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481 results for “freezing”
Data and code used in manuscript: Basal freeze-on generates complex ice-sheet stratigraphy
<p>Mapped plumes location obtained from ice-sheet radio echo sounding data of North Greenland (https://data.cresis.ku.edu/data/rds/ for 2010-2014_Greenland files) and map of calculated freeze-on index are found in 'FreezeOnIndex_MappedPlume_Data.nc'. Model code of the three models used to obtain the findings shown in the manuscript 'Basal freeze-on generates complex ice-sheet stratigraphy'. As well as code to calculate the freeze-on index.</p>
Indicative distribution map for Ecosystem Functional Group F2.4 Freeze-thaw freshwater lakes
<p>This archive contains indicative distribution maps and profiles for <strong>F2.4 Freeze-thaw freshwater lakes</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Black Rock Forest Spring Freeze Defoliation Radial Growth and Leaf-Level Gas Exchange
These data are from a study conducted at Black Rock Forest in Cornwall, New York, USA during 2020 and 2021. This study was conduct to assess the ecophysiological responses of red oak (Quercus rubra) and red maple (Acer rubrum) trees in a temperate broadleaf forests to a spring frost in 2020 that that defoliated red oak trees, but not red maples. We used 2021—a year without a defoliation event—as a reference year. The datasets include tree-level measurements of (1) basal area increment for the early growing season, late growing season, and entire growing season and (2) leaf-level gas exchange (Amax, gsw, and WUE) for red oak (Quercus rubra) and red maple (Acer rubrum). These data are associated with the manuscript “Compensatory Responses of Leaf Physiology Reduce Effects of Spring Frost Defoliation on Temperate Forest Tree Carbon Uptake” by Reinmann et al.2023 in Frontiers in Forests and Global Change.
Microwave assisted freezing equipment
<p>Image representing the equipment used for microwave assisted freezing during the implementation of COLDμWAVE project.</p> <p> </p>
A Repository of 100+ Years of Measured Soil Freezing Characteristic Curves
<p>The temperature of the soil can be used as a proxy to represent the soil ice content through a soil freezing characteristic curve (SFCC). This mathematical construct relates the soil ice content to a specific temperature for a particular soil. SFCCs depend on many factors including soil properties (e.g., porosity, composition, etc.), soil pore water pressure, dissolved salts, (hysteresis in) freezing/thawing point depression, and degree of saturation, all of which can be site-specific and time varying. SFCCs have been measured using various methods for diverse soils since 1921, and to date this data has not been broadly compared, in part because it has not previously been compiled in a single data set. The dataset presented in this publication includes SFCC data digitized or received from authors, and includes both historic and modern studies.</p>
Kerosene freeze data for paper to be published:
<p>This dataset originates in an oil refinery producing, among other products, kerosene. The freeze point of the kerosene is an important specification. In the paper to be published the authors use data quality assessment methods to define periods of the data suitable for the derivation of an inferential model.</p>
Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts"
<p>Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts"<br><br>Open data concerning experimental work. <span>This study investigates the influence of a crystalline admixture (CA) in Ultra-high performance (fibre-reinforced) concrete under freeze-thaw (FT) cycles with de-icing salts with focus on single cracks with a width of around 120 µm, specifically focusing on the ability of the healing products of CA to survive and the ability to re-heal after a healing regime following FT exposure. </span></p>
Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement"
<p>Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement" submitted to The Cryosphere.</p>
Model data and code for "Freeze-thaw effects on daily sediment transport in an Alpine river"
<p>Supporting information for the research article "Freeze-thaw effects on daily sediment transport in an Alpine river" by Skålevåg et al., submitted to Water Resources Research.</p> <p>This data repository contains the processed data, model code, and results presented in the research article. Please refer to the article and its supplementary information for details on primary data.</p> <p> </p> <p><strong>Contents:</strong></p> <ul> <li>processed data: <ul> <li>Standardised target and predictor variables, in addition to non-standardised data used for freeze-thaw state classification <a href="https://zenodo.org/api/records/13928999/draft/files/model_variables.csv/content" target="_blank" rel="noopener noreferrer">model_variables.csv</a></li> <li>Means and standard deviations of standardised variables <a href="https://zenodo.org/api/records/13928999/draft/files/regression_variables_mean_std.csv/content" target="_blank" rel="noopener noreferrer">regression_variables_mean_std.csv</a></li> </ul> </li> <li>model code: <ul> <li>final model presented in research article: <a href="https://zenodo.org/api/records/13928999/draft/files/model.py/content" target="_blank" rel="noopener noreferrer">model.py</a></li> <li>model comparison performed as part of model development: <a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_predictors_and_segmentation.html/content" target="_blank" rel="noopener noreferrer">model_comparison_predictors_and_segmentation.html</a></li> </ul> </li> <li>results: <ul> <li>final model: <ul> <li>Inference trace from the pymc model <a href="https://zenodo.org/api/records/13928999/draft/files/inference.nc/content" target="_blank" rel="noopener noreferrer">inference.nc</a></li> <li>Summary table of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary.csv/content" target="_blank" rel="noopener noreferrer">inference_summary.csv</a></li> <li>Visualisation of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_trace.png/content" target="_blank" rel="noopener noreferrer">inference_trace.png</a></li> </ul> </li> <li>other models: <ul> <li>non-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_SRC.nc/content" target="_blank" rel="noopener noreferrer">inference_SRC.nc</a> and <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_SRC.csv</a></li> <li>non-segmented "pooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_nonsegmented.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_nonsegmented.csv</a></li> <li>freeze-thaw-state-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_segm_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_segm_SRC.csv</a></li> <li>freeze-thaw-state-segmented "unpooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_unpooled.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_unpooled.csv</a></li> </ul> </li> <li>model comparison: <ul> <li><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_waic.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_waic.csv</a></li> <li> <div><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_loo.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_loo.csv</a></div> </li> </ul> </li> </ul> </li> </ul>
Arctic Sea Ice Freeze Onsest
<p>The product contains yearly maps of early freeze onset and freeze onset for the sea ice surface based on the improved PMW algorithm.</p> <p>The data were derived using brightness temperature observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), Sea Ice Concentration (SIC) observations using the Enhanced NASA Team (NT2) algorithm, and sea ice age dataset.</p> <p>They are gridded on the NSIDC northern hemisphere polar stereographic grid at 12.5 km, with a time range of 2003 to 2021 (2011 and 2012 missing).</p>
NEON Biorepository Aquatic Microalgae Collection (Freeze-dried) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains freeze-dried subsamples of aquatic microalgae (NEON sample class: ptx_taxonomy_in.freezeDried). Periphyton and phytoplankton samples are collected three times per year at wadeable stream, river, and lake sites during aquatic biology bout windows, roughly in spring, summer, and fall. Benthic samples are collected using the most appropriate sampler for the habitat and substratum type, including rock scrubs, grab samples, and epiphyton. In wadeable streams, periphyton samples are collected in the two most dominant benthic habitat types (e.g. riffles, runs, pools, step pools), and seston samples were collected from the water column near the S2 sensor (seston samples were discontinued in 2018). In lakes, water-column phytoplankton samples are collected near the buoy and littoral sensors using a Kemmerer sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. In rivers, phytoplankton samples are collected near the buoy and two other deep-water locations using a Kemmerer or Van Dorn sampler, and in littoral areas using the best benthic sampling method for the dominant substratum type. All field-collected samples are split into subsamples in the domain support facility, preserved, and shipped to a contracting taxonomy laboratory where samples are further subsampled for analysis and archiving. Freeze dried subsamples contained cleaned, freeze dried diatoms. Samples are archived in 20 mL glass scintillation vials and stored at room temperature. See related links below for protocols and NEON related data products.
Hubbard Brook Experimental Forest: Soil Freezing Study (SFS) In Situ Measurements of Snow and Soil Frost Depth
Climate models for the northeastern United States (U.S.) over the next century predict an increase in air temperature between 2.8 and 4.3 °C and a decrease in the average number of days per year when a snowpack will cover the forest floor (Hayhoe et al. 2007, 2008; Campbell et al. 2010). Studies of forest dynamics in seasonally snow-covered ecosystems have been primarily conducted during the growing season, when most biological activity occurs. However, in recent years considerable progress has been made in our understanding of how winter climate change influences dynamics in these forests. The snowpack insulates soil from below-freezing air temperatures, which facilitates a significant amount of microbial activity. However, a smaller snowpack and increased depth and duration of soil frost amplify losses of dissolved organic C and NO3- in leachate, as well as N2O released into the atmosphere. The increase in nutrient loss following increased soil frost cannot be explained by changes in microbial activity alone. More likely, it is caused by a decrease in plant nutrient uptake following increases in soil frost. We conducted a snow-removal experiment at Hubbard Brook Experimental Forest to determine the effects of a smaller winter snowpack and greater depth and duration of soil frost on trees, soil microbes, and arthropods. A number of publications have been based on these data: Comerford et al. 2013, Reinmann et al. 2019, Templer 2012, and Templer et al. 2012. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Campbell JL, Ollinger SV, Flerchinger GN, Wicklein H, Hayhoe K, Bailey AS. Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA. Hydrological Processes. 2010; 24:2465–2480. Comerford DP, PG Schaberg, PH Te
Hubbard Brook Experimental Forest: Soil Freeze Study - Tree Growth
The climate is changing in many temperate forests with the amount of forest area dominated by sugar maple experiencing an insulating snowpack expected to shrink between 49 and 95% compared to 1951-2005 values. A reduced snowpack and increased depth and duration of soil frost can injure or kill fine roots, which are essential for plant water and nutrient uptake. These adverse impacts on tree roots can have important impacts on tree growth and ecosystem carbon sequestration. We evaluated the effects of changing winter climate, including snow and soil frost dynamics, by using tree cores to measure sugar maple radial growth rates in the Soil Freezing Study plots at the Hubbard Brook Experimental Forest. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Analysis of these data are published in: Reinmann AB, Susser JR, Demara EMC, and Templer PH. 2019. Declines in northern forest tree growth following snowpack decline and soil freezing. Global Change Biology. 25(2):420-430. https://doi.org/10.1111/gcb.14420
Trace metal, ion, and nutrient concentrations in aeolian samples subjected to experimental freeze-thaw cycles, collected from Taylor Valley, McMurdo Dry Valleys, Antarctica (2013-2016)
This data package contains measurements of trace metal, ion, and nutrient concentrations in aeolian samples collected from several locations throughout Taylor Valley in the McMurdo Dry Valleys of Antarctica during the 2013-2014, 2014-2015, and 2015-2016 austral summers. Samples were collected by the McMurdo Dry Valleys Long Term Ecological Research Program (MCM LTER) using Big Spring Number Eight (BSNE) isokinetic wind samplers located at Explorer’s Cove, Lake Fryxell at F6, East Lake Bonney, and Taylor Glacier. Samples were then subjected to experimental freeze-thaw cycles in a controlled laboratory setting to simulate supraglacial weathering processes and then analyzed to understand how freeze-thaw cycles affect nutrient, ion, and trace metal concentrations over time.
How to transport veterinary drugs in insulated boxes to avoid thermal damage by heating or freezing
<p><strong>Documentation of the empiric data, measured in the heating and cooling chamber to determine the thermal constant for the investigated transport boxes.</strong></p> <p> </p> <p><strong>Background: </strong>The transport of veterinary drugs must comply with the general standards for drug storage. Although many vehicles are equipped with active heating and/or cooling devices assuring recommended storage conditions, simple insulated transport boxes are also often used. In this study, measurements for typical transport boxes were performed under laboratory conditions by the use of a climate chamber for a temperature of -20°C and 45°C to investigate the impact of box size, insulation material, liquid vs. dry filling products, filling degree and other parameters on the thermal performance of insulated boxes. Model calculations and instructions are presented to predict the retention time of recommended drug storage temperatures.</p> <p><strong>Results: </strong>The measurements and the model calculations showed that the loading of the transport boxes with additional water bottles to increase the heat capacity is appropriate to prolong the retention time of the recommended temperature range of the drugs. Insulated transport boxes are not suitable to store drugs over a period of more than approximately 12 hours. For practical use, a recipe is presented to measure the thermal properties of a transport box and the related retention time for which the recommended storage temperatures can be assured.</p> <p><strong>Conclusions: </strong>The following principles for drug transportation in vehicles are recommended: (1) Before transfer into boxes, drugs should always be thermally preconditioned (2) Increase the filling degree of the boxes with thermally preconditioned water bottles or re-usable thermal packs will increase the heat capacity. Do not deep-freeze the bottles or packs below 0°C to avoid drug freezing due to contact. (3) Open the lid of the boxes only to uncase drugs that are immediately needed. (4) The bigger the box and the higher the filling degree, the longer the retention time of the transport box. (5) Wherever possible, place the drug box at a cool site inside the vehicle. (6) The monitoring of the inside temperature of the transport boxes is recommended. <br> By the proper use of such transport boxes the recommended temperatures can be maintained over one working day.</p> <p> </p>
Snow flies self-amputate freezing limbs to sustain behavior at sub-zero temperatures
<p><span>All living things are profoundly affected by temperature. In spite of the thermodynamic constraints on biology, some animals have evolved to live and move in extremely cold environments. Here, we investigate behavioral mechanisms of cold tolerance in the snow fly (<em>Chionea</em> spp.), a flightless crane fly that is active throughout the winter in boreal and alpine environments of the northern hemisphere. Using thermal imaging, we show that adult snow flies maintain the ability to walk down to an average body temperature of -7 °C. At this supercooling limit, ice crystallization occurs within the snow fly's hemolymph and rapidly spreads throughout the body, resulting in death. However, we discovered that snow flies frequently survive freezing by rapidly amputating legs before ice crystallization can spread to their vital organs. Self-amputation of freezing limbs is a last-ditch tactic to prolong survival in frigid conditions that few animals can endure. Understanding the extreme physiology and behavior of snow insects is important at this moment when the alpine ecosystems they inhabit are rapidly changing due to anthropogenic climate change.</span></p>
Figure 7 in Before the freeze: otoliths from the Eocene of Seymour Island, Antarctica, reveal dominance of gadiform fishes (Teleostei)
Figure 7. Fossil otolith record in Antarctica, Australia, New Zealand and the North Sea Basin and distribution and estimated abundance of gadiform otoliths (number of species, recognized or inferred, not shown). Ranicipitidae shown in family ranking following Nelson (1994); other families following Nelson (2006). Data compiled and altered from Nolf (2013), Schwarzhans (1980, 1985, 1994, 2003) and Stinton (1965, 1966).
Figure 6. Eocene otoliths from Seymour Island. A in Before the freeze: otoliths from the Eocene of Seymour Island, Antarctica, reveal dominance of gadiform fishes (Teleostei)
Figure 6. Eocene otoliths from Seymour Island. A, Argentina antarctica sp. nov., holotype, NRM-PZ P.15964, mirror imaged, inner face. B, C, Diaphus? marambionis sp. nov., holotype, NRM-PZ P.15966; B, inner face; C, ventral view. D—F, Macruronus eastmani sp. nov., holotype, NRM-PZ P.15970, mirror imaged; D, inner face; E, ventral view; F, outer face. G—I, Palimphemus seymourensis sp. nov., holotype, NRM-PZ P.15973, mirror imaged; G, inner face; H, ventral view; I, outer face. J—L, Coelorinchus nordenskjoeldi sp. nov., holotype, NRM-PZ P.15978; J, inner face; K, ventral view; L, outer face. M, N, Coelorinchus balushkini sp. nov., holotype, NRM-PZ P.15976; M, inner face; N, ventral view. O, P, Hoplobrotula? antipoda sp. nov., holotype, NRM-PZ P.15984, mirror imaged; O, inner face; P, ventral view. Q, Notoberyx cionei gen. nov., sp. nov.; holotype, NRM-PZ P.15987, inner face. R, Cepola anderssoni sp. nov., holotype, NRM-PZ P.15996, mirror imaged, inner face.
Figure 5 in Before the freeze: otoliths from the Eocene of Seymour Island, Antarctica, reveal dominance of gadiform fishes (Teleostei)
Figure 5. Drawings of Eocene otoliths from Seymour Island. A—C, Hoplobrotula? antipoda sp. nov.; A, B, holotype, NRM-PZ P.15984, mirror imaged; A, inner face; B, ventral view; C, paratype, NRM-PZ P.15985, inner face. D—G, Notoberyx cionei gen. nov., sp. nov.; D—F, holotype, NRM-PZ P.15987; D, anterior view; E, inner face; F, ventral view; G, paratype, NRM-PZ P.15988, inner face. H, Centroberyx sp., NRM-PZ P.15986, mirror imaged, inner face. I, J, Acanthopterygii indet., NRM-PZ P.15990, mirror imaged; I, inner face, J, ventral view. K, L, Percoidei indet., NRM-PZ P.15992, mirror imaged; K, inner face; L, ventral view. M, N, Haemulidae? indet., NRM-PZ P.15993, mirror imaged; M, inner face; N, ventral view. O, P, Sparidae? indet., NRM-PZ P.15994, mirror imaged; O, inner face; P, ventral view. Q, R, Cepola anderssoni sp. nov., holotype, NRM-PZ P.15996, mirror imaged; Q, inner face; R, ventral view.
Figure 4 in Before the freeze: otoliths from the Eocene of Seymour Island, Antarctica, reveal dominance of gadiform fishes (Teleostei)
Figure 4. Drawings of Eocene otoliths from Seymour Island. A, B, Tripterophycis immutatus Schwarzhans, 1980, NRM-PZ P.15969, mirror imaged; A, inner face; B, dorsal view. C—F, Macruronus eastmani sp. nov.; C—E, holotype, NRM-PZ P.15970, mirror imaged; C, inner face; D, outer face; E, ventral view; F, paratype, NRM-PZ P.15971, inner face. G—M, Palimphemus seymourensis sp. nov.; G—I, holotype, NRM-PZ P.15973, mirror imaged; G, inner face; H, ventral view; I, outer face; J, paratype, NRM-PZ P.15975, mirror imaged, inner face; K—M, paratypes, NRM-PZ P.15974; K, inner face, mirror imaged; L, ventral view; M, inner face. N—R, Coelorinchus nordenskjoeldi sp. nov.; N—P, holotype, NRM-PZ P.15978; N, inner face; O, ventral view; P, outer face; Q, R, paratype, NRM-PZ P.15979, mirror imaged; Q, inner face; R, ventral view. S—U, Coelorinchus balushkini sp. nov., holotype, NRM-PZ P.15976; S, inner face; T, ventral view; U, outer face. V, W, Coelorinchus sp., NRM-PZ P.15911; V, inner face (strongly eroded); W, ventral view.
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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)
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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.