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Fig. 1 in Preliminary observations on the circadian variation in site fidelity in Atelopus hoogmoedi (Lescure, 1974) (Anura, Bufonidae)
Fig. 1. (A) Orange and (B) Yellow color morphs of Atelopus hoogmoedi, both encountered at the studied locality in the Iwokrama Mountains, Guyana. (C) Typical breeding habitat of A. hoogmoedi in the Iwokrama Mountains. Photos by PJRK.
Rock magnetic data from IODP Exp. 382 Sites U1537 and U1538 to support Reilly et al. "A geochemical mechanism for >10 m offsets of magnetic reversals inferred from the comparison of two Scotia Sea drill sites"
<p>Rock magnetic data from IODP Exp. 382 Sites U1537 and U1538 to support Reilly et al. "A geochemical mechanism for >10 m offsets of magnetic reversals inferred from the comparison of two Scotia Sea drill sites"</p><p>Excel Files:</p><ul><li>U1537_CubeSummary_Zenodo.xlsx : Summary of NRM, ARM, IRM, and magnetic susceptibility investigations on U1537 cube samples</li><li>U1538_CubeSummary_Zenodo.xlsx : Summary of NRM, ARM, IRM, and magnetic susceptibility investigations on U1538 cube samples</li></ul><p>Zip Files:</p><ul><li>FORC_Data.zip : First order reversal curve data files in MicroMag format for samples discussed in paper</li><li>DCD_Data.zip : DC Demagnetization curve data files for samples discussed in paper</li><li>Hysteresis_Data.zip : Hysteresis Loops for samples discussed in paper</li><li>MPMS_Data.zip : Data collected on Magnetics Property Measurement System 3, including Field Cooled/Zero Field Cooled Curves, Low Temperature Cycling of Room Temperature IRM, and AC Susceptibility</li></ul><p> </p><p>NRM = Natural Remanent Magnetization; ARM = Anhysteretic Remanent Magnetization; IRM = Isothermal Remanent Magnetization</p>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Fig. 2 in Ichthyofaunal Diversity Of A Ramsar Site In Kashmir Himalayas - Wular Lake
Fig. 2. Fishes caught during sampling: A — Cyprinus carpio var. specularis; B — Cyprinus carpio var. specularis; C — Carassius carassius; D — Puntius conchonius; E — Schizothorax niger; F — Schizothorax curvifrons; G — Schizothorax esocinus; H — Crossocheilus diplochilus; I — Triplophysa marmorata.
Fig. 1 in Additional data on mollusks of the archaeological site Chernyatino-2 (Primorye)
Fig. 1. Plan-scheme of the ancient settlement Chernyatino-2: A – plan of the north-eastern part of the settlement; В – situational plan of the excavations.
Figure 1 in Why may the same species have different elevational ranges at different sites in New Guinea?
Figure 1. Example of the Massenerhebung effect: variation in the elevational ceiling and floor of the Island Leaf Warbler Seicercus poliocephalus on New Guinea mountains. Note that the warbler's ceiling (upper graph) and floor (lower graph) are higher on Central Range peaks than on outlier peaks; that they tend to be higher on higher peaks (summit elevation, abscissa); that they tend to be higher on inland-facing or interior-facing outlier transects than on coast-facing outlier transects; and that they tend to be higher on outlier transects further from the coast than on outlier transects nearer the coast. Data from Table 4.
Figure 2 in Why may the same species have different elevational ranges at different sites in New Guinea?
Figure 2. Google Earth view of the environment of Diamond's 1979 Foja coastal transect. Visible are the narrow north-flowing stream on which Diamond's camp was sited; the western and eastern ridges flanking the valley in which the stream lies; at the bottom of the field of view, the ridges south of Diamond's camp, rising towards the Foja summits; the near-vertical east / west barrier ridge constituting the northern flank of the basin, and pierced by the stream; and, at the top, New Guinea's lowland coastal plain beyond the east / west ridge, and in which the stream flows in a braided gravel bed. A line traces the crest of the western, eastern and northern ridges, with numbers denoting elevation readings in metres. Diamond's camp in the basin, labelled 610 m, was at 02°45'S, 138°63'E.
Figure 3 in Why may the same species have different elevational ranges at different sites in New Guinea?
Figure 3. Google Earth close-up view, from the south, of the east / west ridge closing to the north the basin viewed in Fig. 2. The ridge has near-vertical southern and northern faces, and is pierced by the basin's stream via a cleft only c.20 m wide.
Fig. 9 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 9: Histogram of the Mantel test assessing the relationship between genetic and morphologic distance for Gobius niger. Sim: simulations; Frequency: frequency values of the correlation between the genetic and morphologic distances. The dot represents the original value of the correlation between the distance matrices.
Fig. 6 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 6: PCA of the morphological variables of Gobius niger (standard length, SL; body height, BH; head length, HL; snout length, SnL; eye diameter, ED; first dorsal fin, DF1; second dorsal fin, DF2; anal fin, AF; pectoral fin, PF; ventral fin, VF) with projection of phenotypic groups. PC1 vs. PC2 and PC2 vs. PC3. The percentage of variation explained by each PC axis is given within parentheses.
Fig. 3 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 3: Cluster analysis associated with the similarity profile test (SIMPROF), based on abundances of Gobius niger, reveals reciprocal relations among the 20 sampled stations in the Marchica Lagoon using the Bray–Curtis distance.
Fig. 2 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 2: Picture of Gobius niger from the Marchica Lagoon showing the main measurements taken: total length (TL), standard length (SL), head length (LT), snout length (SnL), body height (BH), and eye diameter (ED).
Fig. 7 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 7: Linear regression of the principal component score axis (PC1) from morphometric measurements on the log standard length of Gobius niger with projection of phenotypic groups.
Fig. 8 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 8: Haplotype network constructed from 16S rDNA sequences of Gobius niger. The size of a particular circle reflects the haplotype frequency. The numbers indicate the nodes.
Fig. 1 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 1: Map showing the geographical localization of the Marchica Lagoon and the sampling stations of Gobius niger.
Fig. 4 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 4: Two-dimensional redundancy analysis (RDA) ordination representing the spatial distribution of Gobius niger related to the predictor variables selected through the best linear models based on distance (DISTLM). SM: suspended matter.
Atmospheric CO2 simulations over Indian sites using STILT driven by WRF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by WRF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
Atmospheric CO2 simulations over Indian sites using STILT driven by ECMWF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by ECMWF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
Figure 1. A in Survey of the Non-Migratory Birds of the Rock Islands Southern Lagoon World Heritage Site in Palau
Figure 1. A. Map of Palau. B. Map of the island groups of the Rock Islands Southern Lagoon World Heritage Site study area (inset). Our survey included all 10 island groups. Scale marker = 5 km..
Ten-a-day: bumblebee pollen loads reveal high consistency in foraging breadth among species, sites, and seasons
<p>Pollen and nectar are crucial resources for bees, but vary greatly amongst plant species in their quantity, nutritional quality, and timing of availability. This makes it challenging to identify an appropriate range of plants to meet the nutritional needs of pollinators through the year, though this information is important in the design of pollinator conservation schemes.</p> <p>Using DNA metabarcoding of pollen loads, we record the floral resource use of UK farmland bumblebees at different stages of their colony lifecycle, and compare this with null models of 'expected' resource use based on landscape-scale resource availability (pollen and nectar), to identify foraging priorities and preferences. We use this approach to ask three main questions: i) what is the foraging breadth of individual bumblebees?; ii) do bumblebees utilise a greater or lesser diversity of plant species than expected if they foraged in proportion to resource availability?; iii) which plant species do bumblebees preferentially utilise?</p> <p>Individual bumblebees foraged from a highly consistent number of different plant taxa (mean: 10 ±0.37 SE per bee), regardless of their species, sampling site, or time of year. This high consistency in foraging breadth, despite large changes in the quantity, identity, and diversity of resource availability, implies a strong behavioural tendency towards a fixed range of foraging resources. This effect was most striking in April when foraging diversity was maintained despite very low landscape-level resource diversity.</p> <p>Bumblebees used some plant taxa significantly more than predicted from their landscape-level floral abundance, nectar, or pollen supply, implying certain desirable characteristics beyond the mere quantity of resource. These included <em>Allium</em> spp. and <em>Vicia</em> spp. in April; <em>Trifolium repens</em> and <em>Lotus corniculatus</em> in July; and <em>Cynareae</em> spp. (thistles) and <em>Taraxacum officinale</em> in September.</p> <p>Our results strongly indicate that resource quantity is not the only factor driving bumblebee foraging patterns, and that resource diversity and quality are also important factors. Thus, in addition to providing large quantities of floral resources, we recommend that pollinator conservation schemes also focus on providing a sufficient diversity of preferred floral resources, enabling pollinators to self-select a diverse and nutritious diet.</p>
ScienceDex guides
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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.