Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,837
datasets available to search
ShareScore release 0.9.0
Dataset results
2,837 results for “Climate Data”
A four-dimensional, multiyear, and near-global climate data record of the fine-mode (sub-micrometer in terms of diameter) and coarse-mode (super-micrometer in terms of diameter) components of atmospheric pure-dust.
<p>A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (sub-micrometer in terms of diameter) and coarse-mode (super-micrometer in terms of diameter) components of atmospheric pure-dust, is presented. The separation of the two modes of dust in detected atmospheric dust layers is based on a combination of (1) the total pure-dust product provided by the well-established European Space Agency (ESA) - “LIdar climatology of Vertical Aerosol Structure” (LIVAS) database and (2) the coarse-mode component of pure-dust provided by the first-step of the two-step POlarization LIdar PHOtometer Networking (POLIPHON) technique, developed in the framework of European Aerosol Research Lidar Network (EARLINET). The fine-mode component of pure-dust is extracted as the residual between the total pure-dust and the coarse-mode component of pure-dust. Intermediate steps involve the implementation of regionally-dependent lidar-derived lidar-ratio values and AErosol RObotic NETwork (AERONET) based climatological extinction-to-volume conversion factors, facilitating conversion of dust backscatter into extinction and subsequently extinction into mass concentration. The decoupling scheme is applied to Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations at 532 nm. The final products consist of the submicrometer (particles with diameter less than 1 μm) and supermicrometer (particles with diameter greater than 1 μm) modes of atmospheric pure-dust, of quality-assured profiles of backscatter coefficient at 532 nm, extinction coefficient at 532 nm, and mass concentration for each of the two components. The datasets are provided primarily with the original L2 horizontal (5 km) and vertical (60 m) resolution of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) along the CALIPSO orbit-path, and secondly in averaged profiles of seasonal-temporal resolution, 1<sup>o</sup>×1<sup>o</sup> spatial resolution, and with the original vertical resolution of CALIPSO, focusing on the latitudinal band extending between 70<sup>o</sup>S and 70<sup>o</sup>N and covering more than 15-years of Earth Observation (06/2006-12/2021). The quality of the dust products is justified by using AERONET fine-mode and coarse-mode aerosol optical thickness (AOT) interpolated to 532 nm and AERosol properties – Dust (AER-D) campaign airborne in-situ particle size distributions (PSDs) as reference datasets, during atmospheric conditions characterized by dust presence. The near-global fine-mode and coarse-mode pure-dust climate data record is considered unique with respect to a wide range of potential applications, including climatological, time-series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and reanalysis datasets, assimilation activities, investigation of the role of airborne dust on radiation, and air quality.</p>
Calibration data for Climate Change Tower temperature sensors - may 2017
<p>Calibration data for the temperature sensors hosted at Climate Change Tower in Ny-Ålesund, Svalbard, during May 2017. Each namefile contains the temperature point (e.g. m25 is -25 °C, while p5 is +5 °C) and the sensor type ("ref" for reference, "DUT" for Device under test).</p> <p>Reference files contain data for the 2 reference sensors employed, while DUT files contain data for the 4 CCT sensors.</p>
Data from: The effect of shrub cover on conifer water-use patterns, growth, and response to climate change in the southern Sierra Nevada
<p>As wildfire increases in size and severity, large areas of forest are undergoing substantial increases in shrub cover. In forests where water is the limiting resource, the paritioning of soil water between shrubs and trees may determine how shrubs affect tree growth and water-stress. We analyzed hydrogen and oxygen isotopes in the xylem water for two conifer species and two shrub species to assess how shrub cover affects the water-uptake patterns of conifers in the southern Sierra Nevada. Further, we analyzed tree growth and stable carbon isotopes in tree rings to assess how shrub cover affects tree growth, intrinsic water-use efficiency and response to climate change.</p>
Data for: Climatic and management-related drivers of endemic European spruce bark beetle populations in boreal forests
<p>Climate change is already reducing carbon sequestration in Central European forests dramatically through extensive droughts and bark beetle outbreaks. Further warming may threaten the enormous carbon reservoirs in the boreal forests in northern Europe, unless disturbance risks can be reduced by adaptive forest management. The European spruce bark beetle (<em>Ips typographus</em>) is a major natural disturbance agent in spruce-dominated forests and can overwhelm the defences of healthy trees through pheromone-coordinated mass-attacks.</p> <p>We used an extensive dataset of bark beetle trap counts to quantify how climatic and management-related factors influence bark beetle population sizes in boreal forests. Trap data was collected during a period without outbreaks and can thus identify mechanisms that drive populations towards outbreak thresholds.</p> <p>The most significant predictors of bark beetle population size were volume of mature spruce, extent of newly exposed clearcut edges, temperature, and soil moisture. For clearcut edge, temperature, and soil moisture, a three-year time lag produced the best model fit. We demonstrate how a model incorporating the most significant predictors, with a time lag, can be a useful management tool by allowing spatial prediction of future beetle population sizes.</p> <p><em>Synthesis and Applications</em>: Some of the population drivers identified here, i.e., spruce volume and clearcut edges, can be targeted by adaptive management measures to reduce the risk of future bark beetle outbreaks. Implementing such measures may help preserve future carbon sequestration of European boreal forests.</p>
Data from: Disentangling the dynamics among climate, food availability, and reproduction in a pair-living, monogamous primate
<p>We examined the hypothesis that pair-living, monogamous social systems evolved in environments with evenly distributed food resources, a homogenous distribution of females in space prevents males from monopolizing more than one female. Our study investigated the interplay between climatic variables (ambient temperature, rainfall), food availability (forest structure, phenology data), and infant production in a pair-living monogamous owl monkey population (322 births, 21 years, 22 groups) in the humid Chaco of Argentina. Associations between climatology and fruit availability ranged from -0.29 to 0.38, and some extreme events negatively impacted fruit availability. Variation in fruit availability was higher among years than territories, whereas infant production varied more among territories than it did across years. Our study makes specific contributions to examining the hypothetical "homogeneity" in the temporal and spatial distribution of food available to a pair-living monogamous primate and how such variation relates to infant production in the population.</p>
Data for the "Deforestation could push Amazonia close to a tipping point under future climate change." paper
<p>Matlab datafiles for the output of the HYBRID-ICBL <span>coupled land surface-atmosphere single column model presented in the "Deforestation could push Amazonia close to a tipping point under future climate change"</span></p> <p><span>Values are daily mean values and their names are self explanatory as follows.</span></p>
Data for "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region"
<p>The data presented here were used the following study:</p> <p>Africa's Climate Response to Regional Marine Cloud Brightening Strategies.<span> </span></p> <p><span>Romaric C. Odoulami<sup>1</sup>, Haruki Hirasawa<sup>2,3</sup>, Kouakou Kouadio<sup>4</sup>, Trisha D. Patel<sup>1</sup>, Kwesi A. Quagraine<sup>5,6</sup>, Izidine Pinto<sup>6,7</sup>, Temitope S. Egbebiyi<sup>6</sup>, Babatunde J. Abiodun<sup>6</sup>, Christopher Lennard<sup>6</sup>, Mark G. New<sup>1</sup> </span></p> <p><sup><span>1</span></sup><span>African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa.</span></p> <p><sup><span>2</span></sup><span>School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada.</span></p> <p><sup><span>3</span></sup><span>Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA</span></p> <p><sup><span>4</span></sup><span>Laboratory of Atmospheric Physics and Fluid Mechanics, University Felix Houphouet-Boigny, Abidjan, Côte d’Ivoire.</span></p> <p><sup><span>5</span></sup><span>National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA.</span></p> <p><sup><span>6</span></sup><span>Climate System Analysis Group (CSAG), Environmental and Geographical Science Department, University of Cape Town, Cape Town, South Africa.</span></p> <p><sup><span>7</span></sup><span>Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands.</span></p> <p> </p> <p>The datasets contains six folders. Each of them contains two folders: (a) "precipitation_indices" containing all precipitation indices and (b) "temperature_indices" containing all temperature indices for the African domain analysed in this study.</p> <p> (i) "historical"<br> This folder contains all historical precipitation and temperature indices analysed.</p> <p> (ii) "ssp245"<br> This folder contains all ssp245 precipitation and temperature indices analysed.</p> <p>(iii) "ssp245_MCB_R1"<br> This folder contains all ssp245+MCB_R1 (MCB over Northeast Pacific: MCB_NEP) precipitation and temperature indices analysed.</p> <p> (iv) "ssp245_MCB_R2"<br> This folder contains all ssp245+MCB_R2 (MCB over Southeast Pacific: MCB_SEP) precipitation and temperature indices analysed.</p> <p> (v) "ssp245_MCB_R3"<br> This folder contains all ssp245+MCB_R2 (MCB over Southeast Atlantic: MCB_SEA) precipitation and temperature indices analysed.</p> <p> (vi) "ssp245_MCB_R1R2R3"<br> This folder contains all ssp245+R1R2R3 (MCB simultaneously over Northeast Pacific, Southeast Pacific, and Southeast Atlantic: MCB_ALL) precipitation and temperature indices analysed.</p> <p> </p>
Blockage, vandalism, and harassment activities for the cause of climate change mitigation: data deposit
<p>The paper describes a dataset of metadata of 89 blockage, vandalism, and harassment events happening in recent years. The dataset comprises three main categories: 1) Events, 2) Activists, and 3) Consequences. For researchers interested in environmental activism, climate change, and sustainability, the dataset is helpful in studying the effectiveness and appropriateness of strategies to raise public awareness and support. For researchers in the field of security studies and green criminology, the dataset offers resources to study features and impacts of blockage, vandalism, and harassment events. The Bayesian Mindsponge Framework (BMF) analytics was employed to validate the dataset. Consequently, the estimated result aligns with the Mindsponge Theory’s theoretical reasoning.</p>
[Data availability]Safety climate in the operating room in the pre-pandemic and pandemic period of COVID-19: A mixed method study
<p>Disponibilidade dos dados analisados no estudo Safety climate in the operating room in the pre-pandemic and pandemic period of COVID-19: A mixed method study.</p>
Fig. 8 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 8. Myosotis antarctica subsp. traillii photographs and distribution map. (a) Habit. (b, d) Rosette leaf tips: (b) adaxial and (d) abaxial sides. (c) Flower. (e) Nutlets. (f) Map of georeferenced herbarium specimens observed by J. M. Prebble (35). Whie scale bars: 2 mm; black scale bars: 1 mm. Photo credits: a, e by J. M. Prebble (a: WELT SP100487, Tiwai Point, Southland, South Island; e: WELT SP104518, cultivated ex Mason Bay, Stewart Island). b, c © Te Papa by H. M. Meudt (b: WELT SP090544, Manihi Rd, Taranaki, North Island; c: WELT SP090629, Hukanui, Gisborne, North Island; d: WELT SP090631, Waipuna, Gisborne, North Island).
Fig. 2 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 2. Maps of MaxEnt niche models for pygmy Myosotis in New Zealand and southern South America. (a) Myosotis glauca (light blue circles). (b) M. pygmaea (green circles). (c, h) M. "Volcanic Plateau" (grey triangles). (d) M. brevis (yellow cir-cles). (e) M. drucei (dark blue circles; excluding individuals identified as M. "Volcanic Plateau"). (f) M. drucei (dark blue circles) + M. pygmaea (green circles) + M. "Volcanic Plateau" (grey triangles) (g) M. antarctica (pink circles; Chilean locations), note scale is the same as for maps of New Zealand. a–f use models based on the nine-layer model (see Table 1), whereas g and h are based on the sevenlayer model.
Fig. 5 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 5. Myosotis glauca photographs and distribution map. (a) Habit. (b) Rosette leaves, adaxial and abaxial sides. (c) Calyces, left to right most to least mature. (d) Nutlets. (e) Map of georeferenced herbarium specimens observed by J. M. Prebble (16). White scale bars: 2 mm; black scale bar: 1 mm. Photo credits: all by J. M. Prebble (WELT SP093285, Nevis Valley, Otago).
Fig. 4 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 4. Myosotis brevis photographs and distribution map. (a) Habit. (b) Inflorescence showing cauline leaf abaxial side. (c) Inflorescence showing cauline leaf adaxial side, calyces, and flower. (d) Rosette leaf adaxial side showing colour morphs. (e) Flower. (f) Nutlet. (g) Map of georeferenced herbarium specimens observed by J. M. Prebble (25). White scale bars: 2 mm; black scale bar: 1 mm. Photo credits: a–e © Te Papa by H. M. Meudt (a: WELT SP090549, Te Ikaamaru Bay, Wellington; b, c: WELT SP090545, Ngawi, Wairarapa; d: WELT SP090543, Stent Road, Taranaki; e: WELT SP090550, Ohau Bay, Wellington); f by J. M. Prebble (WELT SP090543, cultivated ex Stent Road, Taranaki).
Fig. 7. Myosotis antarctica subsp. antarctica. Illustration reproduced from Bot. Antarct. Voy. I in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 7. Myosotis antarctica subsp. antarctica. Illustration reproduced from Bot. Antarct. Voy. I. (Fl. Antarct.) Part I, plate 38 (Hooker 1844). Illustration by W. H. Fitch. This image is in the public domain, downloaded from the Biodiversity Heritage Library (https:// www.biodiversitylibrary.org/page/13448452#page/81/ mode/1up, accessed 8 June 2021). Draft pencil drawings for this figure are attached to the type specimen of M. antarctica (K0007878799; visible online at http:// apps.kew.org/herbcat/getImage.do?imageBarcode= K000787899, accessed 8 June 2021), which was collected by J. D. Hooker from Campbell Island.
Fig. 6 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 6. Myosotis antarctica subsp. antarctica photographs and distribution maps. (a, b) Habit. (c) Rosette leaves abaxial and adaxial sides. (d) Flower. (e) Nutlets. (f) Map of mainland New Zealand distribution based on georeferenced herbarium specimens observed by J. M. Prebble (163). (g) Map of Campbell Island distribution based on georeferenced herbarium specimens observed by J. M. Prebble (14). (h) Map of Chilean distribution based on georeferenced herbarium specimens observed by J. M. Prebble (2). White scale bars: 2 mm; black scale bars: 1 mm. Photo credits: a, c, e by J. M. Prebble (a: WELT SP102777, Mt Azimuth, Campbell Island; c: WELT SP093293, Port Hills, Canterbury, South Island E: WELT SP100466, cultivated ex Mt Peel, Western Nelson. South Island). b, d © Te Papa by H. M. Meudt (b: WELT SP106592, Matiri Range, Western Nelson, South Island; d: WELT SP107322, Mt Starveall, Western Nelson, South Island).
Fig. 3 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 3. Plots displaying (a, c) omission and commission values and (b, d) area under the receiving operating characteristic curve (AUC) for two pygmy forget-me-not taxa: (a, b) M. "Volcanic Plateau" and (c, d) M. drucei, modelled using MaxEnt and all nine environmental layers for the New Zealand extent.
Data for study " Future Climate Change Impacts on Rice in Uttar Pradesh, India's Most Populous Agrarian State "
<p>Crop management practicrs used to calibrate the DSSAT for Uttar Pradesh. The rice genetic coefficients for various rice varieties/ cultivars are also available here.</p>
Data for "The hydrogen economy can reduce costs of climate change mitigation by up to 22%"
<p>This dataset contains the underlying data for all figures in the main text and in the SI of Wolfram et al. "The hydrogen economy can reduce costs of climate change mitigation by up to 22%", published in One Earth (2024). </p>
Fig. 1. Maps displaying all 290 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Fig. 1. Maps displaying all 290 occurrence points used for Myosotis pygmy species group niche modelling (Supplementary Table S1). Maps, clockwise from top: World, New Zealand, Campbell Island, and southern South America. Colour represents a priori species: M. antarctica (pink circles); M. drucei (dark blue circles); M. pygmaea (green circles); M. brevis (yellow circles); M. glauca (light blue circles); M. "Volcanic Plateau" (grey triangles).
Data and code for "Topography and climate drive recent shifts in vegetation productivity dynamics"
<p>This zip file includes data and code for manuscript "<span><span>Topography and climate drive recent shifts in vegetation productivity dynamics</span></span>".</p>
ScienceDex guides
Understand access before you commit
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.