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zenodo52/100

Dataset for "Impact of the flow-field distribution channel cross-section geometry on PEM fuel cell performance: stamped vs. milled channel"

Experimental data comprises raw data from load curve characterisation of a PEM fuel cell used for the validation of the mathematical model. Model data comprise of space-dependent values of hydrogen and oxygen concentration, local current densities, gas pressures and gas velocities in the modelled cell. These data were used for the investigation of the effect of different geometric parameters of flow-field channels on the performance of a PEM fuel cell.

opencc-by-4.0May 2024View details →

zenodo52/100

Indicative distribution map for Ecosystem Functional Group M1.10 Rhodolith/Maërl beds

This archive contains indicative distribution maps and profiles for M1.10 Rhodolith/Maërl beds, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.1). Please refer to Keith et al. (2020) and Keith et al. (2022) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

opencc-by-4.0Oct 2023View details →

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ICARIA: spatially distributed climate projections from statistical downscaling

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose of being freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset, being obtained from a high-quality and high-density set of weather observations that are then interpolated to the case studies of interest in a 100x100m resolution grid, which is the main outcome offered in this publication. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed:          -----                                      The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). <div> <div> <table> <tbody> <tr> <td> CMIP6 MODELS </td> <td> Resolution </td> <td> Responsible Centre </td> <td> References </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> 1,875º x 1,250º </td> <td> Australian Community Climate and Earth System Simulator (ACCESS), Australia </td> <td> Bi, D. et al (2020) </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> 1,125º x 1,121º </td> <td> Beijing Climate Center (BCC), China Meteorological Administration, China. </td> <td> Wu T. et al. (2019) </td> </tr> <tr> <td> CanESM5 </td> <td> 2,812º x 2,790º </td> <td> Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </td> <td> Swart, N.C. et al. (2019) </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> 1,000º x 1,000º </td> <td> Centro Mediterraneo sui Cambiamenti Climatici (CMCC). </td> <td> Cherchi et al, 2018 </td> </tr> <tr> <td> CNRM-ESM2-1 </td> <td> 1,406º x 1,401º </td> <td> CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </td> <td> Seferian, R. (2019) </td> </tr> <tr> <td> EC-EARTH3 </td> <td> 0,703º x 0,702º </td> <td> EC-EARTH Consortium </td> <td> EC-Earth Consortium. (2019) </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> 0,938º x 0,935º </td> <td> Max-Planck Institute for Meteorology (MPI-M), Germany. </td> <td> Müller et al., (2018) </td> </tr> <tr> <td> MRI-ESM2-0 </td> <td> 1,125º x 1,121º </td> <td> Meteorological Research Institute (MRI), Japan. </td> <td> Yukimoto, S. et al. (2019) </td> </tr> <tr> <td> NorESM2-MM </td> <td> 1,250º x 0,942º </td> <td> Norwegian Climate Centre (NCC), Norway. </td> <td> Bentsen, M. et al. (2019) </td> </tr> <tr> <td> UKESM1-0-LL </td> <td> 1,875º x 1,250º </td> <td> UK Met Office, Hadley Centre, United Kingdom </td> <td> Good, P. et al. (2019) </td> </tr> </tbody> </table> The climate projections have been developed over each of the observational locations that were retrieved to run the statistical downscaling. The results from these projections have been spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model considering diverse adjustments and topographic corrections. The results presented here are the median of the 10 models used, obtained for each of the 4 SSPs and each of the time periods considered in ICARIA until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Thermal indicators </td> </tr> <tr> <td> TX90 / TX10 </td> <td> Warm/cold days </td> <td> Zhang et al. (2011) </td> <td> TX </td> <td> nd </td> <td> 90 / 10% </td> </tr> <tr> <td> HD </td> <td> Heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> EHD </td> <td> Extreme heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 35 °C </td> </tr> <tr> <td> TR </td> <td> Tropical nights </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> > 20 °C </td> </tr> <tr> <td> EQ </td> <td> Equatorial nights </td> <td> AEMet 2020, ICARIA </td> <td> TN </td> <td> nd </td> <td> > 25 °C </td> </tr> <tr> <td> IN </td> <td> Infernal nights </td> <td> ICARIA </td> <td> TN </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> FD </td> <td> Frost days </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> < 0 °C </td> </tr> <tr> <td> Max consec </td> <td> Max spell length for above thermal indicators </td> <td> ICARIA </td> <td> - </td> <td> nd </td> <td> - </td> </tr> <tr> <td> Nº events </td> <td> Number of above thermal indicators events </td> <td> ICARIA </td> <td> - </td> <td> ne </td> <td> > 3 days </td> </tr> <tr> <td> TXm </td> <td> Mean maximum temperatures </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TNm </td> <td> Mean minimum temperatures </td> <td> ICARIA </td> <td> TN </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TM </td> <td> Mean temperatures </td> <td> ICARIA </td> <td> TA </td> <td> °C </td> <td> - </td> </tr> <tr> <td> HWle </td> <td> Heatwave length </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWim/HWix </td> <td> Mean and maximum heatwave intensity </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWf </td> <td> Heatwave frequency </td> <td> ICARIA </td> <td> TX </td> <td> ne </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWd </td> <td> Heatwave days </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HI - P90 </td> <td> Heat Index (percentile 90) </td> <td> NWS (1994) </td> <td> TX, RH </td> <td> °C </td> <td> TX>27 °C, HR> 40% </td> </tr> <tr> <td> UTCI </td> <td> Universal Thermal Climate Index </td> <td> Bröde et al. (2012) </td> <td> TA RH, W </td> <td> - </td> <td> - </td> </tr> <tr> <td> UHI </td> <td> Isla de calor (BCN) anual y estacional </td> <td> AMB, Metrobs 2015 </td> <td> T </td> <td> °C </td> <td> TM1-TM2 > 0 °C </td> </tr> <tr> <td> Precipitation indicators </td> </tr> <tr> <td> R20 </td> <td> Number of heavy precipitation days </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> >20 mm </td> </tr> <tr> <td> R50, R100 </td> <td> Days with extreme heavy rain </td> <td> AMB et al. (2017) </td> <td> P </td> <td> nd </td> <td> >50mm >100mm </td> </tr> <tr> <td> Ra </td> <td> Yearly and seasonal rainfall relative change </td> <td> ICARIA </td> <td> P </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> IDF - CCF </td> <td> IDF Curves - Climate Change Factor </td> <td> Arnbjerg-Nielsen (2012) </td> <td> P </td> <td> - </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Forest fire indicators </td> </tr> <tr> <td> Mean FWI </td> <td> Mean Canadian FWI in fire season </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> . </td> <td> June- September </td> </tr> <tr> <td> Very High FWI </td> <td> Very High Canadian FWI </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> nd </td> <td> FWI > 38 </td> </tr> </tbody> </table> </div>   Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Drought indicators </td> </tr> <tr> <td> CDDx </td> <td> Maximum dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> CDDm </td> <td> Mean dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> SPI </td> <td> SPI  of 1, 3, 6, 12, 24 & 36 months </td> <td> McKee et al. (1993)  </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> SPEI </td> <td> SPEI  of 1, 3, 6, 12, 24 & 36 months </td> <td> Vicente-Serrano et al.  (2010) </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Oceanic indicators </td> </tr> <tr> <td> SS </td> <td> Storm surge </td> <td> Bryant et al. (2016) </td> <td> MT </td> <td> cm </td> <td> - </td> </tr> <tr> <td> OW </td> <td> Significant/maximum wave height </td> <td> ICARIA </td> <td> WH </td> <td> m </td> <td> - </td> </tr> <tr> <td> Wind indicators </td> </tr> <tr> <td> EWG </td> <td> Extreme wind gusts </td> <td> ICARIA </td> <td> W </td> <td> km/h </td> <td> - </td> </tr> </tbody> </table> </div>   </div> </div>

opencc-by-4.0Jul 2024View details →

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Simulated metagenomes with quality and abundance distributions derived from real samples

Species abundances and quality values were derived from the following list of samples: <pre><code>SAMEA2466896 SAMEA2466916 SAMEA2466952 SAMEA2466953 SAMEA2466965 SAMEA2466996 SAMEA2467015 SAMEA2467039 SAMEA2621010 SAMEA2621033 SAMEA2621107 SAMEA2621155 SAMEA2621229 SAMEA2621247 SAMEA2621300 SAMEA2622357 </code></pre> Reference abundances (.abund files) were generated using <a href="https://github.com/motu-tool/mOTUs_v2">mOTUs profiler</a>. Metagenomes were simulated with <a href="https://sourceforge.net/projects/cmessi/">cMESSi</a> using <a href="http://progenomes.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> for species and the aforementioned abundances. In cases where a ref_mOTU_v2 corresponded to more than one genome, the abundance of said ref_mOTU was distributed equally over all genomes. GFF location files were produced using location information generated by cMESSi. Two variants of truth values were obtained by intersecting coordinates of simulated reads with coordinates of <a href="http://eggnogdb.embl.de">eggNOG</a> orthologous groups (OG at NOG level) as predicted by <a href="https://github.com/jhcepas/eggnog-mapper">eggNOG-mapper</a>. <ol> <li>.cog-simulated files contain the NOG distribution that was effectively simulated, i.e. a count of the number of reads overlapping with genes annotated with each NOG. A read overlapping multiple genes is considered for each gene. If a gene possesses multiple NOG annotations, each annotation gets assigned the total number of overlapping reads. Longer genes will (in expectation) generate more reads, all else being equal.</li> <li>.cog-distribution file contains the expected distribution for every NOG on all samples. The number of genes annotated with each NOG is multiplied by the abundance of the corresponding species. Length of the gene is not taken into account.</li> </ol> If you use this dataset, please cite: <a href="https://www.biorxiv.org/node/111718.full">NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language</a>

opencc-by-4.0Jan 2019View details →

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Sub-micron aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured sub-micrometer aerosol particles with two scanning mobility particle spectrometers (SMPSs) between 11 and 400 nm (file name ACESPACE_submicron_aerosol_particle_size_distribution.csv) in 100 bins, and 11 and 181 nm in 77 bins - so no data entry in the remaining 23 bins - (ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). Particles in this size range are important for cloud formation because a sub-set of them can act as cloud condensation nuclei (CCN). The time series of the size distribution shows that the particle population over the Southern Ocean can be quite variable featuring three dominant modes: a new particle formation mode (11 – 30 nm); an Aitken mode (20 – 70 nm); and an accumulation mode (> 70 nm). Often a concentration minimum between the Aitken and accumulation mode can be observed. It is known as Hoppel minimum (Hoppel and Frick, 1990; 10.1016/0960-1686(90)90020-N). Typically, particles larger than this minimum act as CCN. The variability of the particle size spectrum is a result of particle sources and atmospheric processes. Sea spray generation adds larger particles likely with a peak in the mode around 200 nm. Trace gas emissions from microbial communities in the ocean, such as dimethylsulfide (DMS) will be oxidized to either sulphuric acid or methanesulfonic acid in the atmosphere which condense onto pre-existing particles, hence growing those. Sulphuric acid can also form new particles (new particle formation mode). Rain and snow will remove particles larger than the Hoppel minimum. The data set can be used to explore the variability of the particle size distribution in three different oceans around Antarctica (Indian, Pacific, Atlantic Oceans) and from Cape Town to Europe in relation to weather patterns, air mass trajectories, microbial activity etc. It is best used in combination with CCN data to explore the importance of particles for cloud formation. This data set cannot be used to unambiguously determine sources of particles over the southern ocean or to trace anthropogenic impact in the region. The data have been cleaned from the influence of the exhaust of the research vessel. We give five-minute average data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/64. To derive the total particle number concentration between 11 and 400 nm one has to integrate over the diameter range taking into account the normalization by dlog(dp). Temporal coverage is from December 20, 2016 to April 10, 2017. The file “ACESPACE_submicron_aerosol_particle_size_distribution.csv” covers the entire time period except between 9 and 14 January 2017 due to instrument issues. The file “ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv” contains data for the period between 9 and 14 January 2017 and can be used to fill the above gap. The second data file stems from another SMPS with a smaller differential mobility analyser, hence the smaller diameter coverage. Dataset contents The data set contains two files with the size distribution of sub-micrometer aerosol particles. The rows are indexed by the time stamp, which is the end of the 5-minutes averaging interval. The columns are the normalized concentrations of particles in the respective size bin. See the data abstract for details. <ul> <li>ACESPACE_submicron_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>ACESPACE_submicron_aerosol_particle_size_distribution_nano.csv, data file, comma-separated values</li> <li>ACESPACE_particle_diameter_bins.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values in a complete row denote missing values because of e.g., calibration periods, ship exhaust contamination, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This sub-micron aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jun 2019View details →

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Coarse mode aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

Dataset abstract The authors would highly appreciate to be contacted if the data is used for any purpose. We measured coarse mode aerosol particle size distributions with an aerodynamic particle sizer (APS, model TSI 3321) at a time resolution of five minutes during the Antarctic Circumnavigation Expedition (ACE). The diameter range is 0.7 to 19 µm. Particles in this size range are indicative of primary sea spray aerosol, biological particles and potentially long-range transported mineral dust. These particles are also important for cloud formation as they act as cloud condensation nuclei or ice nucleating particles, the latter especially in the case of biological particles and mineral dust. Typically the instrument reports data starting from particles with a diameter greater than 500 nm, however, particle number concentrations in the channels below 723 nm were overestimated, which is a common artefact with this instrument. The data have been cleaned from the influence of the exhaust of the research vessel. Temporal coverage is from December 20, 2016 to April 10, 2017. We give five-minute averaged data as dN/dlog(dp), where dN is the particle number concentration per measured size bin normalized over the logarithm of the bin width. The bin width is defined as the distance between two diameters. They are spaced equally in log-space with dlog(dp) = log(d_n+1/d_n) = 1/32. To derive the total particle number concentration one has to integrate over the diameter range taking into account the normalization by dlog(dp). Dataset contents <ul> <li>ACESPACE_coarse_mode_aerosol_particle_size_distribution.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.md, metadata, text</li> </ul> NaN values in a complete row denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure. NaN values which appear individually or only in small groups reflect that data were below detection limit. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. Dataset license This coarse mode aerosol particle size distribution dataset collected during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Jun 2019View details →

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Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).

This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included: <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ53Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ53Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>

opencc-by-4.0Sep 2021View details →

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Dataset for paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis"

Dataset for the paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis" published in Icarus.

opencc-by-4.0Feb 2023View details →

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Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification

Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the <a href="https://www.fao.org/soils-portal/data-hub/soil-classification/world-reference-base/en/">WRB 2022</a> (World Reference Base the international standard for soil classification) classification system. The training data comes from the following 3 main sources: <ol> <li>WOSIS points available via: <a href="https://www.isric.org/explore/wosis">https://www.isric.org/explore/wosis</a>;</li> <li>HWSD v2 (random draw of cca 20,000 points): <a href="https://iiasa.ac.at/models-tools-data/hwsd">https://iiasa.ac.at/models-tools-data/hwsd</a>;</li> <li>Other national datasets / data from publications and projects.</li> </ol> Predictions are based on using Rando Forest algorithm as implemented in the <a href="https://www.randomforestsrc.org/">randomForestSRC package</a> with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar). All TIF files are provided as <a href="https://www.cogeo.org/">COGs</a>, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending. Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk. Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact please report via <a href="https://github.com/OpenGeoHub/SoilTypeMapping">the Github repository</a>. Help us improve this dataset by contributing training points.

opencc-by-4.0Apr 2023View details →

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Raw Particle Number Size-Distribution Data of twin-DMPS equipped with two CPCs for nanoparticle detection for SMEAR II station, Hyytiälä, Finland, Spring 2017

Raw size-Distribution data from twin-DMPS system (Aalto et al., 2001), where the nano-DMA (measuring up to 40 nm, short Hauke type DMA) is quipped with two detectors: a TSI 3776 and a modified Airmodus A20 (Kangasluoma et al., 2015) Data acquired during in March-May 2017 at the SMEAR II station in Hyytiälä, Finland. Data associated with the publication Stolzenburg, Laurila et al. (2023), Atmos. Meas. Techn., "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system" Files DMYYDDMM_A20.Dat contain the raw DMPS data, with YYMMDD indicating the day of the measurement. Data are provided alternating between data acquired with the nano-DMA and with the long-DMA, on a scan by scan basis. First line of each scan cycle (for both DMAs) always indicates the start and end times of the voltage scan. Second line gives the parameters related to the DMPS as given below: (sheath flow in [l per min], aerosol flow in [l per min], DMA inner electrode diameter in [m], DMA outer electrode diameter in [m], DMA classification length in [m], other parameters) Following lines give (for long-DMA): set voltage at DMA [in V], concentration measured by TSI3772 in [per cm3] (for nano_DMA): et voltage at DMA [in V], concentration measured by TSI 3776 in [per cm3], concentration measured by mod. Airmodus A20 in [per cm3] File dmps_data_format_specifier.text gives a conversion from voltage to diameter and indicates the measurement time at each voltage during the stepping of the DMPS. Needs to be used to convert measured concentrations in counts per set-interval. Files GR_J_overview.xlsx gives size-distribution derived quantities during that campaign. Header defines Date, Growth Rate and Formation Rate measured at different sizes [in nm] and by the two different CPCs connected to the nano-DMA. Growth rates in [nm per h], formation rate in [per cm3 per s]. Other data related to the campaign can be obtained from the corresponding author upon reasonable request. juha.kangasluoma@helsinki.fi References: Stolzenburg, Laurila et al. "Improved counting statistics of an ultrafine differential mobility particle size spectrometer system", Atmos. Meas. Techn., in press, 2023 Aalto et al., "Physical characterization of aerosol particles during nucleation events", Tellus B, vol. 53, pp. 344-358, 2001 Kangasluoma et al., "Sub-3 nm Particle Detection with Commercial TSI 3772 and Airmodus A20 Fine Condensation Particle Counters", Aerosol Sci. Techn., vol. 49, pp. 674-681, 2015

opencc-by-4.0May 2023View details →

edi52/100

Distribution and habitat use of juvenile Feather River salmonids: 25 years and ongoing of snorkel surveys

Since 1999, the California Department of Water Resources (DWR) has conducted annual snorkel surveys to monitor juvenile salmon on the Feather River. The objective of this data collection effort is to determine the relative abundance and distribution of rearing juvenile Chinook salmon and steelhead. A secondary objective is to collect baseline data for future monitoring programs associated with habitat restoration projects. Crews survey units within 20 sampling sections on the high flow (HFC) and low flow channel (LFC) between January and September and collect information on species, fish size, substrate, cover, and habitat type. This dataset represents an extensive time series that could be used to identify habitat conditions where juvenile Chinook salmon and steelhead occur and how these conditions have changed over time. These data were published to support the Healthy Rivers and Landscapes Program.

openCC0Sep 2024View details →

edi52/100

Michigan Opossum Distribution from 1899 to 2008

This data set documents the expansion of the distribution of opossums in Michigan. Each row in the data file consists of a specimen record or observation and includes the year and location (latitude/longitude) of the record.

openCC (other)Aug 2024View details →

edi52/100

NEON distributed initial soil characterization dataset (DP1.10047.001) modified for statistical analysis of organic carbon and extractable metals in Hall and Thompson (2021)

We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal.

openCC (other)Sep 2021View details →

edi52/100

Wetland abundance and distribution changes in Sycamore Creek, Arizona, USA (2014-2019)

The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). This dataset was collected to understand: (1) the spatial pattern of wetland distribution and abundance; (2) the influence of multi-annual variability in hydrological regime on wetland distribution and abundance; and (3) the mechanism of the resilience of wetland to different disturbances in terms of hydrology (i.e., drying and flooding).

openCC0Nov 2020View details →

edi52/100

Biomass and abiotic variable data in the study of the ecosystem engeneering effect of oysters on Suaeda linearis distribution in Georgia salt marshes (2008-2009)

Oysters are ecosystem engineers in marine ecosystems, but the functions of oyster shell deposits in intertidal salt marshes are not well understood. The annual plant Suaeda linearis is associated with oyster shell deposits in Georgia salt marshes. We hypothesized that oyster shell deposits promoted the distribution of Suaeda linearis by engineering soil conditions unfavorable to dominant salt marsh plants of the region (the shrub Borrichia frutescens, the rush Juncus roemerianus and the grass Spartina alterniflora). We tested this hypothesis using common garden pot experiments and field transplant experiments. Suaeda linearis thrived in Borrichia frutescens stands in the absence of neighbors, but was suppressed by Borrichia frutescens in the with-neighbor treatment, suggesting that Suaeda linearis was excluded from Borrichia frutescens stands by interspecific competition. Suaeda linearis plants all died in Juncus roemerianus and Spartina alterniflora stands, indicating that Suaeda linearis is excluded from these habitats by physical stress (likely water-logging). In contrast, Borrichia frutescens, Juncus roemerianus and Spartina alterniflora all performed poorly in Suaeda linearis stands regardless of neighbor treatments, probably due to physical stresses such as low soil water content and low organic matter content. Thus, oyster shell deposits play an important ecosystem engineering role in influencing salt marsh plant communities by providing a unique niche for Suaeda linearis, which otherwise would be rare or absent in salt marshes in the southeastern US. Since the success of Suaeda linearis is linked to the success of oysters, efforts to protect and restore oyster reefs may also benefit salt marsh plant communities.

openCustomJan 2020View details →

edi52/100

Mollusc population size distribution monitoring: Fall 2013 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10

This data set is the Fall 2013 report of infaunal and epifaunal mollusk species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-1407. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.

openCustomJan 2020View details →

edi52/100

Mollusc population size distribution monitoring: Fall 2014 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10

This data set is the Fall 2014 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-1507. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.

openCC (other)Jan 2020View details →

edi52/100

Mollusc population size distribution monitoring: Fall 2015 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10

This data set is the Fall 2015 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-1607. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.

openCC (other)Jan 2020View details →

edi52/100

Effect of salt water intrusion on the distribution of invertebrates in a GA tidal freshwater marshes from the GCE Seawater Addition Long-Term Experiment (SALTEx) project.

To characterize the effect of persistent and episodic salt water intrusion on the distribution of common freshwater marsh invertebrates, we monitored the density of adult and juvenile fiddler crabs and snails. Prior to the start of salt water addition treatments, we collected data on the distribution of crabs and snails in all 30 experimental plots (6 replicates of 5 treatments: pressed salt water addition, pulsed salt water addition, fresh water addition, procedural control structure, and control no structure). In each experimental plot, we counted the number of adult and juvenile fiddler crab burrows and snails visible on the marshs surface in a 50cm x 75cm plot (juvenile fiddler crabs were counted in only half of this area) that was positioned in the Northeastern corner of each experimental plot. Initial data was collected in March 2014. A Bentho Torch was used to measure the concentrations of cyanobacteria, diatoms, and green algae on the marsh surface in 2015 and 2016.

openCC (other)May 2021View details →

edi52/100

Mollusc population size distribution monitoring: Fall 2018 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10

This data set is the Fall 2018 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-1907. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.

openCC (other)Oct 2020View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

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