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120 results for “Transit time”
Absolute frequency measurement of the 1 S 0 – 3 P 0 transition of 171 Yb with a link to International Atomic Time
<p>Dataset of the INRIM Yb clock measured respect to TAI collected between October 2018 to February 2019.<br> </p> <p>YbvsSIm-viaEAL.dat: montly data with columns</p> <pre><code>MJDstart: start date in MJD MJDstop: stop date in MJD MJDmed: mid point date in MJD MJDbaro: baricenter date in MJD Ybduty: Yb clock duty time y0=Yb/HM3: ratio between Yb clock and H Maser 03 u0: statistical uncertainty of y0 uB0: systematic uncertainty of y0 y1=extrap.: extrapolation over HM3 udead1: uncertainty of y1 from dead times udrift1: uncertainty of y1 from HM3 drift HM3drift/d: HM3 drift per day udrift/d: uncertainty of HM3 drift y2=HM3/UTCit: ratio between HM3 and UTC(IT) u2: uncertainty of y2 y3=UTCit/TAI: ratio between UTC(IT) and TAI u3: uncertainty of y3 y4=EALext.: extrapolation over EAL udead4: uncertainty of y4 from dead times udrift4: uncertainty of y4 from EAL drift y5=-d: ratio between TAI and the SI second from Circular T u5: uncertainty of y5 uA5: statistical uncertainty of y5 uB5: systematic uncertainty of y5 y=Yb/SI: final ratio beween the Yb clock and the Si second uA: not used uB: not used u: uncertainty of y </code></pre> <p>YbvsTAId.dat: data every 5 days with columns:</p> <pre><code>MJDstart: start date in MJD MJDstop: stop date in MJD MJDmed: mid point date in MJD MJDbaro: baricenter date in MJD Ybduty: Yb clock duty time y0=Yb/HM3: ratio between Yb clock and H Maser 03 u0: statistical uncertainty of y0 uB0: systematic uncertainty of y0 y1=extrap.: extrapolation over HM3 udead1: uncertainty of y1 from dead times udrift1: uncertainty of y1 from HM3 drift HM3drift/d: HM3 drift per day udrift/d: uncertainty of HM3 drift y2=HM3/UTCit: ratio between HM3 and UTC(IT) u2: uncertainty of y2 y3=UTCit/TAI: ratio between UTC(IT) and TAI u3: uncertainty of y3 y=Yb/TAI: final ratio beween the Yb clock and TAI uA: not used uB: not used u: uncertainty of y </code></pre> <p> </p>
Initial conditions to reproduce figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system"
<p>The directory includes the initial conditions of the simulations needed to reproduce the figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system". </p> <p>The simulations were done with Posidonius version v2019.07.30. The information to download and use Posidonius can be found here:<br><a href="https://www.google.com/url?q=https://www.blancocuaresma.com/s/posidonius&sa=D&source=hangouts&ust=1580820901945000&usg=AFQjCNEmxwuCiIgh9d-aTfx-bqB0_u_9BA">https://www.blancocuaresma.com/s/posidonius</a>. <br>That version was only slightly modified to use a maximum timestep size for the IAS15 algorithm. </p> <p>The directory includes:</p> <ul> <li>a README giving all the information defining the test directories</li> <li>test directories with a file called trappist1.json, which can be used to launch Posidonius:<br>posidonius start --silent trappist1.json data_dir/trappist1.bin data_dir/trappist1_history.bin</li> </ul>
Data from: Phylogenomics of elongate-bodied Springtails reveals independent transitions from aboveground to belowground habitats in deep time
<p>Soil has become a major hotspot of biodiversity studies, yet the pattern and timing of the evolution of soil organisms are poorly known because of the scarcity of palaeontological data. To overcome this limitation, we conducted a genome-based macroevolutionary study of an ancient, diversified, and widespread lineage of soil fauna, the elongate-bodied springtails (class Collembola, order Entomobryomorpha). To build the first robust backbone phylogeny of this previously refractory group, we sampled representatives of major higher taxa (6 out of 8 families, 11 out of 16 subfamilies) of the order with an emphasis on the most problematic superfamily Tomoceroidea, applied whole-genome sequencing (WGS) methods, and compared the performance of different combinations of datasets (universal single-copy orthologues/USCO versus ultraconserved elements/UCE) and modelling schemes. The fossil-calibrated timetree was used to reconstruct the evolution of body size, sensory organs, and pigmentation to establish a time frame of the ecomorphological divergences. The resultant trees based on different analyses were congruent in most nodes. Several discordant nodes were carefully evaluated by considering method fitness, morphological information, and topology test. The evaluation favoured the well-resolved topology from analyses using USCO amino acid matrices and complex site-heterogeneous models (CAT+GTR and LG+PMSF (C60)). The preferred topology supports the monophyletic superfamily Tomoceroidea as an early-diverging lineage and a sister relationship between Entomobryoidea and Isotomoidea. The family Tomoceridae was recovered as monophyletic, while Oncopoduridae was recovered as paraphyletic, with <em>Harlomillsia</em> as a sister to Tomoceridae and hence deserving a separate family status as Harlomillsiidae Yu and Zhang <strong>fam. n.</strong> Ancestral Entomobryomorpha were reconstructed as surface-living, supporting independent origins of soil-living groups across the Palaeozoic–Mesozoic, and highlighting the ancient evolutionary interaction between aboveground and belowground fauna.</p>
Text-fig. 2. Aptian to Albian Cerebropollenites taxa, all scale bars in LM and SEM overview images 10 µm, scale bars in SEM detailed images 2 µm. a–c: Cerebropollenites thiergartii from St. Pölten (Austria), a – LM image, equatorial view, b – SEM equatorial overview with visible, less sculptured leptoma, c – detail of echinate verrucae; d–f: Cerebropollenites thiergartii from Khovil basin (Mongolia), d – LM image polar view with well visible thin-walled leptoma, e – SEM of proximal polar view with faintly sculptures leptoma, f – SEM detailed view of transition from leptoma to normal sexine sculpturing; g–i: Cerebropollenites macroverrucosus from Khovil basin (Mongolia), g – LM image of oblique equatorial view, h – SEM of oblique equatorial view with concave leptoma, i – SEM detail of the rugulate to verrucate sexine and smaller sculpturing in leptoma area. in The Occurrence Of Pollen Of Sciadopityaceae Luerss. Through Time
Text-fig. 2. Aptian to Albian Cerebropollenites taxa, all scale bars in LM and SEM overview images 10 µm, scale bars in SEM detailed images 2 µm. a–c: Cerebropollenites thiergartii from St. Pölten (Austria), a – LM image, equatorial view, b – SEM equatorial overview with visible, less sculptured leptoma, c – detail of echinate verrucae; d–f: Cerebropollenites thiergartii from Khovil basin (Mongolia), d – LM image polar view with well visible thin-walled leptoma, e – SEM of proximal polar view with faintly sculptures leptoma, f – SEM detailed view of transition from leptoma to normal sexine sculpturing; g–i: Cerebropollenites macroverrucosus from Khovil basin (Mongolia), g – LM image of oblique equatorial view, h – SEM of oblique equatorial view with concave leptoma, i – SEM detail of the rugulate to verrucate sexine and smaller sculpturing in leptoma area.
Text-fig. 1. Extant Sciadopitys verticillata pollen. a–c: LM images (scale bars 10 µm), a – polar view, b – equatorial view, c – equatorial view with well visible thinning of proximal leptoma; d–e: SEM overview images (scale bar 10 µm), d – distal polar view, e – oblique equatorial view; f – equatorial view with leptoma at top; g–i: SEM detailed images (scale bars 2 µm), g – detail of verrucate, echinate, perforate sexine of distal pol, h – wall break displaying thin nexine and verrucate, echinate sexine, i – ripped open leptoma displaying transition from verrucate sculpturing to nearly psilate state. in The Occurrence Of Pollen Of Sciadopityaceae Luerss. Through Time
Text-fig. 1. Extant Sciadopitys verticillata pollen. a–c: LM images (scale bars 10 µm), a – polar view, b – equatorial view, c – equatorial view with well visible thinning of proximal leptoma; d–e: SEM overview images (scale bar 10 µm), d – distal polar view, e – oblique equatorial view; f – equatorial view with leptoma at top; g–i: SEM detailed images (scale bars 2 µm), g – detail of verrucate, echinate, perforate sexine of distal pol, h – wall break displaying thin nexine and verrucate, echinate sexine, i – ripped open leptoma displaying transition from verrucate sculpturing to nearly psilate state.
Data for: Modeling the transition of death assemblages through the mixed layer predicts a downcore increase in time averaging
<p>Understanding how time averaging changes during the burial is essential for using Holocene and Anthropocene cores to analyze ecosystem change, given the many ways in which the time averaging affects biodiversity measures. Here, we use transition-rate matrices to explore how time averaging changes downcore when shells transit through a taphonomically-complex mixed layer into permanently-buried historical layers: this is a null model, without any temporal changes in rates of sedimentation or bioturbation, to contrast with downcore patterns that might be produced by human activity. Assuming stochastic burial and exhumation movements of shells between increments within the mixed layer and stochastic disintegration within increments, almost all combinations of net sedimentation, mixing, and disintegration produce a downcore increase in time averaging (interquartile range, IQR), typically associated with a decrease in kurtosis and skewness and with a shift from right-skewed to symmetrical age distributions. A downcore increase in time averaging is a null expectation wherever bioturbation generates an internally-structured mixed layer (i.e., a surface well-mixed layer is underlain by an incompletely-mixed layer), so that shells are mixed throughout the entire mixed layer at slower rate than they are buried below it by sedimentation. This downcore trend created by mixing is further amplified by the downcore decline in disintegration rate. Using data from the southern California shelf, we find that transition-rate matrices accurately reproduce the downcore changes in IQR, skewness, and kurtosis observed in sediment cores. The right-skewed distributions typical of surface death assemblages – the focus of most actualistic research – might be fossilized under exceptional conditions of episodic anoxia or sudden burial. However, such right-skewed assemblages will not typically transfer into subsurface historical layers and thus will be geologically transient. The deep-time fossil record will be dominated instead by more time-averaged assemblages with weakly skewed age distributions that form in the lower parts of the mixed layer.</p>
Data for: Modeling the transition of death assemblages through the mixed layer predicts a downcore increase in time averaging
Open the record for dataset details and reuse information.
Data from: Phylogenomics of elongate-bodied Springtails reveals independent transitions from aboveground to belowground habitats in deep time
Open the record for dataset details and reuse information.
Mean transit times simulations with the EcH2O-iso model
<p>Input, forcing and and output files, as well as running and plotting scripts used in the numerical experiments with the EcH<sub>2</sub>O-iso critical zone model described in Kuppel et al. manuscript "<strong>Catchment storage control on the transit times of ecohydrological fluxes</strong>" submitted for publication to Geophysical Research Letters.</p> <p>------------------------------------------------------------------------------<br> <strong>Ensemble simulations</strong></p> <p>NetCDF maps and time series used in the analysis can be found in the <em>Output_w30Yspin</em> directory. Time series covers the whole simulations (02/2013 - 08/2016 + 30-yr spinup) while maps are output<br> The ensemble simulations are launch by executing the jobRuns shellscript. It calls the Multitool_* python scripts , which uses (see also options in the header of Multitool_Main.py):<br> - a global definition file located in the root directory, here <em>defRuns_w30Yspin.py</em><br> - a EcH2O-iso executable; here the <em>ech2o_iso </em>file has been compiled for a linux environment. The source code of EcH<sub>2</sub>O-iso is available <a href="https://bitbucket.org/sylka/ech2o_iso/">here</a> (branch master_2.0 used in this study), and the associated documentation <a href="https://ech2o-iso.readthedocs.io/en/latest/">here</a>.<br> - <em>Input_Configs</em> contains template configurations files for EcH2O-iso: one for general simulations (<em>config_w30Yspin.ini</em>), one for options regarding the "tracking" (i.e. water isotopes and ages) mode (<em>configTrck_w30Yspin.ini</em>)<br> - <em>Input_Forcings</em> contains the meteorological forcing files needed for simulations<br> - <em>Input_Params</em> contains the ensemble parameter file used for the simulations<br> - <em>Input_Maps_100m</em> contains the maps defining the simulated domain at the 100-m resolution used in the study, as well as the vegetation-specifics files, some of which are modified using the values contained in the ensemble parameter file.</p> <p>--------------------------------------------------------------------------<br> <strong>Plots</strong></p> <p>The R plotting scripts found in the <em>Plotting_scripts </em>directory generate the basic plots used in the submitted manuscript (see examples in <em>Plots </em>directory), with some post-editing using a vector graphics editor.</p>
Data from: A rapidly evolved shift in life history timing during ecological speciation is driven by the transition between developmental phases
<p>For insect species in temperate environments, seasonal timing is often governed by the regulation of diapause, a complex developmental program that allows insects to weather unfavorable conditions and synchronize their lifecycles with available resources. Diapause development consists of a series of distinct phases including initiation, maintenance, termination, and post-diapause development. The evolution of insect seasonal timing depends in part on how these phases of diapause development and post-diapause development interact to affect variation in phenology. Here, we dissect the physiological basis of a recently evolved phenological shift in Rhagoletis pomonella (Diptera: Tephritidae), a model system for ecological divergence. A recently derived population of R. pomonella shifted from specializing on native hawthorn fruit to earlier fruiting introduced apples, resulting in a 3-4 week shift in adult emergence timing. We tracked metabolic rates of individual flies across post-winter development to test which phases of development may act either independently or in combination to contribute to this recently evolved divergence in timing. Apple and hawthorn flies differed in a number of facets of their post-winter developmental trajectories. However, divergent adaptation in adult emergence phenology in these flies was due almost entirely to the end of the pupal diapause maintenance phase, with post-diapause development having a very small effect. The relatively simple underpinnings of variation in adult emergence phenology suggest that further adaptation to seasonal change in these flies for this trait might be largely due to the timing of diapause termination unhindered by strong covariance among different components of post-diapause development.</p>
Following up the Kepler field: Masses of Targets for transit timing and atmospheric characterization
<p>These are posterior samples from TTV models presented in "Following up the Kepler field: Masses of Targets for transit timing and atmospheric characterization" by D. Jontof-Hutter, D., A. Wolfgang A., E. B. Ford, J. J. Liassauer, D. C. Fabrycky and J. F. Rowe.</p>
Figures referring to the paper "Radii, masses, and transit-timing variations of the three-planet system orbiting the naked-eye star TOI-396" (Bonfanti et al.) accepted for publication in A&A on 20/11/2024
<p>Fig. A.1: ttv_pla1_2.pdf</p> <p>TTV amplitudes obtained for TOI-396 b (black markers) and TOI-396 c (red markers). This is a superposition of the first two panels of Figure 4 in the paper emphasising the anti-correlation pattern.</p> <p> </p> <p>Fig. A.2: lcGLS*.pdf</p> <p>First column: Generalised Lomb-Scargle periodograms of TESS Sector 3 (lcGLS_3.pdf), 4 (lcGLS_4RawFluxrm.pdf), 30 (lcGLS_30RawFlux.pdf), and 31 (lcGLS_31RawFlux.pdf) raw photometry. Second column: Generalised Lomb-Scargle periodograms of the residuals (lcGLSRaw_3noSine.pdf, lcGLSRaw_4noSinerm.pdf, lcGLSRaw_30noSine.pdf, lcGLSRaw_31noSine.pdf) after removing the best fitting sinusoidal signals at the most significant frequency identified in the periodograms of the corresponding TESS time series. The long dashed red line marks the FAP at 0.1\%. Significant peaks are detected in the 6---8 day range, which we attribute to the presence of active regions co-rotating with the star.</p>
Dataset for "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange"
<p>This dataset contains the sediment models, DNS flow field data, subsurface path data, and calculated transit time distributions for both the regular- and random-interface cases used in the paper: "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange" by Guangchen Shen, Junlin Yuan, and Mantha S. Phanikumar (Submitted to Water Resources Research). <br> Detailed introduction of each data file is as follows.</p> <p>1. DNS flow field data</p> <p>Flowfield_Reg.h5 and Flowfield_Ran.h5 contains the following fields for the regular and random cases, respectively. 'ni', 'nj', 'nk' are the numbers of grid points along x, y, and z directions. 'xc', 'yc', 'zc' are the cell center locations. 'u3d','v3d','w3d' are the three-dimensional time-averaged velocities at each grid point. 'vof' is the volume-of-fluid field used by the immersed-boundary method to prescribe the fluid-solid interface (vof=1 in fluid and 0 in solid), at each grid point. 'vof' contains the information of sediment grain distribution and bed roughness geometry. Only the subsurface data (those below the sediment crest) are shared due to dataset size limit.</p> <p>2. Particle-tracked subsurface flow paths and corresponding transit time distributions</p> <p>The mat files “xxx_pathline” store the (x,y,z) location of each point (saved as 'StrX', 'StrY, 'StrZ') along the subsurface paths, discretized by uniform steps of travel time (with time intervals of 0.1 for 'A' and 0.01 for 'MD' cases, normalized by channel height and friction velocity). The files “xxx_TT” store the array of transit times corresponding to the tracked paths, where the 1d array T is the transit time. 'A' denotes calculations based on time-mean advection only, while 'MD' denotes calculations accounting for additional molecular diffusion. 'Interface' and '3DiameterBelow' indicate that the particles were released at the interface and -3 D below the interface, respectively.</p>
Time-resolved pump-probe ellipsometry of the photoinduced insulator-metal transition in 25 nm VO2 thin films
<h3>Data available</h3> <p>Transient pseudo dielectric function and ellipsometric parameters (\Psi and \Delta) measured with a time-resolved pump-probe ellipsometry on a 25 nm VO2 film deposited on SiO2.</p> <p>The data of the transient pseudo dielectric function and ellipsometric parameters (\Psi and \Delta) are provided for different pump wavelengths and pump fluences.</p> <ul> <li>\lambda_{pump} = 400 nm - F_{pump} = 3.058 mJ/cm2</li> <li>\lambda_{pump} = 400 nm - F_{pump} = 3.823 mJ/cm2</li> <li>\lambda_{pump} = 400 nm - F_{pump} = 5.352 mJ/cm2</li> <li>\lambda_{pump} = 400 nm - F_{pump} = 6.117 mJ/cm2</li> <li>\lambda_{pump} = 400 nm - F_{pump} = 7.646 mJ/cm2</li> </ul> <ul> <li>\lambda_{pump} = 800 nm - F_{pump} = 1.536 mJ/cm2</li> <li>\lambda_{pump} = 800 nm - F_{pump} = 1.919 mJ/cm2</li> <li>\lambda_{pump} = 800 nm - F_{pump} = 2.109 mJ/cm2</li> <li>\lambda_{pump} = 800 nm - F_{pump} = 2.654 mJ/cm2</li> <li>\lambda_{pump} = 800 nm - F_{pump} = 2.793 mJ/cm2</li> </ul> <h3>Naming of the files</h3> <p>In the .zip file that can be downloaded here, there are several folders.</p> <p>Each are named as lambda_pump_XXX_nm_fluence_YYY_mJcm-2 where XXX is either 400 or 800 and YYY is the fluence value.</p> <p>In each folder are four files.</p> <ul> <li>diffdelta.txt - values of the transient ellipsometric parameter \Delta.</li> <li>diffpsi.txt- values of the transient ellipsometric parameter \Delta.</li> <li>diffpseudoepsilon1.txt- values of the real part of the transient pseudo dielectric function.</li> <li>diffpseudoepsilon2.txt- values of the real part of the transient pseudo dielectric function.</li> </ul> <h3>Structure of the files</h3> <p>Each file has the same structure:</p> <p>The first row an array time delays in picosecond for which measurements have been performed.</p> <p>In the rest of rows, the first element of the array are the photon energy, while the rest of the elements are the values of the transient dielectric function or ellipsometric parameters (\Psi and \Delta) at each of the elements in the time delay array.</p> <p>Example:</p> <p>time delay t1 t2 t3</p> <p>E1(eV) Delta(E1, t1) Delta(E1, t2) Delta(E1, t3)</p> <p>E2(eV) Delta(E2, t1) Delta(E2, t2) Delta(E2, t3)</p> <p>E3(eV) Delta(E3, t1) Delta(E3, t2) Delta(E3, t3)</p>
Storage and Transit Time Data and Code
<p><br>Author: Andrew J. Felton<br>Date: 5/5/2024</p> <p>This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis and figure production for the study entitled:</p> <p>"Global estimates of the storage and transit time of water through vegetation"</p> <p>Please note that 'turnover' and 'transit' are used interchangeably in this project.</p> <p>Data information:</p> <p>The data folder contains key data sets used for analysis. In particular:</p> <p>"data/turnover_from_python/updated/annual/multi_year_average/average_annual_turnover.nc" contains a global array summarizing five year (2016-2020) averages of annual transit, storage, canopy transpiration, and number of months of data. This is the core dataset for the analysis; however, each folder has much more data, including a dataset for each year of the analysis. Data are also available is separate .csv files for each land cover type. Oterh data can be found for the minimum, monthly, and seasonal transit time found in their respective folders. These data were produced using the python code found in the "supporting_code" folder given the ease of working with .nc and EASE grid in the xarray python module. R was used primarily for data visualization purposes. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here. </p> <p>#Code information</p> <p>Python scripts can be found in the "supporting_code" folder.</p> <p>Each R script in this project has a particular function:</p> <p>01_start.R: This script loads the R packages used in the analysis, sets the<br>directory, and imports custom functions for the project. You can also load in the <br>main transit time (turnover) datasets here using the `source()` function.</p> <p>02_functions.R: This script contains the custom function for this analysis,<br> primarily to work with importing the seasonal transit data. Load this using the <br> `source()` function in the 01_start.R script. </p> <p>03_generate_data.R: This script is not necessary to run and is primarily<br>for documentation. The main role of this code was to import and wrangle<br>the data needed to calculate ground-based estimates of aboveground water storage.</p> <p>04_annual_turnover_storage_import.R: This script imports the annual turnover and<br>storage data for each landcover type. You load in these data from the 01_start.R script<br>using the `source()` function.</p> <p>05_minimum_turnover_storage_import.R: This script imports the minimum turnover and<br>storage data for each landcover type. Minimum is defined as the lowest monthly<br>estimate.You load in these data from the 01_start.R script<br>using the `source()` function.</p> <p>06_figures_tables.R: This is the main workhouse for figure/table production and <br>supporting analyses. This script generates the key figures and summary statistics <br>used in the study that then get saved in the manuscript_figures folder. Note that all<br>maps were produced using Python code found in the "supporting_code"" folder.</p>
Data for EUA Trends 2024 - European higher education institutions in times of transition
<p>Since 1999, the EUA Trends reports have consistently mapped developments in the European higher education landscape, by presenting comparative data from the perspective of higher education institutions. In the ninth edition of the European University Association’s long-running series, the Trends 2024 report provides an overview of how European higher education institutions have experienced changes over the past five years, due to higher education reforms, and in the wider context of societal, political, economic and technological changes, marked among others by the implications of Covid-19 pandemic and Russia’s war against Ukraine.</p> <p>Trends 2024 is based on survey data collected in April to July 2023.</p> <p>Responses were gathered from 489 higher education institutions in 46 European higher education systems. The survey was open to all higher education institutions in the European Higher Education Area (EHEA) that provide study programmes in at least one of the three degree cycles (bachelor’s, master’s, doctoral). One response per institution was collected.</p> <p>The survey addressed the higher education institutions’ perspectives and strategies regarding:</p> <p>· The institution and its context</p> <p>· The student life cycle and experience</p> <p>· Learning, teaching and teachers </p> <p>· Inclusion, equity and diversity</p> <p>· Engagement and outreach with society and community </p> <p>· Internationalisation</p> <p>Results of the survey are published in “<a href="https://www.eua.eu/publications/reports/trends-2024.html"><strong>Trends 2024 - European higher education institutions in times of transition</strong></a>”.</p> <p>The following files are available:</p> <ul> <li>Codebook including original questionnaire</li> <li>Dataset</li> </ul>
An automated sensitive approach for measuring whole gut transit time
Background <p>Commonly used methods to measure whole gut transit time in rodents have yet to combine high sensitivity, objectivity and automation. We have developed a novel method using oral gavage of non-toxic fluorescent dye particles and their detection by fluorescence imaging to enable unbiased automated detection of gut transit time simultaneously in 8 cages.</p> Methods <p>Naïve mice (n=20) were gavaged with a 4.4% suspension of fluorescent dye in CMC in 3 groups on 2 occasions. Each group was imaged in 8 cages at 5-minute intervals using blue LEDs for illumination and a Sony full-frame mirrorless camera with a green band-pass emission filter. Custom Matlab code counted the number of fluorescent boli per cage post-hoc and provided graphical and spreadsheet output. Boli counts across a wide range of parameters were compared to blind assessments by an experimenter.</p> Results <p>Fluorescent boli were detected with high sensitivity, while unstained boli were readily rejected. All cages showed no fluorescent boli for the first ~20 frames (120 minutes), after which many cages gradually show a rise to 1-6 fluorescent boli. The mean time to first fluorescent bolus in each session was 264±141 and 223±81 minutes post-gavage, with no within subject consistency. There was high correlation between automated scores and that of experimenter (r=0.95±0.02), being robust to parameter changes.</p> Conclusions & Inferences <p>This novel approach provides a reliable, automatic and low-cost method of measuring gastrointestinal transit time in mice.</p>
Transitioning from microsatellites to SNP-based microhaplotypes in genetic monitoring programs: lessons from a 20-year time series of paired data.
<p>Many long-term genetic monitoring programs began before next-generation sequencing became widely available. Older programs can now transition to new marker systems usually consisting of 1000s of SNP loci, but there are still important questions about comparability, precision, and accuracy of key metrics estimated using SNPs. Ideally, transitioned programs should capitalize on new information without sacrificing continuity of inference across the time series. We combined existing microsatellite-based genetic monitoring information with SNP-based microhaplotypes obtained from archived samples of Rio Grande silvery minnow (<em>Hybognathus amarus</em>) across a 20-year time series to evaluate point estimates and trajectories of key genetic metrics. Demographic and genetic monitoring bracketed multiple collapses of the wild population, and included cases where captive-born repatriates comprised the majority of spawners in the wild. Even with smaller sample sizes, microhaplotypes yielded comparable and in some cases more precise estimates of variance genetic effective population size, multilocus heterozygosity and inbreeding compared to microsatellites because many more microhaplotype loci were available. Microhaplotypes also recorded shifts in allele frequencies associated with population bottlenecks. Trends in microhaplotype-based inbreeding metrics were associated with the fraction of hatchery-reared repatriates to the wild, and should be incorporated into future genomic monitoring. Although differences in accuracy and precision of some metrics were observed between marker types, biological inferences and management recommendations were consistent.</p>
Transition and Drivers of Elastic to Inelastic Deformation in the Abarkuh Plain from InSAR Multi-Sensor Time Series and Hydrogeological Data
<p>This repository contains the datasets used in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026430">Mirzadeh et al., 2023</a>. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research.</p> <p>Dataset 1: Envisat descending track 292</p> <ul> <li>Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: ALOS-1 ascending track 569</p> <ul> <li>Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: Sentinel-1 ascending track 130 and descending track 137</p> <ul> <li>Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions)</li> <li>Processor: ISCE/topsStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.</p>
Eccentricity signal in the nannofossil time-series across the Middle Pleistocene Transition in the Northwestern Pacific Ocean
<p>Supplementary data to the paper "Eccentricity signal in the nannofossil time-series across the Middle Pleistocene Transition in the Northwestern Pacific Ocean" of Bordiga et al 2023 in Quaternary Science Reviews.</p>
ScienceDex guides
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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.