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22 results for “darkening”
Dataset for: "Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star"
<p><strong>Summary: </strong>This deposit supplements the manuscript, "<em>Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star</em>", accepted to the Astrophysical Journal. The tar.gz archive file contains an example Cloudy script and associated data. The complete listing of the files included in this archive file are given here: </p> <p> ReadMe Documentation file<br> example_cloudy_input_file.txt Cloudy script<br> spica_i=100_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=110_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=116_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=120_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=130_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=140_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=150_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=160_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=170_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=180_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=90_V5_solar.ascii Spica input stellar spectral energy distribution<br> </p> <p><strong>System requirements:</strong> The input script and SED files correspond to Cloudy code version 17.02:<br> Codebase: <a href="https://trac.nublado.org/">https://trac.nublado.org/</a><br> Primary documentation: Ferland et al. (<a href="https://ui.adsabs.harvard.edu/abs/2017RMxAA..53..385F/abstract">2017RMxAA..53..385F</a>; arXiv:<a href="https://arxiv.org/abs/1705.10877">1705.10877</a>)<br> </p> <p>Additional documentation provided in the ReadMe file.</p>
First release of PLATO consortium stellar limb-darkening coefficients
<p>First official grid of stellar limb-darkening coefficients and intensity profiles computed by the consortium of the PLAnetary Transits and Oscillations of stars (PLATO) Working Package 122400.</p> <p>Linked to the paper "First release of PLATO consortium stellar limb-darkening coefficients", published on the Research Notes of the American Astronomical Society (RNAAS).</p>
Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales
<p>At high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between e.g. the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms are poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long-term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change causing coastal water darkening has gradually delayed the spring bloom up to 1990 after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land-ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.</p>
PLATE IIB. Paratrigonidium Brunner, 1893. (A–E), Paratrigonidium nitidum Brunner, 1893: A, Male elytra membranous & harpvein only one; B, Palpi long, yellowish, fifth joint of maxillary palpi long, feebly widening at apex; C, Lateral field of tegmina inmale blackish, presenting 3 parallel veins and the fourth incomplete; D, The hind femur with a feeble brownish band/stripe; E, The female ovipositor fulvous at base, darkened in the middle. Trigonidium Rambur, 1839. (F–I), Trigonidium humbertianum (Saussure, 1878): F, Anterior tibiae with tympanum on both sides; G, Fifth joint of maxillary palpi large and triangular; H, Female ovipositor curved, compressed, acute at apex; I, Male sub-genital plate feebly notched at apex. in JHABAR MAL, RAJENDRA NAGAR & R. SWAMINATHAN (2014) Record of Natula matsuurai Sugimoto (Orthoptera: Gryllidae: Trigonidiinae) and other sword-tailed crickets from India. Zootaxa, 3760(3): 458-462.
PLATE IIB. Paratrigonidium Brunner, 1893. (A–E), Paratrigonidium nitidum Brunner, 1893: A, Male elytra membranous & harpvein only one; B, Palpi long, yellowish, fifth joint of maxillary palpi long, feebly widening at apex; C, Lateral field of tegmina inmale blackish, presenting 3 parallel veins and the fourth incomplete; D, The hind femur with a feeble brownish band/stripe; E, The female ovipositor fulvous at base, darkened in the middle. Trigonidium Rambur, 1839. (F–I), Trigonidium humbertianum (Saussure, 1878): F, Anterior tibiae with tympanum on both sides; G, Fifth joint of maxillary palpi large and triangular; H, Female ovipositor curved, compressed, acute at apex; I, Male sub-genital plate feebly notched at apex.
PLATE IIA. Paratrigonidium Brunner, 1893. (A–K), Paratrigonidium nitidum Brunner, 1893: A–B, Male and female black, shining;the male with membranous elytra, while female elytra corneous, convex with plain, longitudinal veins. Vertex flattened and sloping; C, Head wide and black; antenae yellow with first joint black; D, Pronotum black, pubescent; E, Palpi long, yellowish, fifth joint of maxillary palpi long, feebly widening at apex; F, Tympanum external; G, Male elytra membranous; H, The hind femurwith a feeble brownish band/stripe; I, Legs yellowish; J, Lateral field of tegmina in male blackish, presenting 3 parallel veins andthe fourth incomplete; K, The female ovipositor fulvous at base, darkened in the middle, cerci long. in JHABAR MAL, RAJENDRA NAGAR & R. SWAMINATHAN (2014) Record of Natula matsuurai Sugimoto (Orthoptera: Gryllidae: Trigonidiinae) and other sword-tailed crickets from India. Zootaxa, 3760(3): 458-462.
PLATE IIA. Paratrigonidium Brunner, 1893. (A–K), Paratrigonidium nitidum Brunner, 1893: A–B, Male and female black, shining;the male with membranous elytra, while female elytra corneous, convex with plain, longitudinal veins. Vertex flattened and sloping; C, Head wide and black; antenae yellow with first joint black; D, Pronotum black, pubescent; E, Palpi long, yellowish, fifth joint of maxillary palpi long, feebly widening at apex; F, Tympanum external; G, Male elytra membranous; H, The hind femurwith a feeble brownish band/stripe; I, Legs yellowish; J, Lateral field of tegmina in male blackish, presenting 3 parallel veins andthe fourth incomplete; K, The female ovipositor fulvous at base, darkened in the middle, cerci long.
A shortcut to calculate SPAM limb-darkening coefficients
<p>This repository contains data files and other supplementary material associated with the manuscript entitled "A shortcut to calculate SPAM limb-darkening coefficients", published on the Research Notes of the American Astronomical Society (RNAAS).</p>
Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales
Open the record for dataset details and reuse information.
Tracking freshwater browning and coastal water darkening from boreal forests to the Arctic Ocean
<p></p> <p class="MsoNormal"><span>The forest cover of Northern Europe has been steadily expanding during the last 120 years. More terrestrial vegetation and carbon fixation leads to more export to surface waters. This may cause freshwater browning, as more degraded plant-litter ends up as chromophoric (coloured) dissolved organic matter. Although most freshwater ultimately drains to coastal waters, the link between freshwater browning and coastal water darkening is poorly understood. Here, we explore this relationship through a combination of centennial records of forest and coastal water clarity, contemporary optical measurements in lakes and coastal waters, as well as an ocean drift model. We suggest a link between forest cover in Northern Europe and coastal water clarity in the Baltic, Kattegat and Skagerrak Sea and show how brown coloured freshwater from Northern European catchments can dictate coastal water clarity across thousands of kilometres, from the Baltic lakes to the Barents Sea.</span></p>
Tables of Limb and Gravity-darkening Coefficients for the Space Mission GAIA
<p><br> File Summary:<br> --------------------------------------------------------------------------------<br> FileName Lrecl Records Explanations<br> --------------------------------------------------------------------------------</p> <p><br> TABLE1 76 574 u linear LDCs <br> PHOENIX-COND models, all metallicities,<br> v.tu=2,0 km/s for GAIA (LSM, FCM)</p> <p> TABLE2 76 1148 a, b quadratic LDCs <br> PHOENIX-COND models, all metallicities,<br> v.tu=2 km/s for GAIA (LSM, FCM) </p> <p> TABLE3 76 1148 c,d square-root LDCs <br> PHOENIX-COND models, all metallicities,<br> v.tu=2 km/s for GAIA (LSM, FCM) <br> </p> <p> TABLE4 76 1148 e,f logarithmic LDCs<br> PHOENIX-COND models, all metallicities, <br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM)</p> <p> TABLE5 49 2296 a_1,a_2,a_3,a_4 LDCs<br> PHOENIX-COND models, all metallicities, <br> v.tu=2 km/s for GAIA (LSM)</p> <p> TABLE6 76 9586 u linear LDCs <br> ATLAS models, all metallicities,<br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM)</p> <p> TABLE7 76 19172 a, b quadratic LDCs <br> ATLAS models, all metallicities,<br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM) </p> <p> TABLE8 76 19172 c,d square-root LDCs <br> ATLAS models, all metallicities,<br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM) <br> </p> <p> TABLE9 76 19172 e,f logarithmic LDCs<br> ATLAS models, all metallicities, <br> v.tu=2,0,1,4,8 km/s for GAIA (LSM, FCM)</p> <p> TABLE10 49 38344 a_1,a_2,a_3,a_4 LDCs<br> ATLAS models, all metallicities, <br> v.tu=2,0,1,4,8 km/s for GAIA (LSM)<br> <br> TABLE11 55 9575 y GDCs <br> ATLAS models, all metallicities,<br> v.tu=0,1,2,4,8 km/s for GAIA(BP,G,RP) <br> --------------------------------------------------------------------------------</p> <p> Byte-by-byte Description of files: TABLE 1<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- u linear LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- u linear LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- u linear LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- u linear LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- u linear LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- u linear LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 2<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- a quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- a quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- a quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- a quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- a quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- a quadratic LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- b quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- b quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- b quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- b quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- b quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- b quadratic LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 3<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- c root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- c root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- c root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- c root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- c root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- c root-square LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- d root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- d root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- d root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- d root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- d root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- d root-square LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 4<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- e logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- e logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- e logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- e logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- e logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- e logar LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- f logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- f logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- f logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- f logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- f logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- f logar LDC (GAIA RP, FCM)<br> -------------------------------------------------------------------------------- <br> <br> Byte-by-byte Description of files: TABLE 5<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a1 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a1 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a1 4 TERMS LDC (GAIA RP,LSM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a2 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a2 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a2 4 TERMS LDC (GAIA RP,LSM)<br> THIRD LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a3 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a3 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a3 4 TERMS LDC (GAIA RP,LSM)<br> FOURTH LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a4 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a4 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a4 4 TERMS LDC (GAIA RP,LSM)<br> --------------------------------------------------------------------------------</p> <p> Byte-by-byte Description of files: TABLE 6<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- u linear LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- u linear LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- u linear LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- u linear LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- u linear LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- u linear LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 7<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- a quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- a quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- a quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- a quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- a quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- a quadratic LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- b quadratic LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- b quadratic LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- b quadratic LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- b quadratic LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- b quadratic LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- b quadratic LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 8<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- c root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- c root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- c root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- c root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- c root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- c root-square LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- d root-square LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- d root-square LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- d root-square LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- d root-square LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- d root-square LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- d root-square LDC (GAIA RP, FCM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 9<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- e logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- e logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- e logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- e logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- e logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- e logar LDC (GAIA RP, FCM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 25- 31 F7.4 --- f logar LDC (GAIA BP, LSM)<br> 34- 40 F7.4 --- f logar LDC (GAIA G, LSM)<br> 43- 49 F7.4 --- f logar LDC (GAIA RP, LSM)<br> 52- 58 F7.4 --- f logar LDC (GAIA BP, FCM)<br> 61- 67 F7.4 --- f logar LDC (GAIA G, FCM)<br> 70- 76 F7.4 --- f logar LDC (GAIA RP, FCM)<br> -------------------------------------------------------------------------------- <br> <br> Byte-by-byte Description of files: TABLE 10<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> FIRST LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a1 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a1 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a1 4 TERMS LDC (GAIA RP,LSM)<br> SECOND LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a2 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a2 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a2 4 TERMS LDC (GAIA RP,LSM)<br> THIRD LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a3 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a3 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a3 4 TERMS LDC (GAIA RP,LSM)<br> FOURTH LINE<br> 1- 5 F5.2 cm/s+2 logg Surface gravity<br> 7- 12 F6.0 K Teff Effective temperature<br> 14- 17 F4.1 Z log [metal/H]<br> 19- 22 F4.1 km/s Vel microturbulent velocity<br> 24- 31 F8.4 --- a4 4 TERMS LDC (GAIA BP,LSM)<br> 34- 40 F7.4 --- a4 4 TERMS LDC (GAIA G,LSM)<br> 43- 49 F7.4 --- a4 4 TERMS LDC (GAIA RP,LSM)<br> --------------------------------------------------------------------------------</p> <p>Byte-by-byte Description of files: TABLE 11<br> --------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> --------------------------------------------------------------------------------<br> 2-6 F5.2 Z log [metal/H]<br> 8-13 F6.3 km/s Vel microturbulent velocity<br> 16-20 F5.2 cm/s+2 logg Surface gravity<br> 22-27 F6.3 K log Teff log Effective temperature<br> 31-37 F7.4 --- y GDC (GAIA, BP)<br> 40-46 F7.4 --- y GDC (GAIA, B)<br> 49-55 F7.4 --- y GDC (GAIA, RP)<br> --------------------------------------------------------------------------------</p>
Dataset for: Tracking freshwater browning and coastal water darkening from boreal forests to the Arctic Ocean
Open the record for dataset details and reuse information.
Distinguishing Between Shock Darkening and Space Weathering Trends in Ordinary Chondrite Reflectance Spectra
<p>The data file contains raw spectra of Chelyabinsk meteorite IM, SD, and SW series as described in the linked journal publication.</p>
FIGURES 13–18. C. bellera, pupa. 13. Immediately after molting, dorsal view. 14. After two hours, dorsal view. 15. After 24 hours, dorsal view. 16. Darkened individual, dorsal view. 17. Idem, lateral view. 18 in Immature stages of Calycopis bellera (Hewitson) and C. janeirica (Felder) (Lepidoptera, Lycaenidae, Theclinae, Eumaeini): Taxonomic significance and new evidence for detritivory
FIGURES 13–18. C. bellera, pupa. 13. Immediately after molting, dorsal view. 14. After two hours, dorsal view. 15. After 24 hours, dorsal view. 16. Darkened individual, dorsal view. 17. Idem, lateral view. 18. Idem, ventral view.
In situ characterization of dust storms and their snow darkening effect over Himalayas
<p>The dataset provided here pertains to the Mukteshwar region. Users are requested to acknowledge the data curators before utilizing it.</p>
Data from: Coastal darkening substantially limits the contribution of kelp to coastal carbon cycles
<p>Macroalgal-dominated habitats are rapidly gaining recognition as important contributors to marine carbon cycles and sequestration. Despite this recognition, relatively little is known about the production and fate of carbon originating from these highly productive ecosystems, or how anthropogenic and climate related stressors affect the role of macroalgae in marine carbon cycles. Here, we examine the impact of increasing turbidity on carbon storage, fixation and loss in southern hemisphere kelp forests. We quantified net primary production (NPP) and biomass accumulation (BA), and estimated carbon release via detritus and dissolved organic carbon (DOC) across a large-scale turbidity gradient. We show that increased turbidity, resulting in a 63 % reduction in light, can result in a 95 % reduction in kelp productivity. When averaged annually, estimates of NPP and BA per plant at high light sites were nearly 6 and 2 times greater than those at low light sites, respectively. Furthermore, the quantity of carbon fixed annually by kelp forests was up to 4.7 times greater than that stored as average annual standing stock. At low light sites, the majority of C goes directly into tissue growth and is subsequently eroded. In contrast, excess production at high light sites accounts for up to 39 % of the total carbon fixed and is likely released as DOC. Turbidity is expected to increase in response to climate change and our results suggest this will have significant impacts on the capacity of kelp forests to contribute to carbon sequestration pathways. In addition to demonstrating that turbidity significantly reduces the quantity of carbon fixed by kelp forests, and subsequently released as detritus, our results highlight the negative impacts of turbidity on a large source of previously unaccounted for carbon.</p>
Data from: Coastal darkening substantially limits the contribution of kelp to coastal carbon cycles
Open the record for dataset details and reuse information.
Rapid beard darkening predicts contest outcome, not copulation success, in bearded dragon lizards
<p><span><span><span><span><span><span><span><span><span><span><span>Rapid colour change is widespread in animals and allows them to respond dynamically to the physical and social environment. However, few studies have examined the information conveyed by dynamic colour signals in different social contexts, such as courtship and rivalry contests. Furthermore, dynamic colour change on different body regions may be subject to different selection pressures and therefore serve different functions. We tested whether male colour or colour change predict contest outcome in male-male interactions, or the likelihood of copulation attempts in male-female interactions in the central bearded dragon, <i>Pogona vitticeps</i>. The extent of beard darkening strongly predicted contest outcome, but neither colour (beard, dorsal) nor colour change predicted copulation attempts. <span><span>Dorsal colour showed little consistent change and did not predict contest outcome or copulation attempts. </span></span>Previous work shows that dorsal but not beard colour change occurs in response to both background colour and temperature in the laboratory and in the wild. <a name="_Hlk33037262">Taken together, these results indicate that dynamic colour change in bearded dragons varies in relation to social context and can serve different functions depending on the body region.</a> Our data add to the growing appreciation of how rapid colour change enables animals to accommodate the multiple functions of colour, including communication to different receivers, camouflage and thermoregulation.</span></span></span></span></span></span></span></span></span></span></span></p>
Improved snow darkening coefficient for large-scale albedo modelling with Crocus
<p><strong>Description</strong></p> <p>Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. The explicit simulation of these effects with advanced snow radiative transfer models can be associated with a large computational cost. Consequently, many albedo schemes used in snowpack models still rely on empirical parameterizations that do not account for the spatial variability of LAP deposition. In Gaillard et al. (2024), a new strategy of intermediate complexity that includes the effects of spatially variable LAP deposition on snow albedo was tested with the snowpack model Crocus. It relies on an optimization of the snow darkening coefficient that controls the evolution of snow albedo in the visible range. A global dataset of LAP-informed and spatially variable values of the snow darkening coefficient was constructed. The revised snow darkening coefficient improved snow albedo simulations at the ten sites considered in the study by 10%, with the largest improvements found in the Arctic (more than 25%). The uncertainties in the values of the snow darkening coefficient resulting from the inter-annual variability of LAP deposition on snow were also computed.</p> <p>The data are distributed in a NetCDF file (gamma<em>_tot_publish_v2.nc</em>). More details about the dataset and the file format are given in the file <em>readme_gamma_v2.pdf</em>. </p>
Rapid beard darkening predicts contest outcome, not copulation success, in bearded dragon lizards
Open the record for dataset details and reuse information.
Transcriptomic insight into molecular processes of light-induced rapid darkening of non-obligative cave dweller Oreolalax rhodostigmatus (Megophryidae, Anura) and their genetic basis of pigmentation s
GEO Series GSE106622. Oreolalax rhodostigmatus. 13 samples. Type: Expression profiling by high throughput sequencing.
Identification of a piscine reovirus-related pathogen in Proliferative Darkening Syndrome infected brown trout (Salmo trutta fario) using a next-generation technology detection pipeline
GEO Series GSE70257. Salmo salar; Oncorhynchus mykiss; Salmo trutta fario. 42 samples. Type: Expression profiling by array.
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.