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365 results for “Spatial modeling”

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Data from: The ecology of spider sociality – A spatial model

The emergence of animal societies offers unsolved problems for both evolutionary and ecological studies. Social spiders are specially well suited to address this problem given their multiple independent origins and distinct geographical distribution. Based on long term research on the spider genus Anelosimus, we developed a spatial model that recreates observed macroecological patterns in the distribution of social and subsocial spiders. We show that parallel gradients of increasing insect size and disturbance (rain, predation) with proximity to the lowland tropical rainforest would explain why social species are concentrated in the lowland wet tropics, but absent from higher elevations and latitudes. The model further shows that disturbance, which disproportionately affects small colonies, not only creates conditions that require group living, but also tempers the dynamics of large social groups. Similarly simple underlying processes, albeit with different players on a somewhat different stage, may explain the diversity of other social systems.

opencc-zeroDec 2020View details →

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Response of East Asian Precipitation to Precessional Orbital Forcing during Glacials –Sensitivity to Model Spatial Resolution

Output data (precipitation, wind vector, etc) from three resolution experiments (F19,F09,F05) . LGM means Last glacial maximum. cntl means control run(21ka), HighSolin means the orbital year modified to 174ka. Please see more in the publication.

opencc-by-4.0Dec 2021View details →

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Supplementary material for "Spatio-temporal modelling of abundance from multiple data sources in an integrated spatial distribution model"

Abstract Aim: In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous data sets are often deemed to be incompatible. Consequently, many available data sets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends. Location: Switzerland (Europe) Taxon: Birds Methods: We developed an integrated, hierarchical species distribution model with a joint likelihood for all data sets using a shared state process (e.g., latent species abundance or occurrence), but distinct observation process for each data set. We show how the abundance submodel of a binomial N-mixture model can fuse four different data types (count, detection/non-detection, presence-only, and absence-only data) and enable improved inferences about spatio-temporal patterns in abundance. As case studies, we use data from multiple avian biodiversity monitoring schemes. In the first, the goal is estimating abundance-based species distribution maps. In the second, we infer trends in population abundance across time. Results: Accuracy and precision of abundance estimates increased when combining data from different sources compared to using a single data source alone. This is particularly valuable when data from each single data source is too sparse for reliable parameter estimation. Main conclusions: We show that exploiting the complementary nature of "cheap", but abundant, citizen-science data and less abundant, but more information-rich, data from structured monitoring programs might be ideal to estimate distribution and population trends more accurately, especially for rare species. Joint likelihoods allow to include a wide variety of different data sets to (1) combine all the available information and to (2) mitigate weaknesses of one by the strength of another.

opencc-by-4.0Jan 2022View details →

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Using seabird and whale distribution models to estimate spatial consumption of krill to inform fishery management

Ecosystem dynamics at the north-west Antarctic Peninsula are driven by interactions between physical and biological processes. For example, baleen whale populations are recovering from commercial harvesting against the backdrop of rapid climate change, including reduced sea-ice extent and changing ecosystem composition. Concurrently, the commercial harvesting of Antarctic krill is increasing, with the potential to increase the likelihood for competition with and between krill predators and the fishery. However, understanding the ecology, abundance, and spatial distribution of krill predators is often limited, outdated, or at spatial scales that do not match those desired for effective fisheries management. We update current knowledge of predator dependence on krill by integrating telemetry-based data, at-sea observational surveys, estimates of predator abundance, and physiological data to estimate the spatial distribution of krill consumption during the austral summer by three species of Pygoscelis penguin, 11 species of flying seabirds, one species of pinniped and two species of baleen whale. Our models show that the majority of important areas for krill-predator foraging are close to penguin breeding colonies in nearshore areas where humpback whales also regularly feed, and along the shelf-break, though we caution that not all known krill predators are included in these analyses. We show that krill consumption is highly variable across the region, and often concentrated at fine spatial scales, emphasising the need for management of the local krill fishery at relevant temporal and spatial scales. We also note that krill consumption by recovering populations of krill predators provides further evidence in support of the krill surplus hypothesis, and highlight that despite less than comprehensive data, cetaceans are likely to consume a significant proportion of the krill consumed by natural predators but are not currently considered directly in the management of the krill fishery. If management of the krill fishery is to be precautionary and operate in a way that minimises the risks to krill predator populations, it will be necessary in future analyses, to include up-to-date and precise abundance and consumption estimates for pack-ice seals, finfish, squid, and other baleen whale species not currently considered.

opencc-zeroMar 2022View details →

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Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"

Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model" This file includes  - Source data for Figs 1-5 and Supplementary Materials - LAMMPS codes and raw log files used to produce the results of this study

opencc-by-4.0May 2022View details →

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Pre-fitted Bayesian models for "Gene panel selection for targeted spatial transcriptomics"

simulation_parameters_DARTFISH_slim.rds: Bayesian model fitted on the Zhang dataset. simulation_parameters_MERFISH_slim.rds: Bayesian model fitted on the Moffit dataset. simulation_parameters_osmFISH_slim.rds: Bayesian model fitted on the Codeluppi dataset.

opencc-by-4.0Jul 2022View details →

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Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model

Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially-varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic dataset, then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.

opencc-zeroSep 2022View details →

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A Multi-Scale Spatial Model of Hepatitis-B Viral Dynamics

Dataset related to an accepted manuscript: A Multi-Scale Spatial Model of Hepatitis-B Viral Dynamic. Cangelosi Q., Means S., Ho H. PLOS One Dataset documented in a ReadMe file.

opencc-by-4.0Nov 2017View details →

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Model data for " Topography Influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"

This dataset is for the paper " Topography influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"

opencc-by-4.0Aug 2023View details →

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Spatial association and modelling of vaccination coverage in Thailand, 2021 - 2022

Information on COVID-19 vaccine services from MOPH immunization Center (MOPH IC) (Department of Disease Control, 2023) Information on COVID-19 patients (per 1,000 population), COVID-19 deaths (per 100,000 population), people with chronic diseases (%), pregnant women (per 1,000 population), medical personnel (per 1,000 population), hospitals (per 100,000 population), subdistrict health promotion hospitals (per 100,000 population), and village health volunteers (per 1,000 population) from the Ministry of Public Health. (Department of Disease Control, 2023; Department Of Health Service Support, 2023; HDC, 2023; Ministry of Public Health, 2022) Information on population density (sq. km.), proportion of population in municipal areas (%), proportion of elderly people (%), proportion of working age (%), business establishments (per 1,000 population), average monthly household income (baht), proportion of population that has a mobile phone (%), and proportion of population internet access (%) from the National Statistical Office. (National Statistical Office, 2023) Information on nighttime light from The Earth Observation Group (EOG., 2023) Information on public transport vehicles (per 1,000 population) and private vehicles (per 1,000 population) from the Ministry of Transport. (Department of Land Transport, 2023) Information on the proportion of treatment rights (%) includes universal coverage scheme rights, social security scheme (SSS), and government rights (OFC) from the National Health Security Office. (National Health Security Office, 2023)   a_pop : population density (sq. km.) in 2021 a_pop65 : population density (sq. km.) in 2022 urban% : proportion of population in municipal areas (%) in 2021 Urban%65 : proportion of population in municipal areas (%) in 2022 Older_21 : proportion of elderly people (%) in 2021 Older_22 : proportion of elderly people (%) in 2022 7CD_21 : people with chronic diseases (%) in 2021 7CD_22 : people with chronic diseases (%) in 2022 Preg_21 : pregnant women (per 1,000 population) in 2021 Preg_22 : pregnant women (per 1,000 population) in 2022 Work_21 : proportion of working age (%) in 2021 Work_22 : proportion of working age (%) in 2022 NTL64 : nighttime light in 2021 NTL65 : nighttime light in 2022 BSN_21 : business establishments (per 1,000 population) in 2021  BSN_22 : business establishments (per 1,000 population) in 2022 P-car_21 : public transport vehicles (per 1,000 population) in 2021 P-car_22 : public transport vehicles (per 1,000 population) in 2022 I-car_21 : private vehicles (per 1,000 population) in 2021 I-car_22 : private vehicles (per 1,000 population) in 2022 Phone_21 : population that has a mobile phone (%) in 2021 Phone_22 : population that has a mobile phone (%) in 2022 Internet_21 : proportion of population internet access (%) in 2021 Intermet_22 : proportion of population internet access (%) in 2022 Income : average monthly household income (baht) PH-P_21 : medical personnel (per 1,000 population) in 2021 PH-P_22 : medical personnel (per 1,000 population) in 2022 VHV_21 : village health volunteers (per 1,000 population) in 2021 VHV_22 : village health volunteers (per 1,000 population) in 2022 Hos-P_21 : hospitals (per 100,000 population) in 2021 Hos-P_22 : hospitals (per 100,000 population) in 2022 Local-p_21 : subdistrict health promotion hospitals (per 100,000 population) in 2021 Local-p_22 : subdistrict health promotion hospitals (per 100,000 population) in 2022 UC_21 : universal coverage scheme rights (%) UC_22 : universal coverage scheme rights (%) SSS_21 : social security scheme (%) SSS_22 : social security scheme (%) OFC_21 : government rights (%) OFC_22 : government rights (%) Covid-p_21 : COVID-19 patients (per 1,000 population) in 2021 Covid-p_22 : COVID-19 patients (per 1,000 population) in 2022 Dcovid-p_21 : COVID-19 deaths (per 100,000 population) in 2021 Dcovid-p_22 : COVID-19 deaths (per 100,000 population) in 2022 Vcovid21_1 : COVID-19 vaccine coverage 1 dose (%) in 2021 Vcovid21_2 : COVID-19 vaccine coverage 2 dose (%) in 2021 Vcovid21_3 : COVID-19 vaccine coverage 3 dose (%) in 2021 Vcovid22_1 : COVID-19 vaccine coverage 1 dose (%) in 2022 Vcovid22_2 : COVID-19 vaccine coverage 2 dose (%) in 2022 Vcovid22_3 : COVID-19 vaccine coverage 3 dose (%) in 2022

opencc-by-4.0Jul 2024View details →

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GIS data for the maps in publication Spatial perspectives enhance modeling of nanomaterial risks

These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology.  The following data sources were used in this modeling effort: National Hydrography Dataset (NHD): United States Geological Survey (USGS) Upstate NY Lakes, ponds, streams, rivers, springs, and wells Critical Environmental Areas in New York State: New York State Department of Environmental Conservation  Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC) National Land Cover Dataset (NLCD): United States Geological Survey (USGS) National Land Cover Database classification schemes based primarily on Landsat data (2011) Elevation Data: United States Geological Survey (USGS) Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000.  Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database Rural and urban highways for New York   Other references Bureau, U.S. Census., American community survey 5-year estimates. 2017. EPA, Toxics Resource Inventory. 2019   .

opencc-by-4.0Nov 2019View details →

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Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics Data Imputation

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →

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Data from: Spatial modeling improves understanding patterns of invasive species defoliation by a biocontrol herbivore

Spatial modeling has proven to be useful in understanding the drivers of plant populations in the field of ecology, but has yet to be applied to understanding variation in biocontrol impact. In this study, we employ multi-scale analysis (Moran's Eigenvector Maps) to better understand the variation in tree canopy exposed to defoliation by a biocontrol beetle (Diorhabda spp.). The control of the exotic tree Tamarix in riparian areas has long been a priority for land managers and ecologists in the American southwest. Diorhabda spp. was introduced as a bio-control agent beginning in 2001 and has since become an inseparable part of Tamarix-dominated river systems in the southwest. Between 2013 and 2016 tamarisk dieback was assessed at 79 sites across Grand County, Utah, arguably the epicenter of Diorhabda impact in the U.S. Canopy cover of Tamarix was between 73%-81% at these sites, with the percent that was live cover fluctuating by year with a minimum of 42%. Using a traditional general linear model, we found that readily and commonly measured environmental factors could explain only up to 26% of the variation in Tamarix live canopy each year, including that number of defoliations was correlated with an increase rather than a decrease in percent live canopy, suggesting compensatory growth. Spatial structure alone explained 22-40% of variation. We found fine scale spatial structure at less than 10 km and broad scale spatial structure from 10-30 km. Combining both traditional and novel spatial statistical methods we increased that percentage to 43-63%, depending on year. These results suggest that scientists and land managers must look beyond commonly measured environmental variables to explain non-random biocontrol impact in this system. In particular, this study points to the potential for biotic interactions and variation in flood cycles for further exploration of the identified spatial structure.

opencc-zeroDec 2017View details →

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Data from: Spatially explicit models of dynamic histories: examination of the genetic consequences of Pleistocene glaciation and recent climate change on the American Pika.

A central goal of phylogeography is to identify and characterize the processes underlying divergence. One of the biggest impediments currently faced is how to capture the spatiotemporal dynamic under which a species evolved. Here we described an approach that couples species distribution models (SDMs), demographic and genetic models in a spatiotemporally explicit manner. Analyses of American Pika (Ochotona priniceps) from the sky islands of the central Rocky Mountains of North America are used to provide insights into key questions about integrative approaches in landscape genetics, population genetics and phylogeography. This includes (i) general issues surrounding the conversion of time-specific SDMs into simple continuous, dynamic landscapes from past to current, and (ii) the utility of SDMs to inform demographic models with deme-specific carrying capacities and migration potentials, as well as (iii) the contribution of the temporal dynamic of colonization history in shaping genetic patterns of contemporary populations. Our results support that the inclusion of a spatiotemporal dynamic is an important factor when studying the impact of distributional shifts on patterns of genetic data. Our results also demonstrate the utility of SDMs to generate species-specific predictions about patterns of genetic variation that account for varying degrees of habitat specialization and life-history characteristics of taxa. Nevertheless, the results highlight some key issues when converting SDMs for use in demographic models. Because the transformations have direct affects on the genetic consequence of population expansion by prescribing how habitat heterogeneity and spatiotemporal variation is related to the species-specific demographic model, it is important to consider alternative transformations when studying the genetic consequences of distributional shifts.

opencc-zeroDec 2011View details →

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Data and analysis for "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"

This contains all of the necessary data and code to reproduce the results of the manuscript submitted to the Journal of Hydrometeorology entitled "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"

opencc-by-4.0Aug 2021View details →

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Spatially explicit models for decision-making in animal conservation and restoration

Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79 %), towards the species and population level (80 %) and towards conservation (rather than restoration) applications (71 %). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10 % of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration, and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by (1) developing a toolbox with multiple, easier-to-use methods, (2) improving calibration and validation of dynamic modelling approaches, and (3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by (4) combining multiple modelling approaches to assess uncertainty, and (5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.

opencc-zeroSep 2021View details →

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Processed data used for spatial analysis of DMD mouse models

This repository contains seurat objects and .H5AD files that were used in the analysis described in the paper titled "Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models" Authors: L.G.M. Heezen, T. Abdelaal, M. van Putten, A. Aartsma-Rus, A. Mahfouz and P. Spitali It contains datafiles obtained from spatial transcriptomics (Visium, 10x Genomics) experiments on skeletal muscle samples from two wildtypes: C57BL10 and DBA/2J and two DMD mouse models: mdx and D2-mdx. All ten weeks old male mice, 10micron thick sections of the quadriceps.

opencc-by-4.0Dec 2022View details →

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Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138

The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). Journal of South American Earth Sciences ). The dataset provided should be cited together with the article.  DSM processing  The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID:  SEN_SPOT7_20180419_142955500_000, delivered by CONAE as DS_SPOT7_20180419). The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps:  <ol> <li> The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable. </li> <li> The scenes were map-projected onto the NASADEM (spatial resolution of 30 m)  elevation dataset, assisted by the results of the orbital adjustment in Step 1. </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way:  Correlation Kernel: 15 x 15 pixels; Sub-pixel Refinement Kernel: 21 x 21 pixels; Subpixel Refinement Mode: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as DSM_Copahue_UTM19S_WGS84_5m_raw.tif). </li> </ol> The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEMTM  elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m.  Comprehensive details on the methodologies evaluated  to create the dataset with ASP, can be found in the corresponding master's thesis  “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>).  Creation of the Final, Filtered DSM product The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps.    <ol> <li> Water Bodies Delineation </li> </ol> The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas  above this threshold were filled in with a constant value and their borders  were smoothed with a median filter to provide smooth shorelines. 2 . Void Filling Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software.   3. Smoothing Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel  window, excluding water bodies filled in the step 1.   Final Remarks and Suggestion The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.   Dataset Description <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> No Data Value = -9999 Format = float 32 bit File Format = GeoTiff Vertical Datum: WGS84 Projection information: EPSG 32719 (UTM19S) Spatial Resolution: 5m (subfix: _5m)  Versions:  <ul> <li> Unfiltered product: without corrections DSM_Copahue_UTM19S_WGS84_5m_raw.tif </li> <li> Final, filtered product: smoothed and void filled DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> No Lake Value = 0 Lakes Values = 1 to 45 File Format= GeoTiff Spatial Resolution: 5m (subfix: _5m) Projection information : EPSG 32719 (UTM19S) WB_mask_5m_UTM19S.tif </td> </tr> </tbody> </table>     Repository structure |__ 01_Scripts     |+ run21_CopahueDSM_AMES_sviotto.sh     |+ stereo.default |__ 02_DSMs     |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif     |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif     |+  WB_mask_5m_UTM19S.tif References Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. Earth and Space Science, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a> Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015. Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. Journal of Geophysical Research: Earth Surface, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a> Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138.  https://doi.org/10.1016/j.jsames.2022.104138

opencc-by-4.0Dec 2022View details →

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Developers' Visuo-spatial Mental Model and Program Comprehension

Dataset for the paper entitled: "Developers' Visuo-spatial Mental Model and Program Comprehension"

opencc-by-4.0Aug 2022View details →

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Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling

Data and scripts for our paper: Lindsø, L.K., Dupont, P., Rød-Eriksen, L. et al. Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling. Oecologia 198, 139–151 (2022). https://doi.org/10.1007/s00442-021-05087-3

opencc-by-4.0Dec 2021View details →

ScienceDex guides

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

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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