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.
3,169
datasets available to search
ShareScore release 0.9.0
Dataset results
3,169 results for “bat”
FIG. 4 in A new species of horseshoe bat (Chiroptera: Rhinolophidae) from Mount Namuli, Mozambique
FIG. 4. Live photographs of R. namuli sp. nov. in the field (♀, MHNG 1971.067), illustrating pelage colour and features of the (a) head and ears, (b) sella and lips and (c) connecting process
FIG. 3 in A new species of horseshoe bat (Chiroptera: Rhinolophidae) from Mount Namuli, Mozambique
FIG. 3. Maximum likelihood tree based on CYT-B sequences and reconstructed with IQ-tree. Sequences of R. hipposideros were used as outgroups. Bootstrap support levels are explained in the box; the scale indicates nucleotide substitutions per site. Newly sequenced taxa are associated with their GenBank accession numbers (see Table 2 and Supplementary Appendix S3 for more details), while the species groups to which they belong appear to the right of the tree. The clade representing the new species (sp. nov.) is highlighted in grey
FIG. 5 in A new species of horseshoe bat (Chiroptera: Rhinolophidae) from Mount Namuli, Mozambique
FIG. 5. Head shape (a and b) and noseleaf (c and d) of holotypes of R. maendeleo SMF 79.643 and R. namuli sp. nov. MHNG 1971.068, respectively; connecting process of R. maendeleo SMF 79.643 (e), SMF 99.760 (f), and of R. namuli sp. nov. MHNG 1971.068 (g)
FIG. 2 in A new species of horseshoe bat (Chiroptera: Rhinolophidae) from Mount Namuli, Mozambique
FIG. 2. PCA biplot for selected (A) craniodental and (B) external measurements. Ellipses represent 95% confidence intervals around each species group (not possible for R. maendeleo due to the low sample size). Large symbols represent the species mean value. Percentage values on each axis are the variances explained. For specimen accession numbers and input data, see Tables 3 and 4
Figure 4 in Structure of three subtropical bat assemblages (Chiroptera) in the Andean rainforests of Argentina
Figure 4 Relative abundance plots for principal guilds of Argentina subtropical rainforest. LM, Laja Morada; RC, Río de Las Conchas; ED, El Durazno. F/und, Frugivore understory; F/can, frugivore/canopy; U/ins, uncluttered space/aerial insectivore; B/ins, background-cluttered space/aerial insectivore; Hem, gleaning hematophagous.
Figure 2 in Structure of three subtropical bat assemblages (Chiroptera) in the Andean rainforests of Argentina
Figure 2 Relative abundance of each bat species in three sites of Argentina subtropical rainforests. Total sample effort was 7538 m-net-h in Laja Morada; 7178 m-net-h in Río de Las Conchas; 12,422 m-net-h in El Durazno. Apl, ArtibeUs planirostris; Ser, StUrnira erythromos; Sli, S. liliUm; Sop, StUrnira oporaphilUm (Tschudi, 1844); Pbi, Pygoderma bilabiatUm (Wagner, 1843); Dro, DesmodUs rotUndUs; Cau, ChrotopterUs aUritUs; Aca, AnoUra caUdiFer; Efu, EptesicUs FUrinalis; Edi, EptesicUs diminUtUs (Osgood, 1915); Deg, DasypterUs ega (Gervais, 1856); Lbl, LasiU- rUs blosseVillii (Lesson and Garnot, 1826); Lci, LasiUrUs cinereUs (Beauvois, 1796); Mal, Myotis albescens; Mdi, Myotis dinellii (Thomas, 1902); Mke, Myotis keaysi (J. A. Allen, 1914); Mri, Myotis ripariUs (Handley, 1960); Hla, HistiotUs laephotis (Thomas, 1916); Cpl, Cynomops planirostris (Peters, 1865); Ebo, EUmops bonariensis (Peters, 1874); Egl, EUmops glaUcinU (Wagner, 1843); Nma, Nyctinomops macrotis (Gray, 1839); Mte, Molossops temminckii (Burmeister, 1854); Pna, Promops nasUtUs (Spix, 1823); Tbr, Tadarida brasiliensis.
Figure 3 in Structure of three subtropical bat assemblages (Chiroptera) in the Andean rainforests of Argentina
Figure 3 Individual-based rarefaction curves (solid lines) with 95% CIs (box or dashed lines) based in mist-netting captures for three sites in rainforests of Argentina. (A) Traditional rarefactions analysis; note the values zero in the both extreme of CI. (B) Conservative analysis using an unconditional variance expression; CI is opened at the end of the curve. LM, Laja Morada; ED, El Durazno; RC, Río de Las Conchas.
Influence of ambient temperature on the phenology of the greater mouse-eared bat (Myotis myotis)
<p>In order to assess the consequences of climate change and evaluate its impacts on wildlife, it is essential to do so on a species-specific level. It is assumed that changes in the ambient temperature influence energy consumption as well as food availability and thus foraging behaviour, reproduction, survival and therefore population dynamics in bats. Based on this assumption, the present study aims to gain insights into the roosting and breeding behaviour of the greater mouse-eared bat (<em>Myotis myotis</em>) in relation to changes of the ambient temperature. For this purpose, we investigated the effect of ambient temperature on the phenology of the greater mouse-eared bat by using activity data of the bats collected using light barriers at the maternity roosts. The light barrier used in this study is a system that detects the interruption of two light beams, e.g. by a flying bat, and displays it as an electrical signal. The investigations have shown that (1) the higher the winter temperatures, the earlier the greater mouse-eared bats returned to the roosts to form the maternity colony; however, this was only true for ambient temperatures below 0.5 °C, (2) birth season started earlier at higher spring temperatures, and (3) the dissolution of maternity roosts occurred earlier with an earlier birth season and at higher ambient temperatures during lactation. The results revealed that ambient temperature has an influence on the phenology of the greater mouse-eared bat. This study highlights that in order to understand the impact of climate change on biodiversity, it is necessary to investigate in detail effects on a species-specific level and also to consider direct and indirect effects of ambient temperature on different life history stages.</p>
Newspaper Coverage and Framing of Bats, and Their Impact on Readership Engagement
<p>Dataset underpinning the following study: </p> <p>Abstract: The media is a valuable pathway for transforming people’s attitudes towards conservation issues. Understanding how bats are framed in the media is hence essential for bat conservation, particularly considering the recent fearmongering and misinformation about the risks posed by bats. We reviewed bat-related articles published online no later than 2019 (before the recent COVID19 pandemic), in 15 newspapers from the five most populated countries in Western Europe. We examined the extent to which bats were presented as a threat to human health and the assumed general attitudes toward bats that such articles supported. We quantified press coverage on bat conservation values and evaluated whether the country and political stance had any information bias. Finally, we assessed their terminology, and, for the first time, modelled the active response from the readership based on the number of online comments. Out of 1096 articles sampled, 17% focused on bats and diseases, 53% on a range of ecological and conservation topics, and 30% only mention bats anecdotally. While most of the ecological articles did not present bats as a threat (97%), most articles focusing on diseases did so (80%). Ecosystem services were mentioned on very few occasions in both types (<30%), and references to the economic benefits they provide were meagre (<4%). Disease-related concepts were recurrent, and those articles that framed bats as a threat were the ones that garnered the highest number of comments. Therefore, we encourage the media to play a more proactive role in reinforcing positive conservation messaging by presenting the myriad ways in which bats contribute to safeguarding human well-being and ecosystem functioning.</p>
Data for: Combining stable isotopes, trace elements, and distribution models to assess the geographic origins of migratory bats
<p>The expansion of industrial-scale wind-energy facilities has increased the production of low-carbon emission energy but has also resulted in mortality of wildlife, including migratory bats. Management decisions can be limited by a lack of understanding of the geographic impact of bats killed at wind-energy facilities. Several studies have leveraged stable hydrogen isotope ratios (δ<sup>2</sup>H) of bat fur to illuminate this issue but are limited in the precision of conclusion because δ<sup>2</sup>H values vary primarily across latitudinal and elevational bands. One approach to increase the precision of geographic assignment is to combine independent inferences about spatial location from additional biomarkers and other related information. To test this possibility, we assigned known-origin individuals of three bat species commonly killed at on-shore wind-energy facilities in North America (<em>Lasiurus</em> <em>borealis</em>, <em>L</em>. <em>cinereus</em>, and <em>Lasionycteris</em> <em>noctivagans</em>) to probable origin using δ<sup>2</sup>H values, trace element concentrations, and species distribution models. We used cross-validation calibrated combined model tuning to determine the degree to which assignment probabilities improved when combining datasets. We found that combining markers typically performed better than single approaches. For <em>L. borealis</em> and <em>L. cinereus</em>, combining all three data sources outperformed any single or other combined approach. With an accuracy set at 80%, an average of 39.7% and 36.0% of each species' total geographic range was considered a potential origin, respectively; stable hydrogen alone included 51.8% and 50.6% of the total geographic area. In contrast, for <em>L. noctivagans</em>, including trace elements did not increase precision, and adding distribution data to δ<sup>2</sup>H values only improved precision by 0.6%. Thus, we found that a combination of multiple biomarkers typically, but not always, outperforms single marker approaches, and optimized combinations of different markers outperform equal weighting of each marker. From a practical perspective, δ<sup>2</sup>H values performed better than trace elements alone; in cases where cost is a limiting factor, the stable hydrogen should be the single biomarker used in conjunction with species distribution models. Overall, these results highlight the importance of validating methods for each species they are applied to and show that combining information from intrinsic biomarker approaches is a useful tool to document bat movements.</p>
FIGURE 11 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 11. Baculum structure of Rhinolophus b. sobrinus [a—dorsal view; b—ventral view; c—lateral view; Scale: 1 mm].
FIGURE 10 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 10. Skull and mandible of Rhinolophus b. sobrinus [a—dorsal view of skull; b—lateral view of skull; c—frontal view of skull; d—ventral view of skull; e—dorsal view of mandible; f—lateral view of mandible; g—frontal view of mandible; Scale: 5 mm].
FIGURE 9 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 9. External features of Rhinolophus b. sobrinus [a—structure of ear; b—close-up view of lower lip, showing one mental groove; c—frontal view of head and ventral view of the sella; d—frontal view of noseleaf; e—lateral view of noseleaf; f—frontal view of penis; g—lateral view of penis; h—hind foot].
FIGURE 7 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 7. Skull and mandible of Rhinolophus b. beddomei [a—dorsal view of skull; b—lateral view of skull; c—frontal view of skull; d—ventral view of skull; e—dorsal view of mandible; f—lateral view of mandible; g—frontal view of mandible; Scale: 5 mm].
FIGURE 5 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 5. External features of Rhinolophus b. beddomei [a—frontal view of noseleaf; b—lateral view of noseleaf depicting the sella; c—frontal view of head and ventral view of the sella; d—structure of the ear; e—closeup of the lower lip showing one mental groove; f—frontal view of penis; g—hind foot].
FIGURE 6 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 6. Coloration of dorsal fur (in live condition) of a—Rhinolophus b. beddomei and b—R. b. sobrinus.
FIGURE 4 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 4. Bayesian inference (BI) tree based on partial COI sequences, using the HKY+G nucleotide substitution model (BIC = 4338.0916). Nodal values represent posterior probability.
FIGURE 2 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 2. PCA plot based on craniodental characters of Rhinolophus species of the R. trifoliatus group.
FIGURE 8 in Taxonomic status of the Lesser Woolly Horseshoe bats (Chiroptera, Rhinolophidae Rhinolophus beddomei) in peninsular India and Sri Lanka
FIGURE 8. Baculum structure of Rhinolophus b. beddomei [a—dorsal view; b—ventral view; c—lateral view; Scale: 1 mm].
To share or not to share: DNA metabarcoding reveals trophic niche overlap between sympatric trawling bats.
<p>All genetic sequences used in the scientific paper entiteled "To share or not to share: DNA metabarcoding reveals trophic niche overlap between sympatric trawling bats".</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.