Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
231
datasets available to search
ShareScore release 0.9.0
Dataset results
231 results for “scale dependence”
Litter decomposition is moderated by scale-dependent microenvironmental variation in tundra ecosystems
<p><strong>QHI_crop.tiff </strong>= We carried out topographic surveys using unoccupied aerial vehicles photogrammetry in August 2017. We used three UAV platforms to collect RGB multispectral data at a fine (3 cm) spatial resolution: DJI Phantom 4 Pro and Advanced (multicopter), and Phantom FX-61 (fixed wing), and used used structure from motion with multiview steriopsis to obtain a fine-grain 10 cm spatial resolution digital surface model and orthomosaic as described in Cunliffe et al. (2019a, 2019b).</p> <p><strong>thermsum.tif </strong>= We used the microclima package in R (Kearney et al., 2020; Maclean et al., 2019) to model surface air temperature at a 1-m spatial grain. Using our fine resolution DSM, we modelled mean surface temperatures at the study site for each day spanning the teabag burial period of 13th July to 9th August 2017. The microclima model incorporates local daily climate, radiation, cloud cover and coastal exposure data from gridded global datasets derived from RCNEP (<a href="https://www.zotero.org/google-docs/?broken=Zl6wgI">Kemp et al., 2012)</a>. We summed the 28 TIF files produced through this modelling technique to produce a 28-day thermal sum variable - a metric which captures the overall heating of the ground surface over the course of the experiment.</p> <p><strong>Cited Works:</strong></p> <p> </p> <p>Cunliffe, A., I. Myers-Smith. J. Kerby and W. Palmer (2019a). Orthomosaic of permafrost landscape on Qikiqtaruk – Herschel Island, Yukon, Canada: August 2017. NERC Polar Data Centre. DOI:10.5285/29bf1c9f-a39a-452c-b9f9-de35d9fb9179.</p> <p> </p> <p>Cunliffe, A., G. Tanski, B. Radosavljevic, W. Palmer, T. Sachs, H. Lantuit, J. Kerby, and I. Myers-Smith (2019b) Rapid retreat of permafrost coastline observed with aerial drone photogrammetry. The Cryosphere 13(5):1513-1528. DOI: 10.5194/tc-13-1513-2019.</p> <p> </p> <p><a href="https://www.zotero.org/google-docs/?hjdBYY">Maclean, I. M. (2020). Predicting future climate at high spatial and temporal resolution. <em>Global Change Biology</em>, <em>26</em>(2), 1003–1011.</a></p> <p> </p> <p>Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. (2020). A method for computing hourly, historical, terrain‐corrected microclimate anywhere on Earth. <em>Methods in Ecology and Evolution</em>, <em>11</em>(1), 38-43.</p> <p> </p> <p>Kemp, M. U., Van Loon, E. E., Shamoun-Baranes, J., & Bouten, W. (2012). RNCEP: global weather and climate data at your fingertips. <em>Methods in Ecology & Evolution</em>, <em>3</em>(1), 65-70.</p> <p><strong>Paper Abstract:</strong></p> <ol> <li> <p><strong>The Arctic tundra is one of the world’s largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. We currently know very little about landscape-scale controls of litter decomposition in tundra ecosystems, which hinders our understanding of the global carbon cycle. </strong></p> </li> <li> <p><strong>Here, we examined how local-scale topography, surface air temperature, soil moisture and permafrost conditions influenced litter decomposition rates across a heterogeneous tundra landscape on Qikiqtaruk - Herschel Island (Yukon, Canada).</strong></p> </li> <li> <p><strong>We used the Tea Bag Index protocol to derive decomposition metrics which we then compared across environmental gradients, including thermal sum surface temperature data derived from fine-resolution microclimate data modelled from drone derived topographic data.</strong></p> </li> <li> <p><strong>We found greater green tea litter mass loss and faster decomposition rates in wetter and warmer areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness.</strong></p> </li> <li> <p><strong>Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at the landscape-scale (> 10 m) better than surface temperature.</strong></p> </li> <li> <p><strong>Surprisingly, there was no strong control of elevation or slope of litter decomposition. We also found higher decomposition rates on North-facing relative to South-facing aspects at microsites that were wetter rather than warmer.</strong></p> </li> </ol>
Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land-cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10, 30, 60 and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (about 7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0 to 100%, each) in a mid-sized city in the Upper Midwest, USA. Variability in daytime air temperature within the urban landscape averaged 3.5 degreeC (range 1.1 to 5.7 degreeC). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale, and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1C (range 1.2 to 3.0 degreeC), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.
Data from: Mean landscape-scale incidence of species in discrete habitats is patch size dependent
<p>Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'.</p> <p>Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants). </p> <p>The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups. </p>
F I G U R E 3 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 3 Relationships between fast-start performance, body size (standard length), acclimation temperature, and test temperature in adult mummichogs. Graphs are arranged by performance variable in rows and by test temperature (24, 30, and 36 C) in columns. Colors indicate acclimation temperatures (blue is 24 C, purple is 30 C, and red is 36 C). Lines are the fitted performance–size relationships based on parameter estimates from best-fit mixed-effects models or model-averaged parameter estimates from supported models (see Table 1 and Table S3.). Effects of size on performance are apparent as nonzero slopes within individual graphs; effects of test temperature are evident as variation in average intercepts across test temperature graphs (i.e., comparing average intercepts for acclimation temperature–specific lines across columns within each row of graphs); effects of acclimation temperature are seen as differences in intercepts between acclimation temperature–specific lines within graphs; and interactions between size and temperature are apparent as variations in average slopes (i.e., averaged over acclimation-specific slopes) across test temperature graphs and as differences in acclimation temperature–specific slopes within graphs.
F I G U R E 1 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 1 Morphological scaling in mummichog ranging from YOY to larger adults. Morphological variables are body dimensions (top row) and median fin areas (bottom row), and standard length is a measure of the overall body size. All data were log transformed. Sample sizes for fin areas were reduced because individuals with damaged fins were not measured. Estimates for scaling coefficients (i.e., the slope of the scaling relationship, b1) ± 1 S.E. are given for each morphological variable. Isometric expectations for scaling coefficients are 1.0 for body dimensions and 2.0 for fin areas; only the caudal fin area deviates significantly from isometry.
F I G U R E 2 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 2 Representative kinematics for fast-starts in small (41 mm SL) and large (66 mm SL) adult mummichogs controlling for temperature. The top panel shows frames from high-speed videos at 10 ms intervals. To facilitate interpretation, original videos have been cropped to the same spatial dimensions and rotated to standardize starting orientation and escape direction (but note that digitization used unmodified videos). The graph in the bottom panel shows the angular displacement of the head (triangles) and the linear displacement of the fish center of mass (circles) for the trials depicted in the video frames, which were maximum performance trials for the small (filled shapes) and large (open shapes) fish tested at their acclimation temperature (24 C). The curve for head rotation was truncated at its maximum value to highlight the difference in time to form the initial C-bend. The curve for linear displacement continued until the end of stage 2 of the fast-start. Whereas small fish had higher rotation rates and executed the fast-start in less time, small and large fish moved their bodies over similar distances given equal amounts of time.
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Data and R code from: Relics of beavers past: time and population density drive scale-dependent patterns of ecosystem engineering
<p><span>Like many ecological processes, natural disturbances exhibit scale-dependent dynamics that are largely a function of the magnitude, frequency, and scale at which they are assessed. Ecosystem engineers create patch-scale disturbances that affect ecological processes, yet we know little about how these effects scale across space or vary through time. Here, we investigate how patch disturbances by beavers (<i>Castor canadensis</i>), ecosystem engineers renowned for their pond-creation behavior, affect ecological processes across space and time. We evaluated how beaver population recovery influenced surface water dynamics in relation to population density over 70 years across multiple spatial scales (pond, watershed, and regional) in northern Minnesota. Surface water area was positively related to population density at the watershed scale; however, despite variation in beaver densities (and therefore surface water area) at the watershed scale, regional-scale surface water area was stable through time. This stability appears to have been driven by asynchronous beaver density fluctuations among watersheds, combined with the increasing importance of abandoned ponds. Beavers initially created and occupied larger ponds with greater surface water area, but through time shifted towards occupying smaller ponds. As ponds accumulated on the landscape proportionally more surface water was stored within abandoned ponds, which offset the smaller size of occupied ponds. Beaver engineering—driven by density-dependent mechanisms and the legacy effects from abandoned ponds—not only follows general patterns of patch disturbance dynamics by creating a spatial mosaic of patches, but the organism-created mosaic also appears to generate ecological stability at greater spatial scales. We suggest restoring beavers to landscapes is a viable method for increasing surface water storage and will ultimately help advance numerous conservation and rewilding objectives. Our study demonstrates that ecosystem engineering effects can be scale-dependent, indicating researchers should evaluate the ecological impact of engineers across diverse spatiotemporal scales to fully understand their functional roles in ecosystems.</span></p>
Environment and density-dependency explain the fine-scale distribution of tree recruits before and after thinning
<p>Dataset used in the article "Environment and density-dependency explain the fine-scale distribution of tree recruits before and after thinning" in PeerJ</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data Revision
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
<p>Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.</p>
Figure 2 in A sense of scale: Foraging cetaceans' use of scale-dependent multimodal sensory systems
Figure 2. Scale-of-senses schematic of the hypothetical interchange of sensory modalities used by baleen whales to locate prey at variable scales. The line for audition of signals from prey is faded to denote a lack of evidence for this sensory system in baleen whales. X-axis on log scale, with equivalent metric distance given in gray type, and associated scale below. Y-axis ranks the relative use of each sensory modality between 0 (no contribution) and 10 (highest contribution) relative to its own information capacity, not relative to other senses.
Figure 1 in A sense of scale: Foraging cetaceans' use of scale-dependent multimodal sensory systems
Figure 1. Scale-of-senses schematic of the hypothetical interchange of sensory modalities used by dolphins to locate prey at variable scales. The line for chemoreception is faded to denote a lack of support for the sensory system in dolphins. X-axis on log scale, with equivalent metric distance given in gray type, and associated scale below. Y-axis ranks the relative use of each sensory modality between 0 (no contribution) and 10 (highest contribution) relative to its own information capacity, not relative to other senses.
Figure 20 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 20. Results of cluster analysis (Simpson index) of species compositions of tintinnid ciliates in the world's oceans; in nodes of dendrogram, the results of bootstrap-analysis are marked.
Figure 10 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 10. Results of cluster analysis (Simpson index) of species compositions of tintinnid ciliates in the Atlantic Ocean; in nodes of dendrogram, the results of bootstrap-analysis are marked.
Figure 23. A in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 23. A log-log plots of semivariance versus distance for tintinnid alpha-diversity (A – for distances ranged 100 to 2,500 km; B – for distances ranged 1,500 to 16,000 km). DF – fractal dimension; r - correlation coefficient.
Figure 5 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 5. Species accumulation curve for tintinnid ciliates diversity in the Black Sea and the Sea of Azov.
Figure 8 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 8. Results of cluster analysis (Simpson index) of species compositions of tintinnid ciliates in the Mediterranean Sea; in nodes of dendrogram, the results of bootstrap-analysis are marked.
Figure 14 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 14. Results of cluster analysis (Simpson index) of species compositions of tintinnid ciliates in the Indian Ocean; in nodes of dendrogram, the results of bootstrap-analysis are marked.
Figure 6 in Biogeography of marine tintinnid ciliates (Ciliophora, Tintinnida): a Scale-Dependent Model
Figure 6. Results of cluster analysis (Simpson index) of species compositions of tintinnid ciliates in the Black Sea and the Sea of Azov; in nodes of dendrogram, the results of bootstrap-analysis are marked.
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.