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Figures 313–318 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 313–318. Dentate margins of pedipalp movable fingers. Hormuridae. Figures 313–315. Hormurus waigiensis ♀. Right finger (313), distal finger (314), and mid-finger (315). Figures 316–318. Liocheles australasiae ♀. Left finger (mirrored) (316), distal finger (317), and midfinger (318). Scale bars: 1 mm (313), 200 μm (314–315), 1 mm (316), and 100 μm (317–318). UV fluorescence.
Figures 223–228 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 223–228. Dentate margins of pedipalp movable fingers. Euscorpiidae. Figures 223–225. Megacormus gertschi ♀. Left finger (mirrored) (223), distal finger (224), and subrow 4 (225). Figures 226–228. Tetratrichobothrius flavicaudis ♂. Left finger (mirrored) (226), distal finger (227), and subrows 4–5 (228). Denticle subrow numbering and arrow conventions as described under Figs. 5–10. Scale bars: 400 μm (223), 100 μm (224–225), 1 mm (226), and 100 μm (227–228). UV fluorescence.
Figures 25–30 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 25–30. Dentate margins of pedipalp movable fingers. Buthidae: 'Buthus' group. Figures 25–27. Androctonus crassicauda ♂. Left finger (mirrored) (25), distal finger (26), and subrows 7–8 (27). Figures 28–30. Anomalobuthus lowei ♀. Right finger (28), distal finger (29), and subrow 7 (30). Denticle subrow numbering and arrow conventions as described under Figs. 5–10. Scale bars: 1 mm (25), 200 μm (26–27), 400 μm (28), and 100 μm (29–30). UV fluorescence.
Figures 31–39 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 31–39. Dentate margins of pedipalp movable fingers. Buthidae: 'Buthus' group. Figures 31–33. Apistobuthus pterygocercus ♂. Right finger (31), distal finger (32), and subrow 3 (33). Figures 34–36. Buthacus nigroaculeatus ♂. Right finger (34), distal finger (35), and subrow 5 (36). Figures 37–39. Compsobuthus acutecarinatus ♂. Left finger (mirrored) (37), distal finger (38), and subrow 6 (39). Denticle subrow numbering and arrow conventions as described under Figs. 5–10. Scale bars: 2 mm (31), 200 μm (32–33), 1 mm (34), 100 μm (35–36), 1 mm (37), 100 μm (38–39). UV fluorescence.
Figures 19–24 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 19–24. Dentate margins of pedipalp movable fingers. Pseudochactidae and Chaerilidae. Figures 19–21. Pseudochactas ovchinnikovi ♀. Right finger (19), distal finger (20), and subrow 3 (21). Figures 22–24. Chaerilus hofereki ♂. Left finger (mirrored) (22), distal finger (23), and subrows 4–5 (24). Denticle subrow numbering and arrow conventions as described under Figs. 5–10. Scale bars: 400 μm (19, 22), 50 μm (20–21), and 50 μm (23–24). UV fluorescence.
Figures 5–10 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 5–10. Dentate margins of pedipalp fingers. Buthidae. Buthus mardochei ♂. Figures 5–7. Left fixed finger (mirrored) (5), distal finger (6), and denticle subrow 7 (7). Figures 8–10. Left movable finger (mirrored) (8), distal finger (9), and denticle subrow 7 (10). In Figs. 5 and 8, denticle subrows are numbered from distal to proximal, with numbers placed near proximal ends of subrows where a subrow proximal
Figures 11–18 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 11–18. Dentate margins of pedipalp fingers. Vaejovidae. Mesomexovis punctatus ♂. Figures 11–13. Left fixed finger (mirrored) (11), distal finger (12), and denticle subrow 4 (13). Figures 14–18. Left movable finger (mirrored) (14), distal finger (15), denticle subrow 3 (16), and enlarged proximal median denticle of subrow 3 with associated subrow proximal sensillum (SPS), internal lateral aspects (17–18). Denticle subrow numbering and arrow conventions as described under Figs. 5–10. Scale bars: 400 μm (11, 14), 100 μm (12–13, 15–16), and 50 μm (17–18). UV fluorescence (11–16, 18) and transmitted white light (17).
Figures 1–4 in A survey of proximal sensilla associated with denticle subrows on scorpion pedipalp fingers (Arachnida: Scorpiones), with observations on scorpion fluorescence
Figures 1–4. Examples of scorpion appendage segments bearing a mixture of Type N sensilla (non-fluorescent 'macrosetae') and Type F sensilla (fluorescent 'microsetae'). Figures 1–2. Right telotarsus, prolateral aspect, of Androctonus australis ♀ (1) and Smeringurus vachoni ♀ (2). Figures 3–4. Right pedipalp chela of Diplocentrus whitei ♂, external (3) and internal (4) aspect. Scale bars: 1 mm (1–2), 2 mm (3–4). UV fluorescence.
Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes
<p>Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variations in pest ecology but also from the widespread use of categorical land-use maps that do not adequately characterize habitat availability for pests. We used 1163 field-year observations of <em>Lygus hesperus</em> (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because <em>L. hesperus</em> often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that <em>L. hesperus</em> densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.</p>
Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction.
Asteroseismology of the young open cluster NGC 2516 I: Photometric and spectroscopic observations
<p>A column-to-column explaination is here:</p> <p>gaia_dr3_source_id: Gaia DR3 source ID</p> <p>TIC: TIC number</p> <p>gaia_RA: RA by Gaia</p> <p>gaia_DEC: DEC by Gaia</p> <p>gaia_G_apparent_mag: Gaia G band magnitude</p> <p>gaia_G_apparent_mag_err: Gaia G band magnitude uncertainty</p> <p>gaia_G_absolute_mag: Gaia G band absolute magnitude, without the correction of extinction</p> <p>gaia_G_absolute_mag_err: uncertainty of gaia_G_absolute_mag</p> <p>log_Luminosity: log of luminosity, calculated by Gaia effective temperature, with the bolometric correction and extinction correction. Use with caution.</p> <p>log_Luminosity_err: uncertainty of log_Luminosity</p> <p>Gaia_Teff: effective temperature provided by Gaia. Use with caution.</p> <p>Gaia_Teff_err: uncertainty of Gaia_Teff. Use with caution. A uncertainty value of '100' means the temperature is absent by Gaia, so we use the temperature from the TIC input catalog.</p> <p>BP-RP: Gaia colour index.</p> <p>BP-RP_err: uncertainty of BP-RP</p> <p>Teff_by_spectra: effective temperature by FEROS spectra, better than Gaia_Teff, only available for nine stars. "9999" means no data available.</p> <p>Teff_by_spectra_err: uncertainty of Teff_by_spectra. "9999" means no data available.</p> <p>log_L_by_Teff_spectra: log of luminosity calculated by Teff_by_spectra, with the bolometric correction and extinction correction, better than log_Luminosity.</p> <p>log_L_by_Teff_spectra_err: uncertainty of log_L_by_Teff_spectra</p> <p>spectra_logg: log g by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_logg_err: uncertainty of spectra_logg</p> <p>spectra_vsini: projected equatorial velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_vsini_err: uncertainty of spectra_vsini</p> <p>spectra_matellicity: matellicity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_matellicity_err: uncertainty of spectra_matellicity</p> <p>spectra_radial_velocity: radial velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_radial_velocity_err: uncertainty of spectra_radial_velocity</p> <p>spectra_microturbulent: microturbulent velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_microturbulent_err: uncertainty of spectra_microturbulent</p> <p>spectra_SNR: signal-to-noise ratio of the FEROS spectra</p> <p>Pi0: asymptotic spacing of g modes, measured by g modes, only available for 11 stars. '9999' means no data available.</p> <p>Pi0_err: uncertainty of Pi0</p> <p>core_rotation_g_mode: near-core rotation rate in unit of days^{-1}, measured by g modes, only available for 11 stars. '9999' means no data available.</p> <p>surface_modulation_period: surface rotation period measured by surface modulations. '9999' means no data available.</p> <p>surface_modulation_period_err: uncertainty of surface_modulation_period</p> <p> </p>
FIG. 2. — A in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 2. — A, Location map of the study area in the northwest of Iran (from Ghaderi et al. 2016); B, geologic map of the study area at the west of Julfa City; the Ali-Bashi section is marked with a white star (from Ghaderi 2014); C, palaeogeographic map of the Lopingian and location of the study area (white star) as a part of a Cimmerian block, close to the equator in the southern hemisphere (from Ruban et al. 2007).
FIG. 4. — A in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 4. — A, Field pictures of the Ali-Bashi section, with the succession of the Khachik Beds lower part (view Northward); B, general view of the upper parts of the Khachik Beds, include thick bedded to massive cherty limestones (view to the Northwest).
FIG. 1 in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 1. — Historical background of the Guadalupian-Lopingian successions in the northwest of Iran; following Stepanov et al. (1969), Teichert et al. (1973), Partoazar (2002), Ghaderi (2014) and Ghaderi et al. (2014a, b, 2016).
FIG. 3 in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 3. — Stratigraphic log and vertical distribution of ostracods (obtained from CH2O2 protocol) in the Khachik Formation from the Ali-Bashi section.
FIG. 5 in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 5. — Ostracods from the Khachik beds of Ali-Bashi section, NW Iran, extracted by CH2O2 technique (A1-J2) and by CH3COOH technique (K1-K3): A1- A6, Bairdia deducta deducta (Zalányi, 1974); A1, right lateral view, sample A. 197, MNHN.F.F72157; A2-A4, right lateral view, sample A. 210, MNHN.F.F72158- F72160; A5, right lateral view, sample A. 189, MNHN.F.F72161; A6, right lateral view, sample A. 207, MNHN.F.F72162; B1-B4, Bairdia hungarica Zalányi, 1974; B1, left lateral view, sample A. 189, MNHN.F.F72163; B2, right lateral view, sample A. 189, MNHN.F.F72164; B3, left lateral view, sample A.170, MNHN.F.F72165; B4, right lateral view, sample A. 176, MNHN.F.F72166; C1-C4, Bairdia sp.; C1, left lateral view, sample A. 189, MNHN.F.F72167; C2, right lateral view, sample A. 189, MNHN.F.F72168; C3, left lateral view, sample A.170, MNHN.F.F72169; C4, right lateral view, sample A. 176, MNHN.F.F72170; D1-D6, Fabalicypris parva Wang, 1978; D1, right lateral view, sample A. 194, MNHN.F.F72171; D2, right lateral view, sample A. 151, MNHN.F.F72172; D3, right lateral view, sample A. 158, MNHN.F.F72173; D4, right lateral view, sample A. 192, MNHN.F.F72174; D5, D6, right lateral view, sample A. 210, MNHN.F.F72175, F72176; E, Fabalicypris sp. 1., right lateral view, sample A. 207, MNHN.F.F72177; F, Fabalicypris sp. 2., right lateral view, sample A. 210, MNHN.F.F72178; G1-G3, Hollinella (Hollinella) herrickana (Girty, 1909); G1, left lateral view, sample A. 15, MNHN.F.F72179; G2, left lateral view, sample A. 238, MNHN.F.F72180; G3, right lateral view, sample A. 238, MNHN.F.F72181; H, Hollinella sp., left lateral view, sample A. 188, MNHN.F.F72182; I, Sargentina transita (Kozur, 1985), left lateral view, sample A. 176, MNHN.F.F72183; J1, J2, Silenites sp.; J1, right lateral view, sample A. 238, MNHN.F.F72184; J2, ventral view, sample A. 238, MNHN.F.F72185; K1-K3, Ostracoda indet. Specimens are stored in the MNHN (Muséum national d'Histoire naturelle) collections (Paris, France). Scale bar: 100 µm.
Europa spectral images of HCN, SO2, H2CO and CH3OH observed using ALMA
<p>This dataset contains observations of Europa carried out using the Atacama Large Millimeter/submillimeter Array (ALMA) on four dates in 2021, using the Band 7 receiver. The targeted spectral lines included HCN ($J=4-3$; 354.505 GHz), H$_2$CO ($J_{K_a,K_c}=5_{1,5}-4_{1,4}$; 351.769 GHz), SO$_2$ ($J_{K_a,K_c}=5_{3,3}-4_{2,2}$; 351.257 GHz), observed at 122~kHz resolution, and CH$_3$OH ($J_K = 4_0 - 3_{-1}\ E$; 350.688 GHz), observed at 61 kHz resolution. The interferometric data were cleaned and imaged using CASA, with a pixel size of 0.02'' and a clean mask of 1.1''. The resulting spatial resolution is ~0.15 arcseconds.</p> <p>The data includes FITS image cubes and PDF spectral maps for each molecule, on each observing date. See FITS headers for further details. For each PDF, the ALMA beam FWHM is indicated lower left (dot-dashed ellipse); upper right axes show physical distances in the plane of the sky, while lower left axes show the spectral units for each sub-panel.</p>
F I G U R E 2 Survival curves for female Eristalis tenax flies observed for 11 in Toward optimising reproductive output of Eristalis tenax (Diptera: Syrphidae) for commercial mass rearing systems
F I G U R E 2 Survival curves for female Eristalis tenax flies observed for 11 weeks in four adult density treatments; 15:15 (n = 45), 30:30 (n = 90), 60:60 (n = 180), 120:120 (n = 360). Letters indicate significant differences (p <0.05) between survival curves of treatments.
Figures 1–4. Platycnemis spp. 1. A in Behavioural observation on Platycnemis latipes revealing novel function of males' patrol
Figures 1–4. Platycnemis spp. 1. A male of P. phyllopoda, arraw indicting the fans. 2. A numbered male of P. latipes for test of marking and recapture. 3. A fans blacked male of P. latipes. 4. An abdomen blacked male of P. latipes in copulation.
Figures 5–6. Platycnemis latipes. 5 in Behavioural observation on Platycnemis latipes revealing novel function of males' patrol
Figures 5–6. Platycnemis latipes. 5. The same male (above) and female (below) in UV light (left) and sunlight (right). 6. Teneral (left) and mature female (right).
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.