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zenodo52/100

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 →

zenodo52/100

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 →

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 →

zenodo44/100

DISTRIBUTION STATISTICS AND ANALYSIS APPLIED TO EPIDEMOLOGY

The Image shows an hypothesis of Sigmoid Distribution with Boltzmann and Gaussiun Probability individually . The Sigmoid Probability is combined with Gaussiun Probability to get a new mathematical function called Gaussiun-Sigmoid function . accordingly the Boltzmann - Sigmoid function .The distribution is applied to epidemological parameters like mortality rate ( MR ), Case fatality rate ( CFR ) , Infection fatality rate ( IFR ) .REF :https://encyclopedia.pub/2560

opencc-by-4.0Nov 2020View details →

zenodo40/100

Fig. 2 Parasite abundance and distribution statistics. A in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)

Fig. 2 Parasite abundance and distribution statistics. A total of 58 Aphrodita longipalpa were dissected and examined for parasite presence. The upper horizontal bars graphically depict the proportional parasitism rates and the corresponding distribution among male, female, and juvenile parasites, along with various cohabitation configurations. The box plots show the relationship between host size and the occurrence of parasites, presented collectively and then individually for female, male, and juvenile parasites

opencc-by-4.0Jan 2024View details →

zenodo40/100

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 4. Statistical Metrics distribution of model on RIO as response of Terasort application

Figure 4 shows the statistical metrics distribution of regression model on read rate as the response of Terasort application. The filled triangle point-up indicates the minimum stable sampling time for statistical metrics. The top-half of figure 4 shows the residual standard error (RSE) distribution as training data size increase. The remaining half is for the distribution of R2.

opencc-by-4.0Apr 2018View details →

zenodo40/100

Codes in R for spatial statistics analysis, ecological response models and spatial distribution models

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022). It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running: In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.).  Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017). Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009). After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable. Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021). Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included. On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017). Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission). <table align="center"> <tbody> <tr> <td>                                      </td> <td> Validation set </td> </tr> <tr> <td> Model </td> <td> True </td> <td> False </td> </tr> <tr> <td> Presence </td> <td> A </td> <td> B </td> </tr> <tr> <td> Background </td> <td> C </td> <td> D </td> </tr> </tbody> </table> We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006). The last code (i.e., <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model. Regarding the model evaluation and estimation, we selected the following estimators: 1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020). 2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

opencc-by-4.0Feb 2023View details →

dryad36/100

Marine ecoregions and subecoregions within Indo-West Australian waters: A statistical approach based on species distributions

Aim: The Marine Ecoregions of the World (MEOW) system delineates the oceans into 232 ecoregions. Here, we aimed to evaluate the suitability of this system to represent species distributions within Indo-West Australian waters, explore alternative ecoregions and new subecoregions, and investigate environmental variables that are correlated with species distributions within those waters. Location: Indo-West Australia Taxa: Vertebrates, invertebrates, marine plants Methods: We downloaded occurrence data for 14,513 marine species from the Ocean Biogeographic Information System. We analysed differences in species composition among nine ecoregions within Indo-West Australian waters using pairwise permutational multivariate analysis of variance to evaluate how well the MEOW system represents species distributions within those waters. We delineated subecoregions within each distinct ecoregion using hierarchical cluster analysis with the unweighted pair-group method using arithmetic averages. We analysed relationships between environmental variables and species composition using distance-based linear models. Results: Species composition was significantly different among ecoregions, except for three adjacent regions, which were combined into a single large ecoregion. Hence, seven distinct ecoregions were further analysed. Our study identified 13 subecoregions within these ecoregions that each separate into 'inshore' and 'offshore' zones. Depth explained the most variation in species composition of the combined taxa and sea surface temperature was the most important parameter in explaining the variability in most taxa. Main conclusion: The MEOW system did not represent well the distribution of marine species within Indo-West Australian waters. Alternatively, we show that those waters encompass seven distinct ecoregions with 13 subecoregions. The main environmental drivers of species distributions could be depth and sea surface temperature. The proposed ecoregions and subecoregions allow us to improve the biogeographic hypotheses for understanding the evolution of marine species and identify representative marine habitats and species composition for the setting of Marine Protected Area networks within Indo-West Australian waters.

opencc-zeroApr 2022View details →

dryad36/100

Seeing shapes in clouds: the fallacy of deriving ecological hypotheses from statistical distributions

The explanations behind observations of global patterning in species diversity pre-date the field of ecology itself. The generation of new species-area theories, in particular, far outpaces their falsification, resulting in a centuries-old accumulation in species diversity theories. We use historical assessment and new data analysis to argue that one of the earliest recognized and most consistent patterns in species diversity is not strictly an ecological phenomenon and, when ecological mechanism is invoked, the range of potential mechanisms is too numerous for tractable hypothesis falsification. We provide a historical parallel in that the normal distribution once was treated as a pattern assuming a biological mechanism rather than a statistical distribution that can be generated by biological and non-biological forces. Similarly, power law distributions are ubiquitous in aggregated data, such as the species-area relationship. That nearly identical broad-scale aggregation patterns are observed for both ecological and non-ecological data as a function of area suggest that these broad-scale patterns reflect a statistical distribution that, in itself, cannot be used to discern between or among ecological and non-ecological mechanisms. We argue that by seeking processes in such a ubiquitous pattern, ecologists may read ecological mechanism into statistical patterns, and we suggest that falsifying broad-scale diversity distribution hypotheses should be a greater priority than generating or parameterizing new ones.

opencc-zeroAug 2022View details →

dryad36/100

Marine ecoregions and subecoregions within Indo-West Australian waters: A statistical approach based on species distributions

Open the record for dataset details and reuse information.

publicApr 2022View details →

dryad36/100

Seeing shapes in clouds: the fallacy of deriving ecological hypotheses from statistical distributions

Open the record for dataset details and reuse information.

publicAug 2022View details →

zenodo32/100

JGR-Statistical Characteristics of Raindrop Size Distributions and Parameters in Central China during the Meiyu Seasons

The dataset for the JGR article "Statistical Characteristics of Raindrop Size Distributions and Parameters in Central China during the Meiyu Seasons"

opencc-by-4.0Apr 2020View details →

zenodo32/100

Video S1: Evolution of CO2 spatial statistical distribution.

Spatio-temporal evolution of CO2 levels in an exam at room 4.1 at the ETSE, in University of Valencia

opencc-by-4.0Jan 2021View details →

zenodo32/100

A strong statistical link between aerosol indirect effects and the self-similarity of rainfall distributions

Supporting data for "A strong statistical link between aerosol indirect effects and the self-similarity of rainfall distributions".  Atmos. Chem. Phys., 22, 1–17, 2022. https://doi.org/10.5194/acp-22-1-2022.

opencc-by-4.0Mar 2022View details →

zenodo32/100

Complete dataset used in the study "A statistical approach to coronavirus classification based on nucleotide distributions"

This is a complete dataset of parameters used in coronavirus classification based on distributions of nucleotide sequences as described in the study "A statistical approach to coronavirus classification based on nucleotide distributions"

opencc-by-4.0May 2024View details →

dryad32/100

Data associated with ecological niche models and post-ENM statistical analyses for Trillium species distributions

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publicMay 2021View details →

dryad32/100

Data from: Insights into the origin and distribution of biodiversity in the Brazilian Atlantic forest hotspot: a statistical phylogeographic study using a low-dispersal organism

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publicDec 2013View details →

zenodo28/100

figure data for Statistical Properties and Distributions of Dayside Whistler-mode Waves Under Various Solar Wind Conditions

Figure dataset saved in csv files with the same binned format as the paper described.

opencc-by-4.0Aug 2024View details →

zenodo24/100

Descriptive Statistics and Town level Geospatial Distribution of Archaeological Settlements of Turkey in Iron Age (1200 –330 BCE)

Context This dataset is a byproduct of my phd thesis. It combines the Archaeological Settlements of Turkey (TAY) Project data with geo spatial data obtained from openstreetmaps. Content For each archaeological settlement, the data contains: <ul> <li>active dates:</li> <li>geo spatial data which points to the town containing the settlement.</li> <li>information with respect to site type and its research status/methodology. These are all contained in the file <code>taydata.json</code>.</li> </ul> The associated <a href="https://www.kaggle.com/dkaane/data-extraction-protocol-for-iaasot">notebook</a> to this dataset gives how each file is produced. We give several important statistics with respect to regions, and cities of Turkey for the Iron Age. If you want to visualize the data on a map. You can use the <code>1200___330_bce_sites_of_turkey.umap</code> file. Just download the file and visualize it on <a href="https://umap.openstreetmap.fr/en/">umap</a> or on <a href="https://www.kaggle.com/dkaane/framacarte.org/">framacarte</a> Acknowledgements Without the immense effort of TAY Project and its researchers, this dataset would not be possible.

opencc-by-nc-sa-4.0Apr 2021View details →

nasa20/100

ACE Solar Wind Ion Composition Spectrometer (SWICS) Solar Wind Plasma Elemental Charge State Distributions with Statistical Uncertainties and Data Quality Flags, Level 2 (L2), 2 h Data

This ACE SWICS/SWIMS Data Set contains actual Charge State, Q, Distributions of Carbon, Oxygen, Neon, Magnesium, Silicon and Iron as opposed to the averaged Charge States that have been available on CDAWeb for some time. The Solar Wind Ion Composition Spectrometer (SWICS), prior to August 23, 2011 and denoted as SWICS Version 1.1, determines uniquely the Chemical and Ionic Charge State Composition of the Solar Wind, the Temperatures and Mean Speeds of major Solar Wind Ions at all Speeds above 300 km/s for Protons and 170 km/s for Fe+16, and resolves Protons and Helium Isotopes of Solar and Interstellar Sources. SWICS 1.1 measures the Distribution Functions of Interstellar Cloud Pick-Up Ions and Interplanetary Dust Cloud Pick-Up Ions up to Energies of 100 keV/e. The ACE SWICS 1.1 Data Products represent a new Release of the Data with significantly improved Time Series Measurements for the Elemental Abundance, Charge State Composition, and Kinetic Properties of Heavy Ions in the Solar Wind. It is a major new Update produced with completely redesigned Analysis Methods to account more rigorously for Instrumental and Statistical Effects (Shearer et al., 2014). Rare Elements are now identified more reliably and Estimates of Statistical Error are provided. Release Notes are available that describe the Data, the Methods used to determine the Data Values, and Issues concerning Data Quality and Measurement Uncertainty. The Quality of ACE Level 2 Data is such that it is suitable for serious Scientific Study. However, to avoid Confusion and Misunderstanding, it is recommended that Users consult with the appropriate ACE Team Members before publishing Work derived from the Data. The ACE Team has worked hard to ensure that the Level 2 Data are free from Errors, but the Team cannot accept Responsibility for Erroneous Data, or for Misunderstandings about how the Data may be used. This is especially true if the appropriate ACE Team Members are not consulted before Publication. At the very least, Preprints should be forwarded to the ACE Team before Publication. For more Information about the SWICS Instrument, visit the SWICS Home Page at http://solar-heliospheric.engin.umich.edu/ace.

restrictednotspecifiedApr 2025View 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