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Fig. 4 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 4: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the Otranto Strait. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the Otranto Strait.
Fig. 1 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 1: SANI model bathymetry (A). The geographical area covered by SANI model (red rectangular) and the divided sub-regions SA (Southern Adiatic - yellow), OS (Otranto Strait - green), NI (Northern Ionian – brown) and SI (Southern Ionian – blue). All the available (966) Argo profiles for the period 2008-2012 from 21 individual floats denoted with different colours according to their WMO number (B).
Output Dataset from "Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks"
<p>Output dataset of stellar parameters and associated uncertainties derived by the machine learning approach, as decribed in <a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener"><em>"Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks", Fallows & Sanders</em> (</a><em><a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener">2024)</a>. </em></p> <p>Included parameters: [Fe/H] (fe_h), effective temperature (teff), surface gravity (logg), [C/Fe] (c_fe), [N/Fe] (n_fe), and [a/M] (a_m). Gaia DR3 source ids are included for matching purposes, alongside Gaia bp_rp_excess_factor and ruwe metrics for quality filtering.</p> <p><em>We include predictions for only Gaia objects with radial velocity measurements ('output_0.0_360.0_v2.txt'), and for all* Gaia objects with XP spectra ('gaiaFull_output_0.0_360.0.zip'). Note our full catalogue has a total size of 30.9GB once uncompressed.</em></p> <p> </p> <p><em>* Not truly all objects with XP spectra; stars with spurious or unreliable measurements in our requred inputs (Gaia, 2MASS, WISE) have been removed.</em></p>
Model outputs from "Holocene thinning in central Greenland controlled by the Northeast Greenland Ice Stream"
<p>Two-dimensional outputs of the best Yelmo model simulation within the ensemble presented in the manuscript by Tabone et al., 2024, titled "Holocene Thinning in Central Greenland Controlled by the Northeast Greenland Ice Stream," published in Nature Communications. For more information, please contact the authors and read the paper. </p>
Data from: Breeding phenology drives variation in reproductive output, reproductive costs and offspring fitness in a viviparous ectotherm
<p>Phenological advances are a widespread response to global warming and can contribute to determine the climate vulnerability of organisms, particularly in ectothermic species which are highly dependent on ambient temperatures to complete their life cycle. Yet, the relative contribution of breeding dates and temperature conditions during gestation on fitness of females and their offspring is poorly documented in reptiles. Here, we exposed females of the common lizard <em>Zootoca vivipara </em>to contrasting thermal scenarios (cold versus hot treatment) during gestation and quantified effects of parturition dates and thermal treatment on life-history traits of females and their offspring for one year. Overall, our results suggest that parturition date has a greater impact than thermal conditions during gestation on life history strategies. In particular, we found positive effects of an earlier parturition date on juvenile survival, growth and recruitment suggesting that environmental dependent selection and/or differences in parental quality between early and late breeders underlie seasonal changes in offspring fitness. Yet, an earlier parturition date compromised the energetic condition of gravid females, which suggests the existence of a mother-offspring conflict regarding the optimisation of parturition dates. While numerous studies focused on the direct effects of alterations in incubation temperatures on reptile life-history traits, our results highlight the importance of considering the role of breeding phenology in assessing the short- and long-term effects of thermal developmental plasticity.</p>
Model Output for CCKW analyses
<p>These files are the output of WRF tropical channel simulation. Three cumulus parameterizations were analyzed, but one is uploaded as a representative (Grell Freitas Scheme). The brief description of each file are as follows :</p> <ol> <li> Q_cu_GF.nc : Diabatic heating anomaly data</li> <li>PREC_filt_cu_GF.nc : Kelvin Wave filtered precipitation anomaly data</li> <li>T_cu_GF.nc : Temperature anomaly data</li> <li>EOF_cu_GF.nc : EOF decomposition data (2 modes)</li> </ol>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: output files (2 of 2)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes daily-mean output of all (ocean circulation, sea ice, and biogeochemistry) modules for September 2015. Similar files for September 2013 are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample input files for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a>, <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a>, and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p>
ISIMIP 3b, Land Use Models outputs intercomparison
<p>These data sets contain the information used to compare the land use models' outputs submitted as human forcings for ISIMIP3b and utilized for different impact analyses and teams under various scenarios (SSP1-RCP2.6, SSP3-RCP7.0, SSP5-RCP8.5). </p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for<br> training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate<br> count, yeast & mould count, and spore count were taken as input parameters, and sensory score as<br> output parameter for developing TDNN single and multilayer models (Fig.1).</p>
Preprints Servers as a Hub for Early-Stage Research Outputs
<p>Data for "Preprints as a Hub for Early-Stage Research Outputs"</p> <p>This data set contains three documents results from a survey focused on how <br> preprint servers relate to open science. See http://researchpreprints.com/2017/12/18/a-short-research-project-where-do-preprints-fit-in/<br> for the research plan.</p> <p> - submission systems and websites.xlsx</p> <p>A check of submission pages for preprint servers. Opportunities to link to other early-stage<br> research outputs were recorded. Information displayed on websites was also checked on an <br> ad hoc basis.</p> <p> - preprint abstracts.xlsx<br> <br> A check of 25 or 50 preprints for selected preprint servers, to see what information linking<br> to other early-stage research outputs was visible.</p> <p> - preprint operator survey<br> <br> Individual responses from a survey of those operating preprint servers. Names and email addresses<br> were collected but are not reported.</p>
Ocean-only simulation outputs based on the IPSL-CM5A-LR and IPSL-CM5A-MR models
<p><br> Name of the simulations<br> piControl2 : COUPLED-LR<br> piControlMR3 : COUPLED-MR<br> OR2L2E : CLIM-LR<br> OR2L2G : CLIM-MR<br> OR2L2M : TOTAL-LR member 1<br> OR2L2M2 : TOTAL-LR member 2<br> OR2L2M3 : TOTAL-LR member 3<br> OR2L2M4 : TOTAL-LR member 4<br> OR2L2M5 : TOTAL-LR member 5<br> OR2L2R : TOTAL-MR member 1<br> OR2L2R2 : TOTAL-MR member 2<br> OR2L2R3 : TOTAL-MR member 3<br> OR2L2R4 : TOTAL-MR member 4<br> OR2L2R5 : TOTAL-MR member 5<br> OR2L2Q : RANDOM-LR<br> OR2L2S : RANDOM-MR<br> piControl2_perm : like COUPLED-LR but with random permutation identical to those used to generate RANDOM-LR<br> piControlMR3_perm : like COUPLED-MR but with random permutation identical to those used to generate RANDOM-MR</p> <p>Variables :<br> zomsfatl : Atlantic meridional overturning streamfunction<br> thetao_700 : averaged potential temperature between 0m and 700m<br> dens : 3D seawater density<br> EOF-NA_SLP : PC1 of the North Atlantic monthly SLP</p> <p> </p>
Coarse-grained outputs from near-global aqua-planet control run with QOBS SST
<p>This is the coarse-grained CTRL NG-Aqua data described in the following two papers:</p> <p>Narenpitak, P., Bretherton, C. S. & Khairoutdinov, M. F. Cloud and circulation<br> feedbacks in a near-global aquaplanet cloud-resolving model: Cloud Feedbacks in<br> a Near-Global CRM. J. Adv. Model. Earth Syst. 9, 1069–1090 (2017).</p> <p>Bretherton, C. S. & Khairoutdinov, M. F. Convective self-aggregation feedbacks<br> in near-global cloud-resolving simulations of an aquaplanet. Journal of<br> Advances in Modeling Earth Systems 7, 1765–1787 (2015).</p> <p><br> The data are generated using the System for Atmospheric Modeling, and then<br> selected fields are averaged onto (160 km)^2 grid boxes for machine learning<br> purposes.</p>
EMAC-L90MA-SD output used in "Stratospheric Injection of Brominated Very Short-Lived Substances: Aircraft Observations in the Western Pacific and Representation in Global Models"
<p>Output of halocarbons, inorganic bromine, and tropopause pressure from EMAC-L90MA-SD used in:</p> <p>Wales et al., Stratospheric Injection of Brominated Very Short-Lived Substances: Aircraft Observations in the Western Pacific and Representation in Global Models." <em>Journal of Geophysical Research: Atmospheres,</em> (2018).</p> <p>The EMAC-L90MA-SD simulation uses ERA-Interim meteorology and was prepared according to: </p> <p>Jöckel, P., Tost, H., Pozzer, A., Kunze, M., Kirner, O., Brenninkmeijer, C. A. M., Brinkop, S., Cai, D. S., Dyroff, C., Eckstein, J., Frank, F., Garny, H., Gottschaldt, K.-D., Graf, P., Grewe, V., Kerkweg, A., Kern, B., Matthes, S., Mertens, M., Meul, S., Neumaier, M., Nützel, M., Oberländer-Hayn, S., Ruhnke, R., Runde, T., Sander, R., Scharffe, D., & Zahn, A.: Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51, <em>Geoscientific Model Development</em>, 9, 1153–1200, doi: 10.5194/gmd-9-1153-2016, URL <a href="http://www.geosci-model-dev.net/9/1153/2016/">http://www.geosci-model-dev.net/9/1153/2016/</a> (2016)</p> <p>For further details, please contact Patrick Joeckel (Patrick.Joeckel@dlr.de) and Phoebe Graf (Phoebe.Graf@dlr.de)</p>
Optimization and improvement of output performance in G-band extended interaction klystron
<p>We optimized and improved the output performance of G-band Extended Interaction Klystron, including the optimized period, influence of gap voltage on the beam modulation, and optimized gap length as well as non-uniform gaps are analyzed, and these figures are the simulation results with .tif format.</p>
Historical climate model output of ECHAM5-wiso from 1871-2011 at T106 resolution
<p>Historical climate model simulation of the isotope-enabled ECHAM5-wiso model from the years 1871 to 2011 at T106 (1 degree) resolution. The model code was provided by Martin Werner of AWI. The simulations were designed and run by Nathan Steiger on the Yellowstone supercomputer. The boundary conditions were interpolated HadISST fields. The simulations also included updated fractionation factors (an option within the ECHAM5-wiso Fortran code). All variables here are at monthly resolution in netcdf format. Please <a href="http://www.ldeo.columbia.edu/~nsteiger/contact.html">contact</a> Nathan Steiger if you have any questions about the simulation. In addition to the data citation, please also cite the following reference for where the data were first published: Steiger, N.J., E.J. Steig, S.G. Dee, G.H. Roe, and G.J. Hakim, (2017): <em>Climate reconstruction using data assimilation of water-isotope ratios from ice cores.</em> Journal of Geophysical Research: Atmospheres, doi:10.1002/2016JD026011.</p> <p>Standard variables include: ECHAM5 T106 orography, 2 m temperature, surface pressure, mean sea level pressure, vertically integrated water vapor, total precipitation, evaporation, soil moisture, relative humidity, specific humidity, atmospheric stream function at 200 hPa, geopotential height at 500 hPa, windspeed at 10 m, and u-velocity wind at 200 hPa. Moisture variables are given at the surface (lowest atmospheric level).</p> <p>Isotope variables include: d18O and dD of total precipitation, d18O and dD of evaporation, d18O and dD of snow fall, d18O and dD of seasonal snow cover, d18O and dD of snow on glaciers, d18O and dD of soil moisture, and specific humidity of water isotopes.</p>
Transcranial direct current stimulation (tDCS) over the left prefrontal cortex does not affect time-trial self-paced cycling performance: Evidence from oscillatory brain activity and power output.
<p>This research will shed new light into the bidirectional relationship between acute aerobic exercise, brain and cognition. This is based on the particular role of executive (cognitive) function during exercise. The rationale of our study is that stimulation of the prefrontal cortex that has been repeatedly associated with executive function, would facilitate or impair self-paced aerobic exercise. This would also affect cognitive performance immediately after exercise. We will use a modified flanker’s task as a form of assessing executive function (see below for further details). The flanker’s task implies two different stimuli, one congruent and one incongruent. Relative to “congruent” stimuli, these “incongruent” stimuli are usually accompanied by increased response times (RTs) and decreased accuracy. To stimulate the prefrontal cortex, we use transcranial direct-current stimulation (tDCS). tDCS is able to induce cortical changes by hyperpolarizing (anodal) or depolarizing (cathodal) neuron’s resting membrane potential.<br> Therefore, the hypotheses of this research are:<br> 1) Anodal stimulation (relative to sham and cathodal stimulation) will improve self-paced aerobic exercise and, consequently it will also improve subsequent cognitive performance.<br> 2) Cathodal stimulation (relative to sham and anodal stimulation) will impair self-paced aerobic exercise and subsequent cognitive performance.<br> </p>
Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic"
<p>Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic", accepted for publication in Atmospheric Chemistry and Physics, 2018, same authors. The intercomparison includes output from three large-eddy simulation models (UCLALES-SALSA, MIMICA, and COSMO-LES) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM) for a case study of high-Arctic tenuous cloud based on observations from the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign. See publication for details. The discussion preprint for peer review can be found at https://doi.org/10.5194/acp-2017-1128.</p>
raw data output from PLL-TRNG
<p>This dataset contains raw data output from PLL-TRNG (Phase Locked Loop - True Random Number Generator).</p> <p>This data has been used for Deliverable D2.4 "Robustness tests on TRNGs and PUFs".</p> <p>Data is organized in folders according to the tested board, temperature and core voltage.</p> <p>In each folder, there are these types of files:<br> - *.r32: 32-bit words of raw data from the TRNG.<br> - *.1BitPerByte.r08: 8-bit words of raw data in the format suitable for statistical testing (1 random bit per byte)<br> - *.1millionBits.1BitPerByte.r08: 1 million of 8-bit words of raw data in the format suitable for statistical testing (1 random bit per byte)<br> - *.r31: results of AIS31 procedure B statistical tests<br> - *.r9i : results of NIST 800-90B IID tests<br> - log_.txt: data acquisition log</p> <p> </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.