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opencc-by-4.0Dec 2020View details →

Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa

Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a> Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are: <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.

opencc-by-4.0Nov 2021View details →

Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm

CHARACTERISTICS Planet: Mars Radius: RM = 3389.5 km (volumetric mean planetary radius) Spacecraft: NASA/Mars Atmosphere and Volatile Evolution (MAVEN) Spacecraft coordinates system: Mars Solar Orbital (MSO) equivalent to Sun-State coordinate system: <ul> <li>+XMSO points towards the Sun from the planet’s centre,</li> <li>+ZMSO towards Mars’ North pole and perpendicular to the orbital plane defined as the XMSO–YMSO plane passing through the centre of Mars,</li> <li>YMSO completes the orthogonal system.</li> </ul> Time span: 01/11/2014 to 30/04/2024 (Mars Years MY32 to MY36 included, part of MY37). Total number N of candidate bow shock crossings in the database: N = 20107 ORIGINAL DATASETS USED The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used. METHOD To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in: Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, Journal of Geophysical Research, 127, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>.  Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a> These algorithms consist of two consecutive steps:  <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> REMARK ON VERSIONS From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that: <ul> <li>45∘<θBn<135∘: quasi-perpendicular shock condition</li> <li>θBn≤45∘ and θBn≥135∘: quasi-parallel shock condition</li> </ul> Uncertainty on these angles is estimated to be ± 5º.  From Version 4 onwards, we also added the solar longitude Ls (in degrees). For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper. VARIABLES DESCRIPTION This database contains the following ASCII variables: <ul> <li>Bow shock times in MAVEN's database (1-s resolution): Tbs</li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius RM (RM = 3389.5 km): XMSO, YMSO, ZMSO and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in RM)</li> <li>Solar Zenith angle in degrees: SZA = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn <  135 deg: quasi-&perp; shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> WARNING This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a first indicator of the shock location, and with caution: it CANNOT, and WILL NOT substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 RM (with RM = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind. Current formatting optimised for MATLAB. ACKNOWLEDGEMENTS C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>.      LICENSE AND RIGHTS This database is shared under a Creative Commons CC-BY-4.0 license. Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF),                                                              Austrian Academy of Sciences (ÖAW), 2021-09-08 Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.   Contact email:        cyril.simon.wedlund@gmail.com

opencc-by-4.0Feb 2018View details →

Data for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect

Dataset for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect, in final revision for Nature Communications. Includes two files - one behavioral data from uniquely identified worker bumblebees, and the second containing metadata for the colonies from which these data were generated (including experimental treatments, locations, sizes, etc.).

opencc-by-4.0Nov 2024View details →

Spatial predictions of the morpho-ecological state of Finnish palsa mires

Spatial predictions of the probability of a good morpho-ecological state are provided for Finnish palsa mires as a TIFF file with a 10 m resolution, using the EUREF FIN TM35FIN coordinate system. These predictions were produced through spatial modeling that combined classified point data on the state of Finnish palsa mires (Ruuhijärvi et al., 2022) with high-resolution (10 m) environmental datasets. The predictions were computed for the extent of palsa mires (Tammilehto et al., 2024).  Modelling was conducted in mgcv package (version 1.9.0; Wood, 2011) in R (version 4.3.2; R Core Team 2023). The predictions were developed during the preparation of the manuscript: "The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling" (Leppiniemi et al., 2024, in-review).   References: Leppiniemi, O., Karjalainen, O., Aalto, J., Yletyinen., E., Luoto, M., & Hjort, J. 2024. The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling. (In-review). R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (accessed 14 October 2024). Ruuhijärvi, R., Salminen, P., & Tuominen, S., 2022. Distribution range, morphological types, and state of palsa mires in Finland in the 2010s. Suo 73, 1–32. (In Finnish with English summary). Tammilehto, A., Härmä, P., Kallio, M., Törmä, M., Saikkonen, A., Tuominen, S., Impiö, M., Heikkinen, M., Kervinen, M., Jussila, T., Böttcher, K., Pääkkö, E., Kokko, A., Mäkelä, K., & Anttila, S., 2024. Ylä-Lapin luonnon kaukokartoitus – Projektin loppuraportti osa 1 – Aineistot ja menetelmät. Vantaa. (In Finnish). Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. Methodol. 73, 3–36. https://doi.org/10.1111/J.1467-9868.2010.00749.X

opencc-by-4.0Mar 2023View details →

Spatial predictions of suitable environments for palsas and peat plateaus in the Northern Hemisphere for recent and future periods

Here we provide raster files of suitable environments for palsas and peat plateaus in the Northern Hemisphere. These files are results of a scientific study by Könönen et al. (2022, preprint). Files are provided in TIFF-format, and they describe the occurrence probability of the suitable environments for palsas and peat plateaus.   Könönen, O. H., Karjalainen, O., Aalto, J., Luoto, M., and Hjort, J.: Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-135, in review, 2022.

opencc-by-4.0Nov 2020View details →

Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner. Conclusions: The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

opencc-zeroJan 2024View details →

Data from: A species' response to spatial climatic variation does not predict its response to climate change

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.

opencc-by-4.0Oct 2024View details →

Four spatial prediction datasets of susceptibility to gully erosion, comparing machine learning models, in the Piraí drainage basin, southeastern Brazil

The data in this repository refer to the article published in the journal Land, entitled: Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba do Sul Middle Valley, Southeast Brazil.

opencc-by-4.0Sep 2021View details →

Multivariate Spatial Predictions of Air Pollutants with INLA

Spatial predictions and uncertainty quantification for air pollutants: PM2.5, PM10, NO3−, NH4+, EC, OC, SO42− , CO, NOx, NO2, SO2 and O3 covering the continental US for the period 2005–2014. The daily prediction is at 12km spatial resolution.

opencc-zeroAug 2023View details →

Data and code for: Spatial cell type enrichment predicts mouse brain connectivity

A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.

opencc-by-4.0Sep 2023View details →

Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'

Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article:  Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405 The .zip file has the following folders: 1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents 2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction 3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction 4) soil_texture_quantile05: quantile 5% of the soil texture prediction 5) soil_texture_quantile95: quantile 95% of the soil texture prediction

opencc-by-4.0Oct 2023View details →

Data --- "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions"

Data to reproduce the results of the manuscript entitled "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions" submitted to Geophysical Research Letters. The companion jupyter notebook can be found in DOI: <a href="https://doi.org/10.5281/zenodo.8387558">10.5281/zenodo.8387558</a>

publicSep 2023View details →

Data and code for: Spatial cell type enrichment predicts mouse brain connectivity

Open the record for dataset details and reuse information.