Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
187
datasets available to search
ShareScore release 0.9.0
Dataset results
187 results for “mixing effects”
Soil nitrogen availability vs. acidification: effects on soil respiration, heterotrophic respiration, and soil physicochemical properties in mixed temperate forests in central New York, USA (2019-2022)
In 2011, an experimental nitrogen x pH manipulation study was initiated in mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains soil physicochemical properties (soil pH, resin available nitrogen), soil temperature, in situ soil respiration, and heterotrophic respiration measured from laboratory incubations of soils collected from experimental plots. Soil pH was measured both pre-treatment (2009-2010) and after 8 and 11 years of experimental treatment. All other properties were measured between 9 and 12 years after treatment initiation.
Soil nitrogen availability and acidity: effects on aboveground production and belowground carbon allocation in mid- and late-successional mixed temperate forests (2009-2021)
In 2011, an experimental nitrogen x pH manipulation study was initiated in mid- and late-successional mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains forest productivity (wood, litterfall, and aboveground net primary production), total belowground carbon flux (TBCF), and leaf litterfall and fine root chemistry (C and N concentration) data collected from all experimental plots. It also includes plot-level, species-weighted estimates of measured and modeled photosynthesis (Anet) for the late-successional stands. Wood production, litterfall production, and litterfall chemistry data were collected between 2009 and 2019. Aboveground net primary production data are reported for a pre-treatment interval (2009-2011) and the interval including years 6-9 of experimental treatment (2016-2019). All other properties were measured between years 9 and 11 of the experiment (2019-2021).
Greenhouse mixed culture experiment from August 2002 to April 2003 (FCE): Evaluate the effect of salinity and hydroperiod on interspecific mangrove seedlings growth rate (mixed culture) / Morphometric variables
A greenhouse experiment (mixed culture experiment) was performed for 8 months to evaluate the effect of salinity and hydroperiod on seedling growth rates of 2 mangrove species( Laguncularia racemosa and Rizhophora mangle). Data analyses are currently being performed.
Data in Support of Effects of Urbanization and Forest Fragmentation on Atmospheric Nitrogen Inputs and Ambient Nitrogen Oxide and Ozone Concentrations in Mixed Temperate Forests.
Urban ecosystems around the globe experience greater atmospheric nitrogen (N) deposition compared to rural areas and are particularly vulnerable to fragmentation due to land-use change. However, while the influences of urbanization and forest fragmentation on atmospheric inputs to temperate forests have been determined separately, the combined effects of the two changes on temperate forest ecosystems have yet to be assessed. To investigate these combined effects, we deployed throughfall collectors to measure atmospheric N inputs and passive samplers to measure nitrogen oxides (NOx) and ozone (O3) throughout the 2018 and 2019 growing seasons in seven temperate forest sites along an urbanization gradient from Boston to central Massachusetts. We found a positive relationship between the amount of impervious surface area surrounding each site (% ISA) and throughfall nitrate (NO3-) inputs at the forest edge, with urban edge NO3- inputs nearly double the rate at rural edge sites. There were higher rates of NO3- inputs in the rural forest interior than edge sites. Urban sites experienced significantly higher concentrations of NOx and O3 both in the interior and at the edge compared to rural sites. Atmospheric N inputs were significantly elevated in the early (May-July) compared to the late (August-November) growing season and concentrations of NOx and O3 were also elevated in the mid-growing season (June-September). Our results demonstrate that together, urbanization and forest fragmentation lead to greater rates of atmospheric N inputs and ambient pollutant concentrations of NOx and O3 in temperate forests of the northeastern U.S.
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
<p>1. In mixed stands, species complementarity (e.g., facilitation and competition reduction) may enhance forest tree productivity. Although positive mixture effects have been identified in forests worldwide, the majority of studies have focused on two-species interactions in managed systems with high functional diversity. We extended this line of research to examine mixture effects on tree productivity across landscape-scale compositional and environmental gradients in the low functional diversity, fire-suppressed, mixed-conifer forests of the U.S. Interior West.</p> <p>2. We investigated mixture effects on the productivity of <i>Pinus ponderosa</i>, <i>Pseudotsuga menziesii</i>, and <i>Abies concolor</i>. Using region-wide forest inventory data, we created individual-tree generalized linear mixed models and examined the growth of these species across community gradients. We compared the relative influences of stand structure, age, competition, and environmental stress on mixture effects using multi-model inference. We analyzed growth of neighboring tree species to infer whether a mixture effect in a single species translated to the stand-level.</p> <p>3. We found support for a positive mixture effect in <i>P. menziesii</i>, although our results were equivocal in light of a weaker but still plausible alternative model. Growth of <i>P. menziesii</i> neighboring species in mixed stands declined or held constant depending on aridity, suggesting that a positive mixture effect in <i>P. menziesii</i> does not necessarily extend to the stand level. We found no evidence for mixture effects in <i>P. ponderosa</i>, <i>A. concolor</i> or their neighboring species.</p> <p>4. Complementarity appears to have a limited influence on tree growth in the mixed-conifer systems of the U.S. Interior West, reflecting limited functional diversity. Historical changes in stand structure following fire exclusion, particularly high stand densities, may limit the potential for positive species mixture effects. The limited species pool of Interior West forests increases the risk that, without careful management, what functional diversity exists could be lost to compositional changes resulting from stand dynamics or disturbance.</p>
Effects of Tide-Induced Mixing on the Surface Temperature Gradients Between the Equator and Poles During the Middle Miocene Climate Optimum -- Dataset
<p>The files contain the data related to the figures in this paper.</p><p>-- Fig.1 The topographic roughness of the PI and MMCO before and after reconstruction</p><p>-- Fig.2 The 300-year time series of the annual mean SAT and SST</p><p>-- Fig.3 The data of SSH for PI_TF experiment</p><p>-- Fig.4 The tidal dissipation and mixing for MMCO_TM, and the ocean vertical mixing</p><p>-- Fig.5 The annual mean SAT and SST for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p><p>-- Fig.6 The global meridional heat transport for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p><p>-- Fig.7 The net sea surface heat flux for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p><p>-- Fig.8 The GMOC and AMOC for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p>
Spin-mixing enhanced proximity effect in aluminum-based superconductor-semiconductor hybrids
<p>In superconducting quantum circuits, aluminum is one of the most widely used materials. It is currently also the superconductor of choice for the development of topological qubits. In this application, however, aluminum-based devices suffer from poor magnetic field compatibility. In this article, we resolve this limitation by showing that adatoms of heavy elements (e.g. platinum) increase the critical field of thin aluminum films by more than a factor of two. Using tunnel junctions, we show that the increased field resilience originates from spin-orbit scattering introduced by Pt. We exploit this property in the context of the superconducting proximity effect in semiconductor-superconductor hybrids, where we show that InSb nanowires strongly coupled to Al/Pt films can maintain superconductivity up to 7 T. The two-electron charging effect, a fundamental requirement for topo- logical quantum computation, is shown to be robust against the presence of heavy adatoms. Additionally, we use non-local spectroscopy in a three-terminal geom- etry to probe the bulk of hybrid devices, showing that it remains free of sub-gap states. Finally, we demonstrate that semiconductor states which are proximi- tized by Al/Pt films maintain their ability to Zeeman-split in an applied magnetic field. Combined with the chemical stability and well-known fabrication routes of aluminum, Al/Pt emerges as the natural successor to Al-based systems and is a compelling alternative to other superconductors, whenever high-field resilience is required. </p> <p> </p>
Performance of Advanced Ambu Bag System among Adult Patients with Mechanical Ventilation: A Mixed-Effects Analysis
<p>We conducted the study at the Department of Stroke Care of the Can Tho Central General Hospital, Vietnam. There are eight intensive care beds for critical illness. The study was performed according to the Helsinki Declaration and approved by the Can Tho Central General Hospital ethics committee. All patients gave written informed consent by a legal surrogate. We enrolled patients with mechanical ventilation between November 2022 and September 2023. The inclusion criteria were: (1) patients aged 16 years and older, (2) pulse rate less than 120 times per minute, (3) systolic blood pressure from 110 to 160 mmHg, (4) peripheral oxygen saturation (SpO<sub>2</sub>) greater than 90%, (5) spontaneous breathing rate less than 28 times per minute, (6) end-tidal carbon dioxide (EtCO<sub>2</sub>) from 20 to 45 mmHg, (7) secretion required suction less than one time per hour, (8) positive end-expiratory pressure less than or equal to 5 cmH<sub>2</sub>O, (9) fraction of inspired oxygen less than or equal to 60%, (10) minute ventilation less than 15 liters per minute, (11) diameter of a tracheal or tracheostomy tube greater than or equal to 7.0 mm, (12) no usage of sedation, (13) normal ST wave in the electrocardiogram. Patients were excluded from the trial if they had one of the following conditions: (1) acute myocardial infarction, (2) acute pulmonary embolism, or (3) new dangerous arrhythmias appeared in this episode (multiform ventricular ectopy, bigeminy or trigeminy ventricular ectopy, coupled ventricular ectopy, R-on-T ventricular ectopy, high-grade atrioventricular heart block, supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, ventricular fibrillation), (4) using vasopressors or inotropic agents. Patients could withdraw from the study at any time without giving any reason. Besides, the patient stopped the trial of the advanced Ambu bag system immediately when one of the signs appeared, such as (1) the peripheral oxygen saturation lower than 90% prolonging more than 1 minute, (2) the end-tidal carbon dioxide greater than 45 mmHg or less than 15 mmHg prolonging more than 10 minutes, (3) pulse rate greater than 120 times per minute or less than 60 times per minute prolonging more than 10 minutes, (4) systolic blood pressure greater than 170 mmHg prolonging more than 10 minutes, (5) appearing dangerous arrhythmias, (6) progressive cognitive impairment (based on Grady coma scale), or (7) any abnormal sign that the physician evaluated the patient required respiratory support immediately with conventional mechanical ventilation.</p> <p> The following is the meaning of the variables in the study:</p> <p>age: Age of study participants.</p> <p>gender: Gender of study participants (0: Woman, 1: Man).</p> <p>day1: Day of admission to the hospital</p> <p>day2: Intervention day.</p> <p>dia1: Major disease.</p> <p>dia2: Cause of respiratory failure.</p> <p>nihss1: National Institute of Health Stroke Scale on admission</p> <p>hsg: Severity of cerebral hemorrhage (0: No hemorrhagic stroke, hi1: Scattered small petechiae, no mass effect, hi2: Confluent petechiae, no mass effect, ph1: Hematoma within infarcted tissue, occupying <30%, no substantive mass effect, ph2: Hematoma occupying 30% or more of the infarcted tissue, with obvious mass effect, 3a: Parenchymal hematoma remote from infarcted brain tissue, 3b: Intraventricular hemorrhage, 3c: Subarachnoid hemorrhage, 3d: Subdural hemorrhage)</p> <p>aspects1: Alberta stroke program early CT score of anterior circulation on CTscan</p> <p>aspect2: Alberta stroke program early CT score of anterior circulation on DWI- Diffusion-weighted Imaging.</p> <p>aspects3: Alberta stroke program early CT score of posterior circulation on CTscan</p> <p>aspects4: Alberta stroke program early CT score of posterior circulation on DWI- Diffusion-weighted Imaging.</p> <p> </p> <p>nihss2: National Institute of Health Stroke Scale before intervention</p> <p>grady: Grady coma scale before intervention</p> <p> </p> <p>rtpa: Use alteplase (0: No, 1: Yes)</p> <p>thromb: Thrombectomy (0: No, 1: Yes)</p> <p>crani: Craniectomy (0: No, 1: Yes).</p> <p>coil: Endovascular coiling (0: No, 1: Yes)</p> <p> </p> <p>mode: Ventilation mode</p> <p>mv: Mechanical ventilation (L/min)</p> <p>fio2: Fraction of inspired oxygen (%)</p> <p>peep: Positive end-expiratory pressure (cmH<sub>2</sub>O)</p> <p>sc: Static compliance (mL/cmH<sub>2</sub>O)</p> <p>alv: Pulmonary consolidation (0: No, 1: ¼ lung, 2: ½ lung, 3: ¾ lung, 4: Complete lung)</p> <p>sf: spo2/fio2 ratio.</p> <p> </p> <p>p13a: Number of pulse beats at time -13 (conventional mechanical ventilation stage)</p> <p>s13a: Systolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>d13a: Diastolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>sp13a: SpO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p>e13a: EtCO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p> </p> <p>p12a: Number of pulse beats at time -12 (conventional mechanical ventilation stage)</p> <p>s12a: Systolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>d12a: Diastolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>sp12a: SpO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p>e12a: EtCO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p> </p> <p>p11a: Number of pulse beats at time -11 (conventional mechanical ventilation stage)</p> <p>s11a: Systolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>d11a: Diastolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>sp11a: SpO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p>e11a: EtCO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p> </p> <p>p10a: Number of pulse beats at time -10 (conventional mechanical ventilation stage)</p> <p>s10a: Systolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>d10a: Diastolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>sp10a: SpO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p>e10a: EtCO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p> </p> <p>p9a: Number of pulse beats at time -9 (conventional mechanical ventilation stage)</p> <p>s9a: Systolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>d9a: Diastolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>sp9a: SpO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p>e9a: EtCO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p> </p> <p>p8a: Number of pulse beats at time -8 (conventional mechanical ventilation stage)</p> <p>s8a: Systolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>d8a: Diastolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>sp8a: SpO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p>e8a: EtCO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p> </p> <p>p7a: Number of pulse beats at time -7 (conventional mechanical ventilation stage)</p> <p>s7a: Systolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>d7a: Diastolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>sp7a: SpO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p>e7a: EtCO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p> </p> <p>p6a: Number of pulse beats at time -6 (conventional mechanical ventilation stage)</p> <p>s6a: Systolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>d6a: Diastolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>sp6a: SpO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p>e6a: EtCO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p> </p> <p>p5a: Number of pulse beats at time -5 (conventional mechanical ventilation stage)</p> <p>s5a: Systolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>d5a: Diastolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>sp5a: SpO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p>e5a: EtCO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p> </p> <p>p4a: Number of pulse beats at time -4 (conventional mechanical ventilation stage)</p> <p>s4a: Systolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>d4a: Diastolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>sp4a: SpO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p>e4a: EtCO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p> </p> <p>p3a: Number of pulse beats at time -3 (conventional mechanical ventilation stage)</p> <p>s3a: Systolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>d3a: Diastolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>sp3a: SpO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p>e3a: EtCO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p> </p> <p>p2a: Number of pulse beats at time -2 (conventional mechanical ventilation stage)</p> <p>s2a: Systolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>d2a: Diastolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>sp2a: SpO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p>e2a: EtCO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1a: Number of pulse beats at time -1 (conventional mechanical ventilation stage)</p> <p>s1a: Systolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>d1a: Diastolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>sp1a: SpO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p>e1a: EtCO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p> </p> <p>p0a: Number of pulse beats at time 0 (conventional mechanical ventilation stage)</p> <p>s0a: Systolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>d0a: Diastolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>sp0a: SpO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p>e0a: EtCO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1b: Number of pulse beats at time +1 (advanced Ambu bag system stage)</p> <p>s1b: Systolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>d1b: Diastolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>sp1b: SpO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p>e1b: EtCO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p> </p> <p>p2b: Number of pulse beats at time +2 (advanced Ambu bag system stage)</p> <p>s2b: Systolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>d2b: Diastolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>sp2b: SpO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p>e2b: EtCO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p> </p> <p>p3b: Number of pulse beats at time +3 (advanced Ambu bag system stage)</p> <p>s3b: Systolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>d3b: Diastolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>sp3b: SpO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p>e3b: EtCO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p> </p> <p>p4b: Number of pulse beats at time +4 (advanced Ambu bag system stage)</p> <p>s4b: Systolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>d4b: Diastolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>sp4b: SpO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p>e4b: EtCO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p> </p> <p>p5b: Number of pulse beats at time +5 (advanced Ambu bag system stage)</p> <p>s5b: Systolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>d5b: Diastolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>sp5b: SpO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p>e5b: EtCO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p> </p> <p>p6b: Number of pulse beats at time +6 (advanced Ambu bag system stage)</p> <p>s6b: Systolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>d6b: Diastolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>sp6b: SpO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p>e6b: EtCO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p> </p> <p>p7b: Number of pulse beats at time +7 (advanced Ambu bag system stage)</p> <p>s7b: Systolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>d7b: Diastolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>sp7b: SpO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p>e7b: EtCO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p> </p> <p>p8b: Number of pulse beats at time +8 (advanced Ambu bag system stage)</p> <p>s8b: Systolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>d8b: Diastolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>sp8b: SpO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p>e8b: EtCO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p> </p> <p>p9b: Number of pulse beats at time +9 (advanced Ambu bag system stage)</p> <p>s9b: Systolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>d9b: Diastolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>sp9b: SpO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p>e9b: EtCO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p> </p> <p>p10b: Number of pulse beats at time +10 (advanced Ambu bag system stage)</p> <p>s10b: Systolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>d10b: Diastolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>sp10b: SpO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p>e10b: EtCO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p> </p> <p>p11b: Number of pulse beats at time +11 (advanced Ambu bag system stage)</p> <p>s11b: Systolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>d11b: Diastolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>sp11b: SpO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p>e11b: EtCO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p> </p> <p>p12b: Number of pulse beats at time +12 (advanced Ambu bag system stage)</p> <p>s12b: Systolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>d12b: Diastolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>sp12b: SpO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p>e12b: EtCO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p> </p> <p>p13b: Number of pulse beats at time +13 (advanced Ambu bag system stage)</p> <p>s13b: Systolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>d13b: Diastolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>sp13b: SpO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p>e13b: EtCO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p> </p> <p>p14b: Number of pulse beats at time +14 (advanced Ambu bag system stage)</p> <p>s14b: Systolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>d14b: Diastolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>sp14b: SpO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p> <p>e14b: EtCO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p>
Fig. 1 in Mixing male lures results in an effective multispecies bait for trapping Bactrocera (Diptera: Tephritidae) fruit flies
Fig. 1. Numbers of Bactrocera dorsalis male flies captured in traps baited with methyl eugenol (ME) alone or a mixture of ME and raspberry ketone (RK, a natural analogue of cue lure). Comparisons involved 3 mixtures in ME:RK ratios (wt:wt) of 95:5, 90:10, and 85:15. Symbols represent means (± SE) of 15 traps per lure type at 2 wk intervals over an 8 wk sampling period.
STRATIFICATION EFFECTS ON FLOW HYDRODYNAMICS AND MIXING AT A CONFLUENCE WITH A HIGHLY DISCORDANT BED AND A RELATIVELY LOW VELOCITY RATIO
<p>The effects of temperature induced stratification on flow hydrodynamics, thermal mixing and the capacity of the flow to entrain sediment at a medium-size stream confluence with a highly discordant bed are investigated. To isolate the effects due to differences in the temperature/density of the incoming streams, two simulations were conducted with identical flow conditions (mean velocity ratio VR=2.44, temperature difference between the two streams ΔT=4.7<sup>0 </sup>C). In the first case the Richardson number was Ri=0 (no coupling between the temperature and the momentum equations via the Boussinesq approximation), while in the second simulation Ri=0.67. Even in the Ri=0 case the structure of the mixing interface (MI) was different from the one expected for concordant bed confluences with a similar confluence angle and VR. The MI contained only co-rotating eddies shed in the shear layer forming on the fast speed side of the confluence apex. In the Ri = 0.67 case no wake region was present but a large recirculation eddy formed not far from the confluence apex. In both cases, the flow near the upstream part of the MI was found to be highly 3D and to allow the passage of particles from one side of the confluence to the other. While in the Ri = 0 case mixing was driven by the MI eddies, in the Ri = 0.67 case mixing was controlled by large near-bed intrusions of heavier fluid from the tributary containing colder water and also by the fluid advected in and out of the recirculation eddy.</p>
Dataset - Production of polyhydroxybutyrates and carbohydrates in a mixed cyanobacterial culture: effect of nutrients limitation and photoperiods
<p>The data set attached is two excel files where the data from the article <strong>“</strong><strong>Production of polyhydroxybutyrates and carbohydrates in a mixed cyanobacterial culture: effect of nutrients limitation and photoperiods</strong>”, published in New Biotechnology (Vol. 42, 25<sup>th</sup> May 2018, 1-11), can be found, as well as the different equations and formulae that were used to obtain the published results.</p>
Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses
<p>1. Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect non-zero correlations has rarely been evaluated and these few studies have not focused on tag-recovery data, which is a common data type.</p> <p>2. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (<em>Anas</em> <em>platyrhychos</em>) tag-recovery dataset and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery datasets than total individuals tagged.</p> <p>3. Different priors lead to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (–1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery.</p> <p>4. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.</p>
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
Open the record for dataset details and reuse information.
Relative effects of seed mix design, consumer pressure, and edge proximity on community structure in restored prairies
Open the record for dataset details and reuse information.
Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses
Open the record for dataset details and reuse information.
Intraspecific phytochemical diversity increases with productivity but has mixed effects on herbivory
Open the record for dataset details and reuse information.
Strength in numbers? Cytotype frequency mediates effect of reproductive barriers in mixed-ploidy arrays.
When differentiated lineages come into contact, their fates depend on demographic and reproductive factors. These factors have been well-studied in taxa of the same ploidy, but less is known about sympatric lineages that differ in ploidy, particularly with respect to demographic factors. We assessed prezygotic, postzygotic, and total reproductive isolation in naturally-pollinated arrays of diploid-tetraploid and tetraploid-hexaploid population mixes of Campanula rotundifolia by measuring pollinator transitions, seed yield, germination rate, and proportion of hybrid offspring. Four frequencies of each cytotype were tested, and pollinators consistently overvisited rare cytotypes. Seed yield and F1 hybrid production were greater in 4X-6X arrays than 2X-4X arrays, while germination rates were similar, creating two distinct patterns of reproductive isolation. In 2X-4X arrays, postzygotic isolation was near-complete (3% hybrid offspring), and prezygotic isolation associated with pollinator preference is expected to facilitate the persistence of minority cytotypes. However, in 4X-6X arrays where postzygotic isolation permitted hybrid formation (44% hybrids), pollinator behavior drove patterns of reproductive isolation, with rare cytotypes being more isolated and greater gene flow expected from rare into common cytotypes. In polyploid complexes, both the specific cytotypes in contact and local cytotype frequency, likely reflecting spatial demography, will influence likelihood of gene exchange.
Simulated data for paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4) with a rich (scenarios 1 and 3) and a sparse design (scenarios 2 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p>
Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
<p>The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.</p>
Dataset for Dichotomic core solidification caused by nonideally-mixing effects in rocky planets
<p>All data involved in the manuscript.</p>
ScienceDex guides
Understand access before you commit
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.