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251 results for “spatial heterogeneity”
Effects of compositional heterogeneity and spatial autocorrelation on richness and diversity in simulated landscapes
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Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
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Large differences in herbivore performance emerge from simple herbivore behaviors and fine-scale spatial heterogeneity in phytochemistry
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Spatial Distribution of the International Food Prices: Unexpected Heterogeneity and Randomness
<p>Global <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/food-prices">food prices</a> are typically analysed in a time-series framework. We complement this approach by focusing on the spatial <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/price-dispersion">price dispersion</a> of the country-pair bilateral trade in the international food trade network (<em>IFTN</em>), for ten relevant commodities. The main purposes are to verify if the <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/price-convergence">Law of One Price</a> (<em>LOP</em>) holds and to investigate the emergence of randomness in the price-formation mechanism.</p> <p>We distinguish between the “internal” variance, which indicates the magnitude of price discrimination, and the “external” variance, that is a measure of price dispersion. We find that, for some commodities, spatial price dispersion is remarkable and persistent over time (i.e., failure of the <em>LOP</em>) and that there exists a strict correlation between price spikes and peaks in spatial price variability.</p> <p>We test whether the price distribution can be replicated through a <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/stochastic-process">stochastic process</a> of extraction. Surprisingly, the actual distribution of prices, for several commodities, is well described by a random distribution. Then, the process of data aggregation is not neutral because the information at the micro-level scale might be lost at the macro-scale, due to the complexity of the <em>IFTN</em>. Finally, we discuss some possible economic explanations of these outcomes and the main methodological, environmental, and policy consequences.</p>
Foraging in nature: contrasting responses to resource heterogeneity at small and large spatial scales.
<p>A key problem faced by foragers is how to forage when resources are distributed heterogeneously in space. This heterogeneity and associated trade-offs may change with spatial scale. Furthermore, foragers may also have to optimize acquiring multiple resources. Such complexity of decision-making while foraging is poorly understood. We studied the butterfly <i>Ypthima huebneri</i> to examine how foraging decisions of adults are influenced by spatial scale and multiple resources. We predicted that, at a small-spatial scale, the time spent foraging in a patch should be proportional to resources in the patch, but at large-spatial scales, due to limitations arising from large travel costs, this relationship should turn negative. We also predicted that both adult and larval resources should jointly affect foraging butterflies. To test these predictions, we laid eleven plots, and sub-divided them into patches. We mapped nectar and larval resources and measured butterfly behavior in these patches and plots. We found that adult foraging behavior showed contrasting relationships with adult resource density at small versus large-spatial scales. At the smaller-spatial scale, butterflies spent more time feeding in resource-rich patches, whereas at the large-scale, butterflies spent more time feeding in resource-poor plots. Furthermore, both adult and larval resources appeared to affect foraging decisions, suggesting that individuals may optimize search costs for different resources. Overall, our findings suggest that the variation in foraging behavior seen in foragers might result from animals responding to complex ecological conditions, such as, resource heterogeneity at multiple spatial scales and the challenges of tracking multiple resources.</p>
Spatial heterogeneity in soil pyrogenic carbon mediates tree growth and physiology following wildfire
<table> <tbody> <tr> <td>Pyrogenic carbon (PyC) is a ubiquitous legacy of wildfire in terrestrial soils, yet how it affects the growth and function of regenerating plants has received little research attention.<br> <br> We examined responses to a natural gradient of PyC deposition five years following a severe fire in a northern boreal forest, based on measurements of growth (height, basal area increment, and leader extension), physiological performance (Fv/Fm), and foliar nutrition (foliar C, N, P, K, Mg) of Pinus banksiana Lamb. We determined the concentration of PyC, expressed as a dosage (t·ha-1), in mineral soils collected from the rhizospheres of each sapling and used it as an independent factor to model trait responses to increasing PyC levels, in conjunction with measurements of soil physio-chemical properties (pH, EC, VOC, Ash, N, P, K, Ca, and Mg).<br> <br> Quantification and spatial analysis of PyC reveals heterogeneous deposition across the landscape with fine-grained patchiness at scales <0.5 m. In response to this heterogeneity, phenotypic and nutritional adjustments followed dose-dependent response patterns. Beneficial effects of PyC on sapling growth occurred to an optimum point of ~30-60 t·ha-1, while declining patterns were found for trees in dosages exceeding 100 t·ha-1. Some traits were positively and negatively related to soil K and N, respectively, and shared strong negative associations with soil pH and volatile matter.<br> <br> Synthesis. This study supports the longstanding hypothesis that soil PyC enhances growth and physiological function of fire-adapted plants, but indicates that responses are highly dosage-dependent, with natural levels of PyC deposition commonly exceeding an optimum point. These results also suggest that the main mechanisms for observed responses to PyC include: i) enhanced supply of base cations, ii) immobilization of N, and iii) pronounced liming. Future changes in climate are expected to increase fire frequency, particularly in circumpolar boreal forests. We predict shifts in PyC to frequently exceed the threshold resulting in reduced plant growth and ultimately ecosystem productivity.</td> <td> </td> <td> </td> </tr> </tbody> </table>
Data from: Soil carbon response to woody plant encroachment: Importance of spatial heterogeneity and deep soil storage
1. Recent global trends of increasing woody plant abundance in grass-dominated ecosystems may substantially enhance soil organic carbon (SOC) storage and could represent a strong carbon (C) sink in the terrestrial environment. However, few studies have quantitatively addressed the influence of spatial heterogeneity of vegetation and soil properties on SOC storage at the landscape scale. In addition, most studies assessing SOC response to woody encroachment consider only surface soils, and have not explicitly assessed the extent to which deeper portions of the soil profile may be sequestering C. 2. We quantified the direction, magnitude, and pattern of spatial heterogeneity of SOC in the upper 1.2 m of the profile following woody encroachment via spatially-specific intensive soil sampling across a landscape in a subtropical savanna in the Rio Grande Plains, USA, that has undergone woody proliferation during the past century. 3. Increased SOC accumulation following woody encroachment was observed to considerable depth, albeit at reduced magnitudes in deeper portions of the profile. Overall, woody clusters and groves accumulated 12.87 and 18.67 Mg C ha-1 more SOC compared to grasslands to a depth of 1.2 m. 4. Woody encroachment significantly altered the pattern of spatial heterogeneity of SOC to a depth of 5 cm, with marginal effect at 5-15 cm, and no significant impact on soils below 15 cm. Fine root density explained greater variability of SOC in the upper 15 cm, while a combination of fine root density and soil clay content accounted for more of the variation in SOC in soils below 15 cm across this landscape. 5. Synthesis: Substantial SOC sequestration can occur in deeper portions of the soil profile following woody encroachment. Furthermore, vegetation patterns and soil properties influenced the spatial heterogeneity and uncertainty of SOC in this landscape, highlighting the need for spatially specific sampling that can characterize this variability and enable scaling and modeling. Given the geographic extent of woody encroachment on a global scale, this undocumented deep soil C sequestration suggests this vegetation change may play a more significant role in regional and global C sequestration than previously thought.
Spatial and temporal heterogeneity in pollinator communities maintains within-species floral odour variation
<p>Flowering plants emit complex bouquets of volatile organic compounds (VOCs) to mediate interactions with their pollinators. These bouquets are undoubtedly influenced by pollinator-mediated selection, particularly in deceptively-pollinated species that rely on chemical mimicry. However, many uncertainties remain regarding how spatially and temporally heterogeneous pollinators affect the diversity and distribution of floral odour variation. Here, we characterized and compared the floral odours of ten populations of deceptively-pollinated <em>Arum maculatum</em> (Araceae), and inter-annual and decadal variation in pollinator attraction within these populations. Additionally, we transplanted individuals from all sampled populations to two common garden sites dominated by different pollinator species (<em>Psychoda phalaenoides</em> or <em>Psycha grisescens</em>), and compared pollinator attraction rates to investigate whether populations maintained odour blends adapted to a specific pollinator. We identified high within- and among-population variation in a common blend of VOCs found across the range of <em>A. maculatum</em>. We also observed shifts in pollinator community composition within several populations over 1-2 years, as well as over the past decade. Common garden experiments further revealed that transplanted inflorescences generally attracted the dominant local pollinator species in both transplant sites. However, one population (Forêt du Gâvre, France) appears to exclusively attract <em>P. grisescens</em>, even when transplanted to a <em>P. phalaenoides</em>-dominated site. Together, our results suggest that maintaining diverse floral odour bouquets within populations may be advantageous when pollinator communities vary over short timescales. We propose that temporally-replicated ecological data are one potential key to understanding variation in complex traits such as floral odour, and in some cases may reveal resiliency to shifting pollinator communities.</p>
Data from: Spatial heterogeneity of plant-soil feedbacks increases per capita reproductive biomass of species at an establishment disadvantage
Plant–soil feedbacks have been widely implicated as a driver of plant community diversity, and the coexistence prediction generated by a negative plant–soil feedback can be tested using the mutual invasibility criterion: if two populations are able to invade one another, this result is consistent with stable coexistence. We previously showed that two co-occurring Rumex species exhibit negative pairwise plant–soil feedbacks, predicting that plant–soil feedbacks could lead to their coexistence. However, whether plants are able to reproduce when at an establishment disadvantage ("invasibility"), or what drivers in the soil might correlate with this pattern, are unknown. To address these questions, we created experimental plots with heterogeneous and homogeneous soils using field-collected conditioned soils from each of these Rumex species. We then allowed resident plants of each species to establish and added invader seeds of the congener to evaluate invasibility. Rumex congeners were mutually invasible, in that both species were able to establish and reproduce in the other's resident population. Invaders of both species had twice as much reproduction in heterogeneous compared to homogeneous soils; thus the spatial arrangement of plant–soil feedbacks may influence coexistence. Soil mixing had a non-additive effect on the soil bacterial and fungal communities, soil moisture, and phosphorous availability, suggesting that disturbance could dramatically alter soil legacy effects. Because the spatial arrangement of soil patches has coexistence implications, plant–soil feedback studies should move beyond studies of mean effects of single patch types, to consider how the spatial arrangement of patches in the field influences plant communities.
Spatial data for Chaplin-Kramer et al. "Spatial heterogeneity in forest carbon storage affects priorities for reforestation"
<p>Spatial data generated for the forest carbon edge effects analysis and optimization performed in Chaplin-Kramer et al. "Spatial heterogeneity in forest carbon storage affects priorities for reforestation". The files included are described below.</p><p>fc_stack_hansen_forest_cover2014_compressed_std_forest_edge_result.tif - Carbon (Mg/ha) predicted by the regression model for Hansen forest cover 2014 (this is the LULC map that produced the model with the best fit and was used in the model validation)</p><p>fig1_edge_effect_hansen_compressed_nr_md5_63366a.tif – Figure 1 in the manuscript</p><p>fig2_ipcc1_regression2_overlap3_md5_43e130.tif – Figure 2 in the manuscript</p><p>figS5_ipcc1_regression2_overlap3_avgov_md5_fe8b72.tif – Alternate styling of manuscript Fig. 2 (resampling with "average" instead of "near"), shown in Supplemental Fig. S5</p><p>ipcc_carbon_restoration_limited_md5_cd012d.tif - Carbon (Mg/ha) in restoration LULC calculated with the IPCC Tier 1 approach</p><p>ipcc_tier1_carbon_data.zip - Data needed to run the IPCC approach are contained within two files in the zip folder: the carbon table (carbon zones are rows and ESA LULC codes are columns) IPCC_carbon_table_md5_a91f7ade46871575861005764d85cfa7.csv; and the carbon zones map: carbon_zones_md5_aa16830f64d1ef66ebdf2552fb8a9c0d.gpkg</p><p>ipcc_carbon_esa_compressed_md5_5b4803.tif - Carbon (Mg/ha) in ESA 2014 LULC calculated with the IPCC Tier 1 approach (shown in Fig. S1)</p><p>PNV_jsmith_060420_md5_8dd464e0e23fefaaabe52e44aa296330.tif – Potential Natural Vegetation (PNV) created by Jeff Smith (unpublished) through a cross-walk of ESA to Dinerstein biomes</p><p>regression_carbon_esa_compressed_md5_c867a0.tif - Carbon (Mg/ha) in ESA 2014 LULC calculated with the regression approach (shown in Fig. S1)</p><p>regression_carbon_restoration_md5_1f5ca8.tif - Carbon (Mg/ha) in restoration LULC calculated with the regression approach</p><p>restoration_limited_md5_372bdfd9ffaf810b5f68ddeb4704f48f.tif – the LULC scenario of reforestation (based on PNV, but excluding agriculture and urban areas) </p>
High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"
<p>High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"</p>
Spatial heterogeneity effects on land surface modeling of water and energy partitioning
<p>Related code and data used in the manuscript https://doi.org/10.5194/gmd-2022-4, <Spatial heterogeneity effects on land surface modeling of water and energy partitioning>. The latest source code of ELMv1 is available from https://github.com/E3SM-Project/E3SM (last access: September 2020). If you have any questions, please contact lingchengliwhu@gmail.com</p>
Distinct oral-associated gastric microbiota and Helicobacter pylori communities for spatial microbial heterogeneity in gastric cancer
<p><span>STROBE Statement—Checklist of items that should be included in reports of <strong><em>case-control studies</em></strong> </span></p>
Quantifying the spatial heterogeneity of forest conversion costs and how it relates to biodiversity, conservation and land use history
<b>Description: </b><p>Start and end dates of salvage logging activity at the SAFE Project experimental site</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/6"><b>Quantifying the spatial heterogeneity of forest conversion costs and how it relates to biodiversity, conservation and land use history</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Sime Darby (grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence na)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3266827">here</a></p><p><b>Files: </b>This dataset consists of 2 files: template_Symes.xlsx, SAFE_COUPE.zip</p><p><b>template_Symes.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Salvage logging records</b> (described in worksheet Data)</p><p>Description: Dates of earliest and latest known salvage logging activity in logging coupes</p><p>Number of fields: 10</p><p>Number of data rows: 187</p><p>Fields: </p><ul><li><b>CoupeNumber</b>: Coupe number (Field type: Location)</li><li><b>StartDateTrack</b>: Earliest date of logging activity recorded through GPS loggers on bulldozers (Field type: Date)</li><li><b>EndDateTrack</b>: Last date of logging activity recorded through GPS loggers on bulldozers (Field type: Date)</li><li><b>StartDateLocation</b>: Earliest date of logging activity recorded through 'Location' method (Field type: Date)</li><li><b>EndDateLocation</b>: Last date of logging activity recorded through 'Location' method (Field type: Date)</li><li><b>StartDateMeasurement</b>: Earliest date of logging activity recorded through 'Measurement' method (Field type: Date)</li><li><b>EndDateMeasurement</b>: Last date of logging activity recorded through 'Measurement' method (Field type: Date)</li><li><b>Contractor</b>: Name of the contractor responsible for the coupe (Field type: ID)</li><li><b>GlobalStart</b>: Earliest date of any recorded salvage logging activity (Field type: Date)</li><li><b>GlobalEnd</b>: Last date of any recorded salvage logging activity (Field type: Date)</li></ul></li></ol><p><b>SAFE_COUPE.zip</b></p><p>Description: SAFE coupe data</p><p><b>Date range: </b>2013-01-02 to 2015-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Data from: Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilization
Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta-diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilization and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilization led to higher rates of local extinction whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.
Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries: Datasets
<p>Sinogram XRD-CT data used in the manuscript entitled "Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries".</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Spatial heterogeneity in benthic foraminiferal assemblages track regional impacts of paleoenvironmental change across Cretaceous OAE2
<p>The impact of global climate events on local ecosystems can vary spatially. Understanding this heterogeneity can illuminate which environments will be most impacted and the proximal drivers of ecosystem responses. Cenomanian-Turonian marine deposits of the Western Interior Seaway (WIS) record paleoceanographic changes associated with transgression and the onset of Oceanic Anoxic Event 2 (OAE2). They provide an ideal setting to study basinwide paleoecological responses during a global perturbation. Here, we integrate benthic foraminiferal assemblages from before, during, and after OAE2 via multivariate ordination analysis to examine spatial patterns in faunal responses across the western United States on a common scale and to interrogate a previously defined faunal marker used for basin-wide correlation, the Benthonic Zone (BZ). We identify oxygenation and organic matter quality as primary and secondary controls of variation across the 10 stratigraphic records and use this variation to infer paleoenvironmental changes. Stratigraphic trends reveal, in contrast to previous studies, deoxygenation at the onset of OAE2. They also reveal temporal patterns in oxygenation and productivity consistent with the gradual northward migration of a southern water mass into the WIS. This spatial heterogeneity hinders the use of the BZ in biostratigraphy because assemblages change in response to diachronous environmental change, and thus timing of the BZ with respect to OAE2 is not consistent across the basin. Our study demonstrates that regional processes can overshadow ecosystem responses to global events and underscores the importance of considering how changes in the position of water masses impact the expression of global biogeochemical perturbations.</p>
Areography, environmental heterogeneity and spatial models explain patterns of past and present diversity in Edraianthus (Campanulaceae)
<p>Our study aims to shed light on distribution patterns and the relationship between species diversity and environmental heterogeneity of 17 <em>Edraianthus</em> taxa. We examined present-day and past distribution (Last Glacial Maximum) by applying ecological niche modelling. The environmental space occupied by the taxa was analysed using niche comparison methods. The results support the idea that the central and southern Dinaric Alps represent a centre of diversity and endemism in the western Balkan Peninsula. <em>Edraianthus</em> diversity is best explained by topographic (slope) and bioclimatic predictors (maximum temperature of warmest month, precipitation seasonality, precipitation of warmest quarter, precipitation of coldest quarter), suggesting that mountain areas with the most diversified relief have higher diversity. Niche modelling results suggest considerable range dynamics during the climatically unstable Quaternary. Thermophitic and lowland taxa responded with longitudinal shifts to ecologically suitable areas farther south, while high mountain taxa responded mainly with elevational shifts. The greatest niche similarity is found among phylogenetically closely related taxa and among ecologically similar and often sympatric taxa. The least similarity was observed in species with marked differences in habitat elevation and in more geographically isolated species. Our results suggest that the extent of range and niche overlap varied among some taxa throughout their evolutionary history, which may have led to diversification at both allopatric and sympatric levels. The correlation between the genetic diversity of the most widespread taxon and the richness of taxa could be a consequence of past and present contacts. On the other hand, alternate periods of isolation may have favoured the emergence of taxa that are reproductively isolated and ecologically specialised.</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </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.