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9,882
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ShareScore release 0.9.0
Dataset results
9,882 results for “Scale”
78 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>43 new map files to add to the set of 63k/50k Maps of South Asia, Block 78. An updated Index Map for Block 78 is included in this set of maps.</p>
72 Block--63kor 50k scale Maps of South Asia--additional 1--20251024
<p>96 new map files to add to the set of 63k/50k Maps of South Asia, Block 72. An updated Index Map for Block 72is included in this set of maps.</p>
66 Block--63k or 50k scale Maps of South Asia
<p>69 maps of the Survey of India covering block 66.</p>
38 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>67 new map files to add to the set of 63k/50k Maps of South Asia, Block 38. An updated Index Map from Block 38 is included in this set of maps.</p>
34 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>82 new map files to add to the set of 63k/50k Maps of South Asia, Block 34. An updated index map for Block 34 is included in this set of maps.</p>
55 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>60 new map files to add to the set of 63k/50k Maps of South Asia, Block 55. An updated Index Map for Block 55 is included in this set of maps.</p>
54 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>72 new map files to add to the set of 63k/50k Maps of South Asia, Block 54. An updated Index Map for Block 54 is included in this set of maps.</p>
83 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>33 new map filesto add to the set of 63k/50k Maps of South Asia, Block 83. An updated Index Map for Block 83 is included in this set of maps.</p>
44 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>88 new map files to add to the set of 63k/50k Maps of South Asia, Block 44. An updated Index Map for Block 44 is included in this set of maps.</p>
65 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>72 new map files to add to the set of 63k/50k Maps of South Asia, Block 65. An updated Index Map for Block 65 is included in this set of maps.</p>
40 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>81 new map files to add to the set of 63k/50k Maps of South Asia, Block 40. An updated Index Map for Block 40 is included in this set of maps.</p>
62 Block--63k or 50k scale Maps of South Asia
<p>43 maps of the Survey of India covering block 62.</p>
64 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>94 new map files to add to the set of 63k/50k Maps of South Asia, Block 64. An updated Index Map for Block 64 is included in this set of maps.</p>
58 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>97 new map files to add to the set of 63k/50k Maps of South Asia, Block 58. An updated Index Map for Block 58 is included in this set of maps.</p>
An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix
<p>Appendix of our "An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix" paper.</p>
FESOM2 scaling experiments
<p>Spreadsheet tables with results of FESOM2 scaling experiments performed for the paper "Measuring and optimizing the parallel performance of unstructured ocean model FESOM2" submitted to GMD.</p>
Dataset 2 for Large‐ and small‐scale geographic structures affecting genetic patterns across populations of an Alpine butterfly
<p>Understanding factors influencing patterns of genetic diversity and the population genetic structure of species is of particular importance in the current era of global climate change and habitat loss. These factors include the evolutionary history of a species as well as heterogeneity in the environment it occupies, which in turn can change across time. Most studies investigating spatio-temporal genetic patterns have focused on patterns across wide geographical areas rather than local variation, but the latter can nevertheless be important particularly in topographically complex areas. Here we consider these issues in the Sooty Copper butterfly (<i>Lycaena tityrus</i>) from the European Alps, using genome-wide SNPs identified through RADseq. We found strong genetic differentiation within the Alps with four genetic clusters, indicating western, central, and eastern refuges, and a strong reduction of genetic diversity from west to east. This reduction in diversity may suggest that the southwestern refuge was the largest one in comparison to other refuges. Also, the high genetic diversity in the West may result from (1) admixture of different western refuges, (2) more recent demographic changes, or (3) introgression of lowland <i>L. tityrus</i> populations. At small spatial scales, populations were structured by several landscape features and especially by high mountain ridges and large river valleys. We detected 36 outlier loci likely under altitudinal selection, including several loci related to membranes and cellular processes. We suggest that efforts to preserve alpine <i>L. tityrus </i>should focus on the genetically diverse populations in the western Alps, and that the dolomite populations should be treated as genetically distinct management units, since they appear to be currently more threatened than others. This study demonstrates the usefulness of SNP-based approaches for understanding patterns of genetic diversity, gene flow and selection in a region that is expected to be particularly vulnerable to climate change.</p>
Limited seed dispersal shapes fine-scale spatial genetic structure in a Neotropical dioecious large-seeded palm
<p><span>Seed and pollen dispersal contribute to gene flow and shape the genetic patterns of plants over fine spatial scales. We inferred fine-scale spatial genetic structure (FSGS) and estimated realized dispersal distances in Phytelephas aequatorialis, a Neotropical dioecious large-seeded palm. We aimed to explore how seed and pollen dispersal shape this genetic pattern in a focal population. For this purpose, we genotyped 138 seedlings and 99 adults with 20 newly developed microsatellite markers. We tested if rodent-mediated seed dispersal has a stronger influence than insect-mediated pollen dispersal in shaping FSGS. We also tested if pollen dispersal was influenced by the density of male palms around mother palms in order to further explore this ecological process in large-seeded plants. Rodent-mediated dispersal of these large seeds occurred mostly over short distances (mean 34.76 ± 34.06 m) while pollen dispersal distances were two times higher (mean 67.91 ± 38.29 m). The spatial extent of FSGS up to 35 m and the fact that seed dispersal did not increase the distance at which male alleles disperse suggest that spatially limited seed dispersal is the main factor shaping FSGS and contributes only marginally to gene flow within the population. Pollen dispersal distances depended on the density of male palms, decreasing when individuals show a clumped distribution and increasing when they are scattered. Our results show that limited seed dispersal mediated by rodents shapes FSGS in P. aequatorialis, while more extensive pollen dispersal accounts for a larger contribution to gene flow and may maintain high genetic diversity.</span></p>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part I)
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} }</pre> </blockquote>
Distinctive, fine-scale distribution of Eastern Caribbean sperm whale vocal clans reflects island fidelity rather than environmental variables
<p>Environmental variables are often the primary drivers of species' distributions as they define their niche. However, individuals, or groups of individuals, may sometimes adopt a limited range within this larger suitable habitat as a result of social and cultural processes. This is the case for Eastern Caribbean sperm whales. While environmental variables are reasonably successful in describing the general distribution of sperm whales in the region, individuals from different cultural groups have distinct distributions around the Lesser Antilles islands. Using data collected over two years of dedicated surveys in the Eastern Caribbean, we conducted habitat modelling and habitat suitability analyses to investigate the mechanisms responsible for such fine-scale distribution patterns. Vocal clan-specific models were dramatically more successful at predicting distribution than general species models, showing how a failure to incorporate social factors can impede accurate predictions. Habitat variation between islands did not explain vocal clan distributions, suggesting that cultural group segregation in the Eastern Caribbean sperm whale is driven by traditions of site/island fidelity (most likely maintained through conformism and homophily) rather than habitat type specialization. Our results provide evidence for the key role of cultural knowledge in shaping habitat use of sperm whales within suitable environmental conditions and highlight the importance of cultural factors in shaping sperm whale ecology. We recommend that social and cultural information be incorporated into conservation and management as culture can segregate populations on fine spatial scales in the absence of environmental variability.</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.