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.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “Slow Loris”
Triangular Mesh of the Brain of a Slow Loris (Nycticebus)
<p>Triangular Mesh of the Brain of a Slow Loris (<i>Nycticebus</i>) from http://braincatalogue.org/Slow_loris</p>
Fig. 2. Simplified neighbor-joining tree reconstructed from partial cox1 in Lurking in the dark: Cryptic Strongyloides in a Bornean slow loris
Fig. 2. Simplified neighbor-joining tree reconstructed from partial cox1 gene (716 bp) sequences of Strongyloides spp. S. fuelleborni sequences for Bornean primates cluster within the S. fuelleborni group, together with previously described sequences for the parasite found in African and Japanese primates. The S. stercoralis cluster includes sequences from humans from Laos, Africa and Japan, captive chimpanzees, and dogs. The Strongyloides sp. cluster corresponds to sequences from the slow loris. An alternative hypothesis is presented next to the tree, where instead of representing a different species, Strongyloides sp. would be part of a cryptic assemblage within the S. stercoralis group.
Fig. 1. Sampling sites within Lots 6 and 7 in Lurking in the dark: Cryptic Strongyloides in a Bornean slow loris
Fig. 1. Sampling sites within Lots 6 and 7 of the Lower Kinabatangan Wildlife Sanctuary, Malaysian Borneo (Nm = Nycticebus menagensis, Pp = Pongo pygmaeus, Mf = Macaca fascicularis, Nl = Nasalis larvatus, Tc = Trachypithecus cristatus, DGFC = Danau Girang Field Centre).
Magnetic Resonance Imaging Scan of the Brain of a Slow Loris (Nycticebus)
<p>Magnetic Resonance Imaging Scan of the Brain of a Slow Loris (<i>Nycticebus</i>) from http://braincatalogue.org/Slow_loris</p>
On following pages: 7. Red Slender Loris (Loris tardigradus); 8. Bengal Slow Loris (Nycticebus bengalensis); 9. Sunda Slow Loris (Nycticebus coucang); 10. Javan Slow Loris (Nycticebus javanicus); 11. Bornean Slow Loris (Nycticebus menagensis); 12. Pygmy Slow Loris (Nycticebus pygmaeus). in Lorisidae
On following pages: 7. Red Slender Loris (Loris tardigradus); 8. Bengal Slow Loris (Nycticebus bengalensis); 9. Sunda Slow Loris (Nycticebus coucang); 10. Javan Slow Loris (Nycticebus javanicus); 11. Bornean Slow Loris (Nycticebus menagensis); 12. Pygmy Slow Loris (Nycticebus pygmaeus).
Supplementary data from: The Holocene fossil record of the slow loris (Nycticebus sp.) in Java (Indonesia)
<p>Supplementary data from: The Holocene fossil record of the slow loris (Nycticebus sp.) in Java (Indonesia)</p> <p>Abstract:</p> <p>Fossil lorises are rare in Southeast Asia. Their taxonomic relationship with extant populations, and the extent to which their distribution and morphology are influenced by changing environmental conditions, remain poorly understood. This study provides a synthesis of <em>Nycticebus</em> occurrences in Holocene Java (Indonesia). A morphometric analysis of a sample of craniodental remains aims to improve our understanding of their taxonomic status. Morphometrics were also used to explore potential size changes during the Holocene.</p> <p>Based on the literature and a review of museum catalogs, a synthesis was compiled of fossil slow loris occurrences in Java. Morphometric data on the mandible and maxilla of 11 fossil lorises were compared with a dataset of extant specimens to assess variation in size and shape.</p> <p>Five Holocene <em>Nycticebus</em> occurrences were identified in eastern Java. All specimens fall in the range of <em>N. javanicus</em> and <em>N. coucang</em>. The specimens from Hoekgrot, Gua Jimbe and Sampung suggest a closer affinity to <em>N. javanicus</em>. The fossils from Gua Jimbe and Hoekgrot gave values close to the largest <em>N. javanicus</em> specimens, but the (presumably older) Song Terus fossil was of average size.</p> <p>The distribution of <em>Nycticebus</em> suggests that it originally occurred throughout the island. The fossils are probably best identified as <em>N. javanicus</em> or <em>N. coucang</em>, but the Neolithic finds from Hoekgrot and Gua Jimbe are presumably <em>N. javanicus</em>. Size variation in <em>Nycticebus</em> was clinal, but although some large specimens were present, no evidence was found for size diminution during the Holocene.</p>
Figure 2 in Noxious arthropods as potential prey of the venomous Javan slow loris (Nycticebus javanicus) in a West Javan volcanic agricultural system
Figure 2. Mean abundance of the most frequently captured arthropod taxa per trap type. Sample size: Malaise trap n = 21, sweep net n = 17, pitfall trap n = 9. Error bars: ± 1 SE.
Data from: Failure of the ILD to determine data combinability for slow loris phylogeny
Tests for incongruence as an indicator of among data partition conflict have played an important role in conditional data combination. When such tests reveal significant incongruence, this has been interpreted as rationale for not combining data in a single phylogenetic analysis. In this study of lorisiform phylogeny, we employ the incongruence length difference (ILD) test to assess conflict among three independent data sets. A large morphological data set and two unlinked molecular data sets, the mitochondrial cytochrome b gene and the nuclear interphotoreceptor retinoid binding protein (exon 1), are analyzed with various optimality criteria and weighting mechanisms in order to determine the phylogenetic relationships among slow lorises (Primates, Loridae). When analyzed separately, the morphological data show impressive statistical support for a monophyletic Loridae. Both molecular data sets resolve the Loridae as paraphyletic, though with different branching order depending on optimality criterion and/or character weighting employed. When the three data partitions are analyzed in various combinations, an inverse relationship between congruence and phylogenetic accuracy is observed. Nearly all combined analyses that recover monophyly indicate strong data partition incongruence (p = 0.00005, in the most extreme case) whereas all analyses that recover paraphyly indicate lack of significant incongruence. Numerous lines of evidence verify that monophyly is the accurate phylogenetic result. Therefore, this study contributes to a growing body of information that affirms that measures of incongruence should not be employed as indicators of data set combinability.
Data from: Failure of the ILD to determine data combinability for slow loris phylogeny
Open the record for dataset details and reuse information.
Figure 1 in Noxious arthropods as potential prey of the venomous Javan slow loris (Nycticebus javanicus) in a West Javan volcanic agricultural system
Figure 1. Map of the study location Cipaganti near Garut, West Java.
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.