Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,805
datasets available to search
ShareScore release 0.9.0
Dataset results
5,805 results for “Data model”
Raw data and MTEX script for the article : "Quantification of grain boundary mobilities in natural olivine by annealing experiments and full-field modelling"
<p>This dataset contains the EBSD (.ctf) files of the different sample areas analyzed in the article : "Quantification of grain boundary mobilities in natural olivine by annealing experiments and full-field modelling" by J. Furstoss, S. Demouchy, A. Tommasi, E. Gardès, F. Barou and N. Marino, which is expected to be published in Tectonophysics. This repository also contains the Analyze.m MTEX script which has been used for the post-processing of the EBSD files. This script has to be run in matlab and outputs different figures and .csv files containing the raw data used in the article. For any questions please contact jfurstoss@orange.fr </p>
Data for "Electric polarization near vortices in the extended Kitaev model"
<p>We formulate a Majorana mean-field theory for the extended <span><span><span><span>J</span><span>K</span><span>Γ</span></span></span></span> Kitaev model in a magnetic Zeeman field of arbitrary direction, and apply it for studying spatially inhomogeneous states harboring vortices. This mean-field theory is exact in the pure Kitaev limit and captures the essential physics throughout the Kitaev spin liquid phase. We determine the charge profile around vortices and the corresponding quadrupole tensor. The quadrupole-quadrupole interaction between distant vortices is shown to be either repulsive or attractive, depending on the parameters. We predict that electrically biased scanning probe tips enable the creation of vortices at preselected positions. Our results open new perspectives for the electric manipulation of Ising anyons in Kitaev spin liquids.</p> <p> </p>
Data from: A qualitative analysis of an Aβ-monomer model with inflammation processes for Alzheimer's disease
<p>We introduce and study a new model for the progression of Alzheimer's disease incorporating the interactions of Aβ-monomers, oligomers, microglial cells and interleukins with neurons through different mechanisms such as protein polymerization, inflammation processes and neural stress reactions. In order to understand the complete interactions between these elements, we study a spatially-homogeneous simplified model that allows to determine the effect of key parameters such as degradation rates in the asymptotic behavior of the system and the stability of equilibriums. We observe that inflammation appears to be a crucial factor in the initiation and progression of Alzheimer's disease through a phenomenon of hysteresis, which means that there exists a critical threshold of initial concentration of interleukins that determines if the disease persists or not in the long term. These results give perspectives on possible anti-inflammatory treatments that could be applied to mitigate the progression of Alzheimer's disease. We also present numerical simulations that allow to observe the effect of initial inflammation and concentration of monomers in our model.</p>
Supporting Data for "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of Timothy M. Merlis, Kai-Yuan Cheng, Ilai Guendelman, Lucas Harris, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, Gabriel A. Vecchi, and Stephan Fueglistaler (2024): "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change".</p>
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
Data to support plankton model construction
<p>This work provides a database of references supportive of modelling targeted at developing plankton digital twins.</p> <p>The database, containing over 120 entries, can be explored using multi-level sort functions. </p> <p>Additional contributions are welcome, and the published database will be re-published to make such additions available on open access.</p>
Integrated Model Data Repository
<p>ALLFED integrated food system model supplemental data associated with the paper "Food System Adaptation and Maintaining Trade Greatly Mitigate Global Famine in Abrupt Sunlight Reduction Scenarios"</p>
Reduced optical data for Bayesian model trial
<p>The dataset is a subset of a few selected optical indices from the column experiment. </p> <p>The dataset contains all the observations after the column reversal, with day00 indicating the day before the reversal where all the columns are samplead and day0 indicating the day of the reversal. It does not contain any of the control measurements before day00. </p> <p>From the absorbance and fluoresence measurements, we have calculated several indices among which bix (biological index), fi (fluoresence index), hix, (humification index), a254 (decadal absorption coefficient at 254 nm) ,E2_E3 (ratio of absorbance E2 to E3) , SR (slope ratio). All of the indices are calcuated using the StaRdom package from the raw data. </p> <p>Sample name is in the samplingDay_replicate_columnNo format. </p>
The data for Time-dependent Stellar Flare Models of Deep Atmospheric Heating
<p>This repository contains model output (within two .tar.gz files) and a pdf document (analysis_tools_mdwarfradyngrid-v1.0.pdf) that explains the contents and use. The models are described in Kowalski, A.F., Allred, J.C., & Carlsson, M. <em>Time-dependent Stellar Flare Models of Deep Atmospheric Heating, </em>published 2024 July 5 in <em>The Astrophysical Journal</em> Volume 969, Number 2 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad4148">10.3847/1538-4357/ad4148</a>).</p> <p>The Jupyter notebook demo, radyn_xtools_Demo.ipynb is included in the PyPI package installation that is described in analysis_tools_mdwarfradyngrid-1.0.pdf.</p>
The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome
<p>Data associated with my PhD thesis titled "The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome".</p>
Ambient noise data and velocity model from DEEPEN array in Hengill geothermal field, Iceland
<p>This repository contains the data and velocity model associated with the manuscript entitled "<em>Crustal characterization of the Hengill geothermal fields: Insights from isotropic and anisotropic seismic noise imaging using a 500-node array</em>" by Wu et al. (2024), to be published in <em>Journal of Geophysical Research: Solid Earth</em>. </p> <p>The dataset is the nine component cross-correlation functions (ZZ, ZN, ZE, NZ, NN, NE, EZ, EN, EE) after stacking over seismic array deployment time period (summer 2021) and after spatial averaging (bin stacking). The bin locations are provided in "bin_locations.txt".</p> <p>The derived VOIGT velocity and radial anisotropy model can be found in "Hengill_Voigt_Aniso_DEEPEN_4share.txt".</p> <p> </p>
Data associated to the manuscript "Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model</p> <p>Camille Bacon, Patrice Simon, Mathieu Salanne and Alessandra Serva</p> <p><em>ChemRxiv, </em>10.26434/chemrxiv-2024-9577m, 2024</p> <p>The folder <em>input_files</em> contains typical MetalWalls input files used to perform the simulations.</p> <p>The folder <em>raw_data</em> contains the processed data used to plot all the figures of the paper.</p>
Data for Spaulding-Astudillo and Mitchell (2024a), "A simple model for the emergence of relaxation-oscillator convection"
<p>Data for Spaulding-Astudillo and Mitchell (2024a), "A simple model for the emergence of relaxation-oscillator convection"</p> <p>The main directories have a common nomenclature: e.g., minimal_1p1S_FSC_335K_N7200, where 1p1S indicates a solar constant 1.1 times the present day (1360), FSC indicates that it is a full-sky radiation run, 335 K is the surface temperature (fixed), and N7200 refers to the relaxation timescale of 7200 seconds for CAPE in the quasi-equilibrium closure to the convection scheme. </p> <p>In every directory, each .nc file contains 5 years of model output. There are 5 types of .nc files, which correspond to different output streams of ECHAM6. The main output stream is ..._echam.nc, which has daily-averaged output.</p> <p>The tendencies for gross deposition (gdep_ls) and gross condensation (gcnd_ls; both with units of kg/m2/s) from the large-scale scheme are in ..._g1am.nc. This is the mean-value stream, which records daily-averaged values. </p> <p>The tendencies for gross condensation in convective updrafts are stored in the variable "ddf13" in ..._debugs.nc (units of kg/m2/s). This is another output stream, which records hourly values usually for debugging purposes. The total pressure as a function of height (units of Pa) is stored in the variable "ddf8". Also, the implied surface heat source/sink in the surface energy budget that keeps the surface temperature fixed in time is tracked in the variable "zdf8" (units of W/m2). </p> <p>The experimental range of surface temperatures is 290-360 K, which remain fixed as a function of time in the simulations through the use of an artificial heat sink. The simulations are from the ECHAM6 climate model, which we ran in single-column mode. </p>
Supplementary data to article "From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey"
<p>This repository provides regression models and annual prediction rasters for tree mortality in Germany. </p> <p><strong>Regression models:</strong><br>Logistic regression models which predict tree mortality for the species (beech = Fagus sylvatica, <br>oak = Quercus petraea and robur, pine = Pinus sylvestris, spruce = Picea abies) and species <br>groups (OB = other broadleaves, OC = other conifers) based on observations of dead trees in the<br>German Crown Condition Survey (Waldzustandserhebung) and a set of environmental predictor <br>variables. The predictors come from the domains of climate (clim), site conditions (site, i.e. <br>topography, soil, land cover, deposition), tree age (age) and some models contain pairwise <br>interaction terms between predictors (inter). All models were fit in R and are represented as <br>objects of the class glm and stored in files of the type rds.</p> <p><strong>Prediction rasters:</strong><br>Spatial predictions of the mortality rate across Germany for each tree species and species group <br>and for each year from 1998 to 2022. The rasters have a spatial resolution of 100 m. Missing values<br>mark areas where the species/group does not occur. The mortality values are given as integers <br>between 0 (no mortality) and 10000 (100% mortality). The coordinate reference system is Lambert <br>Azimuthal Equal Area (LAEA; EPSG:3035). The rasters are provided in the file format GeoTIFF (tif).</p> <p>A detailed description of the data sources and analyses can be found in the following article.</p> <p><strong>Citation:</strong><br><em>Knapp, N., Wellbrock, N., Bielefeldt, J., Dühnelt, P., Hentschel, R., Bolte, A., 2024. </em><br><em>From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey.</em></p> <p><strong>Contact:</strong><br>nikolai.knapp@thuenen.de</p> <p> </p> <p> </p>
PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data
<p>Describes the taxonomy used in the paper "PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data", presented at the Second Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data<em> </em>at the Extended Semantic Web Conference (ESWC) 2024.</p>
The fully executable procedure of the U-Net model combined with the Multi-textRG algorithm to achieve fine ice-water classification ---- another 332 scenes of data-fused SIC labels.
<p>This data source is related to the manuscript titled "Combining the U-Net model and a Multi-textRG algorithm for fine SAR ice-water classification", which will be submitted to the journal---The Cryosphere. </p> <ul> <li>The"ready-to-train-fused_01.zip" to "ready-to-train-fused_10.zip" includes 200 scenes of data-fused SIC labels accessible with doi: 10.5281/zenodo.10973107, https://zenodo.org/records/10973107. </li> <li>The "ready-to-train-fused_11.zip" to "ready-to-train-fused_21.zip" includes another 332 scenes of data-fused SIC labels. </li> </ul>
Data and Code for "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" Paper Submission to AGU GRL
<p>This contains the emergent constraint code, the offline CMIP6 hydrology data, and the shapefiles for each region used in the paper "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" submitted to AGU GRL.</p>
Data for: Seasonal differences in observed versus modeled new particle formation at two European boreal stations
<p>Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.4.0 which contains the control case and four experiments with the NPF lookup table. This paper is under review.</p> <p>The files contain:</p> <ul> <li>NetCDF files from TM5 general output for each simulation at the two location grid points.</li> </ul> <p>File-name description of the EC-Earth3 experiments: "Hyyt" = Hyytiälä, "Mossa" = Hyltemossa.</p> <p>"ricc2" = CLUST-HIGH case</p> <p>"dlpno" = CLUST-Low case</p> <p>"bono" = CLUST-Low+Riccobono case</p> <p>"ctrl" = Model control run case</p> <p>"nonpf" = No NPF case</p> <ul> <li>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 3-hourly grib datasets.</li> </ul> <p> </p> <ul> <li>ADCHEM base-case particle number size distributions from Hyytiälä and Hyltemossa.</li> </ul>
Data from: "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied"
<h2>Abstract</h2> <p>This data contains occurrence observations (presence-absence) of 11 aquatic macrophytes from Bothnian Sea and Lake Puruvesi, and environmental covariates used to build species distribution models (SDMs) in paper "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied" in Ecological Modelling.</p> <p>In addition to data files, also R code for fitting the SDMs is supplied, as is the R code to replicate the analysis conducted in the paper. The data is stored in rdata format (point data), without coordinate information due to data policy restrictions. The species in the data are <em>Isoëtes lacustris, Isoëtes echinospora, Ranunculus reptans, Ranunculus schmalhausenii, Potamogeton berchtoldii, Potamogeton perfoliatus, Potamogeton gramineus, Myriophyllum alterniflorum, Equisetum fluviatile, Eleocharis acicularis and Elodea canadensis. </em>The environmental covariates are bottom water salinity, turbidity, sandy substrate occurrence, colored dissolved organic matter (CDOM), surface fetch, sampling depth, total nitrogen and total phosphorus, and distance to closest <em>Phragmites australis </em>reed bed. </p> <h2>Objective of the study </h2> <p>The modelling objective of the paper was species distribution model (SDM) transferability assesment. Transferability was assessed using models built in marine areas in projecting the distributions of the target species in Lake Puruvesi, Saimaa, Eastern Finland. Macrophyte mapping data from Lake Puruvesi was used as independent test data, against which transferability of the models was assessed.</p> <h2>Location</h2> <p>The species data was collected from two geographic areas: Bothnian Bay (Baltic Sea) and Lake Puruvesi (Eastern Finland). The marine observations from Bothnian Bay were split into three overlapping areas (areas 1-3), to test the effect of input data gradient length to SDM transferability. The largest marine sampling area (Area 3) ranged from 62.95, 65.91 latitude and 19.14, 27.99 longitude. Area 2 ranged from 63.95, 65.91 latitude and 21.53, 27.99 longitude. Area 1 ranged from 64.91, 65.91 latitude and 23.82, 27.99 longitude. The Hummonselkä subbasin of Lake Puruvesi, where the macrophyte test data was collected, is located at 61.89, 62.05 latitude and 29.58, 29.78 longitude.</p> <h2>Species data </h2> <p>The species observations were collected using diving transects placed in the floor of the sea or lake, and species observations were recorded in 2 m22 grid cells separated by 10 meters along the transect or 1 meters depth, depending which criteria was met first. The species data was collected in 2010 - 2020 from marine area, and 2017 from Lake Puruvesi. All macrophytes in 2 x 1 m frames were identified to species level by the diver, and the data contained information on species presence or absence in each grid cell. The diving transects were conducted using systematic survey protocol used in the underwater inventories of the Finnish Underwater Biodiversity Survey Program (VELMU) (Frosblom & Virtanen et al. 2024). The locations of the diving transects were not randomly distributed, but were placed using expert judgement. As all our study species are macroscopic and relatively easily identifiable in the field (with the exception of possibility of mixing <em>I. echinospora</em> and <em>I. lacustris</em>), we consider the absences in our observation data to indicate true absences. That said, as the observation area is rather small (2 m2), it is possible that a species may be found in the site of investigation (e.g. a small lagoon) but be located outside the vegetation sampling grid.</p> <h2>Environmental data</h2> <h3>Bottom water salinity</h3> <p>The seasonal mean bottom water salinity was modeled using a generalized additive model with mean salinity as response, with log-link and gamma distribution for the errors. This was necessary to keep the resulting predictions positive. Bottom depth, CDOM, river influence and spatial location were used as predictors. Data from 448 locations were used and each location had a minimum of three observations. The model was validated using 30 % of the data left outside of the model fitting. The explained deviance of the model was 0.94 and the correlation between raw data and predicted values was 0.95 with few outliers.</p> <h3>Turbidity</h3> <p>Maps of turbidity (in FNU, Formazin Nephelometric Unit) were generated from Sentinel-2 Multi-Spectral Imager (MSI) observations using the Case-2 Regional Coast Colour (C2RCC) bio-optical inversion model, containing separate atmospheric correction and water quality parts. Before computing C2RCC, the original 10-meter input data was downsampled to 60 meters. The output variable of the C2RCC processor correlative to turbidity is the backscattering of total suspended sediments at 443 nm, which was further calibrated into turbidity (FNU) values using SYKE's empirical equations for coastal waters and clear lakes (for a similar approach, see Attila et al. (2013) and Sagerman, Hansen, and Wikström (2020)). Monthly observations of turbidity were aggregated into median composites to reduce the effects of cloud cover and other disturbances. Due to low solar elevation and ice cover in winter, the turbidity distribution maps are generated only for the summer months (May to September). The current processing covers years 2017 to 2021. An average raster layer was created from monthly observations as input for SDM building.</p> <h3>Probability of sandy substrate </h3> <p>Random forest model was used to classify sandy bottoms from Sentinel 2 MSI satellite images in shallow water areas. Identifying sandy substrate is based on the higher reflectance compared to other substrates. The model was trained and validated using diver recorded field observations in the Baltic area, and diver recorded and echo sounding observations in the freshwater area. For full coverage including areas beyond the shallow water, the satellite image classification was combined with boosted regression tree modelling result in the Baltic, and echo sounding based product in the freshwater region. The resulting layers were probabilities of sandy substrate with 10-meter cell resolution.</p> <h3>CDOM</h3> <p>We used different methods to estimate the CDOM levels in Bothnian Bay and Lake Puruvesi, based on biogeochemical model data and satellite images. For Lake Puruvesi, we applied the Finnish Environment Institute's (Syke) in-house CDOM algorithm to the Sentinel-2 MSI images processed by the C2RCC bio-optical processor (Brockmann et al. 2016). The observations in 10 m resolution were aggregated as monthly averages for each month of the summer season (May to October) from 2017 to 2021. For Bothnian Bay, we used Syke's in-house Sentinel-2 MSI CDOM layers (resolution: 60 m) aggregated as seasonal averages (1 Jul to 7 Sep). CDOM values are given as absorption coefficient of CDOM at 400 nm [m⁻¹].</p> <h3>Surface fetch</h3> <p>A surface fetch raster was produced to the Puruvesi and Bothnian Bay. The analysis required a feature layer of shorelines from Puruvesi and Baltic Sea. First, we created polyline from north to south spanning over the whole area of interest with a gap of 20 meters which is also the resolution of the output raster. These lines were then cut each time they hit the shoreline and the part of the line that was overlapping land was removed. The distance of the remaining lines was then calculated and a point with the distance value was created every 20 meters. Each time the line was cut when hitting an island for example and starting again from the other side of the island, the distance calculation started from 0. This created a point dataset with a distance value in each point. We repeated the procedure for 15 times for different compass directions with 22.5 degree intervals and calculated average fetch for each point location on 20 meters grid from these 15 point layers.</p> <h3>Depth</h3> <p>Depth was measured by a diver using a dive computer while surveying each vegetation grid cell, and measured depth was used when projecting model results to Puruvesi (transferability performance). In addition, a depth model for the freshwater region was created from Sentinel 2 MSI satellite image using the logarithmic band ratio model of blue and red band. The model was calibrated using diver recorded field observations and validated against echo sounding measurements. For more complete coverage and to include deep areas, echo soundings from multiple sources were combined with the satellite derived bathymetry. The cell resolution of the resulting depth layer was 10 meters.</p> <h3>Total nitrogen and phosphorus</h3> <p>Mean total nitrogen and phosphorus layers for marine area were produced using ArcGIS "splines with barriers" tool for the EEZ of Finland with 20 meters spatial resolution (Virtanen et al. 2018). Summer (July - September) nutrient measurements from 0 to 10 meters depth between 2010 and 2020, obtained from the VESLA database, were used as input data for the interpolation.</p> <p>Nitrogen and phosphorus measurements in Puruvesi between 2010 and 2020 was gathered from the VESLA database. Data from July to September was selected to represent the growing season. A mean value of NTOT and PTOT was then calculated for each location. Spline with Barriers (SwB) tool was used to interpolate the values (Arcmap 10.7.1). The tool uses a feature layer as barrier to create the raster representing only the area of interest. For the barrier and the extent of the interpolated raster we used a shapefile representing Lake Puruvesi shoreline. The resolution was set to 5x5 meters. SwB tool created an "extent box" around the area of interest which was removed with Extract by Mask tool using the shoreline feature layer. After the interpolation we noticed that either one of the locations was situated on land or the polygon used as barrier was "leaking". SwB doesn´t interpolate areas that doesn't have locations with values or aren´t connected to the main body of water. To fix this, the raster was extended outwards based on the values of nearby cells and after that the raster was masked again to remove any cells on land. The phosphorus interpolation provided negative values in southern parts on Enanlahti in Kontiolahti and Muholanlahti. These negative values were caused by considerably larger phosphorus values in Enanlahti Lamminniemi (9m) Enanlahti Lamminniemi (4m) locations when compared with the nearby Puruvesi Enanlahti location. The interpolation apparently continued to decrease the values according to the trend set by the difference between these locations and caused it to reach negative values. The southern parts of the bay, about 750 meters, was removed and new values were calculated based on the surrounding cells with Focal Statistics tool. The interpolations were validated by removing 20 % of the locations and reproducing the interpolation. The removed locations and their values were then compared to the interpolated raster. R∗2∗2 value from phosphorus interpolation model was 0.91 after removing two outliers and R22 value from nitrogen interpolation model was 0.715 after removing one outlier.</p> <h3>Distance to closest reed </h3> <p>The aquatic vegetation (<em>Phragmites australis</em> reeds) presence/absence maps were also generated from Sentinel-2 MSI data. The processing included extracting one month of data (July 2019) from green and near-infra-red bands from Sentinel-2 Global Mosaic (S2GM) service and transforming those to normalized-difference vegetation indices (NDVIs). After that, Bayesian statistics were used to predict the posterior probability of vegetation occurrence when distance from shore and NDVI were used as predictor variables. The posterior variable was thresholded and the resulting vegetation presence areas were sieved so that both too small vegetation areas (fewer than 5 pixels) or areas that were not directly attached to shoreline were removed. The resulting map has 10 m pixel size and tentatively represents the locations of reed belts or other shoreline-attached vegetation. This EO-based layer could also be referred to as helophytes or helophytic macrophytes, as it denotes a specific zone of vegetation with emergent aquatic plants containing leaf-green, particularly those that grow densely and have horizontally oriented leaves. In some lakes, this layer can represent, for example, thick stands of <em>Equisetum fluviatile</em>, although in most cases, it is associated with common reed belts. The approach is described in more detail in Koponen et al. (2022).</p> <h2>Data partitioning </h2> <p>Data was partitioned with 70/30 splitting into training and test (interpolation accuracy) data. The splitting was repeated 100 times for each species by randomly selecting 70 % of observations which were used to build each of the SDMs (GLM, GAM, BRT and BART). The partitioning was repeated for each of the three input data areas and 11 species. The input data indexes for replicating the split are supplied in the data files. </p> <h2>R code </h2> <p>Code files contain scripts for fitting the SDM models described in the paper using the data. Also code for calculating calidation statistics (modelling results) and code for statistical analyses for making inferences in the paper, are supplied. </p> <h2>Additional info</h2> <p>More details on modelling protocol may be found in the original paper in Ecological Modelling, and in the Supplementary Information file of the original paper, which follows the model reporting template "ODMAP" by Zurell et al. (2020).</p> <h2>References </h2> <p><span>Attila, J., et al. 2013. MERIS Case II water processor comparison on coastal sites of the northern Baltic Sea. - Remote Sensing of Environment 128: 138-149.</span></p> <p><span>Brockmann, C., et al. 2016. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters. - In: Living Planet Symposium. p. 54.</span></p> <p><span>Forsblom, L., et al. 2024. Finnish inventory data of underwater marine biodiversity<span> </span>-Scientic data</span></p> <p><span>Koponen, S., et al. 2022. Blue Carbon Habitats: – a comprehensive mapping of Nordic salt marshes for estimating Blue Carbon storage potential. - Nordisk Ministerråd.</span></p> <p><span>Sagerman, J., et al. 2020. Effects of boat traffic and mooring infrastructure on aquatic vegetation: A systematic review and meta-analysis. - Ambio 49: 517-530.</span></p> <p><span>Zurell, D., et al. 2020. A standard protocol for reporting species distribution models. - Ecography 43: 1261-1277.</span></p> <p></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from:Modelling the spatiotemporal dynamics of soil nitrogen in croplands of Northeast China from 1980 to 2023 using multisource data and machine learning
<p>This dataset include the spatiotemporal distribution and uncertainty of cropland soil total nitrogen content at 0-30, 30-60, 60-100 cm depths in Northeast China from 1980 to 2023. The long-time series of TN were estimated by using an space-time automatic machine learning. The detail information on the products were given below:</p> <p>Period: 1980-2023</p> <p>Spatial resolution: 0.004166667 degree (~500 m)</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Data format: GeoTIFF</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.