Find research datasets worth reusing

opencc-by-4.0Oct 2019View details →

Makeup Interpolation Experimental Result Video

This is a supplementary video for a submitted paper to Symmetry Journal, which contains the experimental results including the convergence for the matt refinement process for Figure 9, the makeup morphing comparison results for Figures 10, 11, 13 and 14, and the interactive demo of the system.

opencc-by-4.0Feb 2024View details →

ESA 4DMED-SEA - Mediterranean Multivariate Optimal Interpolated Salinity and Density fields

Daily Mediterranean gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/24° of resolution from 2016 to 2022, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA’s Soil Moisture Active Passive (SMAP) and ESA’s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite UHR SST information.

opencc-by-4.0Sep 2024View details →

Self-organization of conducting pathways explains complex wave trajectories in procedurally interpolated fibrotic cardiac tissue: a digital-twin study

In precision cardiology, digital twinning technology (DT) holds promise for predicting arrhythmias by leveraging patient data and biophysics knowledge. However, current DTs are designed to directly reproduce biopotential conduction in cardiac tissue, while only indirect non-invasive methods can be clinically implemented on real organs. This discrepancy challenges our understanding of DT applicability limits. This study aims to enhance DT by developing an in-vitro training complement. We conducted a frame-by-frame comparison of in-vitro optical mapping of biopotential conduction with machine learning (ML) optimized DT predictions. Patient-specific self-organized tissue samples of human induced pluripotent stem cells-derived cardiomyocytes (CMs) with diffuse fibrosis served as DT prototypes. High spatiotemporal resolution optical mapping recordings (Δx=117 ± 4 μm, Δt=7.69 ms) and immunostainings were used to reproduce fibrotic samples with a linear size of 7.5 mm. Using data-driven ML-optimization of the Cellular Potts model, we examined wave propagation at the subcellular level. The modified Glazier-Graner-Hogeweg model accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs self-organization. The percolation threshold of virtual conductive pathways reached 26% (26.7 ± 2.9% of CMs in-vitro), resulting in a spatial correlation of amplitude maps between prototype samples and their DT with Pearson’s coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated the ability of ML-optimized DT to predict and interpolate wavefront trajectories in optical mapping recordings. We found that mathematical approximation of fibrosis distribution played a key role in DT prediction accuracy, potentially informing the implementation of LGE-MRI detection of fibrosis within cardiac DT frameworks. Dataset A: Immunostaining images were sorted based on the day of enzymatic disaggregation (before and after day 20). We collected and sorted α-actinin, Connexin43 and DAPI immunostainings for Cellular Potts Model optimization. During data processing, cell shapes (n=109 and n=69 for CM and BPs respectively after day 20, n=209 and n=90 for CM and BPs respectively before day 20) were formalized. Dataset C corresponds to FluoVolt recordings (3 samples). Dataset B corresponds to Fluo-4 AM recordings in iPSC-CMs samples with diffuse fibrosis imitation (4 samples).

dryad36/100

An interpolated biogeographic framework for tropical Africa using plant species distributions and the physical environment

Aim: Existing phytogeographic frameworks for tropical Africa lack either spatial completeness, unit definitions smaller than the regional scale, or a quantitative approach. We investigate whether physical environmental variables can be used to interpolate floristically defined vegetation units, presenting an interpolated, hierarchical, quantitative phytogeographic framework for tropical Africa, which is compared to previously defined regions. Location: Tropical mainland Africa 24°N to 24°S. Taxon: 31,046 vascular plant species and infraspecific taxa. Methods: We calculate a betasim dissimilarity matrix from a comprehensive whole-flora database of plant species distributions. We investigate environmental correlates of floristic turnover with local non-metric multidimensional scaling. We derive a hierarchical biogeographic framework by clustering the dissimilarity matrix. The framework is modelled using a classification decision tree method and 12 physical environmental variables to interpolate and downscale the framework across the study region. Results: Floristic turnover is related strongly to water availability and temperature, with smaller contributions from land cover, topographic ruggedness and lithology. Region can be predicted with 90% accuracy by the model. We define 19 regions and 99 districts. We find a novel arrangement of the arid regions. Regional subdivision within the savanna biome is supported with minor variation to borders. Within the forests of west and central Africa, our whole-flora gridded regionalisation supports the divisions identified by a previous analysis of trees only. Main conclusions: Physical environmental variables can be used to predict floristically defined vegetation units with very high accuracy, and the approach could be pursued for other inc ompletely sampled taxa and areas outside of tropical Africa. Geographic coherence is higher than in previous quantitative phytoregional definitions. For most tropical African vascular plant species, we provide predictions of which species will occur within each mapped district and region of tropical Africa. The framework should be useful for future studies in ecology, evolution and conservation.

opencc-zeroSep 2021View details →

opencc-by-4.0Jun 2023View details →

Land cover maps: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes

Land cover maps obtained with mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE models for the year 2018 with Sentinel-2 acquisitions. For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.

opencc-by-4.0Sep 2023View details →

IDW interpolation dataset of ash and tephra deposition following the 2021 Tajogaite volcanic eruption on La Palma, Canary Islands, Spain

The compiled dataset is the result of a field collection campaign to measure the depth of the ash and tephra layer in the aftermath of the 2021 volcanic eruption (Tajogaite) on the island of La Palma, Canary Islands, Spain. The dataset consists of two files: a shapefile and a GeoTIFF raster. The shapefile is a point layer file that displays the location of all ash depth measurements (415 points) taken in the field. To improve our sampling near the crater, where safety and time constraints prevented field collection, we manually sampled additional drone-based measurements (66 points). We combined these data into a single dataset ("ash_depth"; 481 total points) that was then used as input for a spatial interpolation using Inverse Distance Weighting (IDW). The IDW interpolation was performed using the Spatial Analyst toolbox in ArcMap 10.8.1 (Esri, 2021). As an exact deterministic interpolation, IDW estimates pixels values of unknown points by using average distance and a weight between sample points (Watson & Philip, 1985). This is ideal for a dataset that includes many field measurements since IDW interpolates between the minimum and maximum of the collected data. The model parameters were adjusted manually but the best results were yielded using the default settings, with the exception of the output cell size. The output cell size was calibrated to 2 m. The IDW parameters can be viewed in Table 1. The sample point locations were resampled from the raster file to estimate the Root Square Mean Error (RMSE) and were saved to the shapefile as "ash_idw". The RMSE of the dataset is 0.34 m. For further inquiries please contact Christopher Shatto (email: christopher.shatto@uni-bayreuth.de).   Please cite the data paper link to this repository as:  C. Shatto, F. Weiser and A. Walentowitz et al., Volcanic tephra deposition dataset based on interpolated field measurements following the 2021 Tajogaite Eruption on La Palma, Canary Islands, Spain, Data in Brief, https://doi.org/10.1016/j.dib.2023.109949   <table> <tbody> <tr> <td>Power</td> <td>2</td> </tr> <tr> <td>Output cell size</td> <td>2</td> </tr> <tr> <td>Search neighborhood type</td> <td>Standard (circular)</td> </tr> <tr> <td>Major/minor semiaxis</td> <td>12161.79/12161.79</td> </tr> <tr> <td>Max/minimum neighbors</td> <td>15/10</td> </tr> <tr> <td>Sector type</td> <td>1</td> </tr> <tr> <td>Angle</td> <td>0</td> </tr> <tr> <td>Weight field</td> <td>None</td> </tr> </tbody> </table>

dryad36/100

An interpolated biogeographic framework for tropical Africa using plant species distributions and the physical environment

Open the record for dataset details and reuse information.

publicSep 2021View details →

FIGURE 13 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 13. Dendogram based on Bray-Curtis similarity for the South African sponge fauna of 35 coastal sections based on the predicted dataset.

FIGURE 7 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 7. Comparison between regional species diversity (total number of species collected in each region = predicted data; = actual data), endemism (number of unique species in each region) and effort with regions arbitrary sorted on increasing species diversity.

FIGURE 14 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 14. Two-dimensional ordination plot based on the predictive species composition of the 35 coastal sections.

FIGURE 4 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 4. Species accumulation curve for the coastal sections around South Africa as delineated in Fig. 1.

FIGURE 10 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 10. Comparison between regional species diversity patterns of total number of species collected (actual) in each region vs. predictive species pattern in each region (results of ANOVA between the two datasets for species diversity is p=0.01). The results indicated that there was a significant difference between the two datasets (P <0.05) for species richness, and therefore indicate that the true species patterns are what are reflected by the interpolated (or predicted) dataset.

FIGURE 12 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 12. Two-dimensional ordination plot based on the actual species composition of the 35 coastal sections. Datapoint 1 (sector 1) was deleted and reanalyzed to better represent the patterns within the main cluster group.

FIGURE 2 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 2. Summary diagramme indicating South African marine biogeographic provinces as previously outlined by a) Stephenson and Stephenson (1972); b) Brown and Jarman (1978); c) Emanuel et al., (1992); d) Branch and Branch (1981), Field and Griffiths (1991); e) Prochazka (1994); f) Turpie et al., (2000); g) Day et al., (1981); h) Whitfield (1994).

FIGURE 1 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 1. Map of South Africa showing the 35 sectors used as a method of geographical comparison (adapted from Millard, 1978 and Williams, 1992).

FIGURE 6 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 6. Regression analysis comparing (log) number of endemics species and predicted species richness for 35 coastal sections

FIGURE 11 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 11. Dendogram based on Bray-Curtis similarity for the South African sponge fauna of 35 coastal section based on the actual dataset.

FIGURE 9 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 9. Distribution of sponge species richness and endemicity based on the predictive dataset around South Africa on the coastal units shown in Fig. 1.

FIGURE 3 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 3. Distribution of sponge species richness and endemicity based on the actual dataset around South Africa on the coastal units shown in Fig. 1.

FIGURE 5 in Biodiversity " hotspots ", patterns of richness and endemism, and distribution of marine sponges in South Africa based on actual and interpolation data: A comparative approach

FIGURE 5. Regression analysis comparing (log) number of endemics species and actual species richness for 35 coastal sections.