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openmit-licenseSep 2022View details →

Supplemental Data Sets for "Buried Ice Deposits in Lunar Polar Cold Traps were Disrupted by Ballistic Sedimentation"

Supporting Data Sets for manuscript "Buried Ice Deposits in Lunar Polar Cold Traps were Disrupted by Ballistic Sedimentation". Contains Data Sets S1-S7 as described in the manuscript and Supplementary information S1 (see <a href="https://doi.org/10.1029/2022JE007567">https://doi.org/10.1029/2022JE007567</a>).

opencc-by-4.0Feb 2023View details →

Dataset for the "a parameterization for cloud organization and propagation by evaporation-driven cold pools edges"

When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub-cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid-scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry-to-wet 10-days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas.

opencc-by-4.0Jun 2023View details →

Cold rolling mill: Dataset for Recommender system for process optimization

The dataset (pickle formatted with version pickle=4.0) contains a collection of process values obtained from a process line. Each value represents the mean measurement within a window at specific intervals along the distance domain. These intervals were equidistant and sampled under steady-state conditions, ensuring consistent data collection. The process values included in the dataset cover a range of parameters and variables relevant to the process line. These values provide information about the behavior and characteristics of the process at different points along the distance domain.   Associate source code is available at: <a href="https://github.com/CuAuPro/opti-rec-sys">Recommender system for process optimization (github.com)</a>.

opencc-by-4.0Jul 2023View details →

Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island / Model dataset

ECHAM6wiso and LMDZ6iso simulations, and python script analyzing model outputs, associated with the article : <ul> <li>Amaelle Landais, Cécile Agosta, Françoise Vimeux, Olivier Magand, Cyrielle Solis, Alexandre Cauquoin, Niels Dutrievoz, Camille Risi, Christophe Leroy Dos Santos, Elise Fourré, Olivier Cattani, Bénédicte Minster, Frédéric Prié, Mathieu Casado, Aurélien Dommergue, Yann Bertrand, and Martin Werner (submitted to <a href="https://www.atmospheric-chemistry-and-physics.net/">Atmospheric Chemistry and Physics</a>, 2023) Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island.</li> </ul> If you use the data or the python script, please cite the last version of this article available on <a href="https://www.egusphere.net/">https://www.egusphere.net/</a> or <a href="https://acp.copernicus.org/">https://acp.copernicus.org/</a>. Please also cite the articles related to the model simulations: <ul> <li> Risi, C., Bony, S., Vimeux, F., and Jouzel, J.: Water-stable isotopes in the LMDZ4 general circulation model: Model evaluation for present-day and past climates and applications to climatic interpretations of tropical isotopic records, Journal of Geophysical Research Atmospheres, 115, https://doi.org/10.1029/2009JD013255, 2010. </li> <li> Cauquoin, A. and Werner, M.: High-Resolution Nudged Isotope Modeling With ECHAM6-Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data, Journal of Advances in Modeling Earth Systems, 13, e2021MS002532, https://doi.org/10.1029/2021MS002532, 2021. </li> <li> Cauquoin, A., Werner, M., and Lohmann, G.: Water isotopes -- climate relationships for the mid-Holocene and preindustrial period simulated with an isotope-enabled version of MPI-ESM, Climate of the Past, 15, 1913–1937, https://doi.org/10.5194/cp-15-1913-2019, 2019. </li> </ul>

edi44/100

Cold-air pooling characterization and forest composition, New England, USA

This dataset corresponds to a project investigating whether cold-air pooling influences forest composition and function. The data include hourly sub-canopy air temperatures (measured continuously via ibuttons) and forest forest composition data for 48 plots along 9 transects in 3 sites across New England, USA. The temperature data also include surface lapse rates and temperature gradients across transects, as well as a designation indicating the presence or absence of a temperature inversion. We found that sites with the most frequent temperature inversions also displayed vegetation inversions across slopes, with more cold-adapted species at low instead of high elevations.

openCC (other)Mar 2024View details →

edi44/100

SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Cold Springs Basin (ColdSprings210)

Precipitation was collected by the Santa Barbara County Flood Control District at Cold Springs Basin (ColdSprings210) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc

openCC (other)Sep 2019View details →

opencc-by-4.0Sep 2020View details →

COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms

This dataset release supports the results presented in the paper "COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms" by A. U. Gias and G. Casale, accepted in IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2020. When referring to the dataset please cite the paper above.

opencc-by-4.0Jan 2021View details →

Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.   We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).   Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots. In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. Hot and cold spots for each sign are publicly available as binary labels in NIfTI format.

dryad40/100

Data from: Heat tolerance is more variable than cold tolerance across species of Iberian lizards after controlling for intraspecific variation

<ol> <li>The widespread observation that heat tolerance is less variable than cold tolerance ('cold-tolerance asymmetry') leads to the prediction that species exposed to temperatures near their thermal maxima should have reduced evolutionary potential for adapting to climate warming. However, the prediction is largely supported by species-level global studies based on single estimates of both physiological metrics per taxon.</li> <li>We ask if cold-tolerance asymmetry holds for Iberian lizards after accounting for intraspecific variation in critical thermal maxima (CTmax) and minima (CTmin). To do so, we quantified CTmax and CTmin for 58 populations of 15 Iberian lizard species (299 individuals). Then, we randomly selected one population from each study species (population sample = 15 CTmax and CTmin values), tested for variance homoscedasticity across species, and repeated the test for thousands of population samples as if we had undertaken the same study thousands of times, each time sampling one different population per species.</li> <li>The ratio of variances in CTmax to CTmin across species varied up to 16-fold depending on the populations chosen. Variance ratios show how much CTmax departs from the cross-species mean compared to CTmin, with a unitary ratio indicating equal variance of both thermal limits. Sampling one population per species was six times more likely to result in the observation of greater CTmax variance ('heat-tolerance asymmetry') than cold-tolerance asymmetry. The null hypothesis of equal variance was twice as likely for cases of cold-tolerance asymmetry than for the opposite scenario.</li> <li>Range-wide, population-level studies that quantify heat and cold tolerance of individual species are urgently needed to ascertain the global prevalence of cold-tolerance asymmetry. While broad latitudinal clines of cold tolerance have been strongly supported, heat tolerance might respond to smaller-scale climatic and habitat factors hence go unnoticed in global studies. Studies investigating physiological responses to climate change should incorporate the extent to which thermal traits are characteristic of individuals, populations and/or species.</li> </ol>

opencc-zeroDec 2017View details →

opencc-zeroDec 2004View details →

FIGURE 1. Chiridota heheva new species. Approximately 4 in Chiridota heheva, new species, from Western Atlantic deepsea cold seeps and anthropogenic habitats (Echinodermata: Holothuroidea: Apodida)

FIGURE 1. Chiridota heheva new species. Approximately 4 individuals in situ near whitish bacterial mats (?) at Florida Escarpment seep site, eastern Gulf of Mexico, 3,270 meters. Alvin Dive 1343. Approximate diameter of body 5 mm. Photo, S. Golubic.

opencc-zeroDec 2004View details →

FIGURE 3. A – C, E – J in Chiridota heheva, new species, from Western Atlantic deepsea cold seeps and anthropogenic habitats (Echinodermata: Holothuroidea: Apodida)

FIGURE 3. A – C, E – J, Chiridota heheva new species; D, Chiridota laevis (Fabricius). A, Left ventral radial piece from calcareous ring. Note absence of perforation for radial nerve. Length of piece 1. 9 mm. B, Right ventral interradial piece from calcareous ring; length of piece 1. 6 mm. C, Bipartite right dorsal radial piece from calcareous ring. Note absence of perforation for radial nerve. Length of piece 2. 7 mm. D, Chiridota laevis (Fabricius), bipartite right dorsal radial piece from calcareous ring. Note perforation for radial nerve. Length of piece mm. 1.6 mm. E, Rods from tentacles. Length of longest rod 177 µm. F, Inner surface of wheel from wheel papilla. Diameter of wheel 186 µm. G, Outer surface of wheel from wheel papilla. Diameter of wheel 154 µm. H, Three wheels from wheel papillae, two showing inner surface, one showing outer surface. Largest wheel abnormal in having teeth on margin of inner rim. Diameter of largest wheel 184 µm. I, Wheel in lateral view. Diameter of wheel 132 µm. J, Wheel in slightly oblique view. Diameter of wheel 190 µm.

opencc-zeroDec 2004View details →

FIGURE 2. Chiridota heheva new species. A in Chiridota heheva, new species, from Western Atlantic deepsea cold seeps and anthropogenic habitats (Echinodermata: Holothuroidea: Apodida)

FIGURE 2. Chiridota heheva new species. A, At Bathymodiolus heckeri mussel beds, Blake Ridge, closeup view showing anterior end of body with white spots (wheel papillae), and extended tentacles. Note fingerlike digits forming a fringe around tentacle terminal disc. Approximate diameter of tentacle stem 1 mm. From Van Dover et al., 2003, with permission. B, One individual at Bathymodiolus heckeri mussel beds, Blake Ridge, showing conspicuous white spots (wheel papillae) against bluish ground color of body wall. Approximate diameter of body 5 mm. Shrimp at right center is Alvinocaris sp. From Van Dover et al., 2003, with permission. C, At Central America wreck showing conspicuous white spots (wheel papillae) against bluish ground color of body wall. Image taken from videotape, Charles E. Herdendorf. Size of specimen unknown. D, At Central America wreck, showing extended feeding tentacles with conspicuous, discrete digits. Size of specimen unknown. Image taken from videotape, Charles E. Herdendorf. E, Oral field with 12 tentacles in a partially contracted state. Note absence of a “ ventral gap ” between tentacles. Long axis of mouth is 2 mm. F, Closeup view of contracted tentacles showing infolded digits. G, Partially contracted tentacle showing discrete digits. Approximate length of digits 1. 5 mm.

FIGURE 18. a – c in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 18. a – c: Turbonilla micans (Monterosato, 1875), sample BC 51, scale bars: 0.5 mm (a), 0.2 mm (b – c, protoconch); d – f: Graphis gracilis (Monterosato, 1874), sample BC 72, scale bars: 0.5 mm (d), 0.2 mm (e – f, protoconch); g – i: Crenilabium exile (Jeffreys, 1870), samples BC 67 (g) and BC 04 (h – i), scale bars: 2 mm (g), 0.2 mm (h – i, protoconch); j – l: Japonacteon pusillus (MacGillivray, 1843), sample BC 72, scale bars: 2 mm (j), 0.2 mm (k – l, protoconch); m – o: Callostracon thyrrenicum (Smriglio & Mariottini, 1996), sample BC 71, scale bars: 1 mm (m), 0.2 mm (n – o, protoconch).

FIGURE 19. a – b in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 19. a – b: Ringicula sp., sample BC 72, scale bars: 0.5 mm (a), 0.1 mm (b, protoconch); c – d: Diaphana globosa (Lovén, 1846), sample BC 05, scale bars: 1 mm (c), 0.2 mm (d, protoconch); e – f: Diaphana cf. lactea (Jeffreys, 1877), sample BC 05, scale bars: 1 mm (e), 0.2 mm (f, protoconch); g – j: Philine quadrata (Wood, 1839), samples BC 72 (g – h) and BC 05 (i – j), scale bars: 2 mm (g – h), 0.2 mm (i – j, protoconch); k – n: Philine scabra (Müller, 1784), sample BC 72, scale bars: 2 mm (k), 0.5 mm (l, juvenile), 0.2 mm (m – n, protoconch); o – q: Roxania monterosatoi Dautzenberg & Fischer, 1896, samples BC 67 (o) and BC 72 (p – q), scale bars: 1 mm (o), 0.5 mm (p, juvenile), 0.2 mm (q, protoconch).

FIGURE 17. a – c in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 17. a – c: Mathilda cochlaeformis Brugnone, 1873, samples BC 71 (a) and BC 66 (b – c), scale bars: 2 mm (a), 0.2 mm (b – c, protoconch); d – g: Mathilda coronata Monterosato, 1875, sample BC 71, scale bars: 2 mm (d), 0.2 mm (e – g, protoconch); h – j: Eulimella scillae (Scacchi, 1835), sample BC 72, scale bars: 1 mm (h), 0.2 mm (i – j, protoconch); k – m: Eulimella unifasciata (Forbes, 1844), sample BC 72, scale bars: 2 mm (k), 0.2 mm (l – m, protoconch); n – o: Chrysallida flexuosa (Monterosato, 1874), sample BC 67, scale bars: 0.5 mm (n), 0.2 mm (o, protoconch); p – r: Liostomia sp., sample BC 72, scale bars: 1 mm (p), 0.2 mm (q – r, protoconch).

FIGURE 16. a – c in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 16. a – c: Pleurotomella gibbera Bouchet & Warén, 1980, sample BC 66, scale bars: 1 mm (a), 0.2 mm (b – c, protoconch); d – f: Discotectonica discus (Philippi, 1844), sample BC 72, scale bars: 2 mm (d – e), 0.2 mm (f, protoconch); g – h: Pseudomalaxis zanclaeus (Philippi, 1844), sample BC 72, scale bar 5 mm; i – k: Solatisonax alleryi (Seguenza, 1876), sample BC 72, scale bars: 2 mm (i – j), 0.2 mm (k, protoconch); l – n: Solatisonax bannocki (Melone & Taviani, 1980), sample BC 71, scale bars: 2 mm (l – m), 0.2 mm (n, protoconch); o – q: Spirolaxis centrifugus (Monterosato, 1890), sample BC 72, scale bars: 1 mm (o – p), 0.2 mm (q, protoconch).

FIGURE 13. a – c in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 13. a – c: Akritogyra conspicua (Monterosato, 1880), sample BC 05, scale bars: 0.5 mm (a – b), 0.1 mm (c, protoconch); d – f: Epitonium tiberii (de Boury, 1889), sample BC 66, scale bars: 0.5 mm (d), 0.1 mm (e – f, protoconch); g – i: Alvania cimicoides (Forbes, 1844), sample BC 72, scale bars: 1 mm (g), 0.1 mm (h – i, protoconch); j – l: Alvania testae (Aradas & Maggiore, 1844), sample BC 72, scale bars: 0.5 mm (j), 0.1 mm (k – l, protoconch); m – o: Benthonella tenella (Jeffreys, 1869), sample BC 04, scale bars: 0.5 mm (m), 0.1 mm (n – o, protoconch).

FIGURE 15. a – d in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 15. a – d: Drilliola loprestiana (Calcara, 1841), sample BC 05, scale bars: 1 mm (a), 0.5 mm (b – c, protoconch), 0.1 mm (d, PI detail); e – h: Mangelia nuperrima (Tiberi, 1855), samples BC 05 (e) and BC 72 (f – h), scale bars: 2 mm (e), 0.5 mm (f – g, protoconch), 0.1 mm (h, PI detail); i: Mangelia serga (Dall, 1881), sample BC 72, scale bar 2 mm; j – l: Pleurotomella eurybrocha (Dautzenberg & Fischer, 1896), sample BC 21, scale bars: 0.5 mm (j), 0.2 mm (k – l, protoconch); m – p: Teretia teres (Reeve, 1844), sample BC 72, scale bars: 1 mm (m), 0.2 mm (n – o, protoconch), 0.1 mm (p, PI detail); q – t: Pleurotomella demosia (Dautzenberg & Fischer, 1896), sample BC 71, scale bars: 2 mm (q), 0.2 mm (r – s, protoconch), 0.1 mm (t, PI detail).

FIGURE 14. a – c in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 14. a – c: Pagodula echinata (Kiener, 1840), sample BC 72, scale bars: 5 mm (a), 0.5 mm (b – c, protoconch); d – g: Nassarius lima (Dillwyn, 1817), sample BC 72, scale bars: 5 mm (d), 1 mm (e, juvenile), 0.5 mm (f – g, protoconch); h – j: Amphissa acutecostata (Philippi, 1844), sample BC 72, scale bars: 2 mm (h), 0.5 mm (i – j, protoconch); k – m: Fusinus rostratus (Olivi, 1792), sample BC 72, scale bars: 5 mm (k), 0.5 mm (l – m, protoconch); n – p: Drilliola emendata (Monterosato, 1872), sample BC 67, scale bars: 2 mm (n), 0.5 mm (o – p, protoconch).

FIGURE 12. a – c in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 12. a – c: Anatoma aspera (Philippi, 1844), sample BC 71, scale bars: 1 mm (a – b), 0.1 mm (c, protoconch); d: Danilia tinei (Calcara, 1839), sample BC 72, scale bar 5 mm; e – g: Cirsonella romettensis (Granata – Grillo, 1877), sample BC 67, scale bars: 1 mm (e – f), 0.1 mm (g, protoconch); h – j: Putzeysia wiseri (Calcara, 1842), sample BC 66, scale bars: 2 mm (h), 0.2 mm (i – j, protoconch); k – m: Mikro? sp., sample BC 52, scale bars: 0.2 mm (k – l), 0.1 mm (m, protoconch); n – p: Adeuomphalus densicostatus (Jeffreys, 1884), sample BC 72, scale bars: 0.2 mm (n – o), 0.1 mm (p, protoconch).