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1,620 results for “springs”

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zenodo36/100

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PROMET spring wheat simulations

This data set contains output data from simulations with the model PROMET for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the ERA-Interim (Dee et al. 2011) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').

opencc-by-4.0Mar 2019View details →
zenodo36/100

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from CARAIB spring wheat simulations

<p>This data set contains output data from simulations with the model CARAIB for spring wheat as part of AgMIP&#39;s Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= &#39;none&#39;, A1=&#39;regain original growing season&#39;).</p>

opencc-by-4.0Mar 2019View details →
zenodo36/100

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE spring wheat simulations

This data set contains output data from simulations with the model APSIM-UGOE for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').

opencc-by-4.0Mar 2019View details →
zenodo36/100

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES spring wheat simulations

<p>This data set contains output data from simulations with the model JULES for spring wheat as part of AgMIP&#39;s Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= &#39;none&#39;, A1=&#39;regain original growing season&#39;).</p>

opencc-by-4.0Mar 2019View details →
zenodo36/100

Figure 1 in Monthly variations in the shell structure of two freshwater ostracod (Crustacea) species in Karapınar Spring (Bolu, Turkey)

Figure 1. Location of Karapınar Spring.

opencc-by-4.0Apr 2015View details →
zenodo36/100

Figure 5 in Ephemeroptera, Plecoptera, and Trichoptera assemblages of karst springs in relation to some environmental factors: a case study in central Bosnia and Herzegovina

Figure 5. Functional feeding type composition of EPT assemblage.

opencc-by-4.0May 2016View details →
zenodo36/100

Figure 2 in Ephemeroptera, Plecoptera, and Trichoptera assemblages of karst springs in relation to some environmental factors: a case study in central Bosnia and Herzegovina

Figure 2. Values of: A) EPT index of studied springs, B) Shannon's diversity index.

opencc-by-4.0May 2016View details →
zenodo36/100

Figure 1 in Vigilance behavior of common crane Grus grus in flocks during spring, summer, and autumn

Figure 1. Vigilance proportion of each social unit.

opencc-by-4.0Nov 2023View details →
zenodo36/100

Figure 3 in Vigilance behavior of common crane Grus grus in flocks during spring, summer, and autumn

Figure 3. Vigilance proportion of juveniles in each period.

opencc-by-4.0Nov 2023View details →
zenodo36/100

Figure 2 in Vigilance behavior of common crane Grus grus in flocks during spring, summer, and autumn

Figure 2. Vigilance proportion of nonparents in each flock size.

opencc-by-4.0Nov 2023View details →
zenodo36/100

Biogeochemistry of Svalbard Glacial and Periglacial Groundwater Springs

<p>Biogeochemical data collected from water samples of periglacial and glacial groundwater springs on Svalbard between 2018 and 2023.&nbsp;</p>

opencc-by-4.0Sep 2024View details →
zenodo36/100

April 2024 Meteorological Analysis for Chios (Chiostown): Transition to Milder Spring Conditions

<h3><strong>April 2024 &ndash; Chios (Chiostown)</strong></h3> <p>April 2024 saw a significant increase in temperature and generally mild weather conditions compared to the previous months. The average temperature for the month was <strong>19.3&deg;C</strong>, reflecting the transition from winter to spring.</p> <ul> <li><strong>Highest temperature</strong>: Recorded on <strong>April 15th</strong> at <strong>27.0&deg;C</strong>.</li> <li><strong>Lowest temperature</strong>: Recorded on <strong>April 20th</strong> at <strong>11.7&deg;C</strong>.</li> </ul> <h3>Rainfall</h3> <p>Total rainfall for April was relatively low, with only <strong>28.1 mm</strong> recorded. The highest daily rainfall occurred on <strong>April 20th</strong>, with <strong>36.2 mm</strong>. There were <strong>5 days</strong> with recorded rainfall, but only one day showed significant levels (&gt;20 mm).</p> <h3>Winds</h3> <p>Winds in April were moderate, with an <strong>average wind speed</strong> of around <strong>9.2 km/h</strong>. The strongest winds were recorded on <strong>April 7th</strong> and <strong>April 24th</strong>, with maximum speeds of <strong>53.1 km/h</strong> and <strong>51.5 km/h</strong> respectively. The prevailing wind direction was northerly and north-northeasterly, indicating the influence of cooler air masses from the north.</p> <h3>Conclusion</h3> <p>April 2024 in Chios was characterized by mild spring weather, with gradually increasing temperatures and limited rainfall. Winds remained moderate, with occasional stronger gusts, mostly from northerly directions.</p>

opencc-by-4.0Oct 2024View details →
zenodo36/100

ICON_spring_2020_0320_0409

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →
zenodo36/100

Data from ONSET OF EARLY SPRING ACTIVITY BY AMPHIBIANS IN ITHACA, NEW YORK, USA

<p>This dataset was generated by conducting a search of all verifiable, research grade observations with photographs of amphibians (Category = Amphibia) in Ithaca, New York for the period 1 February to 30 April for the years 2016&ndash;2024. We constrained the search to an area within a 5 km radius of Cornell University, Ithaca, New York, USA (42.45&deg; N, 76.48&deg; W). The data output was modified to remove observer names as well as datafields that were extraneous to the analysis.</p>

opencc-by-4.0Oct 2024View details →
zenodo36/100

Diffraction data for Zajdel et al Turning Molecular Springs into Nano-Shock Absorbers ACS Appl. Mater. Interfaces 2022, 14, 26699−26713

<p>Datasets required to reproduce:</p> <p>Individual files</p> <p>Fig. 1,- XRDs of materials collected on DiscoverD, CuKa</p> <p>(BT-1.zip)</p> <p>Fig. 4a. - neutron powder diffraction of ZIF-8 + 2D2O/1H2O mix collected at the BT-1 diffractometer of the NIST Center of Neutron Research at 30C. For&nbsp; plot the data were normalized to a common scale in counts/h</p> <p>Fig. 4c. Fullprof PCR file and VESTA model with the volmetric data *.pgrid</p> <p>Fig. 4d Single detector file Z8221010.bt1 and raw pressure output + reduced data (Zenodo_30C_drops_Fig4d.DAT).</p> <p>Fig. 4b VSANS (110) integration results Up and Down (VSANS.zip)</p>

opencc-by-4.0Oct 2024View details →
zenodo36/100

Variation in soil microbiome impacts on spring foliar phenology and productivity (Van Nuland et al. 2021 New Phytologist)

<p>Data associated with &quot;<em>Natural soil microbiome variation affects spring foliar phenology with consequences for plant productivity and climate-driven range shifts</em>&quot; (Van Nuland et al. 2021 New Phytologist). Please contact corresponding author for any questions.</p>

opencc-by-4.0Jun 2021View details →
zenodo36/100

Hull Springs Dock YSI Station 2021-05-03 to 2021-06-09

<pre># General Metadata for Hull Springs Dock-YSI Sampling Station ## Files Specific metadata for each deployment can be found as text files with the file format of: HS_YSI_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. ## File Created * 2021-07-14 by KF ## File Modified ## Description These data are from the YSI EXO3 sampling station in Aimes Creek near the lab dock (38.125367, -76.659537). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The sensors are sampled every 15 minutes ### Measurements Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * date - the date that the record was collected ( * time - the time that the record was collected ( * site_name - the ID name provided by the KOR software. * unit_ID - the sonde identification number. * user_ID - the user that created the sampling template. * Temp_dC - the water temperature in degrees C. * DO_perc - the dissolved oxygen percent saturation (%). * DO_percL - the &quot;local&quot; dissolved oxygen percent saturation where the calibration is locally always 100% saturated independent of the barometric pressure (%). * DO_mg-L - the concentration of dissolved oxygen (mg/L). * SPC_uS-cm - the specific conductance (uS/cm). * C_uS-cm - the conductivity (uS/cm). * nLFC_uS-cm * TDS_mg-L - the total dissolved solids (mg/L). * SAL_PSU - the salinity of the water (PSU). * pH - the pH of the water. * pH_mV - the millivolt measurement of the pH meter (mV). * FNU - the turbidity of the water (FNU). * TSS_mg-L - the total suspended solids (mg/L). * BGA_PC_RFU - the phycocyanin fluorescence level which is an indicator of blue-green algae (RFU). * BGC_PC_ug-L - the concentration of phycocyanin in the water, which is an indicator of blue-green algae (ug/L). * Chl_RFU - the chlorophyll fluorescence level, which is an indicator of phytoplankton biomass (RFU). * Chl_ug-L - the concentration of chlorophyll in the water, which is an indicator of phytoplankton biomass (ug/L). * fDOM_RFU - the fluorescent DOM fluorescence level (RFU). * fDOM_QSU - the standardized fluorescent DOM concentration (QSU). * Wiper_V - the voltage output of the wiper (V). * Cabel_V - the voltage output of the cable (V). * Batt_V - the voltage output of the internal batteries (V). </pre>

opencc-by-4.0Jul 2021View details →
dryad36/100

Winds aloft over three water bodies influence spring stopover distributions of migrating birds along the Gulf of Mexico coast

<p>Migrating birds contend with dynamic wind conditions that ultimately influence most aspects of their migration, from broad-scale movements to individual decisions about where to rest and refuel. We used weather surveillance radar data to measure spring stopover distributions of northward migrating birds along the northern Gulf of Mexico coast and found a strong influence of winds over non-adjacent water bodies, the Caribbean Sea and Atlantic Ocean, along with the contiguous Gulf of Mexico. Specifically, we quantified the relative influence of meridional (north-south) and zonal (west-east) wind components over the three water bodies on weekly spring stopover densities along western, central, and eastern regions of the northern Gulf of Mexico coast. Winds over the Caribbean Sea and Atlantic Ocean were just as, or more, influential than winds over the Gulf of Mexico, with the highest stopover densities in the central and eastern regions of the coast following the fastest winds from the east over the Caribbean Sea. In contrast, stopover density along the western region of the coast was most influenced by winds over the Gulf of Mexico, with the highest densities following winds from the south. Our results elucidate the important role of wind conditions over multiple water bodies on region-wide stopover distributions and complement tracking data showing Nearctic-Neotropical birds flying non-stop from South America to the northern Gulf of Mexico coast. Smaller-bodied birds may be particularly sensitive to prevailing wind conditions during non-stop flights over water, with probable orientation and energetic consequences that shape subsequent terrestrial stopover distributions. In the future, the changing climate is likely to alter wind conditions associated with migration, so birds that employ non-stop over-water flight strategies may face growing challenges. </p>

opencc-zeroAug 2021View details →
dryad36/100

The risk faced by the early bat: individual plasticity and mortality costs of the timing of spring departure after hibernation

<p>Hibernation is a widespread adaptation in animals to seasonally changing environmental conditions. In the face of global anthropogenic change, information about plastic adjustments to environmental conditions and associated mortality costs are urgently needed to assess population persistence of hibernating species. Here, we used a five-year data set of 1,047 RFID-tagged individuals from two bat species, Myotis nattereri and Myotis daubentonii that were automatically recorded each time they entered or left a hibernaculum. Because the two species differ in foraging strategy and activity pattern during winter, we expected species–specific responses in the timing of hibernation relative to environmental conditions, as well as different mortality costs of early departure from the hibernaculum in spring. Applying mixed-effects modelling, we were able to disentangle population-level and individual-level plasticity in the timing of departure. To estimate mortality costs of early departure, we used both a capture mark recapture analysis and a novel approach that takes into account individual exposure times to mortality outside the hibernaculum. We found that the timing of departure varied between species as well as among and within individuals, and was plastically adjusted to large-scale weather conditions as measured by the NAO (North Atlantic Oscillation) index. Individuals of M. nattereri, which can exploit milder temperatures for foraging during winter, tuned departure more closely to the NAO index than individuals of M. daubentoniid which do not hunt during winter. Both analytical approaches used to estimate mortality costs showed that early departing individuals were less likely to survive until the subsequent hibernation period than individuals that departed later. Overall, our study demonstrates that individuals of long-lived hibernating bat species have the potential to plastically adjust to changing climatic conditions, although the potential for adjustment differs between species.</p>

opencc-zeroNov 2022View details →
dryad36/100

No risk – no fun: Penalty and recovery from spring frost damages in deciduous temperate trees

<p>Phenological shifts in response to changing climatic conditions are a key acclimation process for the persistence of perennial plants in temperate and boreal climates. The optimal time to leaf-out is the result of evolutionary processes determined by the trade-off between minimizing the risk of freezing damages and herbivory pressure while maximizing resource uptake to increase competitiveness against the other plants.</p> <p>We quantified the penalty exerted by frost exposure at the time of leaf emergence on plant development (reduction in leaf area, canopy duration, and growth) over the potential gains without frost (increased biomass and non-structural carbohydrate reserves), depending on when leaf-out occurs. To this purpose, we exposed 960 saplings of four temperate deciduous tree species with contrasting cold hardiness to two frost intensities shortly after leaf emergence, which was artificially induced at four occasions to reflect the whole range of natural leaf-out dates.</p> <p>One year above-ground biomass (AGB) increments following the frost revealed a clear ranking among the species depending on their strategy to cope with damaging frosts. Prunus avium (-41% of AGB-increment compared to control saplings) resprouted from the stem base, Quercus robur (-62%) rapidly produced new leaves from dormant reserve buds, Fagus sylvatica (-98%) showed the highest chlorophyll content in autumn and delayed senescence together with Carpinus betulus (-105%), which overcompensated NSC reserves after the growing season but showed highest mortality (up to 32%). In all species, NSC reserves recovered rapidly their initial stage at the expense of growth.</p> <p>The timing of leaf-out (advanced and delayed artificially) significantly affected the performance and recovery (regreening and growth) of both frozen and non-frozen saplings, with the lowest performance found at the most delayed leaf-out date. We propose that the potential to recover from frost damages is an important component of a tree's performance, particularly at the juvenile stage. The ability to recover may become even more decisive in the future with the predicted increase of false springs in many extra-tropical regions.</p>

opencc-zeroNov 2022View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record