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.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “Annapurna Conservation Area”
Data associated with manuscript titled "Landscape predictors of Human-Leopard conflicts in and around Annapurna Conservation Area."
<p>The data set belongs to the research conducted in Annapurna Conservation Area regarding Human Leopard Conflict (HLC). </p>
Supplementary material 4 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Median-joining networks of Himalayan wolf and related wolf and dog clades : Explanation note: Median-joining networks of Himalayan wolf and related wolf and dog clades. Golden jackal and Ethiopian wolf haplotypes are shown for comparison. Circle size and branches are proportional to sampled haplotype frequency and number of nucleotide mutation steps among haplotypes, respectively. Branch numbers refer to mutation steps separating individual haplotypes. Scat samples sequenced in this study are represented by arrows falling within the Himalayan wolf (HWF) and Indian feral dog (IDH) haplotypes. Nomenclature for the African wolf follows Koepfli et al. (2015).
Supplementary material 2 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Genebank sequences analysed in this study : Explanation note: GenBank accession numbers for sequences analyzed in this study.
Supplementary material 1 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Best model selection for Bayesian analysis : Explanation note: Estimating the best model of molecular substitution inferred using Log Bayes Factors (LBF) from Bayesian posterior distributions in Mr Bayes. Rate variation for tested models - gamma distributed rate variation across sites (gamma); gamma distributed with proportion of invariable sites (invgamma); rate variation with proportion of invariable sites (propinv); equal rate variation across sites (equal).
Supplementary material 3 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Aligned CR sequences of selected samples of wolves and dogs from GenBank, with the obtained scat samples : Explanation note: Aligned CR sequences of selected samples of wolves, dogs from GenBank, with scat samples obtained in this study. Numbers refer to mtDNA nucleotide positions referenced with respect to the complete mtDNA genome of gray wolf (GenBank Accession No. KF857179). Scat sequences D2137, D2138, D2139 and D2143 match each other differs from known Himalayan wolf haplotypes by at least two substitutions, while D2140 completely matches domestic dog and a wolf sequence in GenBank.
Applying the double observer methodology for assessing blue sheep population size in Nar Phu Valley, Annapurna Conservation Area, Nepal
<p>This study was undertaken in spring, 2019 to assess the applicability of the Double-Observer survey method for estimating blue sheep <i>Pseudois nayaur</i> abundance in Nar-Phu valley of Manang District located in Annapurna Conservation Area of northern Nepal. Since counting large mammals in rugged mountain habitat poses a special challenge, we tested the efficacy of the Double Observer method for generating robust population estimates for this important protected area. The overall detection probability for observers (O1 and O2) was 0.94 and 0.91 for a total of 106 groups comprised of 2,059 individual blue sheep. We estimated the area's blue sheep population at 2,070 (SE<span> ±</span>168.77; 95% CI 2,059 – 2,405) for the 246.2 km² of sampled habitat. We determined blue sheep to be widely distributed within the study area with a mean density of 8.4 individuals per km<sup>2</sup> based on a total study area of 246.2 km<sup>2</sup>. We discuss demographic population structure and identify limitations when applying the <i>Double Observer</i> approach, along with recommending viewshed mapping for ensuring more robust density estimates of mountain-dwelling ungulates like blue sheep or ibex that inhabit extremely heterogeneous terrain which strongly influences sighting distances and overall animal detection rates.</p>
Applying the double observer methodology for assessing blue sheep population size in Nar Phu Valley, Annapurna Conservation Area, Nepal
Open the record for dataset details and reuse information.
Supplementary material 5 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Blast report of sample D2140 : Explanation note: Blast search result for D2140 scat sequence.
Figure 3 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Figure 3 - Phylogenetic trees constructed using 229 bp of aligned CR sequence data. The values at nodes correspond to A bootstrap support > 50% in maximum likelihood (ML) and maximum parsimony (MP) analyses; and B Bayesian posterior probability > 0.50. Scat samples sequenced in this study are highlighted in bold. Four samples (D2137, D2138, D2139 and D2143) represented a novel haplotype HWF within the Himalayan wolf clade, while a fifth sample (D2140) matched with existing domestic dog haplotypes.
Figure 1 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Figure 1 - A Himalayan wolf photographed in Upper Mustang of Annapurna Conservation Area, Nepal (29.17356°N, 84.13422°E; datum WGS84, elevation 5,050 m) during May 2014.
Figure 2 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Figure 2 - Fecal sample and direct sighting locations of the Himalayan wolf in Upper Mustang of Annapurna Conservation Area, Nepal.
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.