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33 results for “Transition rates”
Data and code for "Phase transitions in inorganic halide perovskites from machine learning potentials: The impact of size, rate, and the underlying exchange-correlation functional"
<p>This record contains databases with data from density functional theory calculations used for training a series of neuroevolution potentials (NEPs), which are also included here. Information is also included for how to access the databases and run the NEP models.</p> <p><strong>Databases</strong><br> The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <p><strong>Models</strong><br> The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <p><strong>Primitive structures</strong><br> Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p>
Disk population synthesis data for the article: "Large gaps and high accretion rates in photoevaporative transition disks with a dead zone"
<p>We make available here the results of our gas evolution simulations for the article "Large gaps and high accretion rates in photoevaporative transition disks with a dead zone".</p> <p>We include the result of our population synthesis study, consisting in 10 tables corresponding to 9 models including both dead zone and photoevaporation, and 1 control model including photoevaporation only. The results can be plotted using the "Plotting-PopulationSynthesisModels.ipynb" jupyter notebook.</p> <p>The tables including a dead zone are named as: Macc_Rhole_Pop_AD(X)_RD(Y).txt, where (X)*1.e-4 corresponds to the turbulence in the dead zone, and (Y)*AU corresponds to the dead zone radial extent.</p> <p>The tables include the following fields: Simulation ID, Time [Myr] at which the snapshot was taken, Gap size [AU] measured at the outer edge, Accretion rate log[Msun/yr], and Gas disk mass. We also include a table called "LxRc_Distribution.txt", that indicates the X-ray luminosity log[erg/s] and initial disk characteristic radius [AU] used for the corresponding Simulation ID.</p> <p> </p> <p>We also include the gas surface density evolution of a Control simulation, with photoevaporation only (with Lx = 1.e30 erg/s), and a Dead Zone + Photoevaporation simulation (with dead zone turbulence 1.e-4, and dead zone radial extend 10 AU). These can be plotted using the "Plotting-SingleGasEvolution.ipynb" jupyter notebook.</p>
Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?
Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.
Data for "Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?"
<p>Abstract</p> <p>Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.</p> <p>Methods</p> <p>Subjects walked and ran on a classic Quinton 18-60 motorized treadmill with a rigid steel deck (Quinton Instrument Company, Bothell, WA).</p> <p><strong>Determination of PTS: </strong>The average of the walk-to-run transition speed (WRTS) and run-to-walk transition speed (RWTS) defined the PTS as per Hreljac et. al. (2007). We first determined the WRTS in the walk-first group and then their RWTS and <em>vice versa</em> for the run-first group. Based on pilot experiments, we selected starting speeds such that there was no doubt which gait would be preferred at the initial speed. Once the speed of the treadmill was correctly set, subjects mounted the treadmill and chose their gait <em>ad libitum</em>. After we determined the preferred gait at the particular speed, the subject straddled the treadmill belt while we changed the speed by 0.1 m/s (increased during WRTS trials, decreased during RWTS trials). The process repeated until a gait transition occurred and was sustained for 30 seconds.</p> <p><strong>Determination of EOTS and HROTS: </strong>For the energetics and heart rate trials, we set the initial speed based on pilot experiments that indicated it would be near the EOTS. Subjects in the walk-first group walked at the incline-specific initial speed for 5 min, rested for ∼5 min and then ran at that speed for 5 min. Subjects in the run-first group did the opposite. During the rest periods, we re-weighed the subject and they drank just enough water to compensate for the weight loss due mostly to sweating. Thus, each subject maintained a nearly constant weight throughout all the trials.</p> <p>To measure metabolic rate during walking and running, we used an open-circuit, expired gas analysis system (TrueOne 2400; ParvoMedics, Sandy, UT). Subjects wore a mouthpiece with a one-way breathing valve and a nose clip allowing us to collect their expired air. The ParvoMedics software calculated the STPD rates of oxygen consumption (V□O<sub>2</sub>) and carbon dioxide production (V□CO<sub>2</sub>) and we averaged the last 2 minutes of each 5-minute trial. We then calculated metabolic power using the equation of Péronnet and Massicotte (1991) equation, as clarified by Kipp et al. (2018). We only included trials with respiratory exchange ratios (RER) <1.0 to ensure that metabolic energy was predominantly being provided from oxidative pathways. We used an R7 Polar iWL (Polar Electro Oy, Kempele, Finland) to measure heart rate in beats per minute (bpm) and averaged the values for the last 2 min of each trial.</p> <p>Immediately after both gait trials were completed for the initial speed, we calculated and compared the metabolic power required for walking and running. If walking was the more economical gait, we increased the treadmill speed by 0.1 m/s, and the process repeated. If running was the more economical gait, we decreased the treadmill speed by 0.1 m/s, and the process repeated. Each subject performed three speeds, both walking and running at each incline. However, some subjects needed to complete walking and running trials at a fourth speed so that we could obtain energetics data for one speed faster and one speed slower than their EOTS.</p> <p>For the three speeds at which the differences between metabolic rates between walking and running were least, we calculated linear regression equations for both metabolic power and heart rate as functions of speed for both walking and running for each subject and incline. The speeds at which the two equations intersected defined the EOTS and HROTS for each subject.</p> <p>Overall, we analyzed ten subjects at four different inclines, i.e. 40 determinations of EOTS and HROTS. Of those 80 linear regression analyses, the walking vs. running regressions intersected at a speed < 3 m/sec for all but two subjects (one subject for EOTS at 15° and a different subject for HROTS at 10°). Essentially, those individuals’ regression lines were nearly parallel. We chose to exclude those two conditions from further statistical analysis and aggregate data compilation.</p> <p>Usage Notes</p> <p>There are two missing values, as noted in the methods: HROTS for subject 5 at 10 degrees and EOTS for subject 4 at 15 degrees.</p>
Data from: Axial conduit widening, tree height and height growth rate set the hydraulic transition of sapwood into heartwood
<p><span>The size-related xylem adjustments required to maintain</span><span> a constant leaf-specific sapwood conductance (<em>K<sub>LEAF</sub></em>) with increasing height (<em>H</em>) are still under discussion. Alternative hypotheses are that: (i) the conduit hydraulic diameter (<em>Dh</em>) at any position in the stem and/or (ii) the number of sapwood rings at stem base (<em>NSWr</em>) increase with <em>H.</em> In addition, (iii) lower stem elongation (</span><em>Δ<span>H</span></em><span>) increases the tip-to-base conductance through inner xylem rings, thus possibly the <em>NSWr</em> contributing to <em>K<sub>LEAF</sub></em>.</span></p> <p><span>A detailed stem analysis showed that </span><em><span>Dh</span></em><span><em> </em>increased with the distance from the apex (<em>DCA</em>) in all rings of a <em>P. abies</em> and a <em>F. sylvatica</em> tree. Net of <em>DCA</em> effect, <em>Dh</em> did not increase with <em>H</em>. Using sapwood traits from a global dataset, <em>NSWr</em> increased with <em>H</em> and decreased with </span><em>Δ<span>H</span></em><span>, and the mean sapwood ring width (<em>SWrw</em>) increased with </span><em>Δ<span>H</span></em><span>. A numerical model based on anatomical patterns predicted the effects of <em>H</em> and </span><em>Δ<span>H</span></em><span> on the conductance of inner xylem rings.</span></p> <p><span>Results suggested the sapwood/heartwood transition depends on both <em>H</em> and </span><em>Δ<span>H</span></em><span>, and is set when the C allocation to maintenance respiration of living cells in inner sapwood rings produces a lower gain in total conductance than investing the same C in new vascular conduits.</span></p>
Data from: Comparison of carbon and nitrogen accumulation rate between bog and fen phases in a pristine peatland with the fen-bog transition
<p>Long-term carbon and nitrogen dynamics in boreal peatlands are affected by both vegetation production and decomposition processes. Here, we examined the carbon accumulation rate (CAR), nitrogen accumulation rate (NAR) and δ<sup>13</sup>C, δ<sup>15</sup>N of plant residuals in a peat core dated back to ~8500 cal yr BP in a temperate peatland in Northeast China. Impacted by the tephra during 1160 and 789 cal yr BP and climate change, the peatland changed from a fen dominated by vascular plants to a bog dominated by <em>Sphagnum mosses</em>. We used the Clymo model to quantify peat addition rate and decay constant for acrotelm and catotelm layers during both bog and fen phases. Our studied peatland was dominated by <em>Sphagnum fuscum</em> during the bog phase (789 ~ -59 cal yr BP) and lower accumulation rates for the upper sections in the acrotelm layer during this phase, suggesting the dominant role of volcanic eruption in the CAR of the peat core. Both mean CAR and NAR were higher during the bog phase than during the fen phase in our study, consistent with the results of the only one similar study in the literature. Because the input rate of organic matter was considered to be lower during the bog phase, the decomposition process must have been much lower during the bog phase than during the fen phase and potentially controlled CAR and NAR. During the fen phase, CAR was also lower under higher temperature and summer insolation, conditions beneficial for decomposition. δ<sup>15</sup>N of <em>Sphagnum </em>hinted that nitrogen fixation had positive effect on nitrogen accumulation, particular in recent decades. Our study suggested that decomposition is more important for carbon and nitrogen sequestration than production in peatlands in most conditions and if future climate changes or human disturbance increase decomposition rate, carbon sequestration in peatlands will be jeopardized.</p>
Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?
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Data from: Axial conduit widening, tree height and height growth rate set the hydraulic transition of sapwood into heartwood
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Data from: Comparison of carbon and nitrogen accumulation rate between bog and fen phases in a pristine peatland with the fen-bog transition
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Factors on the Mesozoic transition from flat to steep subduction of the Paleo-Pacific Plate beneath South China: Thickened oceanic crust and subduction rate
<p>This dataset contains simulation results used for visualizing Figures in "Factors on the Mesozoic transition from flat to steep subduction of the Paleo-Pacific Plate beneath South China: Thickened oceanic crust and subduction rate"</p>
The data for the work "Verification of the modified Bixon-Jortner-Plotnikov model by calculating rates of non-adiabatic transitions in aromatic compounds"
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Data from: Phenotypic rate and state are decoupled in response to river-to-lake transitions in cichlid fishes
<p>Geographic access to isolated ecosystems is an important catalyst of adaptive radiation. Cichlid fishes repeatedly colonized rift, crater, and volcanic lakes from surrounding rivers. We test the "lake effect" on the phenotypic rate and state across 253 cichlid species. The rate of evolution was consistently higher (~10-fold) in lakes, and consistent across different dimensions of the phenotype. Rate shifts tended to occur coincident with or immediately following river-to-lake transitions, generally resulting in 2- to 5-fold faster rates than in the founding riverine lineage. By contrast, river- and lake-dwelling cichlids exhibit considerable overlap in phenotypes, generally with less disparity in lakes, but often different evolutionary optima. Taken together, these results suggest that lake radiations rapidly expand into niches largely already represented by ancestral riverine lineages, albeit in different frequencies. Lakes may provide ecological opportunity via ecological release (e.g., from predators/competitors), but need not be coupled with access to novel ecological niches.</p>
Data for "Why are Mountaintops Cold? The Transition of Surface Lapse Rate on Dry Planets"
<p>Edited by Bowen Fan on 06/02/2023</p> <p>0. This Dataset is for the paper “Why are Mountaintops Cold? The Transition of Surface Lapse Rate on Dry Planets”. </p> <p>1. data_Fig1.mat: data for figure 1<br> lon: longitude<br> lat: latitude<br> Ts: surface temperature (unit in K) as a function of longitude, latitude, and case. <br> Z: surface elevation (unit in meter) as a function of longitude latitude, and case. <br> a: surface lapse rate (unit in %) for the cases in figure 1c<br> a1, a2, a3, a4, a5, a6: surface lapse rate (unit in %) for the cases in figure 1d<br> b1, b2, b3, b4, b5, b6, b7: surface lapse rate (unit in %) for the cases in figure 1e</p> <p>2. data_Fig2.mat: data for figure 2<br> GSW: net solar heating (unit in W/m2) as a function of longitude, latitude, and case. <br> GLW: atmospheric heating (unit in W/m2) as a function of longitude, latitude, and case.<br> TSK: surface emission (unit in W/m2) as a function of longitude, latitude, and case.<br> HFX: sensible cooling (unit in W/m2) as a function of longitude, latitude, and case.</p> <p>3. data_Fig3.mat: data for figure 3<br> LWS_diff: the difference of surface emission between highland and lowland <br> LWA_diff: the difference of atmospheric emission between highland and lowland <br> SH_diff: the difference of sensible heat flux between highland and lowland </p> <p>4. data_Fig4.mat: data for figure 4</p> <p>a1, a2, a3, a4: surface lapse rate (unit in %) for the cases in figure 4a<br> c1, c2: surface lapse rate (unit in %) for the cases in figure 4b</p> <p>5. data_FigS1.mat: data for supplementary figure 1<br> lon: longitude<br> lat: latitude<br> Ts: surface temperature (unit in K) as a function of longitude, latitude, and case. <br> Z: surface elevation (unit in meter) as a function of longitude latitude, and case. <br> a1, a2: surface lapse rate (unit in %) for the cases in figure S1c</p> <p>6. data_FigS2.mat: data for supplementary figure 2<br> lon: longitude<br> lat: latitude <br> HGT: surface elevation (unit in meter) as a function of longitude latitude. <br> a1, a2: surface lapse rate (unit in %) for the cases in figure S2b<br> b1, b2: surface lapse rate (unit in %) for the cases in figure S2c</p> <p>7. data_FigS3.mat: data for supplementary figure 3<br> lon: longitude<br> lat: latitude<br> T: near-surface air temperature (unit in K) as a function of longitude, latitude, and case. <br> Z: surface elevation (unit in meter) as a function of longitude latitude, and case. <br> U: near-surface zonal wind (unit in m/s) as a function of longitude, latitude, and case.<br> V: near-surface meridional wind (unit in m/s) as a function of longitude, latitude, and case.</p> <p>8. data_FigS4.mat: data for supplementary figure 4<br> GSW: net solar heating (unit in W/m2) as a function of longitude, latitude, and case. <br> GLW: atmospheric heating (unit in W/m2) as a function of longitude, latitude, and case.<br> LWS: surface emission (unit in W/m2) as a function of longitude, latitude, and case.<br> HFX: sensible cooling (unit in W/m2) as a function of longitude, latitude, and case.</p> <p>9. data_FigS3.mat: data for supplementary figure 5<br> Ts_local: surface temperature (unit in K) as a function of case and location (1 = highland, 2 = lowland).<br> Ps_local: surface pressure (unit in Pa) as a function of case and location.<br> T_local: atmospheric temperature profile as a function of height, case and location. <br> P_local: atmospheric pressure profile as a function of height, case and location. <br> T_ad: adiabatic atmospheric temperature profile as a function of height, case and location.</p> <p>10. data_FigS6.mat: data for supplementary figure 6<br> 10a. Cases with varied greenhouse forcing<br> SW_G LWA_G LWS_G SH_G: surface energy budgets from the GCM. The dimensions are location (1 for highland, 2 for lowland) and case.<br> LWA1_G, LWS1_G, SH1_G: surface energy budgets from the highland part of two-column model<br> LWA4_G, LWS4_G, SH4_G: surface energy budgets from the lowland part of two-column model<br> 10b. Cases with varied surface pressure<br> SW_A LWA_A LWS_A SH_A: surface energy budgets from the GCM. The dimensions are location (1 for highland, 2 for lowland) and case.<br> LWA1_A, LWS1_A, SH1_A: surface energy budgets from the highland part of two-column model<br> LWA4_A, LWS4_A, SH4_A: surface energy budgets from the lowland part of two-column model</p>
Data from: Phenotypic rate and state are decoupled in response to river-to-lake transitions in cichlid fishes
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Macroevolution of flower color patterning: biased transition rates and correlated evolution with flower size
<p>Floral pigmentation patterns can both mediate plant-pollinator interactions and modify the abiotic environment of reproductive structures. To date there have been no inquiries into the rate and directionality of macroevolutionary transitions between patterned and non-patterned petals despite their ecological importance and ubiquity across angiosperms. Petals in the Potentilleae tribe (Rosaceae) display color patterns in the ultraviolet (UV) and human-visible spectrum, or can be uniform in color (i.e., patternless). Using a phylogeny of Potentilleae, I test whether evolutionary transition rates between patterned and non-patterned petals are biased in either direction. I then examine whether UV and human-visible patterns are phylogenetically correlated and test the prediction that color patterns will evolve in concert with larger flowers if they function as guides to orient pollinators to floral rewards. I found that transition rates were biased toward petals that were uniform in color. Transition rates from patterned to uniformly-colored petals were two and six times higher than the reverse for UV and human-visible pattern, respectively. The presence of UV and human-visible pattern evolved independently from one another. However, the evolution of human-visible pattern was associated with the evolution of larger flowers but the evolution of UV pattern was correlated with the evolution of smaller flowers. I posit that the transition bias towards non-patterned flowers may reflect developmental constraints on spatial regulation of pigments required to produce floral color patterning. The correlated evolution of larger flowers and human-visible pigmentation patterns support the hypothesis that nectar or pollen guides are more likely to evolve in larger-flowered species. This work provides insight into how transition rate bias and trait correlations can shape phylogenetic patterns of floral color pattern diversity.</p>
Data from: Is specialization an evolutionary dead-end? Testing for differences in speciation, extinction and trait transition rates across diverse phylogenies of specialists and generalists.
Specialization has often been claimed to be an evolutionary dead end, with specialist lineages having a reduced capacity to persist or diversify. In a phylogenetic comparative framework, an evolutionary dead end may be detectable from the phylogenetic distribution of specialists, if specialists rarely give rise to large, diverse clades. Previous phylogenetic studies of the influence of specialization on macroevolutionary processes have demonstrated a range of patterns, including examples where specialists have both higher and lower diversification rates than generalists, as well as examples where the rates of evolutionary transitions from generalists to specialists are higher, lower or equal to transitions from specialists to generalists. Here, we wish to ask whether these varied answers are due to the differences in macroevolutionary processes in different clades, or partly due to differences in methodology. We analysed ten phylogenies containing multiple independent origins of specialization and quantified the phylogenetic distribution of specialists by applying a common set of metrics to all datasets. We compared the tip branch lengths of specialists to generalists, the size of specialist clades arising from each evolutionary origin of a specialized trait and whether specialists tend to be clustered or scattered on phylogenies. For each of these measures, we compared the observed values to expectations under null models of trait evolution and expected outcomes under alternative macroevolutionary scenarios. We found that specialization is sometimes an evolutionary dead end: in two of the ten case studies (pollinator-specific plants and host-specific flies), specialization is associated with a reduced rate of diversification or trait persistence. However, in the majority of studies, we could not distinguish the observed phylogenetic distribution of specialists from null models in which specialization has no effect on diversification or trait persistence.
Data from: Experiments and modelling of rate-dependent transition delay in a stochastic subcritical bifurcation
Complex systems exhibiting critical transitions when one of their governing parameters varies are ubiquitous in nature and in engineering applications. Despite a vast literature focusing on this topic, there are few studies dealing with the effect of the rate of change of the bifurcation parameter on the tipping points. In this work, we consider a subcritical stochastic Hopf bifurcation under two scenarios: the bifurcation parameter is first changed in a quasi-steady manner and then, with a finite ramping rate. In the latter case, a rate-dependent bifurcation delay is observed and exemplified experimentally using a thermoacoustic instability in a combustion chamber. This delay increases with the rate of change. This leads to a state transition of larger amplitude compared to the one that would be experienced by the system with a quasi-steady change of the parameter. We also bring experimental evidence of a dynamic hysteresis caused by the bifurcation delay when the parameter is ramped back. A surrogate model is derived in order to predict the statistic of these delays and to scrutinise the underlying stochastic dynamics. Our study highlights the dramatic influence of a finite rate of change of bifurcation parameters upon tipping points and it pinpoints the crucial need of considering this effect when investigating critical transitions.
Supplementary material 4 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: statistical data
Supplementary material 5 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: measurement
Supplementary material 3 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: measurements
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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)
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.