceDex guides
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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.