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47 results for “Specific heat”
Specific Heat of Holmium in Gold and Silver at Low Temperatures - Data
<p>Data from measurements on the specific heat of a variety of Au:Ho and Ag:Ho alloys. This data is associated with the manuscript:</p> <p>Herbst, M., Reifenberger, A., Velte, C. <em>et al.</em> Specific Heat of Holmium in Gold and Silver at Low Temperatures. <em>J Low Temp Phys</em> <strong>202, </strong>106–120 (2021). https://doi.org/10.1007/s10909-020-02531-1</p> <p>For information on the motivation, measurement techniques, equipment, and data processing, please refer to this manuscript.</p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
Energy and specific heat for J=7/2 magnetic crystals
<p>Complementary data for the manuscript "Specific heat of Gd3+ and Eu2+ -based magnetic compounds" by D.J. Garcia, J. Sereni and A.A. Aligia[1].</p> <p>We include the energy and specific heat for many magnetic lattices. The magnetic moment is S=7/2. Interactions are taken as Heisenberg-like</p> <p>H = Sum_{i,\delta} J_\delta S_i S_{i+\delta}</p> <p>and \delta is taken to consider first, second, and possibly more nearest neighbors.</p> <p>Interactions are either all FM or AF to keep the system non-frustrated. We take all |J_\delta|=1K unless otherwise stated.</p> <p>Computations are performed using quantum Monte Carlo (QMC) simulations from the ALPS libraries (“dirloop_sse” package) [2, 3]. Most calculations are done using 20^3 conventional unit cells.</p> <p><strong>Directory name</strong> convention is as follows:</p> <p>fig*data/{Lattice Name}( {effective z}) , J_2 = ... , J_3 =... , K=... , L={size of the linear length} )</p> <p>fig*data refers to data of the corresponding figure in reference [1].</p> <p><em>Lattice names</em> are BCC, Diamond, FCC, HCP, and SC with their classic meaning of Body-Centered Cubic, Face-centered cubic, Hexagonal close-packed, and simple cubic.</p> <p>LHc corresponds to a layered honeycomb lattice with three NN within the plane of the honeycomb layer and one above or below the layer in alternating sites.</p> <p><em>magnetic order</em> is either ferromagnetic (FM) or antiferromagnetic (AF).</p> <p><em>effective z </em>agree<em>s </em>with the<em> number of neighbors</em> when all J_\delta are equal. It is computed by increasing the considered neighbor radius. See [1].</p> <p>The file<strong> SpecificHeatAndEnergy.tgz</strong> contains the raw data for all lattices in txt format. This version updates results with larger statistics. An ALPS example directory is also included.</p> <p>Finite size effects are almost negligible in these cases except for the antiferromagnetic simple cubic lattice ( SC_AF(6) ).</p> <p> </p> <p>[1] D. J. Garcia, J. G. Sereni, A. A. Aligia, "Specific heat of Gd3+ and Eu2+-based magnetic compounds", arXiv:2410.23519</p> <p>[2] A. Albuquerque, F. Alet, P. Corboz, P. Dayal, A. Feiguin, S. Fuchs, L. Gamper, E. Gull, S. Gürtler, A. Honecker, R. Igarashi, M. Körner et al., The ALPS project release 1.3: Open- source software for strongly correlated systems, Journal of Magnetism and Magnetic Materials 310(2, Part 2), 1187 (2007), doi:https://doi.org/10.1016/j.jmmm.2006.10.304, Proceedings of the 17th International Conference on Magnetism. </p> <p>[3] B. Bauer, L. D. Carr, H. G. Evertz, A. Feiguin, J. Freire, S. Fuchs, L. Gamper, J. Gukelberger, E. Gull, S. Guertler, A. Hehn, R. Igarashi et al., The ALPS project release 2.0: open source software for strongly correlated systems, Journal of Statistical Mechanics: Theory and Experiment 2011(05), P05001 (2011), doi:10.1088/1742-5468/2011/05/P05001.</p>
Life stage-specific effects of heat stress on spermatogenesis and oogenesis in Drosophila melanogaster
<p><br>#Life stage-specific effects of heat stress on spermatogenesis and oogenesis in Drosophila melanogaster</p> <p>##Authors</p> <p>Abhishek Meena, Alessio N. De Nardo, Komal Maggu, Sonja Sbilordo, Benjamin Eggs, Rawaa Al Toma Sho, Stefan Lüpold</p> <p><br>## Citation of associated article<br>Meena, A., Maggu, K., De Nardo, A.N., Sbilordo, S.H., Eggs, B., Al Toma Sho, R., Lüpold, S., 2024. Life stage-specific effects of heat stress on spermatogenesis and oogenesis in Drosophila melanogaster. Journal of Thermal Biology 125, 104001. https://doi.org/10.1016/j.jtherbio.2024.104001</p> <p> </p> <p>##Overview<br>This dataset was collected to assess the impact of heat stress across various life stages on reproductive performance in Drosophila melanogaster. <br>The study focuses on stage- and sex-specific reproductive metrics such as: mating success, fertility, fecundity, hatching success, which for females were summed over four days and for males were assayed for four consecutive 24-hour periods, each with a different female. </p> <p>The dataset is structured to analyze these outcomes at different temperatures and developmental stages, providing insights into sex- and stage-specific vulnerabilities to heat stress. </p> <p>The analyses are performed in an R Markdown (.Rmd) file, using data stored in a CSV file</p> <p>##Contents</p> <p>The dataset includes the following files:</p> <p>Rscript.Rmd: An R Markdown file containing code for analyzing sex- and stage-specific reproductive fitness based on the data in Data.csv.<br>Data.csv: A CSV file with the raw data used for the analysis.</p> <p>##Data Description</p> <p>Note: NA's in each data file represent missing data (data not available). </p> <p>The Data.csv file contains the following columns:</p> <p>Unique.ID: Unique identifier for each experimental individual.</p> <p>SampleID: Identifier for each treatment group.</p> <p>Temperature: Thermal exposure temperature (measured in degrees Celsius) for 4 hours, with levels: 24.5°C, 28°C, 32°C, 36°C, and 38°C.</p> <p>Lifestage: Developmental stage during which thermal treatment was applied.</p> <p>Sex: Sex of the individual exposed to the thermal treatment.</p> <p>Day: Days post adult treatment exposure.</p> <p>Block: Experimental block identifier for randomized grouping.</p> <p>Total: Total number of eggs laid within 24 hours of oviposition.</p> <p>Hatched: Number of eggs that successfully hatched.</p> <p>Unhatched: Number of eggs that did not hatch (derived from the difference between Total and Hatched).</p> <p><br>##Usage</p> <p>To analyze the dataset, open and run the respective R Markdown (Rmd) files in RStudio or any compatible R Markdown environment. The Rmd files contain all the necessary code to reproduce the analyses described in the overview.</p> <p> </p>
Data Files for "Low-Temperature Specific Heat Capacity of Water--Ammonia Mixtures Down to the Eutectic"
<p>The zip file contains all data files and plots used and created in the publication:<br> Low-Temperature Specific Heat Capacity of Water--Ammonia Mixtures Down to the Eutectic</p>
Specific heat of tungsten from 23 °C to 3266 °C
<p>This dataset contains specific heat values determined from 23 °C to 3266 °C by five laboratories on a batch of specimens machined in the same block of tungsten.</p>
Specific heat of molybdenum from 23 °C to 2607 °C
<p>This dataset contains specific heat values determined from 23 °C to 2607 °C by five laboratories on a batch of specimens machined in the same block of molybdenum.</p>
Specific heat of isotropic graphite from 1000 °C to 2800 °C
<p>This dataset contains specific heat values determined from 1000 °C to 2800 °C by three laboratories on a batch of specimens machined in the same block of isotropic graphite.</p>
Isobaric specific heat of gases and vapors under standard conditions
<p><strong>Isobaric specific heat of gases and vapors under standard conditions</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Gases are treated as a large collection of tiny particles subject to the laws of physics. Their properties are attributed primarily to the motion of the molecules and can be explained by the kinetic theory of gases. It is not obvious that this should be the case, and for many years a static picture of gases was instead espoused, in which the pressure, for instance, was attributed to repulsive forces between essentially stationary particles pushing on the container walls. How the kinetic-molecular picture finally came to be universally accepted is a fascinating piece of scientific history and is discussed briefly below in the section Kinetic theory of gases. Any theory of gas behaviour based on this kinetic model must also be a statistical one because of the enormous numbers of particles involved. The kinetic theory of gases is now a classical part of statistical physics and is indeed a sort of miniature display case for many of the fundamental concepts and methods of science. Such important modern concepts as distribution functions, cross sections, microscopic reversibility, and time-reversal invariance have their historical roots in kinetic theory, as does the entire atomistic view of matter.</p> <p>Gases and vapors, Isobaric specific heat (kilojoules per kelvin per kilogram)</p> <p>Acetone 1.47</p> <p>Acetylene 1.69</p> <p>Air 1.01</p> <p>Alcohol (ethanol) 1.88</p> <p>Alcohol (methanol) 1.93</p> <p>Ammonia 2.19</p> <p>Argon 0.520</p> <p>Benzene 1.09</p> <p>Blast furnace gas 1.03</p> <p>Bromine 0.25</p> <p>Butane 1.67</p> <p>Carbon dioxide 0.844</p> <p>Carbon monoxide 1.02</p> <p>Carbon disulphide 0.67</p> <p>Chlorine 0.48</p> <p>Chloroform 0.63</p> <p>Ethane 1.75</p> <p>Ether (diethyl ether) 1.95</p> <p>Ethylene 1.53</p> <p>Helium 5.19</p> <p>Hydrochloric acid 0.795</p> <p>Hydrogen 14.32</p> <p>Hydrogen chloride 0.8</p> <p>Hydroxyl 1.76</p> <p>Krypton 0.25</p> <p>Methane 2.22</p> <p>Natural gas 2.34</p> <p>Neon 1.03</p> <p>Nitric oxide 0.995</p> <p>Nitrogen 1.04</p> <p>Nitrogen tetroxide 4.69</p> <p>Nitrous oxide 0.88</p> <p>Oxygen 0.919</p> <p>Propane 1.67</p> <p>Propene (propylene) 1.5</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Isobaric specific heat of metals and metalloids under standard conditions
<p><strong>Isobaric specific heat of metals and metalloids under standard conditions</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>A metal is any of a class of substances characterized by high electrical and thermal conductivity as well as by malleability, ductility, and high reflectivity of light. Approximately three-quarters of all known chemical elements are metals. The most abundant varieties in the Earth's crust are aluminum, iron, calcium, sodium, potassium, and magnesium. The vast majority of metals are found in ores (mineral-bearing substances), but a few such as copper, gold, platinum, and silver frequently occur in the free state because they do not readily react with other elements. Metals are usually crystalline solids. In most cases, they have a relatively simple crystal structure distinguished by a close packing of atoms and a high degree of symmetry. Typically, the atoms of metals contain less than half the full complement of electrons in their outermost shell. Because of this characteristic, metals tend not to form compounds with each other. They do, however, combine more readily with nonmetals (for example, oxygen and sulfur), which generally have more than half the maximum number of valence electrons. Metals differ widely in their chemical reactivity. The most reactive include lithium, potassium, and radium, whereas those of low reactivity are gold, silver, palladium, and platinum. The high electrical and thermal conductivities of the simple metals (namely the non-transition metals of the periodic table) are best explained by reference to the free-electron theory. According to this concept, the individual atoms in such metals have lost their valence electrons to the entire solid, and these free electrons that give rise to conductivity move as a group throughout the solid. In the case of the more complex metals (namely the transition elements), conductivities are better explained by the band theory, which takes into account not only the presence of free electrons but also their interaction with so-called d electrons. The mechanical properties of metals, such as hardness, ability to resist repeated stressing (fatigue strength), ductility, and malleability, are often attributed to defects or imperfections in their crystal structure. The absence of a layer of atoms in its densely packed structure, for example, enables a metal to deform plastically, and prevents it from being brittle. A metalloid, in chemistry, is an imprecise term used to describe a chemical element that forms a simple substance having properties intermediate between those of a typical metal and a typical nonmetal. The term is normally applied to a group of between six and nine elements (boron, silicon, germanium, arsenic, antimony, tellurium, and possibly bismuth, polonium, astatine) found near the center of the P-block or main block of the periodic table. There is no single property which can be used to unambiguously identify an element as a metalloid. Since most metalloids tend to display semiconducting properties in at least one of their allomorphic modifications, the class might reasonably be extended to also include gray silicon (which, unlike white silicon, is a semiconductor rather than a metal) and the graphite form of carbon (which, unlike the diamond form, is a semimetal rather than an insulator). Chemically, metalloids correspond to atoms having intermediate electronegativities and an ability to display a range of both positive and negative oxidation states in their compounds.</p> <p>Metals and metalloids, Isobaric specific heat (kilojoules per kelvin per kilogram)</p> <p>Aluminum 0.91</p> <p>Antimony 0.21</p> <p>Barium 0.20</p> <p>Beryllium 1.83</p> <p>Bismuth 0.13</p> <p>Cadmium 0.23</p> <p>Calsium 0.63</p> <p>Carbon Steel 0.49</p> <p>Cast Iron 0.46</p> <p>Cesium 0.24</p> <p>Chromium 0.46</p> <p>Cobalt 0.42</p> <p>Copper 0.39</p> <p>Gallium 0.37</p> <p>Germanium 0.32</p> <p>Gold 0.13</p> <p>Hafnium 0.14</p> <p>Indium 0.24</p> <p>Iridium 0.13</p> <p>Iron 0.45</p> <p>Lanthanum 0.195</p> <p>Lead 0.13</p> <p>Lithium 3.57</p> <p>Lutetium 0.15</p> <p>Magnesium 1.05</p> <p>Manganese 0.48</p> <p>Mercury 0.14</p> <p>Molybdenum 0.25</p> <p>Nickel 0.44</p> <p>Niobium 0.27</p> <p>Osmium 0.13</p> <p>Palladium 0.24</p> <p>Platinum 0.13</p> <p>Plutonium 0.13</p> <p>Potassium 0.75</p> <p>Rhenium 0.14</p> <p>Rhodium 0.24</p> <p>Rubidium 0.36</p> <p>Ruthenium 0.24</p> <p>Scandium 0.57</p> <p>Selenium 0.32</p> <p>Silicon 0.71</p> <p>Silver 0.23</p> <p>Sodium 1.21</p> <p>Strontium 0.30</p> <p>Tantalum 0.14</p> <p>Thallium 0.13</p> <p>Thorium 0.13</p> <p>Tin 0.21</p> <p>Titanium 0.54</p> <p>Tungsten 0.13</p> <p>Uranium 0.12</p> <p>Vanadium 0.39</p> <p>Yttrium 0.30</p> <p>Zinc 0.39</p> <p>Zirconium 0.27</p> <p>Wrought Iron 0.50</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Isobaric specific heat for liquid water at different temperatures
<p><strong>Isobaric specific heat for liquid water at different temperatures</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Heat capacity is the ratio of heat absorbed by a material to the temperature change. It is usually expressed as calories per degree in terms of the actual amount of material being considered, most commonly a mole (the molecular weight in grams). The heat capacity in calories per gram is called specific heat. The definition of the calorie is based on the specific heat of water, defined as one calorie per degree Celsius. At sufficiently high temperatures, the heat capacity per atom tends to be the same for all elements. For metals of higher atomic weight, this approximation is already a good one at room temperature, giving rise to the law of Dulong and Petit.</p> <p>Temperature (degrees Celsius), Isobaric specific heat (joules per kelvin per mole), Isobaric specific heat (kilojoules per kelvin per kilogram), Isobaric specific heat (kilowatts-hour per kelvin per kilogram), Isobaric specific heat (kilocalories per kelvin per kilogram)</p> <p>0.01 76.026 4.2199 0.001172 1.0079</p> <p>10 75.586 4.1955 0.001165 1.0021</p> <p>20 75.386 4.1844 0.001162 0.9994</p> <p>25 75.336 4.1816 0.001162 0.9988</p> <p>30 75.309 4.1801 0.001161 0.9984</p> <p>40 75.300 4.1796 0.001161 0.9983</p> <p>50 75.334 4.1815 0.001162 0.9987</p> <p>60 75.399 4.1851 0.001163 0.9996</p> <p>70 75.491 4.1902 0.001164 1.0008</p> <p>80 75.611 4.1969 0.001166 1.0024</p> <p>90 75.763 4.2053 0.001168 1.0044</p> <p>100 75.950 4.2157 0.001171 1.0069</p> <p>110 76.177 4.2283 0.001175 1.0099</p> <p>120 76.451 4.2435 0.001179 1.0135</p> <p>140 77.155 4.2826 0.001190 1.0229</p> <p>160 78.107 4.3354 0.001204 1.0355</p> <p>180 79.360 4.4050 0.001224 1.0521</p> <p>200 80.996 4.4958 0.001249 1.0738</p> <p>220 83.137 4.6146 0.001282 1.1022</p> <p>240 85.971 4.7719 0.001326 1.1397</p> <p>260 89.821 4.9856 0.001385 1.1908</p> <p>280 95.285 5.2889 0.001469 1.2632</p> <p>300 103.60 5.7504 0.001597 1.3735</p> <p>320 117.78 6.5373 0.001816 1.5614</p> <p>340 147.88 8.2080 0.002280 1.9604</p> <p>360 270.31 15.004 0.004168 3.5836</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Isochoric specific heat for liquid water at different temperatures
<p><strong>Isochoric specific heat for liquid water at different temperatures</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Specific heat is the quantity of heat required to raise the temperature of one gram of a substance by one Celsius degree. The units of specific heat are usually calories or joules per gram per Celsius degree. For example, the specific heat of water is one calorie (or 4.186 joules) per gram per Celsius degree. The Scottish scientist Joseph Black, in the 18th century, noticed that equal masses of different substances needed different amounts of heat to raise them through the same temperature interval, and, from this observation, he founded the concept of specific heat. Measurements of specific heats of substances allow calculation of their atomic weights.</p> <p>Temperature (degrees Celsius), Isochoric specific heat (joules per kelvin per mole), Isochoric specific heat (kilojoules per kelvin per kilogram), Isochoric specific heat (kilowatts-hour per kelvin per kilogram), Isochoric specific heat (kilocalories per kelvin per kilogram)</p> <p>0.01 75.981 4.2174 0.001172 1.0073</p> <p>10 75.505 4.1910 0.001164 1.0010</p> <p>20 74.893 4.1570 0.001155 0.9929</p> <p>25 74.548 4.1379 0.001149 0.9883</p> <p>30 74.181 4.1175 0.001144 0.9834</p> <p>40 73.392 4.0737 0.001132 0.9730</p> <p>50 72.540 4.0264 0.001118 0.9617</p> <p>60 71.644 3.9767 0.001105 0.9498</p> <p>70 70.716 3.9252 0.001090 0.9375</p> <p>80 69.774 3.8729 0.001076 0.9250</p> <p>90 68.828 3.8204 0.001061 0.9125</p> <p>100 67.888 3.7682 0.001047 0.9000</p> <p>110 66.960 3.7167 0.001032 0.8877</p> <p>120 66.050 3.6662 0.001018 0.8757</p> <p>140 64.306 3.5694 0.000992 0.8525</p> <p>160 62.674 3.4788 0.000966 0.8309</p> <p>180 61.163 3.3949 0.000943 0.8109</p> <p>200 59.775 3.3179 0.000922 0.7925</p> <p>220 58.514 3.2479 0.000902 0.7757</p> <p>240 57.381 3.1850 0.000885 0.7607</p> <p>260 56.392 3.1301 0.000869 0.7476</p> <p>280 55.578 3.0849 0.000857 0.7368</p> <p>300 55.003 3.0530 0.000848 0.7292</p> <p>320 54.819 3.0428 0.000845 0.7268</p> <p>340 55.455 3.0781 0.000855 0.7352</p> <p>360 59.402 3.2972 0.000916 0.7875</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Data for "Ecosystem heating experiment reveals sex-specific growth responses in fish"
<p>Data for the paper "Ecosystem heating experiment reveals sex-specific growth responses in fish" in the Canadian Journal of Fisheries and Aquatic Sciences.</p> <p>One file contains the back-calculated fish length-at-age used for all analyses in the study. The other file contains the temperature data used to create Figure S2.</p>
Data for: Parent-specific transgenerational immune priming enhances offspring defense – unless heat stress negates it all
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How hornbills handle heat: sex-specific thermoregulation in the southern yellow-billed hornbill
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Data from: Stage-specific genotype-by-environment interactions for cold and heat hardiness in Drosophila melanogaster.
Environments often vary across a life cycle, imposing fluctuating natural selection across development. Such fluctuating selection can drive evolutionary responses specific to distinct life-history stages. However, selection and genetic variation, phenotypic plasticity, and their interaction (GxE), as well as genetic correlation across development dictate stage-specific evolution. Thus, quantifying genetic covariance of fitness-related traits and plasticity across development is vital to determine whether stage-specific adaptation occurs in nature. Additionally, the interaction of genetic variation and environmental plasticity (GxE) may be stage-specific, leading to a 3-way interaction between genotype, environment, and development or GxDxE. To test for these patterns in a natural system, we exposed larvae and adults of Drosophila melanogaster isogenic lines derived from a natural population to extreme heat and cold after developmental acclimation to cool (18°C) and warm (25°C) conditions and measured genetic variance for thermal hardiness. We detected significant GxE that was specific to larvae and adults for cold and heat hardiness (GxDxE), but no significant genetic correlation across development for either trait at either acclimation temperature. However, cross-development phenotypic correlations for acclimation responses suggest that plasticity itself may be developmentally constrained, though rigorously testing this hypothesis requires more experimentation. In general, we find evidence for thermal niche adaptation across development as larvae are more heat-hardy while adults are more cold-hardy. These results illustrate the potential for stage-specific adaptation within a complex life cycle and highlight the importance of measuring traits at appropriate developmental stages and environmental conditions when predicting evolutionary responses to changing climates.
Data from: Transgenerational effects of mild heat in Arabidopsis thaliana show strong genotype specificity that is explained by climate at origin
Transgenerational environmental effects can trigger strong phenotypic variation. However, it is unclear how cues from different preceding generations interact. Also, little is known about genetic variation for these life-history-traits. Here we present effects of grandparental and parental mild heat, and their combination, on four traits of the third-generation phenotype of 14 Arabidopsis thaliana genotypes. We tested for correlations of these effects with climate and constructed a conceptual model to identify the environmental conditions that favour the parental effect on flowering time. We observed strong evidence for genotype-specific transgenerational effects. On average, A. thaliana accustomed to mild heat, producing more seeds after two generations. Parental effects overruled grandparental effects in all traits but reproductive biomass. Flowering was generally accelerated by all transgenerational effects. Notably, the parental effect triggered earliest flowering in genotypes adapted to dry summers. Accordingly, this parental effect was favoured in the model when early summer heat terminated the growing season and environments were correlated across generations. Our results suggest that A. thaliana can partly accustom to mild heat over two generations and genotype-specific parental effects show non-random evolutionary divergence across populations that may support climate change adaptation in the Mediterranean.
Data from: Species-specific germination responses to fire-associated heat of elephant-dispersed seeds in Nimule National Park, South Sudan
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Data from: Transgenerational effects of mild heat in Arabidopsis thaliana show strong genotype specificity that is explained by climate at origin
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Data from: Stage-specific genotype-by-environment interactions for cold and heat hardiness in Drosophila melanogaster.
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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.