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.
3,136
datasets available to search
ShareScore release 0.9.0
Dataset results
3,136 results for “Terrestrialization”
MAP 339 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 339. Distribution of Chamaelycus parkeri in Angola.
MAP 56 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
MAP 56. Distribution of Hyperolius rhizophilus in Angola.
FIGURE 14 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 14. Kaokoveld desert near Espinheira, Namibe Province (Photo by Luis Ceríaco).
FIGURE 4 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 4. The geology of Angola (after Sayre et al. 2013).
FIGURE 9 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 9. Western Congolian Forest-Savanna Mosaic in Uíge Province (Photo by Raffael Ernst).
FIGURE 3 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 3. River basins of Angola (data from https://www.arcgis.com/).
FIGURE 6 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 6. Annual mean precipitation in Angola (after Hijmans et al. 2005).
Figure 8 from: Miu IV, Chisamera GB, Popescu VD, Iosif R, Nita A, Manolache S, Gavril VD, Cobzaru I, Rozylowicz L (2018) Conservation priorities for terrestrial mammals in Dobrogea Region, Romania. ZooKeys 792: 133-158. https://doi.org/10.3897/zookeys.792.25314
Figure 8 Dissimilarity map of Dobrogea mammal species, Romania (S2 index).
Figure 7 from: Miu IV, Chisamera GB, Popescu VD, Iosif R, Nita A, Manolache S, Gavril VD, Cobzaru I, Rozylowicz L (2018) Conservation priorities for terrestrial mammals in Dobrogea Region, Romania. ZooKeys 792: 133-158. https://doi.org/10.3897/zookeys.792.25314
Figure 7 Corrected weighted rarity map of Dobrogea mammal species.
Figure 5 from: Miu IV, Chisamera GB, Popescu VD, Iosif R, Nita A, Manolache S, Gavril VD, Cobzaru I, Rozylowicz L (2018) Conservation priorities for terrestrial mammals in Dobrogea Region, Romania. ZooKeys 792: 133-158. https://doi.org/10.3897/zookeys.792.25314
Figure 5 The mammal species richness at 5 × 5 km grid resolution within Dobrogea.
Figure 1 from: Pohl GR, Landry J-F, Schmidt BC, deWaard JR (2019) Lepidoptera of Canada. In: Langor DW, Sheffield CS (Eds) The Biota of Canada – A Biodiversity Assessment. Part 1: The Terrestrial Arthropods. ZooKeys 819: 463-505. https://doi.org/10.3897/zookeys.819.27259
Figure 1 Map of all Canadian macrolepidopteran type localities for species described since 1978.
Figure 10 from: Urgiles VL, Székely P, Székely D, Christodoulides N, Sanchez-Nivicela JC, Savage AE (2019) Genetic delimitation of Pristimantis orestes (Lynch 1979) and P. saturninoi Brito et al., 2017 and the description of two new terrestrial frogs from the Pristimantis orestes species group (Anura, Strabomantidae). ZooKeys 864: 111-146. https://doi.org/10.3897/zookeys.864.35102
Figure 10 Holotype of Pristimantisquintanai sp. nov. in life.
Figure 5 from: Urgiles VL, Székely P, Székely D, Christodoulides N, Sanchez-Nivicela JC, Savage AE (2019) Genetic delimitation of Pristimantis orestes (Lynch 1979) and P. saturninoi Brito et al., 2017 and the description of two new terrestrial frogs from the Pristimantis orestes species group (Anura, Strabomantidae). ZooKeys 864: 111-146. https://doi.org/10.3897/zookeys.864.35102
Figure 5 Holotype of Pristimantiscajanuma sp. nov. in life. A dorsolateral view B ventral view.
Figure 2 in Assessment of terrestrial snails' diversity and composition in six forests of west central - Morocco along an altitudinal gradient
Figure 2. Geographical location of the prospected stations.
Map 6 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 6. Distribution map for Haplophthalmus mengii.
Plot-level semantically labelled terrestrial laser scanning point clouds
<p><strong>Abstract</strong></p> <p>Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function. However, unlocking their full potential currently requires extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture, competition, space optimisation and physiology, and is a key step in common approaches to individual tree extraction. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture developed from PointNet++ and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, applied to diverse natural European forests. Our model combines meticulously labelled data with voxel-based sampling and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across an extensive dataset, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet++-based approach for leaf/wood segmentation on a high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We tested our model against others’ open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, finding consistently strong performance, showcasing the transferability of our model to a wide range of ecosystems and sensors. Our newly developed evaluation metric for assessing performance in the outer parts of the canopy, such as in twigs and small branches, found our model to clearly outperform the most widely used approach.</p> <p><strong>Methods</strong></p> <p>Within each country, we scanned a subset of the 30 m x 30 m FUNDIV plots using a Riegl VZ400i TLS instrument (RIEGL Gmbh, Horn, Austria), scanning at 600MHz and with an angular resolution of 0.04 mrad. All plots were scanned following a 10m grid system with a minimum of 16 upright and 16 tilt scans (following Wilkes et al. 2017), with additional scans to minimise occlusion in dense areas, and on the plot perimeter. To ensure high-quality data with minimal noise, scanning was paused when wind conditions rose above 5 m/s (measured with an anemometer on the ground) or when gusts were visually evident. In order to create our labelled dataset, we used a semi-automated approach informed by existing approaches followed by significant manual cleaning. Vicari et al. (2019) found anisotropy, verticality and linearity to be informative features for leaf-wood separation, so we created these geometric features at spatial scales of approx. 5 cm - 0.5 m (using CloudCompare, 2023). Alongside these, we used reflectance and xyz information for each point and labelled leaf-wood by thresholding these features. We followed this with intensive manual checking and cleaning to ensure high label quality, especially in the smaller branches and twigs. Our dense scanning and labelling strategy means that this dataset is an ideal candidate for training and testing the capabilities of processing algorithms. Our dataset comprises nine 10 m x 10 m blocks of x, y, z coordinates with corresponding reflectance values and labels, all at 1 cm resolution achieved through voxel downsampling. This data was used for training and validation. For comprehensive evaluation, we incorporated additional openly available datasets, which we cropped to reduce size and cleaned to rectify erroneous labels. The original, unmodified versions of these datasets can be accessed as follows:</p> <div> <p>*Mspace Lab (2024) ‘ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing’, Geo-spatial Information Science. Zenodo. <a href="https://doi.org/10.5281/zenodo.13285640">https://doi.org/10.5281/zenodo.13285640</a>. Distributed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International licence.</p> <p>Wang, Di; Takoudjou, Stéphane Momo; Casella, Eric (2021). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.np5hqbzp6">https://doi.org/10.5061/dryad.np5hqbzp6</a>. Distributed under a Creative Commons 0 1.0 Universal licence.</p> <p>Wan, Peng; Zhang, Wuming; Jin, Shuangna (2021). Plot-level wood-leaf separation for terrestrial laser scanning point clouds [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rfj6q5799">https://doi.org/10.5061/dryad.rfj6q5799</a>. Distributed under a Creative Commons CC0 1.0 Universal licence.</p> <p>Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", <a href="https://doi.org/10.11588/data/UUMEDI">https://doi.org/10.11588/data/UUMEDI</a>, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]. Distributed under a Creative Commons Attribution 4.0 International Deed.</p> </div> <p>*For licensing reasons these data re not included in this repository but can be downladed from the doi provided. </p> <p> </p> <p> </p>
Data from: Environmental DNA metabarcoding of wild flowers reveals diverse communities of terrestrial arthropods
Terrestrial arthropods comprise the most species rich communities on Earth, and grassland flowers provide resources for hundreds of thousands of species. Diverse grassland ecosystems worldwide are threatened by various types of environmental change, which has led to decline in arthropod diversity, while monitoring grassland arthropod diversity is time consuming and strictly dependent on declining taxonomic expertise. Environmental DNA (eDNA) metabarcoding of complex samples has demonstrated that information on species compositions can be efficiently and non-invasively obtained. Here, we test the potential of wild flowers as a novel source of arthropod eDNA. We performed eDNA metabarcoding of flowers from several different plant species using two sets of generic primers, targeting the mitochondrial genes 16S rRNA and COI. Our results show that terrestrial arthropod species leave traces of DNA on the flowers that they interact with. We obtained eDNA from at least 135 arthropod species in 67 families and 14 orders, together representing diverse ecological groups including pollinators, parasitoids, gall inducers, predators and phytophagous species. Arthropod communities clustered together according to plant species. Our data also indicate that this experiment was not exhaustive, and that an even higher arthropod richness could be obtained using this eDNA approach. Overall, our results demonstrate that it is possible to obtain information on diverse communities of insects and other terrestrial arthropods from eDNA metabarcoding of wild flowers. This novel source of eDNA represents a vast potential for addressing fundamental research questions in ecology, obtaining data on cryptic and unknown species of plant-associated arthropods, as well as applied research on pest management or conservation of endangered species such as wild pollinators.
Orientation, latitude, and elevation of 1501 cavities excavated by 25 avian species across 12 terrestrial ecoregions (15°S – 55°S) in South America.
<p><span><span>In the Northern Hemisphere, several avian cavity excavators (e.g., woodpeckers) orient their cavities increasingly toward the equator as latitude increases (i.e., farther north), and it is proposed that they do so to take advantage of incident solar radiation at their nests. If latitude is a key driver of cavity orientations globally, this pattern should extend to the Southern Hemisphere. Here, we test the prediction that cavities are oriented increasingly northward at higher (i.e., colder) latitudes in the Southern Hemisphere and describe the preferred entrance direction(s) of 1501 cavities excavated by 25 avian species (<i>n</i> = 22 Picidae, 2 Trogonidae, 1 Furnariidae) across 12 terrestrial ecoregions (15°S – 55°S) in South America. We used Bayesian projected normal mixed-effects models for circular data to examine the influence of latitude, and potential confounding factors, on cavity orientation. Also, a probability model-selection procedure was used to simultaneously examine multiple orientation hypotheses in each ecoregion to explore underlying cavity-orientation patterns. Contrary to predictions, and patterns from the Northern Hemisphere, birds did not orient their cavities more toward the equator with increasing latitude, suggesting that latitude may not be an important underlying selective force shaping excavation behavior in South America. Moreover, unimodal cavity-entrance orientations were not frequent among the ecoregions analyzed (only in four ecoregions), whereas bimodal (in five ecoregions) or uniform (in three ecoregions) orientations were also present, although many of these patterns were not very clear. Our results highlight the need to include data from under-studied biotas and regions to improve inferences at macroecological scales. Furthermore, we suggest a re-analysis of Northern Hemisphere cavity orientation patterns using a multi-model approach, and a more comprehensive assessment of the role of environmental factors as drivers of cavity orientation at different spatial scales in both hemispheres.</span></span></p>
Figure 2 from: Lombardo B, Messina G, Pezzino E, Montesanto G, Caruso D (2012) The diversity of terrestrial isopods in the natural reserve "Saline di Trapani e Paceco" (Crustacea, Isopoda, Oniscidea) in northwestern Sicily. ZooKeys 176: 215-230. https://doi.org/10.3897/zookeys.176.2367
Figure 2 - Total frequency (%) of catches for the observed families.
Figure 1 from: Gümüs B, Neubert E (2012) New taxa of terrestrial molluscs from Turkey (Gastropoda, Pristilomatidae, Enidae, Hygromiidae, Helicidae). ZooKeys 171: 17-37. https://doi.org/10.3897/zookeys.171.2273
Figure 1 - Vitrea gostelii sp. n., D = 2.25 mm. – All figures scaled × 20, phot H. Baur, NMBE.
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.