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2,837 results for “Climate Data”
LAGOS-NE Annual, seasonal, and monthly climate data for lakes and watersheds in a 17-state region of the U.S.
These data describe climate at different temporal scales and can easily be integrated into analyses using limnological and geospatial data in the LAGOS-NE database. This includes 1) monthly, seasonal and annual PRISM temperature and precipitation data at the hydrologic unit code-12 (HUC-12) watershed scale, 2) monthly, seasonal and annual Palmer Hydrological Drought Index (PHDI) data for all lakes over 4ha in LAGOS-NE by lagoslakeid (unique lake identifier), and 3) 30-year averages (1981-2010) of monthly, seasonal and annual PRISM temperature and precipitation data at the HUC-12 watershed scale linked to all lakes over 4ha in LAGOS-NE by lagoslakeid.
Data for Lynn et al. “Soil microbes that may accompany climate warming increase alpine plant production”; accepted at Oecologia
Climate change is causing species with non-overlapping ranges to come in contact, and a key challenge is to predict the consequences of such species re-shuffling. Experiments on plants have focused largely on novel competitive interactions; other species interactions, such as plant-microbe symbioses, while less studied, may also influence plant responses to climate change. In this greenhouse study, we evaluated interactions between soil microbes and alpine-restricted plant species, simulating a warming scenario in which low elevation microbes migrate upslope into the distribution of alpine plants. We examined three alpine grasses from the Rocky Mountains, CO, USA (Poa alpina, Festuca brachyphylla, Elymus scribneri). We used soil inocula from within (resident) or below (novel) the plants' current elevation range and examined responses in plant biomass, plant traits, and fungal colonization of roots. Resident soil inocula from the species' home range decreased biomass to a greater extent than novel soil inocula. The depressed growth in resident soils suggested these soils harbor more carbon-demanding microbes, as plant biomass generally declined with greater fungal colonization of roots, especially in resident soil inocula. Although plant traits did not respond to the provenance of soil inocula, specific leaf area declined and root:shoot ratio increased when soil inocula were sterilized, indicating microbial mediation of plant trait expression. Contrary to current predictions, our findings suggest that if upwardly migrating microbes were to displace current soil microbes, alpine plants may benefit from this warming-induced microbial re-shuffling.
Micro-macro Climate Data for shrubs and artificial shelters in Panoche Hills, California, USA, 2019
Micro-climatic data for solar radiation and temperature recorded in Panoche Hills Managament Area in Califronia, U.S.A. The study was conducted Spring-Summer 2019 (May 20th- June 12th). Micro-climate was recorded using in situ loggers under the shrubs Ephedra californica, artificial shelters, and the open to test for differences. Macro-site level data was retrieved from the nearby weather station and all data were compiled into one dataset.
Tree ring, leaf mining, climate, and remote sensing data from aspen leaf miner survey sites: I - Basal area increment and d13C
This dataset contiains basal area increment (BAI) and d13C chronologies of 47 aspen cored in 2016 across four sites where leaf mining has been documented since 2004. Chronologies of BAI extend as far back as 1957 and up to 2015. Tree ring d13C chronologies extend from 2004-2015 and were measured on 23 trees from two fo the four sites.
Tree ring, leaf mining, climate, and remote sensing data from aspen leaf miner survey sites: II - Tree DBH and age
This dataset contiains tree level measurements of diameter at breast height (DBH) and age of aspen that were sampled in 2015 for tree ring anlyses. The tree ages provided are the age of the tree in 2015.
Interior Alaska long-term effects of climate and wildfire on permafrost soil temperature regimes: hourly data, 2013-2018
This dataset contains the hourly output from soil temperature sensors at depths from 5 to 150 cm . Three pairs of burned and unburned sites were instrumented in 2013 to measure permafrost temperature dynamics of the surface permafrost.
Seasonal climate data (growing season, spring, fall/winter, and previous growing season) at an annual resolution for eight sites from the Cooperative Alaska Forest Inventory (CAFI) from 1945 - 2013.
This dataset contains site-level measurements from 1945 - 2013 of seasonal (growing season, spring, fall/winter, and previous growing season) climate moisture index (CMI), average temperature, and total precipitation at an annual resolution for eight sites from the Cooperative Alaska Forest Inventory (CAFI) from 1945 - 2013.
Plant aboveground biomass data: BAC: Biodiversity and Climate
Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
Climate Data Summaries for Long-Term Ecological Research Sites
This dataset contains historical climate data and climate summaries from Long-Term Ecological Research sites and other climate stations in their vicinity. It has as its basis "A Climatic Analysis Of Long-Term Ecological Research Sites" created by David Greenland, Timothy KIttel, Bruce Hayden and David Schimel in 1996 (http://climhy.lternet.edu/documents/climdes/). The dataset includes monthly data and summaries aggregating years and months to produce climatic summaries.
Climate and N flux data for Saddle, 1992 - 1995.
Atmospheric gaseous nitric acid (HNO3) plus particulate matter nitrate (NO3) and ammonium (NH4) concentrations were being measured at the Saddle site on an approximately biweekly basis during the winter and on a weekly to twice-weekly basis from April through September. These N species are the dominant contributors to atmospheric N loading at the Saddle site alpine tundra. A 47-mm filter pack with a teflon front filter for NO3 and NH4 collection followed by a nylon filter for HNO3 collection is used to sample the ambient air for 3 to 6 hr. Subsequent colorimetry analysis, performed at the MRS Kiowa Laboratory, is used to obtain the mass of HNO3, NO3, and NH4 on each filter. Air concentrations are then determined by dividing masses by the volume of air sampled.
Climate data for A1 chart recorder, 1952 - 1970.
Climatological data were collected from a ridgetop climate station east of Niwot Ridge (A1 at 2195 m) throughout the year. Parameters measured were temperature, relative humidity, solar radiation, and precipitation. The station was initially instrumented with a thermohygrograph (regularly calibrated and checked with maximum and minimum thermometers and psychrometers), standard 8-inch precipitation gauge installed with the rim 1 m above ground, totalizing anemometer (approximately 0.7 m above the general level of tree crowns), and maximum and minimum soil thermometers at approximately 15 and 30 cm depth. A recording precipitation gauge was installed in 1965. The installation in the clearing was positioned so that the gauge rim subtended an angle of 45 degrees to the tree crowns although the most effective angle is 30 degrees according to Leaf (1962).
Climate data for B1 chart recorder, 1952 - 1970.
Climatological data were collected from a ridgetop climate station east of Niwot Ridge (B1 at 2591 m) throughout the year. Parameters measured were temperature, relative humidity, solar radiation, and precipitation. The station was initially instrumented with a thermohygrograph (regularly calibrated and checked with maximum and minimum thermometers and psychrometers), standard 8-inch precipitation gauge installed with the rim 1 m above ground, totalizing anemometer (approximately 0.7 m above the general level of tree crowns), and maximum and minimum soil thermometers at approximately 15 and 30 cm depth. A recording precipitation gauge was installed in 1965. The installation in the clearing was positioned so that the gauge rim subtended an angle of 45 degrees to the tree crowns although the most effective angle is 30 degrees according to Leaf (1962).
Climate data for D1 chart recorder, 1952 - 1982.
Climatological data were collected from a Niwot Ridge climate station (D1 at 3743 m) throughout the year. Parameters measured were temperature, relative humidity, solar radiation, precipitation, barometric pressure, and wind speed. The station was initially instrumented with a thermohygrograph (regularly calibrated and checked with maximum and minimum thermometers and psychrometers), which was equipped with a Bourdon tube (to measure temperature) and a banjo-spread hair element (to measure relative humidity). The thermohygrograph was situated in a white, all wood, louvered Stevenson screen which is oriented with the door facing north. The station was initially instrumented with a standard 8-inch precipitation gauge installed with the rim 1 m above ground. This gauge was not shielded prior to October 1964. A recording precipitation gauge was installed in 1965. An Alter shield and snow fence were placed around the gauge to give more accurate precipitation measurements during windy conditions. Precipitation was caught in a bucket containing ethylene glycol (to melt snow) and light oil (to prevent evaporation). As the weight of the bucket increased, a pen moved up via a spring mechanism and recorded on a rotating chart. Solar radiation was recorded on a bimetalic strip mechanical actinometer. Ninety percent of solar radiation from 360 to 2000 nm was transmitted through the instrument's glass dome. The station was initially instrumented with a totalizing anemometer (2 m height). Wind speed (peak gust) was subsequently measured with a 3-cup, AC-generating anemometer that continuously recorded onto an Esterline Angus strip chart recorder. Wind direction was recorded as a pen position on a continuously recording strip chart. The thermohygrograph, rain gauge, and actinometer all used wind-up or battery-driven clock drives that rotated the recording chart on a right cylindrical drum with a fixed period between 24 h and 861 h depending on the gears used. These clock mechanisms
Climate data for Saddle chart recorder, 1982 - 1988.
Climatological data were collected from a Niwot Ridge climate station (Saddle at 3525 m) throughout the year. Parameters measured were temperature, relative humidity, solar radiation, wind speed, and run of wind. The station was instrumented with a thermohygrograph (regularly calibrated and checked with maximum and minimum thermometers and psychrometers), which was equipped with a Bourdon tube (to measure temperature) and a banjo-spread hair element (to measure relative humidity). The thermohygrograph was situated in a white, all wood, louvered Stevenson screen which was oriented with the door facing north. Solar radiation was recorded on a bimetalic strip mechanical actinometer. Ninety percent of solar radiation from 360 to 2000 nm was transmitted through the instrument's glass dome. Wind speed (peak gust) was measured near T-Van (3440 m), at a location approximately 400 m south of the Saddle climate station, with a 3-cup, AC-generating anemometer that continuously recorded onto an Esterline Angus strip chart recorder. Run of wind was measured with a Belford totalizing cup anenometer with a counter (at a height of approximately 3 meters above ground) and was located near T-Van as well. Average wind speed was calculated from the peak gust trace. The thermohygrograph, and actinometer both used wind-up or battery-driven clock drives that rotated the recording chart on a right cylindrical drum with a fixed period between 24 h and 861 h depending on the gears used. These clock mechanisms were virtually identical and therefore completely interchangeable among the instruments.
Data for the publication "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model"
<p>These data are a set of 6yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments (diagnostic and prognostic) of precipitation under the present-day (PD, aerosol emission at the year 2000) and preindustrial (PI, aerosol emission at the year 1850) conditions.<br> The data are used in the manuscript entitled "Reconciling compensating errors between<br> precipitation constraints and the energy budget in a climate model".</p>
JUMP - Data collection - Part I: Jets from a Global Climate Model.
<p>We conduct in-depth analysis of statistical flow properties from Global Circulation Model that reproduce Saturn's macroturbulence, namely large-scale zonal winds. We use a high performance Global Climate Models (GCMs), named DYNAMICO, to model the atmospheric circulation of gas giants with appropriate physical parametrizations for Saturn's atmosphere. The high-resolution model DYNAMICO solves for 3D primitive equations of motion. We ran a Saturn simulation covering 15 Saturn years using the Saturn DYNAMICO GCM. Wind fields are output every 20 Saturn days at 32 pressure levels onto 1/2° latitude-longitude grid maps. Details on this Saturn reference simulation are given in Spiga et al. (2020). In addition, to diagnose the relevant 3D dynamical mechanisms in Saturn's turbulent atmosphere, we run a set of four simulations using an idealized version of our Global Climate Model devoid of radiative transfer, with a well-defined Taylor-Green forcing and over several rotation rates (4, 1, 0.5, and 0.25 times Saturn's rotation rate). Here, we deliver a full data set, including velocity maps, at different pressure levels and time steps, from which it is possible to recompute the statistical analysis detailed in Cabanes et al. (2020). The delivered data set includes:</p> <p>Files of our (1) data collection and (2) numerical codes that lead to the statistical analysis:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-Icarus.pdf</strong> that describes in depththe data set and the associated nomenclature.</li> <li>A netcdf file of velocity fields from our Saturn Reference Simulation (SRS) <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> <li><strong>StatisticalData.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 4 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-4-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 1 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-1-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.5 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.5-istep-20626.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.25 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.25-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry are on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> </ul> <p> </p> <p><strong>Acknowledgments</strong>:</p> <p>The authors acknowledge exceptional computing support from Grand Équipement National de Calcul Intensif (GENCI) and Centre Informatique National de l’Enseignement Supérieur (CINES). All the simulations presented in this paper were carried out on the Occigen cluster hosted at CINES. This work was granted access to the High-Performance Computing (HPC) resources of CINES under the allocations A001-0107548, A003-0107548, A004-0110391 made by GENCI. The authors acknowledge funding from Agence Nationale de la Recherche (ANR), project HEAT ANR-14-CE23-0010 and project EMERGIANT ANR-17-CE31-0007. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 797012. Fruitful discussions with Sandrine Guerlet, Ehouarn Millour, Thomas Dubos, Frédéric Hourdin and Alexandre Boissinot from our team helped refine some discussions in the paper.</p>
Data and Python script for article "A text mining analysis of the climate change literature in industrial ecology'
<p>The data and Python script are part of the forum article "A text mining analysis of the climate change literature in industrial ecology" authored by Dayeen, F.R., Sharma, A.S., and Derrible, S., and published in the <em>Journal of Industrial Ecology</em> in 2020.</p> <p>The Python script and instructions are included in the LiTCoF_v1.00-py.zip file. The original data is available in two formats: .csv and .pkl.</p> <p>Updates of the script will be posted at https://github.com/csunlab/LiTCoF and at https://csun.uic.edu/codes/LiTCoF.html. The data is also available at https://csun.uic.edu/datasets.html#AbstractsIE.</p> <p>Feel free to contact any of the authors for information and questions about the data and code.</p>
RESCCUE (RESilience to cope with Climate Change in Urban arEas) EU Project - WP1 Data
<p>These files contain the data generated for the Work Package 1 of the RESCCUE project. RESCCUE project was devised to analyse future urban impacts due to climate change so as to improve resilience of three target cities: Barcelona, Bristol and Lisbon. To achieve that, future climate projections and changes in extreme events were obtained at a local scale for the Work Package 1. Several past studies were analysed to identify all the climate variables and extreme events that could affect urban areas, e.g. heavy rainfall, heat waves and storm surge. All available meteorological observations in the considered areas were collected and filtered through several tests (general consistency, outliers and inhomogeneities) in order to handle datasets long enough and of good quality. As a way to obtain the best input possible, every valid station was extended in time by downscaling process with the ERA-Interim reanalysis.</p> <p>Future climate projections were obtained for ten different global climate models considering two of the main Representative Concentration Pathways (RCP4.5 and RCP8.5) established in the last IPCC report. These models were downscaled through a sophisticated statistical methods (analogous stratification and transfer functions among others) to project local climate according to the identified climate drivers: temperature, precipitation, wind, relative humidity, sea level pressure, potential evapotranspiration, snowfall, wave height and sea level; and for both climate and decadal timescales. Already downscaled models were first validated for the method and afterwards verified, obtaining small errors and good coherent simulations.</p> <p>Extreme events of the main climate drivers were obtained and analysed for both historical and future scenarios through the combination of several statistical methods as well as through the analysis of several teleconnectionpatterns. Derived events such as heat waves, drought, snowstorms, storm surges, wave height among others were afterwards inferred for climate, decadal and seasonal scale.</p> <p><strong>FILES</strong></p> <p>The data generated have been grouped into three different files, one for each studied area: the hydrological basin of the rivers Ter and Llobregat (the area that influences Barcelona), the geographical area between England and South Wales (the area that influences Bristol) and the Lisbon area.</p> <p>Each of the files contains a self-explanatory file detailing the structure of the information contained and the way in which it is provided.</p> <p><strong>ABOUT THE RESCCUE PROJECT</strong></p> <p>The RESCCUE project, Resilience to cope with Climate Change in Urban Areas, –a multisectorial approach focusing on water– aims to provide practical and innovative models and tools to end-users facing climate change challenges to build more resilient cities.</p> <p>The project provides tools to assess urban resilience from a multisectorial approach, for current and future climate scenarios and including multiple hazards. This holistic approach to urban resilience will enable city managers and urban systems operators to decide the optimal investments to cope with future situations.</p> <p>For more information, please visit <a href="http://www.resccue.eu/">www.resccue.eu</a></p>
RCP8.5-ECEARTH-RACMO-LARSIM_ME Climate Flow Projection Data for German Waterways
<p>The datasets provided here were produced as part of the IMPREX project for work package 4, task 4 „<em>Improving prediction on the climate scale</em>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 4.4 „<em>Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale</em>“ (Falloon et al 2019). The aim was to study the impact of internal climate model variability and bias correction method on the climate change signal of relevant flow indicators for the German waterways Rhine, Elbe and Danube.</p> <p>To assess the impact of internal variability of the global climate model on future changes of flow, precipitation, temperature and global radiation of the 16-member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH 2.3 provided by WP3 of IMPREX were used. EC-EARTH was run 16 times from 1850 to 2100, each member starting from a slightly different initial state, under forcing of historical emissions until 2005 and the RCP8.5 greenhouse gas concentration pathway from 2006 onwards. Each of the EC-EARTH members was subsequently dynamically downscaled using KNMI-RACMO2 on a 0.11° (~12 km) resolved domain (Aalbers et al. 2018).</p> <p>To correct the systematic model biases of climate models different bias correction methods were applied: (1) no bias correction, (2) linear scaling (Lenderink et al. 2007) and (3) quantile-quantile mapping (Piani et al. 2010). Bias correction relationships were derived for five-day periods (for each variable and location, in total 73 bias correction relationships were derived) including 13 days before and after the considered five-day period (total window size was 31 days) from the observations and values of the regional climate simulations. The period used to estimate the bias correction relationships was 1971-2000.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. As observed meteorological forcings, precipitation, air temperature and global radiation from the HYRAS data set (Rauthe et al. 2013) available for the 5 km x 5 km model grid and the period 1951-2015 were used. The hydrological model was calibrated using the automatic calibration scheme Shuffled Complex Evolution SCE-UA algorithm (Duan et al. 1994). For more details about the model see Meißner et al. (2017).</p> <p>The meteorological variables air temperature, precipitation and global radiation produced by the KNMI RACMO-EC-EARTH 16 member ensemble (period 1951-2100) were interpolated to a 25 km x 25 km grid and afterwards bias corrected with respect to the observation data (HYRAS) used for calibration of the hydrological model LARSIM. From this 25 km x 25 km grid the bias corrected variables were downscaled to the 5 km x 5 km model grid of LARSIM using monthly background climatology fields on the 5 km x 5 km target grid of the HYRAS dataset. The bias-corrected and downscaled data was then used as meteorological forcing of LARSIM to calculate flow projections for the rivers Rhine, Elbe and Upper Danube (up to the German/Austrian border).</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <em><strong>q_obs(time=24472, stations=8)</strong></em>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <pre><code>float q_obs(time=24472, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "observed streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_HYRAS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the HYRAS dataset stored as variable <em><strong>q_sim (time=23741, stations=8)</strong></em>. Period 1951-2015, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=23741, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Q_RCP85_ECEARTH_RACMO_[bc]_LME.nc:</strong></p> <p>Mean daily projected flow of the hydrological model LARSIM-ME forced by 16 realizations of RCP8.5-ECEARTH-RACMO stored as variable <em><strong>q_sim(time=54787, realization=16, stations=8)</strong></em>, first dimension time, second dimension realization and third dimension stations. Bias correction of meteorological forcings [bc]: NOBC: no bias correction, LS: linear scaling, QQMAP Quantile-Quantile Mapping. Period 1951-2100, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=54787, realization=16, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "projected streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Aalbers, E. E., G. Lenderink, E. van Meijgaard & B. J. J. M. van den Hurk (2018): Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50(11), 4745-4766</p> <p>Duan, Q., S. Sorooshian & V. K. Gupta (1994): Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158(3–4), 265-284</p> <p>Falloon, P., K. Williams, J. Andreu, A. Solera, S. Suárez-Almiñana, B. Klein, D. Meissner, J. Hunink, J. Eekhout & J. de Vente (2019): Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale. Deliverable 4.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf">https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf</a></p> <p>Lenderink, G., A. Buishand & W. van Deursen (2007): Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences 11(3), 1143-1159</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Rauthe, M., H. Steiner, U. Riediger, A. Mazurkiewicz & A. Gratzki (2013): A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift 22(3), 235-256</p>
Supplementary data to Clade-specific biogeographic history and climatic niche shifts of the southern Andean-southern Brazilian disjunction in plants
<p>Georeferenced locality data points used in the climatic analyses. Column "clade" indicate whether the species corresponds to the southern Andean (SA) or to the southern Brazilian (SB) clade. See the book chapter for details.</p> <p>Part of this dataset was built with information downloaded from GBIF (www.GBIF.org) for the following taxa:</p> <p><em>Araucaria</em><br> GBIF.org (06 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.2nc359<br> GBIF.org (06 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.aym9bi</p> <p><em>Butia</em><br> GBIF.org (30th May 2018) GBIF Occurrence Download https://doi.org/10.15468/dl.k42ama</p> <p><em>Colliguaja</em><br> GBIF.org (28 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.nvqo4p<br> GBIF.org (13th January 2016) GBIF Occurrence Download http://doi.org/10.15468/dl.aoovox<br> GBIF.org (15th May 2018) GBIF Occurrence Download https://doi.org/10.15468/dl.mownio</p> <p><em>Griselinia</em><br> GBIF.org (28 November 2014) GBIF Occurrence Download https://doi.org/10.15468/dl.eytdse</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.